The developmental transcriptome of contrasting Arctic charr (Salvelinus alpinus) morphs

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Ahi" }, { "@type": "Person", "name": "Sigridur R. Franzdottir" }, { "@type": "Person", "name": "Kalina H. Kapralova" }, { "@type": "Person", "name": "Bjarni K. Kristjansson" }, { "@type": "Person", "name": "S. Sophie Steinhaeuser" }, { "@type": "Person", "name": "Valerie H. Maier" }, { "@type": "Person", "name": "Isak M. Johannesson" }, { "@type": "Person", "name": "Sigurdur S. Snorrason" }, { "@type": "Person", "name": "Zophonias O. Jonsson" }, { "@type": "Person", "name": "Arnar Palsson" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": "Species and populations with parallel evolution of specific traits can help illuminate how predictable adaptations and divergence are at the molecular and developmental level. Following the last glacial period, dwarfism and specialized bottom feeding morphology evolved rapidly in several landlocked Arctic charr Salvelinus alpinus populations in Iceland. To study the genetic divergence between small benthic morphs and limnetic morphs, we conducted RNA-sequencing charr embryos at four stages in early development. We studied two stocks with contrasting morphologies: the small benthic (SB) charr from Lake Thingvallavatn and Holar aquaculture (AC) charr. The data reveal significant differences in expression of several biological pathways during charr development. There was also an expression difference between SB- and AC-charr in genes involved in energy metabolism and blood coagulation genes. We confirmed differing expression of five genes in whole embryos with qPCR, including lysozyme and natterin-like which was previously identified as a fish-toxin of a lectin family that may be a putative immunopeptide. We also verified differential expression of 7 genes in the developing head that associated consistently with benthic v.s.limnetic morphology (studied in 4 morphs). Comparison of single nucleotide polymorphism (SNP) frequencies reveals extensive genetic differentiation between the SB and AC-charr (~1300 with more than 50% frequency difference). Curiously, three derived alleles in the otherwise conserved 12s and 16s mitochondrial ribosomal RNA genes are found in benthic charr. The data implicate multiple genes and molecular pathways in divergence of small benthic charr and/or the response of aquaculture charr to domestication. Functional, genetic and population genetic studies on more freshwater and anadromous populations are needed to confirm the specific loci and mutations relating to specific ecological traits in Arctic charr." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/4-136/v3", "name": "The developmental transcriptome of contrasting Arctic charr (Salvelinus..." } } ] } Home Browse The developmental transcriptome of contrasting Arctic charr (Salvelinus... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Gudbrandsson J, Ahi EP, Franzdottir SR et al. The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.12688/f1000research.6402.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] Johannes Gudbrandsson 1 , Ehsan P. Ahi 1 , Sigridur R. Franzdottir 1 , [...] Kalina H. Kapralova 1 , Bjarni K. Kristjansson 2 , S. Sophie Steinhaeuser 1 , Valerie H. Maier 1 , Isak M. Johannesson 1 , Sigurdur S. Snorrason 1 , Zophonias O. Jonsson 1 , Arnar Palsson 1 Johannes Gudbrandsson 1 , Ehsan P. Ahi 1 , [...] Sigridur R. Franzdottir 1 , Kalina H. Kapralova 1 , Bjarni K. Kristjansson 2 , S. Sophie Steinhaeuser 1 , Valerie H. Maier 1 , Isak M. Johannesson 1 , Sigurdur S. Snorrason 1 , Zophonias O. Jonsson 1 , Arnar Palsson 1 PUBLISHED 02 Dec 2016 Author details Author details 1 Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 2 Holar University College, Saudarkrokur, 551, Iceland OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Species and populations with parallel evolution of specific traits can help illuminate how predictable adaptations and divergence are at the molecular and developmental level. Following the last glacial period, dwarfism and specialized bottom feeding morphology evolved rapidly in several landlocked Arctic charr Salvelinus alpinus populations in Iceland. To study the genetic divergence between small benthic morphs and limnetic morphs, we conducted RNA-sequencing charr embryos at four stages in early development. We studied two stocks with contrasting morphologies: the small benthic (SB) charr from Lake Thingvallavatn and Holar aquaculture (AC) charr. The data reveal significant differences in expression of several biological pathways during charr development. There was also an expression difference between SB- and AC-charr in genes involved in energy metabolism and blood coagulation genes. We confirmed differing expression of five genes in whole embryos with qPCR, including lysozyme and natterin-like which was previously identified as a fish-toxin of a lectin family that may be a putative immunopeptide. We also verified differential expression of 7 genes in the developing head that associated consistently with benthic v.s.limnetic morphology (studied in 4 morphs). Comparison of single nucleotide polymorphism (SNP) frequencies reveals extensive genetic differentiation between the SB and AC-charr (~1300 with more than 50% frequency difference). Curiously, three derived alleles in the otherwise conserved 12s and 16s mitochondrial ribosomal RNA genes are found in benthic charr. The data implicate multiple genes and molecular pathways in divergence of small benthic charr and/or the response of aquaculture charr to domestication. Functional, genetic and population genetic studies on more freshwater and anadromous populations are needed to confirm the specific loci and mutations relating to specific ecological traits in Arctic charr. READ ALL READ LESS Keywords Salmonids, Aquaculture, ecomorphs, Polymorphism, parallel evolution, immunology, craniofacial divergence, mtDNA Corresponding Author(s) Johannes Gudbrandsson ( [email protected] ) Arnar Palsson ( [email protected] ) Close Corresponding authors: Johannes Gudbrandsson, Arnar Palsson Competing interests: No competing interests were disclosed. Grant information: This project was supported by The Icelandic Center for Research (grant number: 100204011) to SSS, AP, ZOJ and BKK, The University of Iceland Research/Doctoral Fund to JG and KHK and University of Iceland research fund to AP, SSS and ZOJ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2016 Gudbrandsson J et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). How to cite: Gudbrandsson J, Ahi EP, Franzdottir SR et al. The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.12688/f1000research.6402.3 ) First published: 01 Jun 2015, 4 :136 ( https://doi.org/10.12688/f1000research.6402.1 ) Latest published: 02 Dec 2016, 4 :136 ( https://doi.org/10.12688/f1000research.6402.3 ) Revised Amendments from Version 2 The changes to the manuscript are rewriting of the introduction, results and discussion to present more clearly the biology of the Aquaculture charr used here as a reference strain, and the results from the contrast of the SB and AC transcriptomes and their implication both for evolutionary questions and also biology of the AC-charr. We also set out to reduce the emphasis on the ecological divergence in the description and interpretation of the results. We also rewrote part of the introduction, to provide better flow from general to specific background, and shortened the summary of molecular genetics of the craniofacial genes in the discussion. We clarified several issues, like the potential transgenerational effects in common garden experiment, the description of the Salmonid ancestor genome duplication and the age of the clade, and the qPCR results on both the Nattl genes and the summary of validated genes. We amended figures 1, 2 and 4, and cleaned or adjusted the language/spelling in accordance with the suggestions of the reviewers. We thank them dearly for their thoughtful comments and suggestions, which have clearly improved the manuscript. The changes to the manuscript are rewriting of the introduction, results and discussion to present more clearly the biology of the Aquaculture charr used here as a reference strain, and the results from the contrast of the SB and AC transcriptomes and their implication both for evolutionary questions and also biology of the AC-charr. We also set out to reduce the emphasis on the ecological divergence in the description and interpretation of the results. We also rewrote part of the introduction, to provide better flow from general to specific background, and shortened the summary of molecular genetics of the craniofacial genes in the discussion. We clarified several issues, like the potential transgenerational effects in common garden experiment, the description of the Salmonid ancestor genome duplication and the age of the clade, and the qPCR results on both the Nattl genes and the summary of validated genes. We amended figures 1, 2 and 4, and cleaned or adjusted the language/spelling in accordance with the suggestions of the reviewers. We thank them dearly for their thoughtful comments and suggestions, which have clearly improved the manuscript. See the authors' detailed response to the review by Örjan Östman See the authors' detailed response to the review by Daniel Macqueen See the authors' detailed response to the review by Anne Dalziel READ REVIEWER RESPONSES Introduction Historical contingencies and chance shape organisms during evolution 1 , 2 , but convergence in phenotype and molecular systems indicates that evolution is to some extent predictable 3 , 4 . Identification of genes and variants that influence evolved differences is not a trivial task 5 . Ideal systems to study the role of chance and necessity in ecological evolution would be related species or populations with readily observable phenotypic variation, living in a tractable ecological setting, and showing parallel evolution of specific traits within/among species/populations. Examples of such species complexes are provided by finches of the Galapagos islands 6 , while cichlids of the African great lakes also provide an exciting multi-species system in the same respect 7 . The threespine stickleback has also emerged as a model “single species” system 8 . The amount of diversity in the feeding specializations of fish provide great opportunities for studying adaptation and divergence at the developmental and genetic level. Some northern freshwater fish species exhibit frequent parallelism in trophic structures and life history and in several cases are found as distinct resource morphs 8 – 13 . Local adaptation has been extensively studied in the salmonid family, to which our study species Arctic charr ( Salvelinus alpinus ) belongs 14 . This species is well suited for studying the developmental underpinnings of trophic divergence and parallel evolution. The common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago, the fourth vertebrate whole- genome duplication (Ss4R) 15 – 18 . This has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event) in salmonid lineages. Estimates from the rainbow trout ( Oncorhynchus mykiss ) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that around half of the original Ss4R ohnologue pairs are still functionally retained in rainbow trout 16 . One approach to identify pathways related to function or morphological differences between species, populations or ecomorphs is to study gene expression during development 19 , 20 . For example a microarray study of liver samples from anadromous and resident populations of brown trout ( Salmo trutta ), revealed that gene expression in juveniles was more influenced by life history than relatedness 21 . Furthermore, Filteau et al. (2013) 22 found a set of coexpressed genes differentiating two whitefish morphotypes, implicating Bone morphogenesis protein (BMP) signalling in the development of ecological differences in trophic morphology. Thus we were quite keen to apply RNA-sequencing to analyse ecomorphs in Arctic charr. De novo assembly of genomes and transcriptomes is complicated if many paralogs are present, which is the case in Arctic charr – see 23 , 24 . In this study we opted for mapping the reads (36 bp) to a related reference genome/transcriptome 25 , instead of de novo assembly. Two previous studies have used RNA-seq to study salinity tolerance in adult Arctic charr, and found links between gene expression and quantitative trait loci 23 , 24 . Molecular studies of the highly polymorphic Arctic charr Following the end of the last glacial period, about 10.000 years ago, Arctic charr colonized northern freshwater systems 26 . It is found as anadromous or lake/stream residents and exhibits high level of within species polymorphism 11 , 26 . Charr is also known to harbour substantial phenotypic plasticity, which may promote or reduce divergence 9 , 27 . Resource polymorphism in charr correlates with ecological attributes 28 – 30 . For instance small charr with benthic morphology, are found in multiple lavaspring and stream habitats in Iceland 31 , and a comparative study of Icelandic lakes 30 found that lakes with greater limnetic habitat, lower nutrients levels, and greater potential for zooplankton consumption appeared to promote resource polymorphism. Some of the larger lakes contain two or more distinct morphs, typically limnetic and benthic forms. Multiple lines of evidence show that these differences stem both from environmental and genetic causes 32 – 36 . The best studied example of sympatric charr are the four morphs in Lake Thingvallavatn 37 ; two have a benthic morphotype, a large benthivorous (LB-charr) and a small benthivorous (SB-charr), and two morphs are limnetic, a large piscivorous morph (PI-charr) and small planktivorous morph (PL-charr) 38 . Both PL and PI-charr operate in open water and feed on free-swimming prey, PL on planktonic crustaceans and PI on small fish. The PL, LB and SB-charr are presented in Figure 1 . Figure 1. The Arctic charr morphs used in this study. Adult individuals of the four morphs studied here, A ) the Holar aquaculture charr, B ) the small benthic charr, C ) the planktivorous charr D ) and the large benthic charr. The latter three all come from Lake Thingvallavatn and were sexually ripe. The morphs differ in size at maturation, body and head shape - mainly lower jaw and length of maxilla and colour pattern in the wild. Several population genetics studies, using allozymes or mtDNA revealed no differences among charr morphs in Lake Thingvallavatn 39 – 41 while other studies using microsatellite markers and nuclear genes, found significant 42 – 44 genetic differences among morphs in the lake 45 . Importantly Kapralova et al. (2011) 44 concluded that small benthic morphs have evolved repeatedly in Iceland and that gene flow has been reduced between the PL and SB morphs in Lake Thingvallavatn since its formation approximately 10,000 years ago 46 . We also discovered genetic separation in immunological genes ( MHCIIα and cath2 ) between morphs in Iceland and within the lake 45 , consistent with ecologically driven evolution of immune functions. Recently qPCR analyses showed that expression of mTOR pathway components in skeletal muscle correlates with the SB-charr form in Iceland 47 , but it is unknown whether there is genetic differentiation in those genes or upstream regulators. Because individual genes have distinct histories 48 , 49 , genome wide methods are needed to identify genes and mutation that associate with divergence. AC charr as a reference for sympatric Arctic charr The ideal reference populations for developmental and molecular studies of landlocked and sympatric Arctic charr in Iceland would be local anadromous charr. However, capturing running charr from the wild is not trivial. For this study we chose to use the Icelandic aquaculture charr (AC) as a reference. The AC-charr was founded with fish from the north of Iceland, and has been bred at Holar University College since 1990 50 . Body weight and age at sexual maturity have significant heritability in the Holar AC-charr, and the stock responded well to artificial selection for growth and performance characteristics. It is now the dominant charr breed in aquaculture in Iceland. While clearly a derived breed, it seems to have retained general limnetic craniofacial morphotype ( Figure 1 ). The rationale for comparing SB-charr from Lake Thingvallavatn and AC-charr was threefold: i) SB charr represents an extensively studied and derived form of charr, that has been separated from anadromous fish for approx. 10,000 years, ii) AC charr was readily available for sampling (but wild anadromous charr was not), iii) we wanted an extreme contrast, because of budget reasons we could only sequence 8 samples at the time. Note, the transcriptome itself can only point out differences between the two morphs, but not highlight whether specific genes associate with SB or AC biology and breeding. But by focusing the verification on sympatric benthic and limnetic morphs of Lake Thingvallavatn, we could test and verify a subset of the signals found here. The contrast of SB and AC was justified as the data and studies 51 – 53 building on this data illustrate (see discussion). Our long term research objectives are to investigate the genetics and developmental underpinnings of charr divergence and benthic parallelism. As a step towards this we compared the developmental transcriptome of SB charr and AC charr, reared in common lab environment to minimize the effects of environmentally induced phenotypic plasticity. The aims of this study are threefold. First, to find genes and pathways related to the development of phenotypic differences between small benthic charr from Lake Thingvallavatn and Icelandic aquaculture charr conforming to a limnetic morphotype. Second, to screen for signals of genetic differentiation between these two charr types. Third, we set out to verify a subset of the expression and genetic signals in the high-throughput sequencing data and also studying two more morphs (LB and PL) from Lake Thingvallavatn. The data reveal differential expression of genes that may affect the development of craniofacial and other phenotypic traits in charr. Genetic differences in nuclear and mitochondrial genes are also observed and provide a starting point for studying evolution of wild populations and genetics of domestication in Icelandic Arctic charr. Methods Sampling, rearing and developmental series Overview of the experimental design, RNA sequencing, analyses and follow work is outlined in Figure 2 . We set up crosses and reared embryos in the laboratory as described in 51 . Embryos from four charr morphs were studied: an aquaculture charr (AC-charr) from the Holar breeding program 50 and three natural morphs from Lake Thingvallavatn; SB, LB and PL-charr 54 . Samples of the first two, AC and SB-charr, with contrasting adult size and morphology ( Figure 1 ), were collected in 2009 and material sent for RNA sequencing. The latter two were sampled in 2010 and were used for qPCR and SNP studies of selected genes. Briefly, in September 2009 we got material from spawning AC-charr from the Holar breeding program 50 , from single parent crosses and spawning SB-charr collected via gill netting in Olafsdrattur in Lake Thingvallavatn. Similarly, in the 2010 spawning season SB-, LB- and PL-charr were collected from Lake Thingvallavatn. For each parent group, eggs from several females (3–10) were pooled and fertilized using milt from several males (3–5) from the same group. Embryos were reared at ~ 5°C under constant water flow and in complete darkness at the Holar University College experimental facilities in Verid, Saudárkrókur. The water temperature was recorded twice daily and the average was used to estimate the relative age of the embryos using tausomite units ( τs ) 55 . Embryos and juveniles were sampled at designated time points, placed in RNAlater (Ambion) and frozen at −20°C. Post hatching juveniles were reared at the same temperature on standard Aquaculture food. For the investigation of different tissues of adult aquaculture charr (AC) from Hólar (fish size 20–25 cm) were used. Six randomly selected individuals were killed (by cutting through spinal cord) and dissected, and samples were taken from the skin, heart, liver, gills, spleen, intestine and kidney of each fish. The samples were placed in RNAlater (Ambion) and stored at −20°C. We used DNA for population genetic analyses from our previous study 45 , eight individuals from each of the three types, PL, LB and SB-charr. Figure 2. Schematic of RNA sequencing and follow up qPCR and population genetic work. RNA from embryos of the AC and SB charr at four stages (AC embryos pictured at top) were sequenced with Illumina technology. Reads were mapped to Atlantic salmon expressed sequence tags (ESTs). To verify differentially expressed genes we used RNA from embryos and heads of these four morphs, and tissues from adult AC charr. To verify SNPs we genotyped population samples from three Lake Thingvallavatn morphs (PL, LB and SB). Fishing in Lake Thingvallavatn was done with permissions obtained both from the owner of the land in Mjóanes and from the Thingvellir National Park commission. Ethics committee approval is not needed for regular or scientific fishing in Iceland (The Icelandic law on Animal protection, Law 15/1994, last updated with Law 157/2012). Sampling was performed by Holar University College Aquaculture Research Station (HUC-ARC) personnel. HUC-ARC has an operational license according to Icelandic law on aquaculture (Law 71/2008), which includes clauses of best practices for animal care and experiments. RNA extraction and transcriptome sequencing Embryos of AC- and SB-charr sampled in 2009 were used for transcriptome sequencing. For this we focused on the time covering development of pharyngeal arches and morphogenesis of the head: at 141, 163, 200 and 433 τs (post fertilization). For each combination of morphs and timepoints we pooled RNA from approximately six individuals. RNA extraction and following steps were performed as described earlier 51 , 56 . Briefly, the embryos were dechorionated and homogenized with a disposable Pellet Pestle Cordless Motor tissue grinder (Kimble Kontes, Vineland, NJ, USA) and RNA was extracted into two size-fractions using the Ambion mirVana kit (Life Technologies, Carlsbad, CA, USA). The high molecular weight fraction was further used for mRNA-seq and RNA quality was analysed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA from samples was pooled - equal contribution of each sample - and first and second strand cDNA synthesis, fragmentation, adapter ligation and amplification were performed using the mRNA-Seq 8-Sample Prep Kit (Illumina, San Diego, CA, USA) according to manufacturer’s instructions. Sequencing was performed at DeCode genetics (Reykjavík, Iceland) using SOLEXA GAII technology (Illumina, San Diego, CA, USA). The sequencing reads were deposited into the NCBI SRA archive under BioProject identifier PRJNA239766 and with accession numbers: SRX761559, SRX761571, SRX761575, SRX761577, SRX761451, SRX761461, SRX761490 and SRX761501. The embryos sampled in 2010 were used for qPCR analyses. RNA was extracted from six whole embryos, in two replicates (two repetitions X three fish) (AC and SB sampled at 161 and 200 τs ). For the extraction of RNA from heads of AC, SB, LB and PL, 12 embryos (two repetitions X six fish) at 178, 200 and 216 τs were used. Embryos were dechorionated and decapitated in front of the pectoral fin. RNA extraction and cDNA preparation were performed as described previously in 51 . Similarly, RNA was extracted from a small piece (approximately 2 mm 2 ) of skin, heart, liver, gill, spleen, intestine and liver from six adult AC-charr. Analyses of RNA-seq data and mapping to Salmon EST contigs As no S. alpinus genome is available and de novo assembly of the 36 bp reads yielded an excessive number of short contigs we chose to assess expression and genetic variation by mapping the reads to 59336 S. salar expressed sequence tag (EST) contigs from the SalmonDB [ 57 , downloaded 22. March 2012] and the Arctic charr mitochondrial genome [ 48 , NC_000861]. To estimate expression, reads were aligned with RSEM version 1.1.18 with default parameters. RSEM distributes reads that map to multiple locations to the most likely contig, using expectation maximization 58 . The read counts for contigs with the same annotation were pooled because some genes were represented by more than one contig, and due to whole genome duplication almost the half of salmonid genes exist as ohnologs 16 , 18 . Thus the expression tests are done on gene or paralog group level, instead of the contig level. We acknowledge that paralogous genes are not always expressed similarly, but feel its necessary to do this pooling because of the nature of the data. In the remainder of the paper, we will refer to gene or paralog group (the number of underlying contigs is indicated in relevant tables). This brought the number of genes considered down to 16851. Lastly, paralog groups with fewer than 800 mapped reads in the entire dataset were excluded from the analyses, yielding a total of 10496. A generalized linear model (GLM) with morph and developmental time as explanatory variables was used to find genes with different expression levels between the two charr morphotypes (groups) using the edgeR-package in R 59 . Y=Morph+Time+Error To obtain further insight into the expression profiles of differently expressed genes, we performed clustering analyses on log-transformed cpm-values (counts per million; cpm-function in edgeR). The values for each gene were scaled by mean and standard deviation, and the euclidean distance used for the hclust-function in R 60 with the default settings. We used the hypergeometric-test in goseq 61 to test for gene ontology enrichment. Since we pooled the read-count from different contigs we could unfortunately not take gene length into account in those tests. Tests of differential expression with qPCR We previously identified suitable reference genes to study Arctic charr development 51 . Here we examined the expression of several genes in whole charr embryos, embryonic heads and adult tissues. Primers were designed using the Primer3 tool 62 and checked for self-annealing and heterodimers according to the MIQE guidelines 63 ( S1 Table ). Primers for genes with several paralogs were designed for regions conserved among paralogs, except for natterin-like, where primers were designed to match regions differing in sequence between paralogs. Relative expression was calculated using the 2 −ΔΔ Ct method 64 . For the calculation of relative expression of genes in whole embryos, the geometric mean expression of three reference genes, β - Actin (Actb), elongation factor 1 α and Ubiquitin-conjugating enzyme E2 L3 , was used for normalization. For visual comparisons among samples, the normalized expression was presented as relative to the expression in AC at 161 τs (calibration sample). For the embryonic head samples Eukaryotic Translation Initiation Factor 5A (If5a1) and Actb were used as reference genes and a biological replicate of AC at 178 ( τs ) as the calibrator sample, see 51 , 52 . Standard errors of relative expression were calculated from the standard errors (SE) of the Δ C T -values with the formula 2 −(ΔΔ Ct + SE ) = minimum fold expression and 2 −(ΔΔ Ct − SE ) = maximum fold expression. The statistical analysis was performed using the Δ C T -values with a two-way ANOVA with GLM function in R. Y=Morph+Time+MorphxTime+Error Normal distribution of residuals was confirmed for all data. For the study of expression in the embryonic head we followed a significant morph effect in the ANOVA with Tukey’s post-hoc honest significant difference test, on relative expression ratios (Δ C T s). Three genes had lower efficiency (as low as 1.72). We acknowledge that the data on those genes may be weak. Polymorphisms in the Arctic charr transcriptome For analysis of genetic variation we mapped the reads to the salmon contigs, this time using the Burrow-Wheeler Aligner (BWA) 65 with a seed length of 25, allowing two mismatches. We re-mapped the reads, since BWA allows short indels (RSEM does not) but disregarding them leads to many false SNPs close to indels. To extract candidate polymorphic sites from the Arctic charr transcriptome we ran VarScan2 66 with minimum coverage of 50 reads and minimum minor allele frequency of 0.1 on reads mapped to each S. salar contig for all of the 8 timepoints and morph combinations. This was done separately for reads that mapped uniquely to one contig only (UNI) and reads that mapped to two or more contigs (REP). These SNP-candidates were further processed in R 60 , following established principles for variant calling 67 . SNP-candidates at 90% frequency or higher in all samples were disregarded, as they reflect differences between Arctic charr and S. salar and are not the focus of this study. SNP-candidates with poor coverage in specific samples - i.e. coverage of five or fewer reads in three or four samples of each morph - were removed. As the SNP analysis was done on individual contigs, differences among paralogs appear in the data. To address this we use the fact that each sample is a pool of few individuals, thus true SNPs are unlikely to have the same frequency in all samples. However, variants reflecting differences between paralogs will have similar frequency all samples (assuming steady difference in their expression in all samples). We evaluated differences between samples with Fisher exact tests, and only SNPs significantly different between samples with a p < 0.05 (with no multiple testing correction) were retained. To compare morphs, read numbers were summed over the four samples from each morph. A conservative approach was taken by focusing on SNP-candidates that showed the largest differences in frequency between morphs (delta), without adjusting for multiple testing (Fisher exact test, p > 5%). SNP-candidates with the highest frequency difference (delta > 95%) were manually processed and redundant candidates removed. A similar approach was used to mine for polymorphisms in Arctic charr mtDNA (NC_000861), using S. salar mtDNA as the outgroup (NC_001960.1). We wrote a python script to predict the impact of SNPs within the mRNA sequences. Polymorphisms were categorized according to their location (3’UTR, coding, 5’UTR), and those within the coding region into synonymous or non-synonymous. Verification of candidate SNPs We chose 12 candidate SNPs for verification (see below). As the AC-charr is not a random breeding population, and because our interest is on differences between wild morphs, we took random samples of spawning SB, LB and PL-charr from Lake Thingvallavatn (8 per morph) from our earlier study 45 . Using the same PCR and DNA sequencing approach we genotyped 12 candidate SNPs ( S2 Table ). Briefly, we first compared the Salmon genome and ESTs [ 57 , downloaded 22. March 2012] and short contigs from our preliminary assembly of the Arctic charr transcriptome. This allowed us to infer the placement of the putative polymorphism in the locus, and design paralog specific primers for PCR (less than 1 kb amplicons). MJ tetrad machine was used for PCR and the program was 5 min. at 95°C, followed by 35 cycles of 30 sec. at 52°C, 1 min. at 72°C, 30 sec. at 95°C, ending with 12°C while waiting on the human. Each individual was genotyped by first amplifying the region of interest using PCR, followed by ExoSAP (Affymetrix), direct sequencing (BigDye) and finally run on an Applied Biosystems 3500xL Genetic Analyzer (Hitachi). Raw data was base-called using the Sequencing Analysis Software v5.4 with KBTMBasecaller v1.41 (Applied Biosystems). Ab1 files were run through Phred and Phrap and imported to Consed for visual editing of ambiguous bases and putative polymorphisms, and for trimming primers. The FASTA files were aligned with ClustalW online [ 68 , http://www.ebi.ac.uk/Tools/msa/clustalw2/ ] and manually inspected in Genedoc 69 . All sequences where deposited to Genbank as popsets under the accession numbers KP019972-KP020026. Comparative genomic analyses of sequence polymorphisms Two approaches were used for genomic comparisons of verified SNPs in the mitochondrial genome. Using the charr mtDNA sequence we performed both a BLAST search on salmon ESTs (May 2013) and retrieved multiZ alignments of vertebrates from the UCSC genome browser (in September 2013). This yielded several hundred sequences from related fish and other vertebrates. The list was reduced to 20 sequences for visualization, by keeping members of the major taxa but removing more closely related sequences, aligned with ClustalW and manually adjusted in Genedoc. The species and genome versions used are; Human ( Homo sapiens , hg19), Lamprey ( Petromyzon marinus , petMar1), Fugu ( Takifugu rubripes , fr2), Medaka ( Oryzias latipes , oryLat2), Stickleback ( Gasterosteus aculeatus , gasAcu1), Tetraodon ( Tetraodon nigroviridis , tetNig2), Zebrafish ( Danio rerio , danRer6). We also downloaded from NCBI the sequence of whole or partial mtDNA from several fish species; Brown trout ( Salmo trutta , JQ390057 and AF148843), Broad whitefish ( Coregonus nasus , JQ390058), Legless searsid ( Platytroctes apus , AP004107), Pacific menhaden ( Ethmidium maculatum , AP011602), Icefish ( Salanx ariakensis , AP006231 and HM151535), Chain pickerel ( Esox niger , AP013046) and Western Pacific roughy ( Hoplostethus japonicus , AP002938). The three mitochondrial variants (numbered by the S. alpinus mtDNA - NC_000861) are; m1829G>A (CCACGTTGTGAAACCAAC[G/A]TCCGAAGGTGGATTTAGCAGT), m3211T>C (CGTGCAGAAGCGGGCATAAG[T/C]ACATAAGACGAGAAGACCCT) and m3411C>T (CTCTAAGCACCAGAATTT[C/T]TGACCAAAAATGATCCGGC). Results RNA sequencing characteristics Each sample yielded good quality data, with sequencing depth from 49 to 58 million (average: 55 million) reads. To quantify the expression levels, the reads were aligned to a salmon EST-assembly 57 . Around 20% of the reads mapped uniquely to the EST data ( S3 Table ). A further 30% mapped to two or more contigs, probably representing paralogous genes, recent duplications or repeat-like elements within transcribed regions. A substantial fraction of the RNA-sequencing reads did not map to the contigs from S. salar . Analyses of those reads require an Arctic charr genome sequence or transcriptome assembly from longer and paired end reads, currently underway in our laboratory. Differential expression during Arctic charr development We detected considerable changes in the transcriptome during Arctic charr development ( Figure 3a ). The expression of 1603 and 2459 paralog groups differed significantly between developmental timepoints at the 1% and 5% levels of false discovery rate (FDR), respectively ( Dataset 1 ). The difference was most pronounced between prehatching (timepoints: 141, 163, 200 τs ) and post hatching embryos (timepoint 433 τs ), as more than 70% of the paralog groups with FDR below 1% had higher expression in the latter ( Figure 3a ). Gene Ontology analyses reveal six enriched GO categories (below 10%FDR). The most drastic changes were seen in processes related to glycolysis (GO:0006096, FDR = 0.0009), where the expression of 19 out of 25 paralog groups changed during this developmental period. The other five classes that were differentially expressed during charr development are: ion transport (GO:0006811, FDR = 0.027), blood coagulation (GO:0007596, FDR = 0.03), DNA repair (GO:0006281, FDR = 0.08) and two immune related categories (GO:0019882, FDR = 0.08, GO:0006955, FDR = 0.09). Those results probably reflect developmental changes and/or differences in the environment of embryos before and after hatching. Figure 3. Heatmap of differentially expressed genes in the Arctic charr developmental transcriptome. Two morphs (SB and AC) are represented, at four timepoints. ( A ) The 1603 genes with expression difference among time points, here clustered into four groups. ( B ) The 71 genes differentially expressed between morphs are clustered into 4 groups for each morph. High expression is indicated by blue and low expression by beige. Differential expression between Arctic charr morphs The embryos were reared in a common garden setting, which minimizes the impact of environmental factors, as we are interested in genes showing expression differences between the two morphs. In the data 296 paralog groups were differentially expressed (FDR < 5%) between the morphs (141 higher in SB and 152 higher in AC-charr, Dataset 1 ). Among genes with higher expression in SB-charr two biological GO categories were enriched: blood coagulation (GO:0007596, p = 0.001) and proteolysis (GO:0006508, p = 0.002). Recall, expression of blood coagulation factors also differed between developmental stages (see above). In AC-charr, genes in three categories: respiratory electron transport chain (GO:0022904, p = 0.0006), ATP synthesis coupled electron transport (GO:0042773, p = 0.002) and neurotransmitter transport (GO:0006836, p = 0.009) have higher expression. The first two GO categories both relate to energy generation in mitochondria and could reflect higher expression of genes with mitochondrial functions in AC-charr. At more stringent FDR (1%), 31 paralog groups, with diverse functional annotations, were higher expressed in SB and 40 genes higher in AC-charr ( Figure 3b , Table 1 and Table 2 ). The higher expressed ESTs were clustered into 4 groups for each morph, reflecting in some cases functional similarity. For instance SB cluster 3 has three immune related paralog groups: Complement factor D (9), H-2 class I histocompatibility antigen L-D alpha chain (2) and Sushi domain-containing protein 2 (4) ( Table 1 ). Note, however, that immune genes were not significantly enriched in the GO comparison of morphs. The results suggest genes with mitochondrial function, blood coagulation and other functions are differentially expressed between the morphs. Note, because only two morphs are compared, then those genes implicate pathways involved in either ecological divergence in SB charr or adaptation of the AC charr during breeding 50 . But as few samples were sequenced, qPCR verification was needed. Table 1. Differentially expressed genes, with higher expression in the SB morph from Lake Thingvallavatn. NR Name Abbr Cont logFC logCPM FDR Cluster 3766 Histone H3 embryonic 1 8.71 2.74 7.80E-035 S-1 5103 Natterin-like Nattl 6 2.75 7.12 7.76E-007 S-2 356 A7J6M9 Putative uncharacterized protein n175R 1 2.33 4.66 3.30E-006 S-1 6697 Q1KY05 Main olfactory receptor-like Sorf 5 3.12 6.92 9.96E-005 S-1 8151 Sushi domain-containing protein 2 Susd2 4 2.20 5.55 0.0001 S-3 1682 Carcinoembryonic antigen-related cell adhesion molecule 1 Ceacam1 3 2.55 3.83 0.0002 S-1 6228 Protein FAM98A 2 1.96 4.76 0.0003 S-1 7531 STAM-binding protein-like Stampbl1 2 2.07 2.62 0.0005 S-1 6712 Q1M160 Myc-regulated DEAD box protein 1 1.67 3.23 0.0009 S-1 2300 Cytosolic sulfotransferase 3 Sult3st1 3 1.73 2.13 0.0009 S-1 2063 Complement factor D Cfd 7 1.79 6.42 0.0016 S-3 3326 Galectin-3-binding protein A 4 1.79 3.85 0.0017 S-4 3169 Flocculation protein 11 Flo11 2 1.86 4.05 0.0017 S-1 1203 B5XDY0 H-2 class I histocompatibility antigen L-D alpha chain 2 1.70 2.12 0.0028 S-3 9183 UPI000065D844 related cluster 2 1.97 5.55 0.0028 S-1 2909 Epidermis-type lipoxygenase 3 Loxe3 4 1.68 4.84 0.0029 S-1 4884 Myeloperoxidase Mpo 4 2.20 6.78 0.0029 S-1 10003 Uridine phosphorylase 1 Upp1 4 1.51 3.00 0.0047 S-1 2513 Desmoglein-1-alpha Dsg1 1 1.59 2.80 0.0054 S-2 377 A7SJA8 Predicted protein (Fragment) 1 1.73 2.50 0.0055 S-3 9204 UPI00006A2900 related cluster 2 6.38 3.26 0.0064 S-1 9642 UPI00017B1B0F related cluster 1 2.00 1.92 0.0064 S-2 1965 Coiled-coil domain-containing protein 136 Ccdc136 2 2.15 2.32 0.0064 S-2 9260 UPI0000F1D4BA PREDICTED 1 1.80 2.41 0.0065 S-2 738 Adseverin Scin 8 1.58 5.51 0.0073 S-1 9678 UPI00017B4479 related cluster 1 2.18 1.97 0.0074 S-4 8339 Testin Tes 4 1.50 4.93 0.0080 S-2 6840 Q4SNH3 Chromosome 8 SCAF14543 1 1.42 4.00 0.0080 S-1 1668 Carbohydrate sulfotransferase 6 Chst7 1 2.09 2.08 0.0090 S-4 8341 Testisin Prss21 2 2.01 2.76 0.0090 S-4 6373 Protein asteroid homolog 1 Aste1 6 1.29 4.24 0.0090 S-4 Name: name of unigene or paralog group Abbr: Abbreviated paralog group or gene name Cont: Number of contigs logFC: log Fold Change logCPM: log Counts Per Million FDR: False Discovery Rate The cluster numbering corresponds to Figure 3 . Table 2. Differentially expressed genes, with higher expression in the AC morph. NR Name Abbr Cont logFC logCPM FDR Cluster 3465 Glutathione S-transferase P 1 Gstp1 1 -8.35 2.45 1.12E-019 A-2 2475 Dehydrogenase/reductase SDR family member 7 Dhrs7 2 -4.88 2.15 9.67E-014 A-3 6945 Q6NWE8 Sb:cb283 protein 3 -6.08 3.02 2.15E-013 A-2 399 A8DW32 Predicted protein 1 -5.32 6.38 4.27E-010 A-1 9682 UPI00017B4B48 related cluster 2 -3.70 2.81 2.61E-008 A-2 9817 Uncharacterized protein ART2 5 -12.63 6.89 8.23E-008 A-2 6724 Q2L0Z2 Putative ATP-dependent RNA helicase 1 -3.41 1.89 1.88E-007 A-2 1197 B5XD10 Vacuolar proton pump subunit G 1 Atpv1g1 1 -4.30 2.10 1.84E-006 A-2 5325 Nucleoside diphosphate kinase B Nme2 1 -9.85 7.63 2.51E-006 A-1 9205 UPI0000D5B923: myelin basic protein isoform 1 Mbpa 3 -2.49 3.45 9.18E-006 A-3 6377 Protein broad-minded Tbc1d32 1 -2.11 2.74 4.75E-005 A-1 5711 Pistil-specific extensin-like protein 1 -2.16 2.60 0.0002 A-3 3203 Formin-like protein 20 Fmnl2b 7 -1.98 1.95 0.0002 A-3 9315 UPI0000F2EC69: hypothetical protein 2 -5.60 4.57 0.0005 A-2 363 A7RFV0 Predicted protein (Fragment) 2 -1.74 4.96 0.0010 A-3 6937 Q6AZT1 MGC81677 protein 3 -2.06 3.81 0.0014 A-2 3756 Histone H1 Histh1 3 -2.26 4.54 0.0017 A-2 1133 B5DGN9 Creatine kinase-1 Ckm1 7 -4.72 5.50 0.0017 A-3 309 A1IMH7 CD80-like protein Cd80 12 -1.94 4.29 0.0017 A-2 7651 Serine protease ami 2 -1.54 5.90 0.0017 A-3 9935 Uncharacterized protein C7orf63 homolog 1 -1.87 1.91 0.0025 A-2 5219 Nostrin Nostrin 2 -2.55 3.38 0.0029 A-2 1855 Chondroitin sulfate N-acetylgalactosaminyl- transferase 2 Csgalnact2 5 -2.56 6.14 0.0034 A-1 10203 Xylose isomerase 6 -1.55 2.43 0.0035 A-3 2249 Cytochrome c oxidase subunit 3 Cox3 11 -1.78 11.15 0.0035 A-3 180 40S ribosomal protein S3-B Rps3b 2 -5.31 8.67 0.0050 A-1 1227 B6NBL3 Putative uncharacterized protein 3 -1.59 2.95 0.0050 A-2 5055 NADH-ubiquinone oxidoreductase chain 6 Nd6 2 -1.48 2.65 0.0061 A-3 4634 Metallothionein A Mta 1 -3.33 5.44 0.0064 A-2 342 A5C0J4 Putative uncharacterized protein 2 -2.58 2.47 0.0064 A-2 9698 UPI00019258B4: similar to epithelial cell transforming sequence 2 oncogene protein partial 1 -2.06 2.94 0.0064 A-2 5878 Pro-opiomelanocortin B Pomcb 1 -2.04 5.60 0.0065 A-2 2248 Cytochrome c oxidase subunit 2 Cox2 9 -2.21 9.83 0.0074 A-2 1246 B8JI87 Novel protein similar to vertebrate collagen type VI alpha 3 (COL6A3) (Fragment) 1 -1.69 3.16 0.0080 A-3 7994 Sperm-associated antigen 5 Spag5 1 -2.07 3.71 0.0080 A-2 9515 UPI000175F90F: similar to pleckstrin homology domain containing family A member 7 1 -2.00 1.87 0.0090 A-2 1124 B5DDZ4 Acta1 protein Actc1b 1 -1.52 2.62 0.0090 A-2 1127 B5DG94 2-peptidylprolyl isomerase A Ppia1 2 -2.56 5.67 0.0090 A-1 9175 UPI000054A3C0 PREDICTED: apolipoprotein B 3 -1.32 4.09 0.0090 A-4 9671 UPI00017B3C62 related cluster 1 -1.51 1.92 0.0096 A-1 For column header explanation, see footer of Table 1 . Validation of gene expression differences in whole embryos and paralog specific expression of natterin genes For validation we opted for qPCR analyses of 9 genes/paralog groups in whole embryos and 8 in embryonic heads (see next section), which showed differential expression between AC and SB-charr, with statistical support ranging from <1% to about 10% FDR. We studied paralog groups with less FDR support, in part to be able to cast a wider net (see below). Of the nine paralog groups studied in whole embryos, five were confirmed to be differentially expressed between AC and SB-charr at 161 or 200 τs ( Figure 4 , S4 Table and Dataset 2 ). Part of the reason may be that the transcriptome covered four developmental time points, but the validation only two. Three genes, Nattl, Alkaline phosphatase (Alp) and Lysozyme C II (Lyz2), had significantly higher expression in SB. The other two, Keratin-associated protein 4-3 (Krtap4-3) and Poly polymerase 6 (Parp6) had higher expression in AC embryos ( Figure 4 , S4 Table ). No morph and time interaction was detected for any of the genes. Figure 4. qPCR validation of candidates from transcriptome in whole embryos of Arctic charr. Relative expression of 9 genes ( A – I ) analysed by qPCR in the small benthic (SB) charr from Lake Thingvallavatn and aquaculture (AC) charr at two different developmental timepoints (161 and 200 τs ). 5 genes were differentially expressed between the two morphs ( Alp, Krtap4-3, Lyz, Nattl, Parp6 ), while 4 further genes did not show significant expression differences between morphs ( Cgat2, Cox6B1, Ndub6, Ubl5 ), see S4 Table . Error bars represent standard deviation calculated from two biological replicates. As some genes are represented by different contigs or even paralogs, we set out to disentangle the expression of one paralog group Nattern-like ( Nattl ) in detail. We measured the expression of three natterin paralogs ( nattl1, nattl2 and nattl3 ), by designing qPCR primers that matched divergent regions. These genes caught our interest because the only prior work implicated Natterin as a toxin produced by a tropical fish 70 , 71 . We studied nattl expression in several developmental stages in AC-, SB- and PL-charr as well as in selected tissues of adult AC-charr. The expression level of the three paralogs differed between morphs and timepoints ( Figure 5 and S5 Table ). Overall nattl2 had the highest expression in all morphs. The nattl1 had higher expression in embryos of PL-charr than in AC- and SB-charr, while nattl2 and nattl3 were more expressed in SB-embryos. Note however, the efficiency of the primers for the nattl genes ranged from 1.72 to 1.77, which suggests this data should be interpreted with caution. Figure 5. Relative expression of Natterin-like and its three paralogs during charr development in different morphs. The expression is graphed for different morphs (SB, AC and PL) at four developmental timepoints (161, 200, 256 & 315 τs , relative to AC-charr at timepoint 161. A ) General nattl expression along charr development. B – D ) Expression of nattl paralogs 1–3. ANOVA showing the variation among morphs is summarized in S5 Table . In order to evaluate the hypothesis that nattl genes have immune-related functions we studied expression in adult tissues (in AC-charr). The nattl expression was highest in the gills, followed by expression in kidney, skin and spleen. Low expression levels were detected in liver, intestine and heart ( S1 Figure and S5 Table ). The three nattl paralogs followed different patterns, whilst each of them showed significant expression differences among tissues. Nattl1 was mainly expressed in spleen and kidney, while nattl2 showed a significantly higher expression in skin, liver and in gills. Similarly, the relative expression of nattl3 was highest in the gills and skin. This indicates that the three nattl paralogs are expressed in a tissue specific manner, and also differently during the development of the three charr morphs studied here. Expression differences in the developing heads of benthic and limnetic charr morphs The transcriptome only compared two morphs, but we want to find genes with relationship with benthic form or ecology. Thus we next compared two benthic (SB, LB) and two limnetic charr (AC, PL). To get a handle on the craniofacial divergence between sympatric Arctic charr morphs we used qPCR to study 8 paralog groups with expression difference in the RNA-seq data (all higher in SB). We focused on those with known craniofacial expression in zebrafish development 72 . We analyzed heads at three time-points (178, 200 and 218 τs ) as this period overlaps with early stages of craniofacial skeletal formation in Arctic charr 73 , 74 . The qPCR confirmed the higher expression of seven out of these eight genes in the head of benthic charr compared to limnetic charr ( Figure 6 , S2 Figure and Dataset 3 ). These seven genes are Claudin 4 (Cldn4) , adseverin (Scin) , Junction plakoglobin (Jup) , Lipolysis stimulated lipoprotein receptor (Lsr) , Major vault protein (Mvp) , Transforming growth factor beta receptor II (Tgfbr2) and Vitamin D receptor a (Vdra) . The eighth gene, Retinoic acid receptor gamma-A (Rarg) gave a small but significant response in the head, but the effects were reversed, i.e. the expression was higher in AC. The expression difference of the seven genes was, in almost all cases, consistent over the three timepoints studied (See S2 Figure ). In summary the qPCR confirmed the differential expression of 12 of the 17 paralog groups studied ( Table 3 ), some which had 5–10% FDR support. The data reveal notable expression differences between these two charr morphs, and can lead to hypotheses about morph specific variation in particular structures, like the developing head. However this transcriptome should not be taken at face value, because a substantial fraction of signals were false positives. Figure 6. Expression differences of craniofacial candidate genes in developing head of Arctic charr morphs. Relative expression ratios, calculated from the qPCR data, were subjected to an ANOVA to test the expression differences amongst four charr groups and three time points ( τs ). The underlined gene names reflect significant difference between SB and AC-charr. A post hoc Tukey’s test (HSD) was performed to determine the effects of morphs, time and morph by time interaction (M X T). White boxes represent low expression, while black boxes represent high expression. The shading represents significant different expression between the samples (α = 0.05, NS = not significant). The genes studied were, Claudin 4 (Cldn4) , adseverin (Scin) , Junction plakoglobin (Jup) , Lipolysis stimulated lipoprotein receptor (Lsr) , Major vault protein (Mvp) , Transforming growth factor beta receptor II (Tgfbr2) Vitamin D receptor a (Vdra) and Retinoic acid receptor gamma-A (Rarg) . Table 3. Correspondence of transcriptome and qPCR verification on Arctic charr embryos. Tissue Name Abbr FDRm FRDt Effect qPCR Morph Embryo Alkaline phosphatase Alp 0.070 0.001 0.986 * SB Embryo Chondroitin sulfate N-acetylgalactosaminyltransferase 2 Cgat 0.004 0.331 -2.556 Embryo Cytochrome c oxidase subunit 6B1 Cox6b1 0.058 0.632 -1.208 Embryo B5X596 Keratin-associated protein 4-3 Krtap4-3 0.012 0.278 -1.986 * AC Embryo Lysozyme C II Lyz2 0.041 0.001 1.138 * SB Embryo Natterin-like protein Nattl 0.000 0.000 2.755 * SB Embryo NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 6 Ndub6 0.098 0.670 -1.175 Embryo Poly [ADP-ribose] polymerase 6 Parp6 0.108 0.379 -0.986 * AC Embryo Ubiquitin-like protein 5 Ubl5 0.059 0.003 -1.234 Head Claudin-4 Cldn4 0.068 0.000 1.343 * SB/LB Head Major vault protein Mvp 0.065 0.528 0.958 * SB/LB Head Junction plakoglobin Jup 0.051 0.006 1.147 * SB/LB Head Lipolysis-stimulated lipoprotein receptor Lsr 0.013 0.043 1.369 * SB/LB Head TGF-beta receptor type-2 Tgfbr2 0.065 0.013 1.728 * SB/LB Head Vitamin D3 receptor A Vdra 0.053 0.052 1.312 * SB/LB Head Retinoic acid receptor gamma-A Rarg 0.012 0.001 1.403 Head Adseverin Scin 0.007 0.000 1.578 * SB/LB Tissue: which tissue was studied Abbr: abbreviated paralog group or gene name FRDm: FDR for comparison of SB and AC-charr in transcriptome FDRt: FDR for comparison among developmental timepoints in transcriptome Effect: logarithm of fold change between morphs, positive is higher in SB and negative higher in AC-charr in transcriptome (logFC.morph in supplemental dataset 1) qPCR: results consistent with transcriptome (*), a blank cell reflects lack of correspondence Morph: which morph(s) had higher expression in qPCR verification Analyses of polymorphism in Arctic charr transcriptome The RNA-seq data also revealed segregating variations with large frequency differences between charr morphs. To uncover candidate SNPs we mapped the reads to all of the S. salar EST-contigs. Filtering on coverage yielded 165,790 candidate SNPs ( Table 4 ); of those 66.569 came from reads that mapped uniquely and 57.009 candidate SNPs from reads that mapped to more than one contig; with limited overlap between lists. Assuming that the expression of paralogous genes is stable, then differences among paralogs appear as SNPs at similar frequency in all samples. By requiring variant frequency differences (p < 0.05, uncorrected) between samples we reduced the list of candidates by two thirds, yielding over 20.000 candidate SNPs. Note, as cDNA from charr families was sequenced (not a population sample), estimates of SNP frequencies are imprecise. To err on the side of caution, we chose SNP candidates with 50% or higher frequency difference between morphs for further study. The candidate SNPs were also summarized by frequency of the derived allele, in reference to the S. salar sequence. This gave 672 and 872 SNPs at higher frequency, in AC-charr and SB-charr, respectively. The uniquely and multiply mapped reads, revealed approximately similar numbers of candidate SNPs. Gene ontology analysis showed that for derived SNPs in SB, there was an excess of variants in genes related to translation, both as a broad category and specific subgroups ( S6 Table ). There was also enrichment of SNPs in genes related to DNA-mediated transposition, DNA integration, DNA replication and oxidation-reduction process. No GO categories were enriched for high frequency derived SNPs in AC. Furthermore, functional effects of the candidate SNPs (UTR, synonymous and non-synonymous) were predicted. The distribution among those categories did not differ between variants detected by uniquely or repeatedly mapped reads, χ [ 3 ] 2 = 2.59 , p = 0.46 ( S7 Table ). Table 4. Candidate SNPs in the Arctic charr transcriptome, filtered by coverage, difference between sample and morphs and frequency difference between morphs. SNP-candidates Morph Uni Rep Total Total 96231 74341 165790 Filter coverage 66569 57009 113776 Diff. Bwn. samples 21417 22252 42869 Diff. Bwn. morphs 11385 12953 23974 Delta > 0.5 AC 396 285 672 Delta > 0.5 SB 526 353 872 Delta > 0.75 AC 95 68 159 Delta > 0.75 SB 155 95 248 Delta > 0.95 a AC 17 13 30 Delta > 0.95 a SB 29 4 33 SNP-candidates: found by mapping to S. salar ESTs Uni/REP: from UNIquely or REPeatedly mapped RNA-reads Delta: differences in allele frequency between morphs, categorized by which morph had the higher derived allele frequency a The number of SNP-candidates before the redundant ones were removed A total of 60 candidate SNPs are nearly fixed in one morph, with frequency difference between morphs above 95% (after manual inspection of contigs and SNP position three candidates were removed since they represented the same SNP). Of these “fixed” SNPs 46 came from uniquely mapped reads and 14 from reads that mapped more than twice ( Table 5 and Table 6 ). For the SNPs from uniquely mapped reads, 17 are fixed in AC-charr and 29 in SB-charr. The few genes with two or more polymorphic sites were; Keratin type II cytoskeletal 3 (Krt3) , Cysteine sulfinic acid decarboxylase (Csad) and DNA-directed RNA polymerase I subunit RPA12 (Rpa12) with 5, 5 and 2 SNPs respectively. Krt3 and Csad had significant differentiation in both SB and AC. Similarly, 14 SNPs with large differentiation between morphs were predicted from reads that mapped on two or more contigs ( Table 6 ). Of these, we found two variants in the mitochondrial 60S ribosomal protein L36 (RpL36) and variants in 4 other mitochondrial genes (28S ribosomal protein S18a mitochondrial (MRPS18A) , Apoptosis-inducing factor 1 mitochondrial (AIFM1) , Isocitrate dehydrogenase [NADP] mitochondrial (acIDH1) and Protein S100-A1 (S100a1)) , all at higher frequency in AC-charr. PCR and Sanger sequencing of population samples confirmed SNPs in DNA2-like helicase (Dna2) , a gene with nuclear and mitochondrial function, and two other genes Uroporphyrinogen decarboxylase (Urod) , and Mid1-interacting protein 1-like (Mid1ip1) ( S2 Table ). The candidate variant Eukaryotic translation initiation factor 4 gamma 2 (Eif4g2) was not substantiated by the PCR/sequencing. Table 5. SNP candidates from uniquely mapped reads. (a) Higher frequency in AC morph Contig Annotation Pos Ref Var Freq-SB Freq-AC Effect SS2U026955 Keratin type II cytoskeletal 3 300 A T 0.000 0.984 synonymous SS2U026955 Keratin type II cytoskeletal 3 309 G A 0.000 0.996 synonymous SS2U033960 Cysteine sulfinic acid decarboxylase 192 C G 0.000 1.000 5prime SS2U033960 Cysteine sulfinic acid decarboxylase 416 G T 0.000 0.961 G to V SS2U033960 Cysteine sulfinic acid decarboxylase 945 C A 0.004 0.956 synonymous SS2U043396 Eukaryotic translation initiation factor 2-alpha kinase 1 134 A G 0.000 1.000 5prime SS2U043886 Transcription cofactor HES-6 1308 T C 0.000 1.000 5prime SS2U044339 Intraflagellar transport protein 52 homolog 479 T C 0.021 1.000 D to G SS2U045168 Putative Peptide prediction 1275 G A 0.000 1.000 3prime SS2U045328 E3 ubiquitin-protein ligase DTX3L 388 G A 0.000 0.977 synonymous SS2U045990 Low-density lipoprotein receptor-related protein 1 135 T C 0.000 0.969 synonymous SS2U048125 a Transmembrane protein 131-like 480 G A 0.000 1.000 synonymous SS2U052747 Uridine 5’-monophosphate synthase 914 G A 0.000 0.951 synonymous SS2U054542 Mediator of RNA polymerase II transcription subunit 20 474 C T 0.027 0.995 synonymous SS2U056193 SUMO-conjugating enzyme UBC9 96 A T 0.000 1.000 3prime SS2U057101 ETS domain-containing protein Elk-3 440 C G 0.000 1.000 3prime SS2U058860 Voltage-dependent anion-selective channel protein 2 681 G T 0.000 1.000 3prime (b) Higher frequency in SB morph Contig Annotation Pos Ref Var Freq-SB Freq-AC Effect SS2U000399 Insulin-like growth factor-binding protein 7 598 C A 1.000 0.000 3prime SS2U004484 Titin 387 G A 0.990 0.010 synonymous SS2U026826 L-asparaginase 363 C T 1.000 0.000 H to Y SS2U026955 Keratin type II cytoskeletal 3 116 C A 0.996 0.031 T to N SS2U026955 Keratin type II cytoskeletal 3 264 C T 0.970 0.008 synonymous SS2U026955 Keratin type II cytoskeletal 3 317 C T 1.000 0.002 T to M SS2U033960 Cysteine sulfinic acid decarboxylase 363 C T 1.000 0.025 5prime SS2U033960 Cysteine sulfinic acid decarboxylase 387 C T 1.000 0.030 synonymous SS2U033960 Cysteine sulfinic acid decarboxylase 657 T C 0.990 0.031 synonymous SS2U034322 Cyclin-C 1094 A G 1.000 0.000 3prime SS2U034431 Dolichyl-diphosphooligosaccharide–protein glycosyltransferase subunit 2 436 G A 0.992 0.000 G to S SS2U036025 Nuclear receptor coactivator 4 36 G A 1.000 0.043 5prime SS2U040590 Glutamyl-tRNA(Gln) amidotransferase subunit A homolog 478 G A 0.972 0.000 synonymous SS2U045606 Superkiller viralicidic activity 2-like 2 500 C T 1.000 0.000 synonymous SS2U047816 Squalene synthase 1139 G A 1.000 0.029 synonymous SS2U048063 Lysine-specific demethylase NO66 669 C T 1.000 0.000 synonymous SS2U050394 UPF0542 protein C5orf43 homolog 596 G A 1.000 0.000 synonymous SS2U050880 a Transmembrane protein 131-like 901 C T 1.000 0.000 A to V SS2U052076 Eukaryotic translation initiation factor 3 subunit A 824 C T 1.000 0.031 synonymous SS2U053417 RNA polymerase-associated protein LEO1 454 G A 1.000 0.049 synonymous SS2U054333 Scaffold attachment factor B2 382 G A 0.999 0.000 V to M SS2U054705 Cell division protein kinase 4 122 A G 0.971 0.000 3prime SS2U054965 DNA-directed RNA polymerase I subunit RPA12 106 G A 1.000 0.000 5prime SS2U054965 DNA-directed RNA polymerase I subunit RPA12 411 T G 1.000 0.000 synonymous SS2U055120 Chromatin modification-related protein MEAF6 350 A C 1.000 0.000 H to P SS2U055153 Complexin-1 1191 C A 1.000 0.031 3prime SS2U057635 Mitogen-activated protein kinase 14B 1370 A T 1.000 0.026 3prime SS2U058169 Transmembrane protein 50A 1214 C G 0.973 0.000 3prime SS2U058802 Signal recognition particle 54 kDa protein 607 T A 0.969 0.000 C to S a Those genes are distinct paralogs Table 6. SNP candidates with significant difference frequency between AC and SB morphs, from reads that mapped to two or more contigs. Contig Annotation Pos Ref Var Freq-SB Freq-AC Effect SS2U004839 Actin alpha sarcomeric/cardiac 550 A C 0.015 0.999 3prime SS2U021298 28S ribosomal protein S18a mitochondrial 462 A C 0.000 1.000 synonymous SS2U041264 Apoptosis-inducing factor 1 mitochondrial 341 C T 0.000 0.952 synonymous SS2U054211 a Cytoplasmic dynein 1 intermediate chain 2 136 T C 0.018 0.974 synonymous SS2U054362 a Q08CA8 Dynein cytoplasmic 1 intermediate chain 2 945 A G 0.000 1.000 synonymous SS2U055923 Bystin 1623 A C 0.000 0.983 3prime SS2U058758 Protein S100-A1 253 C T 0.000 0.984 synonymous SS2U059000 Isocitrate dehydrogenase [NADP] mitochondrial 1654 T C 0.000 0.975 3prime SS2U059146 60S ribosomal protein L36 263 T G 0.009 1.000 synonymous SS2U059146 60S ribosomal protein L36 470 A C 0.009 1.000 synonymous SS2U036667 Heterogeneous nuclear ribonucleoprotein K 813 C T 1.000 0.022 5prime SS2U042873 RNA polymerase-associated protein LEO1 460 G A 1.000 0.000 synonymous SS2U058455 Adenylosuccinate lyase 1616 C T 1.000 0.000 3prime SS2U058906 Mid1-interacting protein 1-like 350 G T 0.985 0.000 E to D a Those genes are distinct paralogs Polymorphism and expression of Arctic charr mtDNA Considering the enrichment of differentially expressed genes related to mitochondrial energy metabolism (above), and high frequency candidate SNPs in several genes with mitochondrial function in AC-charr we decided to study the mitochondrial transcriptome further. The charr studied here reflect metabolic extremes, the aquaculture charr was bred for growth while the small benthic morph is thought to have experienced natural selection for slow metabolism and retarded growth 38 , 75 . Although mRNA preparation protocols were used for generating cDNA for the RNA-sequencing, a substantial number of reads came from non-polyadenylated sequences. By mapping the reads to mtDNA sequence of Arctic charr we could estimate expression and infer polymorphism both in genes and intergenic regions. There was a clear difference in sequencing coverage, with more than twice as many reads mapped from the AC- compared to SB-charr (mean fold difference 2.27, Wilcoxon test, p < 0.0004). Note, as only two types of fish are compared, the polarity of expression divergence is unknown. The mapped RNA-reads were used to identify polymorphism and divergence in the entire mitochondrial chromosome. The polymorphisms were found by mapping to mtDNA from a Canadian S. alpinus 48 , but ancestral vs. derived status inferred by comparison to S. salar mtDNA. This revealed 82 candidate sites, including 35 that represent divergence between Icelandic and Canadian charr. A total of 20 candidate SNPs had high (more than 50%) frequency difference between SB- and AC-charr ( Figure 7 ). There was no bias in the distribution of derived SNPs, 11 on the AC branch and 9 in SB. The divergence between Iceland and Canada is particularly little in the 12s and 16s ribosomal RNA genes. Curiously two SNPs in those genes differed strongly in frequency between morphs ( Figure 7 ). To confirm and better estimate the frequency of variants in the ribosomal genes, we PCR amplified and sequenced two ~550 bp regions in the rRNA genes. Because of our interest in the evolutionary genetics of sympatric charr, we three morphs (PL, LB and SB) from Lake Thingvallavatn ( Figure 8A, C & E , S2 Table ). The 12s polymorphism (m1829G>A) differed significantly between the morphs ( χ [ 2 ] 2 = 8.6 , p = 0.014), and was at highest frequency in the SB (0% in PL, 12.5% in LB and 75% in SB). Similarly m3411C>T in the 16s was enriched in SB (62.5%) but found at lower frequency in PL (0%) and LB (12.5%) (it differed significantly between morphs, χ [ 2 ] 2 = 9.3333 , p = 0.009). The Sanger sequencing also revealed three other polymorphisms in the amplified region, not seen in the transcriptome. Among those m3211T>C in the 16s gene was at 75% frequency in LB, but not found in the other morphs ( χ [ 2 ] 2 = 19.76 , p < 0.0001). Figure 7. Genetic divergence in the mtDNA between SB- and AC-charr. The frequency differences between morphs of candidate SNPs, estimated from the RNA-sequencing, graphed along the mtDNA chromosome. The SNPs indicate whether the derived allele is of higher frequency in SB (black dots) or AC (open circles). Sites of divergence between the Icelandic stocks and the Canadian reference sequence are indicated by triangles. The two black boxes represent the rRNA genes and gray boxes the 14 coding sequences (abbreviated names underneath each gene). Figure 8. Comparative genomics and population genetic differentiation in Arctic charr at 3 mtDNA locations. Three variants in the 12s and 16s RNA genes are segregating in charr morphs in Lake Thingvallavatn. A , C , E ) Frequency of each of those variants in three morphs from Lake Thingvallavatn (PL, LB and SB). A total of 8 individuals were genotyped from each morph, see methods. B , D , F ) Aligned are several fish genomes, with Lamprey or humans as outgroups, reflecting a 38 bp window around each of the 3 positions (). Indicated are the two Arctic charr alleles, the reference allele (S._alpinus_REFcharr_WT) and the derived variant (S._alpinus_VARcharr_M). B ) Alignment of variant m1829G>A in the 12s rRNA gene in fishes, using humans as an outgroup. D ) Similar alignment of a 16s variant, m3211T>C and F ) alignment of variant m3411C>T in the 16s rRNA gene. In order to gauge the potential functionality of those variants we aligned the rRNA genes from nearly hundred fishes and several vertebrates. The position affected by m1829G>A and m3211T>C, in the 12s and 16s rRNAs, are not well conserved in fishes or vertebrates ( Figure 8B & D ). However m3411C>T, in the 16s rRNA, alters a position that is nearly invariant in 100 fish genomes ( Figure 8F ). The only exception is Pacific menhaden, which curiously also has T in this position. This region could not be aligned properly in other vertebrates. Thus m3411C>T alters a conserved position, but probably not very drastically as the introduced allele is tolerated in another fish species. NR;Unigene.Description;NR.contigs;logCPM;logFC.morph;logFC.T163;logFC.T200;logFC.T433;FDR.morph;FDR.time;Contigs 1;1;1;2.612484616;-0.225481503;-0.278873417;-0.508896253;-1.277691095;0.747951606;0.249821976;SS2U035087 2;1-acyl-sn-glycerol-3-phosphate acyltransferase epsilon;4;3.761073585;-0.037996738;-0.049978651;-0.214128465;-0.287112446;0.960307572;0.980234357;"SS2U018930;SS2U019592;SS2U039760;SS2U056048 3;1-acyl-sn-glycerol-3-phosphate acyltransferase gamma;5;6.641146181;-0.093743693;0.20009448;-0.238354348;-0.208334783;0.88435103;0.892076961;"SS2U004154;SS2U024396;SS2U048217;SS2U050512;SS2U055142 4;1-acylglycerol-3-phosphate O-acyltransferase ABHD5;1;2.133993891;0.49521831;0.023509611;1.118101512;2.324916304;0.401948832;2.13E-05;SS2U003655 5;1-phosphatidylinositol-3-phosphate 5-kinase;7;5.260268835;-0.38689962;-0.088015122;-0.391712255;-0.113987932;0.465032901;0.944528332;"SS2U002134;SS2U003087;SS2U004468;SS2U006309;SS2U044956;SS2U047955;SS2U052927 6;1-phosphatidylinositol-45-bisphosphate phosphodiesterase delta-3-A;3;2.341483498;0.431834652;0.057641376;1.060875195;1.827508509;0.493633343;0.005589471;"SS2U009205;SS2U022276;SS2U023144 7;1-phosphatidylinositol-45-bisphosphate phosphodiesterase delta-4;2;2.264883133;0.792747977;-0.097681734;0.863114439;1.270034862;0.20906477;0.102563277;"SS2U022953;SS2U040057 8;1-phosphatidylinositol-45-bisphosphate phosphodiesterase gamma-2;10;3.139508136;0.713188012;0.090462865;0.212322212;1.519687203;0.212528803;0.017349671;"SS2U000135;SS2U008251;SS2U009524;SS2U009982;SS2U011052;SS2U017905;SS2U041812;SS2U042526;SS2U043584;SS2U056769 9;10 kDa heat shock protein mitochondrial;6;7.799633887;-0.862114545;-0.110956924;-1.044483907;-1.673802659;0.165095491;0.031331196;"SS2U004717;SS2U005582;SS2U025053;SS2U028699;SS2U050788;SS2U053447 10;116 kDa U5 small nuclear ribonucleoprotein component;3;7.000213188;0.486918577;-0.367990988;-0.215463197;-1.133955925;0.360141222;0.246758537;"SS2U003487;SS2U033745;SS2U052181 11;12-dihydroxy-3-keto-5-methylthiopentene dioxygenase;3;3.911848586;-0.723764887;-0.025457788;-0.792186481;-0.944435434;0.212472367;0.298942451;"SS2U002791;SS2U012428;SS2U056621 12;14 kDa phosphohistidine phosphatase;4;5.244222064;-0.563773699;0.160789827;-0.661330316;-0.340718352;0.344871943;0.628141603;"SS2U004897;SS2U053842;SS2U056111;SS2U057209 13;14-3-3 protein beta/alpha-1;8;9.171313556;0.095614462;0.035511723;0.041090437;-0.19442016;0.88064536;0.988917928;"SS2U009914;SS2U014987;SS2U015011;SS2U038869;SS2U042558;SS2U054347;SS2U058799;SS2U058972 14;14-3-3 protein beta/alpha-2;7;8.957141502;-0.257379786;0.006140266;-0.363991125;-0.695437341;0.656199827;0.592338255;"SS2U003874;SS2U007688;SS2U010089;SS2U013448;SS2U025534;SS2U058852;SS2U058868 15;14-3-3 protein epsilon;8;8.667318144;-0.361145939;0.160119787;-0.156305471;-0.294410443;0.502884329;0.90900795;"SS2U006277;SS2U014641;SS2U032523;SS2U036452;SS2U039538;SS2U052995;SS2U056978;SS2U057649 16;14-3-3 protein eta;1;6.600213965;-0.42158307;0.410260345;0.019051378;-0.168586586;0.428541857;0.814944143;SS2U056360 17;14-3-3 protein gamma-1;3;4.327066324;0.085545254;0.321091797;1.355584646;2.381201245;0.914552447;0.000281919;"SS2U020224;SS2U033530;SS2U046718 18;14-3-3 protein gamma-2;1;4.510861236;0.765738803;0.639947348;2.663870715;3.600739346;0.379845184;2.59E-05;SS2U048701 19;14-3-3 protein zeta;3;6.875487454;-0.335563171;0.145279794;-0.292905615;-0.232669211;0.573194022;0.917840857;"SS2U000658;SS2U051741;SS2U058533 20;14-3-3 protein zeta/delta;2;7.428260156;0.427798182;-0.140877964;0.108542693;0.392081067;0.408709329;0.851513193;"SS2U016659;SS2U057927 21;14-alpha-glucan-branching enzyme;2;3.042413021;0.892710917;0.106418452;1.375322749;1.922063649;0.207401194;0.017934593;"SS2U026017;SS2U028058 22;15 kDa selenoprotein;2;6.253954585;-0.80529457;0.195399573;-0.999801208;-1.240689902;0.227415942;0.115855957;"SS2U056695;SS2U058959 23;15-hydroxyprostaglandin dehydrogenase [NAD+];4;5.251629565;0.183546552;0.135330677;-0.141998365;0.570774647;0.775348257;0.678955932;"SS2U010083;SS2U038717;SS2U057358;SS2U058207 24;17-beta-hydroxysteroid dehydrogenase 14;3;5.185466229;0.31567291;-0.109987412;0.159190769;0.444034696;0.574472376;0.834254827;"SS2U006677;SS2U033760;SS2U058371 25;2'3'-cyclic-nucleotide 3'-phosphodiesterase;2;8.077383965;-0.218525486;0.367732124;-0.01492356;-0.600848565;0.715628762;0.42991632;"SS2U042736;SS2U056939 26;2-acylglycerol O-acyltransferase 2-A;5;2.458927261;0.428000924;0.274668818;0.437833355;0.635828036;0.481229598;0.840235407;"SS2U009307;SS2U011994;SS2U029798;SS2U041706;SS2U052121 27;2-amino-3-carboxymuconate-6-semialdehyde decarboxylase;1;2.922103392;-0.097074048;-0.182504475;-0.036632019;0.038738542;0.912496047;0.998274817;SS2U052082 28;2-amino-3-ketobutyrate coenzyme A ligase mitochondrial;3;4.52535201;-0.263590115;0.139116392;-0.012539865;0.253874869;0.640419788;0.979824326;"SS2U006645;SS2U007274;SS2U058373 29;2-aminoethanethiol dioxygenase;3;3.497900691;-0.321419314;0.36456262;0.001915721;-0.193790211;0.575455719;0.854908114;"SS2U012362;SS2U047445;SS2U055590 30;2-hydroxyacyl-CoA lyase 1;3;5.200967177;0.404513275;-0.131646828;0.359150262;0.759905736;0.481095107;0.449404569;"SS2U003230;SS2U014852;SS2U051718 31;2-oxoglutarate and iron-dependent oxygenase domain-containing protein 1;4;4.173033846;-0.060619158;-0.089973463;-0.374285612;-0.852045365;0.927630034;0.457784822;"SS2U021246;SS2U041176;SS2U043117;SS2U048634 32;2-oxoglutarate and iron-dependent oxygenase domain-containing protein 2;4;2.698309587;-0.684351189;-0.022296588;-0.710502718;-0.073093846;0.212528803;0.656271941;"SS2U020964;SS2U023809;SS2U024697;SS2U041648 33;2-oxoglutarate dehydrogenase mitochondrial;6;6.656969148;0.397717801;0.079638703;0.771518231;1.686102482;0.518480722;0.012739482;"SS2U015898;SS2U027755;SS2U030749;SS2U040484;SS2U042130;SS2U047766 34;2-oxoisovalerate dehydrogenase subunit alpha mitochondrial;2;4.705668206;0.789048388;-0.012571542;0.588147276;1.170022501;0.17017232;0.145758626;"SS2U014675;SS2U043384 35;2-oxoisovalerate dehydrogenase subunit beta mitochondrial;3;6.331992067;-0.184567693;0.013826487;-0.205154242;-0.00152498;0.752680634;0.989961948;"SS2U018362;SS2U049433;SS2U055173 36;24-dienoyl-CoA reductase mitochondrial;1;4.961320773;0.315645176;-0.149037773;0.297366645;1.09369054;0.584644751;0.103584083;SS2U044796 37;25-hydroxycholesterol 7-alpha-hydroxylase;2;3.214382274;-0.222220961;-0.223709437;-0.091760327;0.622466861;0.719686573;0.477324406;"SS2U035375;SS2U055951 38;26S protease regulatory subunit 4;22;8.533552286;-0.367148747;-0.057635086;-0.650346274;-0.945953669;0.515375627;0.295606682;"SS2U000228;SS2U010151;SS2U011752;SS2U011753;SS2U012688;SS2U012933;SS2U013810;SS2U013811;SS2U013935;SS2U020575;SS2U020715;SS2U023197;SS2U036612;SS2U036613;SS2U037972;SS2U040248;SS2U042370;SS2U045373;SS2U047133;SS2U052199;SS2U058522;SS2U058550 39;26S protease regulatory subunit 6A;18;8.253677439;-0.17228184;-0.189995137;-0.675727099;-0.853383109;0.782910075;0.422594111;"SS2U004029;SS2U008932;SS2U009083;SS2U009894;SS2U010142;SS2U011408;SS2U015041;SS2U033647;SS2U033648;SS2U033649;SS2U033650;SS2U040816;SS2U044050;SS2U044051;SS2U050352;SS2U051921;SS2U056860;SS2U059095 40;26S protease regulatory subunit 6B;7;7.126090461;0.009834256;-0.242365433;-0.30581166;-0.745460691;0.989071431;0.642677932;"SS2U002692;SS2U007157;SS2U009523;SS2U028733;SS2U038597;SS2U057664;SS2U058595 41;26S protease regulatory subunit 7;5;8.376547781;-0.257052465;-0.134738764;-0.710431026;-0.872461241;0.675286814;0.374988036;"SS2U000417;SS2U015749;SS2U029765;SS2U034493;SS2U058796 42;26S protease regulatory subunit 8;18;8.230321707;0.05270079;-0.225639511;-0.608322928;-1.023704563;0.941180218;0.309606235;"SS2U007232;SS2U008594;SS2U009304;SS2U013322;SS2U032380;SS2U032381;SS2U032382;SS2U037695;SS2U037696;SS2U040409;SS2U040410;SS2U042743;SS2U049808;SS2U050333;SS2U050641;SS2U051057;SS2U054751;SS2U057654 43;26S protease regulatory subunit S10B;6;7.65562158;-0.203435027;-0.167311415;-0.5384128;-0.974634262;0.72409881;0.306164559;"SS2U000606;SS2U006113;SS2U010477;SS2U044081;SS2U054959;SS2U057794 44;26S proteasome complex subunit DSS1;6;6.887659987;-0.774315096;0.181132612;-0.858714018;-1.019254593;0.208104783;0.175993729;"SS2U000916;SS2U008456;SS2U020081;SS2U020626;SS2U051119;SS2U059023 45;26S proteasome non-ATPase regulatory subunit 1;4;8.917522541;0.078399876;-0.010462799;-0.228966095;-0.21244349;0.90594062;0.981227175;"SS2U012719;SS2U036322;SS2U046523;SS2U057465 46;26S proteasome non-ATPase regulatory subunit 10;4;4.90235986;-0.118297419;-0.214363556;-0.601840846;-1.064868546;0.849579808;0.242014629;"SS2U010644;SS2U032193;SS2U040530;SS2U056913 47;26S proteasome non-ATPase regulatory subunit 11;6;7.109670026;-0.235081304;0.001951351;-0.288034111;-0.641287535;0.681065747;0.66409136;"SS2U018584;SS2U022136;SS2U022137;SS2U040196;SS2U046815;SS2U057146 48;26S proteasome non-ATPase regulatory subunit 12;9;7.13409551;-0.235948125;0.018766092;-0.390877057;-0.580103261;0.681690774;0.685201513;"SS2U011282;SS2U011613;SS2U012390;SS2U012391;SS2U020242;SS2U028769;SS2U053263;SS2U054778;SS2U054831 49;26S proteasome non-ATPase regulatory subunit 13;5;7.292928441;0.025174124;-0.076697993;-0.36904132;-0.774026842;0.972489366;0.534208271;"SS2U007629;SS2U012279;SS2U012322;SS2U038513;SS2U057731 50;26S proteasome non-ATPase regulatory subunit 14;6;7.77420275;-0.355224582;-0.030704389;-0.65508484;-1.140569281;0.541332864;0.163843833;"SS2U005680;SS2U012949;SS2U020653;SS2U024034;SS2U041001;SS2U056977 51;26S proteasome non-ATPase regulatory subunit 2;5;8.186805901;0.2427975;-0.135570494;-0.086270125;-0.313739032;0.675271169;0.976879367;"SS2U005642;SS2U041625;SS2U042926;SS2U051028;SS2U053413 52;26S proteasome non-ATPase regulatory subunit 3;3;7.899890138;-0.067114858;-0.110041332;-0.295265304;-0.626164189;0.917998;0.743983161;"SS2U000463;SS2U052183;SS2U058552 53;26S proteasome non-ATPase regulatory subunit 4;7;7.908815489;0.017318065;-0.133741333;-0.34933992;-0.511466553;0.981079705;0.851731748;"SS2U015561;SS2U029338;SS2U034552;SS2U036709;SS2U046483;SS2U051587;SS2U058665 54;26S proteasome non-ATPase regulatory subunit 5;7;4.869183843;-0.306241424;-0.078909095;-0.580967681;-1.229394279;0.5836968;0.114239288;"SS2U012282;SS2U013293;SS2U017568;SS2U018206;SS2U038185;SS2U048666;SS2U055930 55;26S proteasome non-ATPase regulatory subunit 6;6;7.346566165;-0.42608542;-0.138841393;-0.69599827;-1.110825811;0.434436135;0.189179238;"SS2U011170;SS2U033952;SS2U033953;SS2U054527;SS2U056315;SS2U057164 56;26S proteasome non-ATPase regulatory subunit 7;7;7.470946361;-0.400421495;-0.097805971;-0.772007365;-1.179148614;0.463117205;0.118341502;"SS2U010858;SS2U013445;SS2U019052;SS2U036834;SS2U048656;SS2U056703;SS2U056777 57;26S proteasome non-ATPase regulatory subunit 8;3;6.64641373;-0.313928299;-0.096714438;-0.646478964;-0.895896348;0.571700033;0.32586206;"SS2U016668;SS2U019905;SS2U058847 58;26S proteasome non-ATPase regulatory subunit 9;2;5.773556489;-0.459594803;0.038376908;-0.716815273;-0.782819986;0.436328465;0.383842162;"SS2U040755;SS2U058260 59;28 kDa heat- and acid-stable phosphoprotein;3;6.171767363;0.109339439;-0.128155366;-0.421473556;-0.63830012;0.864347717;0.725119944;"SS2U011068;SS2U047224;SS2U049439 60;28S ribosomal protein S11 mitochondrial;1;5.203915635;0.572861559;-0.470927495;0.190371684;0.239793828;0.323318217;0.728972213;SS2U056130 61;28S ribosomal protein S12 mitochondrial;3;5.427490213;-0.031845995;0.205320505;-0.487298981;-0.309306247;0.970050329;0.757679185;"SS2U017647;SS2U048115;SS2U051845 62;28S ribosomal protein S14 mitochondrial;1;5.128497011;-0.47441701;-0.185092754;-0.777058943;-1.240536351;0.396288574;0.139626385;SS2U055706 63;28S ribosomal protein S15 mitochondrial;4;5.501242752;-0.688277631;-0.073414991;-0.716551038;-0.696018301;0.215652119;0.509195192;"SS2U018180;SS2U028076;SS2U052753;SS2U056438 64;28S ribosomal protein S16 mitochondrial;5;5.422273596;-0.699601404;0.056692936;-0.775105155;-1.095121695;0.23827036;0.195950521;"SS2U019386;SS2U028793;SS2U038914;SS2U051880;SS2U055666 65;28S ribosomal protein S17 mitochondrial;1;3.698179019;-0.781067796;0.013740296;-0.250256492;-0.436823351;0.140788912;0.890427338;SS2U052929 66;28S ribosomal protein S18a mitochondrial;4;4.84299107;-0.63831584;0.042755114;-0.506040998;-0.669871827;0.238808747;0.576445807;"SS2U004691;SS2U021298;SS2U045590;SS2U057048 67;28S ribosomal protein S18b mitochondrial;3;5.508608188;-0.688429994;-0.020533866;-0.912165529;-0.9583115;0.234125835;0.224932303;"SS2U006506;SS2U012078;SS2U058582 68;28S ribosomal protein S18c mitochondrial;2;4.448254959;-1.242911541;0.191601846;-1.035553529;-1.265491362;0.067934031;0.096962162;"SS2U037873;SS2U054775 69;28S ribosomal protein S2 mitochondrial;4;4.156927822;0.506744993;-0.31807741;0.562772152;0.628290306;0.41233619;0.439788593;"SS2U009977;SS2U022108;SS2U022557;SS2U047360 70;28S ribosomal protein S21 mitochondrial;3;4.954435408;-0.897268292;0.297736865;-0.653767446;-0.430177306;0.186885004;0.608125734;"SS2U010989;SS2U010990;SS2U058039 71;28S ribosomal protein S22 mitochondrial;1;4.762075071;-0.167867263;0.084831144;-0.317253119;-0.204942406;0.780057818;0.92373926;SS2U052885 72;28S ribosomal protein S23 mitochondrial;3;5.638584938;-0.514081797;0.016439839;-0.671154438;-0.728621347;0.374825667;0.480773721;"SS2U043224;SS2U050518;SS2U055511 73;28S ribosomal protein S24 mitochondrial;1;4.856519781;-0.489962276;0.024117224;-0.610672455;-0.633718218;0.377381943;0.558476639;SS2U058450 74;28S ribosomal protein S25 mitochondrial;1;4.733867569;-0.790927035;-0.293817414;-1.317193043;-1.865835929;0.238329153;0.022618824;SS2U055707 75;28S ribosomal protein S26 mitochondrial;1;4.345595394;-0.68315987;0.112223002;-0.618365597;-0.460598881;0.286197167;0.71545054;SS2U047977 76;28S ribosomal protein S27 mitochondrial;4;4.731640031;-0.374618796;0.011200305;-0.345597686;-0.441173895;0.489805548;0.851600158;"SS2U034246;SS2U038384;SS2U041131;SS2U046008 77;28S ribosomal protein S28 mitochondrial;1;4.505666963;-1.028222902;0.188322294;-0.779662945;-0.661260452;0.122884871;0.447183828;SS2U058926 78;28S ribosomal protein S29 mitochondrial;1;6.597229352;0.013732677;0.000722904;-0.336260958;-0.237481976;0.985691689;0.947300455;SS2U056402 79;28S ribosomal protein S30 mitochondrial;4;5.240659923;-0.47584848;-0.095550638;-0.457352316;-0.508734647;0.37782767;0.815165066;"SS2U006502;SS2U006503;SS2U047973;SS2U052792 80;28S ribosomal protein S31 mitochondrial;3;4.964838903;-0.27951276;-0.037148585;-0.380414415;-0.321805332;0.635125282;0.919480863;"SS2U021444;SS2U047048;SS2U049762 81;28S ribosomal protein S33 mitochondrial;5;4.898911448;-0.550446693;0.053994499;-0.499990827;-0.739544151;0.296349215;0.472180402;"SS2U003850;SS2U007973;SS2U021798;SS2U036682;SS2U050839 82;28S ribosomal protein S34 mitochondrial;4;5.864085834;-0.040451501;0.040315913;-0.013629602;0.001429209;0.954288703;0.999555498;"SS2U016987;SS2U038996;SS2U056137;SS2U057302 83;28S ribosomal protein S35 mitochondrial;3;5.25740306;-0.052795555;-0.199060509;-0.479862344;-0.533731855;0.941737343;0.829968986;"SS2U002256;SS2U009639;SS2U055926 84;28S ribosomal protein S36 mitochondrial;4;5.43307422;-0.934530715;0.102337898;-0.864566062;-0.519667864;0.137182715;0.463025962;"SS2U013845;SS2U049500;SS2U049950;SS2U051234 85;28S ribosomal protein S5 mitochondrial;1;6.178530985;0.023186476;-0.189013133;-0.131140732;-0.00132169;0.975059328;0.992628004;SS2U052099 86;28S ribosomal protein S6 mitochondrial;1;3.879335649;-0.816243792;-0.00025288;-0.88482926;-0.894100441;0.14962952;0.233227714;SS2U058065 87;28S ribosomal protein S7 mitochondrial;5;4.940592381;-0.454465422;-0.111763416;-0.469467501;-0.74687189;0.411646096;0.619699253;"SS2U013106;SS2U013628;SS2U018480;SS2U054257;SS2U058541 88;28S ribosomal protein S9 mitochondrial;6;5.495421144;-0.111091831;-0.00708016;-0.236545782;-0.190926038;0.862085985;0.983437449;"SS2U006485;SS2U006486;SS2U015726;SS2U020944;SS2U050118;SS2U050872 89;3 beta-hydroxysteroid dehydrogenase type 7;3;3.48671514;-0.174860214;-0.066814768;-0.04981122;0.841527514;0.780265362;0.275895001;"SS2U036774;SS2U042422;SS2U056948 90;3'(2')5'-bisphosphate nucleotidase 1;7;4.849270994;-0.287841079;-0.166702746;-0.775132734;-1.13972179;0.645678099;0.198949412;"SS2U001678;SS2U025147;SS2U025148;SS2U034920;SS2U034921;SS2U042847;SS2U052711 91;3'-5' exoribonuclease 1;4;4.470430025;0.02279238;-0.103065279;-0.406062083;-0.700904269;0.975719147;0.646306174;"SS2U028207;SS2U028632;SS2U039327;SS2U052405 92;3'-5' exoribonuclease CSL4 homolog;1;5.868949005;0.739119967;-0.267601046;-0.044932866;0.061994606;0.147679878;0.967595152;SS2U049850 93;3-hydroxy-3-methylglutaryl-coenzyme A reductase;2;4.557159487;-0.135559043;0.362867627;0.230801883;0.476259806;0.82407821;0.892356028;"SS2U003076;SS2U055576 94;3-hydroxyacyl-CoA dehydrogenase type-2;4;6.698483102;0.146351129;-0.08990561;-0.15821137;0.060377516;0.832016291;0.994002833;"SS2U020095;SS2U023246;SS2U038157;SS2U057905 95;3-hydroxyanthranilate 34-dioxygenase;4;2.806312613;-0.784021498;0.329333314;0.343323797;-0.062139894;0.180123247;0.916947218;"SS2U006638;SS2U009895;SS2U009973;SS2U057913 96;3-hydroxybutyrate dehydrogenase type 2;2;6.274424607;0.125435903;-0.396453228;-0.511679208;-1.088528138;0.839803511;0.273416156;"SS2U034664;SS2U057977 97;3-hydroxyisobutyrate dehydrogenase mitochondrial;7;7.236679117;0.090590492;-0.001247416;0.010709836;0.274198658;0.888504031;0.971597254;"SS2U005697;SS2U008752;SS2U008753;SS2U009052;SS2U021506;SS2U039141;SS2U057617 98;3-hydroxyisobutyryl-CoA hydrolase mitochondrial;2;2.954833712;0.489151851;-0.700792529;0.195210922;0.437298775;0.46067388;0.432283716;"SS2U008687;SS2U045701 99;3-ketoacyl-CoA thiolase mitochondrial;6;7.05521624;0.319854961;-0.134167994;0.299334467;1.958246854;0.557600901;8.39E-06;"SS2U011940;SS2U013257;SS2U015495;SS2U053380;SS2U054192;SS2U058414 100;3-mercaptopyruvate sulfurtransferase;3;4.70146886;0.014550328;-0.2324319;-0.062748838;0.189315049;0.984455486;0.935887704;"SS2U015199;SS2U055198;SS2U057713 101;3-oxo-5-alpha-steroid 4-dehydrogenase 2;8;2.274953094;0.427539106;1.032875885;1.562785999;3.637908425;0.517605632;8.21E-10;"SS2U006473;SS2U010008;SS2U011564;SS2U045326;SS2U047204;SS2U047887;SS2U050754;SS2U052433 102;3-oxo-5-alpha-steroid 4-dehydrogenase 3;2;3.073396424;-1.395723203;0.04121692;-1.061170922;-1.280657543;0.034617034;0.108381277;"SS2U002622;SS2U039252 103;3-oxo-5-beta-steroid 4-dehydrogenase;2;1.881881779;-0.740713388;0.696503513;0.310488911;0.985125315;0.212033724;0.476871638;"SS2U030161;SS2U058423 104;3-oxoacyl-[acyl-carrier-protein] reductase;1;3.451483003;-0.198678211;-0.231535703;-0.861951831;-1.103377868;0.7674607;0.229366518;SS2U055976 105;3-oxoacyl-[acyl-carrier-protein] synthase mitochondrial;5;3.273610448;0.312415135;-0.247170465;-0.355600779;-0.716089603;0.589502488;0.723844181;"SS2U022454;SS2U022455;SS2U029834;SS2U033191;SS2U040650 106;3-phosphoinositide-dependent protein kinase 1;6;5.056949073;0.282977969;-0.045338465;0.438192622;0.787280815;0.638424983;0.442699727;"SS2U003646;SS2U025719;SS2U036683;SS2U052207;SS2U052369;SS2U053579 107;32-trans-enoyl-CoA isomerase mitochondrial;1;5.060236514;0.315473526;-0.291278524;0.162652782;1.062159009;0.604924622;0.093632544;SS2U042027 108;35 kDa SR repressor protein;3;1.824060517;-0.090947361;-0.420009053;-0.984891278;-1.342427156;0.907995592;0.16417265;"SS2U011092;SS2U011093;SS2U044490 109;39S ribosomal protein L1 mitochondrial;1;5.177307963;-0.323738425;-0.049886142;-0.438618079;-0.915599747;0.571141184;0.386355032;SS2U055407 110;39S ribosomal protein L10 mitochondrial;3;5.715818581;-0.223386475;-0.142401725;-0.621886385;-0.791811989;0.727156104;0.528478991;"SS2U034633;SS2U034634;SS2U050021 111;39S ribosomal protein L11 mitochondrial;3;6.129370374;-0.208981484;-0.151828368;-0.459695098;-0.683866177;0.728722982;0.674289321;"SS2U040747;SS2U045809;SS2U058684 112;39S ribosomal protein L12 mitochondrial;2;4.536190266;0.358462055;-0.24703571;0.335856908;-0.279791463;0.534445794;0.74165247;"SS2U033104;SS2U049370 113;39S ribosomal protein L13 mitochondrial;1;5.438678271;-0.827448493;0.130593086;-0.955806005;-1.099559439;0.170893461;0.117431739;SS2U056671 114;39S ribosomal protein L14 mitochondrial;2;5.311487374;-0.34798134;-0.134347978;-0.459120653;-0.725428274;0.516992493;0.602359158;"SS2U032587;SS2U057378 115;39S ribosomal protein L15 mitochondrial;4;5.035147451;-0.241334197;-0.067527135;-0.452019819;-0.599939486;0.678434104;0.702954532;"SS2U022409;SS2U023636;SS2U049843;SS2U053236 116;39S ribosomal protein L16 mitochondrial;4;6.648619769;-0.08631435;-0.149228221;-0.350240078;-0.469581461;0.898604882;0.900911339;"SS2U016460;SS2U016461;SS2U038594;SS2U057356 117;39S ribosomal protein L17 mitochondrial;2;5.86768091;-0.744910067;-0.101125029;-1.035016689;-1.501523128;0.188940894;0.030490293;"SS2U050385;SS2U056116 118;39S ribosomal protein L18 mitochondrial;1;6.309002485;-0.386618785;-0.176737656;-0.831236584;-0.857913722;0.523052614;0.38211911;SS2U056937 119;39S ribosomal protein L19 mitochondrial;1;5.896177159;-0.131518567;0.2313727;-0.063457004;-0.484252344;0.836544192;0.705272715;SS2U053687 120;39S ribosomal protein L2 mitochondrial;1;5.552983554;-0.318211192;-0.032473287;-0.404551784;-0.407090663;0.571141184;0.86445178;SS2U057312 121;39S ribosomal protein L20 mitochondrial;1;5.278083083;-0.077848179;-0.046665525;-0.020785944;-0.096000264;0.912496047;0.999555498;SS2U053878 122;39S ribosomal protein L21 mitochondrial;8;4.224376319;-0.75590183;0.024464784;-1.008466385;-0.90382141;0.21602768;0.231624465;"SS2U010704;SS2U013739;SS2U013933;SS2U013934;SS2U038895;SS2U041427;SS2U047273;SS2U058407 123;39S ribosomal protein L22 mitochondrial;3;5.244309686;-0.516914069;-0.058946198;-0.97044511;-1.263009161;0.407946904;0.121512861;"SS2U016291;SS2U050145;SS2U054934 124;39S ribosomal protein L23 mitochondrial;3;5.197030096;-0.555859684;0.19229398;-0.345522864;-0.39427692;0.326068291;0.791610133;"SS2U008492;SS2U013708;SS2U057159 125;39S ribosomal protein L27 mitochondrial;3;4.99338042;-0.704390323;0.08497137;-0.761488498;-1.06680453;0.214798302;0.164372349;"SS2U038771;SS2U053259;SS2U054678 126;39S ribosomal protein L28 mitochondrial;2;5.300619754;-0.729310668;-0.063198979;-0.601530912;-0.931788269;0.175912764;0.34163887;"SS2U008947;SS2U056040 127;39S ribosomal protein L3 mitochondrial;4;6.266283074;-0.527294063;-0.052400825;-0.866063251;-1.314892154;0.362884154;0.088288292;"SS2U013236;SS2U024727;SS2U031652;SS2U057424 128;39S ribosomal protein L30 mitochondrial;1;4.735344317;-0.754210073;0.143856598;-0.914041748;-1.123315398;0.190623113;0.088956851;SS2U051550 129;39S ribosomal protein L32 mitochondrial;1;4.917150465;-1.207786973;-0.033537429;-1.060099337;-1.582610093;0.065286136;0.04801013;SS2U053873 130;39S ribosomal protein L33 mitochondrial;2;4.092306614;-0.468501766;-0.185186067;-0.280945789;-0.347189797;0.389837556;0.966087757;"SS2U037885;SS2U051385 131;39S ribosomal protein L34 mitochondrial;4;5.34001297;-0.466183609;-0.139518328;-0.88384886;-1.113991936;0.405474401;0.159190878;"SS2U001799;SS2U013652;SS2U027329;SS2U055779 132;39S ribosomal protein L35 mitochondrial;4;4.200381326;-0.696453539;-0.116528193;-0.511421101;-0.967129586;0.19414407;0.368588042;"SS2U005254;SS2U025138;SS2U033600;SS2U058807 133;39S ribosomal protein L36 mitochondrial;3;5.417134086;-0.256032107;-0.052635576;-0.859917079;-1.382752996;0.712641469;0.090561601;"SS2U028768;SS2U043854;SS2U049454 134;39S ribosomal protein L37 mitochondrial;3;6.317582597;-0.438880049;-0.180490262;-0.759362953;-1.155591025;0.457823217;0.20864797;"SS2U035367;SS2U039089;SS2U055924 135;39S ribosomal protein L38 mitochondrial;2;6.321815641;-0.517264645;0.036483579;-0.779815842;-1.037346626;0.38455226;0.213215093;"SS2U048951;SS2U054937 136;39S ribosomal protein L39 mitochondrial;2;6.006336765;-0.220292173;-0.151526374;-0.576115522;-0.219385077;0.739930234;0.869481224;"SS2U017884;SS2U056150 137;39S ribosomal protein L4 mitochondrial;5;5.929808321;-0.673821958;-0.052850131;-0.706406445;-1.038526809;0.23991553;0.267829139;"SS2U006279;SS2U021272;SS2U040354;SS2U048841;SS2U055172 138;39S ribosomal protein L40 mitochondrial;3;4.859063827;-0.344024423;-0.135593088;-0.392436379;-0.54347843;0.526771416;0.819677819;"SS2U013862;SS2U044032;SS2U056037 139;39S ribosomal protein L41 mitochondrial;5;5.298867234;-0.708500843;-0.085491762;-0.775445792;-0.879812344;0.207942093;0.349510887;"SS2U011136;SS2U038797;SS2U046510;SS2U053454;SS2U057739 140;39S ribosomal protein L42 mitochondrial;2;5.285781179;-0.893074076;0.075815787;-0.937984443;-1.144050318;0.143209077;0.12555562;"SS2U056691;SS2U056792 141;39S ribosomal protein L43 mitochondrial;3;5.075688659;-0.667868471;0.148598192;-0.760248287;-0.613131701;0.288868875;0.478151198;"SS2U013477;SS2U029916;SS2U045833 142;39S ribosomal protein L44 mitochondrial;3;4.947761995;-0.716080827;0.087785557;-0.697400642;-0.919080401;0.207518175;0.270779186;"SS2U021189;SS2U049856;SS2U052421 143;39S ribosomal protein L45 mitochondrial;3;5.479839444;0.061910215;-0.048939014;-0.119973616;-0.123651644;0.92581635;0.999257081;"SS2U002125;SS2U053436;SS2U055391 144;39S ribosomal protein L46 mitochondrial;1;2.969432085;-0.013139161;-0.084036457;0.024065176;0.042482158;0.985731307;0.999257081;SS2U051887 145;39S ribosomal protein L47 mitochondrial;4;5.452598664;-1.185380073;0.070689257;-1.134445672;-1.432367081;0.064657979;0.045025455;"SS2U030480;SS2U038270;SS2U041538;SS2U056898 146;39S ribosomal protein L48 mitochondrial;3;4.562146376;-0.700946297;0.039715687;-0.579842882;-0.591631592;0.229378559;0.657349377;"SS2U008519;SS2U048537;SS2U048878 147;39S ribosomal protein L49 mitochondrial;2;4.486326516;-0.182048214;-0.153526508;-0.605490848;-0.623873269;0.774390104;0.66134273;"SS2U006675;SS2U057613 148;39S ribosomal protein L51 mitochondrial;8;5.411348966;-0.161927305;-0.114885415;-0.537744091;-0.543240147;0.800370964;0.756957796;"SS2U010367;SS2U010368;SS2U028986;SS2U031642;SS2U033957;SS2U038559;SS2U050380;SS2U058545 149;39S ribosomal protein L52 mitochondrial;3;4.596197253;-1.120718106;-0.01630417;-0.965513726;-1.393877698;0.093655822;0.113195853;"SS2U034174;SS2U049566;SS2U050758 150;39S ribosomal protein L54 mitochondrial;1;4.507962555;-0.882959802;0.169780439;-0.646385989;-0.809712967;0.152396943;0.39864786;SS2U057626 151;39S ribosomal protein L55 mitochondrial;3;3.975908127;-1.000802122;0.142481288;-1.122761166;-1.279517915;0.147679878;0.10775621;"SS2U012610;SS2U024044;SS2U056943 152;39S ribosomal protein L9 mitochondrial;5;4.362588472;-0.642033502;-0.06748024;-0.714157114;-0.671702207;0.236172797;0.502755469;"SS2U012626;SS2U014974;SS2U016892;SS2U034354;SS2U058394 153;4-aminobutyrate aminotransferase mitochondrial;6;5.857306038;0.369779204;-0.085999639;0.270261912;1.541498659;0.559388013;0.016387721;"SS2U007540;SS2U008525;SS2U020940;SS2U047088;SS2U055984;SS2U058364 154;4-hydroxyphenylpyruvate dioxygenase;7;7.471477059;0.009531085;0.143571144;0.226343226;0.172558698;0.989876121;0.99256856;"SS2U002649;SS2U008845;SS2U009556;SS2U037434;SS2U040342;SS2U049835;SS2U050804 155;4-hydroxyphenylpyruvate dioxygenase-like protein;3;4.71279424;-0.451320374;0.038749918;0.392310753;1.346344415;0.428541857;0.048726422;"SS2U020097;SS2U030418;SS2U046989 156;40S ribosomal protein S10;18;11.30119868;-0.566245133;0.095781669;-0.772424828;-0.422849583;0.372617733;0.586833208;"SS2U000915;SS2U001018;SS2U001035;SS2U005235;SS2U011689;SS2U015412;SS2U025130;SS2U026413;SS2U028704;SS2U028731;SS2U029920;SS2U029957;SS2U031431;SS2U031475;SS2U040262;SS2U054485;SS2U056994;SS2U058977 157;40S ribosomal protein S11;13;11.0846024;-0.714603036;0.032796699;-0.929564137;-1.145767719;0.241069703;0.150685771;"SS2U000933;SS2U001073;SS2U010369;SS2U015637;SS2U015638;SS2U020360;SS2U021165;SS2U030511;SS2U034197;SS2U034201;SS2U036659;SS2U036660;SS2U059274 158;40S ribosomal protein S12;9;11.50626806;-0.334301272;-0.045802339;-0.582038243;-0.868060664;0.585264586;0.434742481;"SS2U001002;SS2U027522;SS2U031125;SS2U031427;SS2U039231;SS2U042211;SS2U043295;SS2U058547;SS2U059037 159;40S ribosomal protein S13;9;11.20602473;-0.77640495;0.116190573;-0.833889481;-1.075463592;0.221767046;0.212906649;"SS2U001145;SS2U018056;SS2U026393;SS2U027172;SS2U028645;SS2U055478;SS2U055880;SS2U057723;SS2U058887 160;40S ribosomal protein S14;14;11.6862451;-0.517183985;-0.094925952;-0.668505245;-0.971394564;0.420996535;0.424075674;"SS2U000838;SS2U001032;SS2U001200;SS2U001201;SS2U012807;SS2U019292;SS2U027247;SS2U029947;SS2U030989;SS2U031546;SS2U053538;SS2U059039;SS2U059101;SS2U059333 161;40S ribosomal protein S15;13;10.6650474;-0.968285907;0.077580942;-0.916051548;-1.145702001;0.134577942;0.164055802;"SS2U001080;SS2U001182;SS2U005067;SS2U025502;SS2U028966;SS2U030990;SS2U033876;SS2U038535;SS2U041663;SS2U042894;SS2U050373;SS2U058602;SS2U058711 162;40S ribosomal protein S15a;5;10.68823035;-0.921118946;0.091858973;-1.159601613;-1.337547735;0.161434355;0.065963864;"SS2U001020;SS2U012306;SS2U013337;SS2U026939;SS2U058636 163;40S ribosomal protein S16;5;10.99852905;-0.890572891;0.048703209;-0.876047848;-1.232254279;0.144650498;0.116624185;"SS2U023651;SS2U028709;SS2U029680;SS2U046325;SS2U059130 164;40S ribosomal protein S17;10;10.62790481;-1.081216954;0.108655709;-1.065323677;-1.243372535;0.117402108;0.122056163;"SS2U015615;SS2U020183;SS2U024386;SS2U026931;SS2U028761;SS2U030994;SS2U036835;SS2U057787;SS2U058292;SS2U058643 165;40S ribosomal protein S18;10;11.00126195;-0.530087872;-0.112187095;-0.815517814;-0.842611783;0.382362706;0.413820925;"SS2U005896;SS2U012455;SS2U027303;SS2U027709;SS2U030953;SS2U032080;SS2U033153;SS2U034242;SS2U058858;SS2U059099 166;40S ribosomal protein S19;9;10.74680263;-0.936461633;0.172100213;-0.62120229;-1.059116276;0.124884688;0.206324332;"SS2U000506;SS2U001777;SS2U005689;SS2U026127;SS2U027732;SS2U029997;SS2U033408;SS2U039901;SS2U059236 167;40S ribosomal protein S2;12;13.3847466;-0.287685533;-0.089449389;-0.595023422;-0.693171185;0.673412751;0.658997786;"SS2U000853;SS2U001028;SS2U001064;SS2U001203;SS2U014886;SS2U016679;SS2U028691;SS2U031477;SS2U031879;SS2U042709;SS2U057652;SS2U059318 168;40S ribosomal protein S20;14;11.51310408;-1.123606917;0.086666863;-1.189320125;-1.401474341;0.105911486;0.065727435;"SS2U001023;SS2U001131;SS2U026123;SS2U028798;SS2U029926;SS2U030006;SS2U030034;SS2U030589;SS2U030988;SS2U031410;SS2U037864;SS2U051707;SS2U056297;SS2U058931 169;40S ribosomal protein S21;5;9.971255377;-0.712505946;-0.019844534;-1.170637268;-1.55730588;0.312892033;0.065228362;"SS2U001052;SS2U014999;SS2U033246;SS2U039140;SS2U057712 170;40S ribosomal protein S23;11;11.31397637;-0.62420653;0.140513699;-0.670928697;-0.658783095;0.292343658;0.4633711;"SS2U001117;SS2U009858;SS2U027752;SS2U029936;SS2U029974;SS2U030924;SS2U045936;SS2U048393;SS2U051471;SS2U058281;SS2U058827 171;40S ribosomal protein S24;14;11.37506593;-1.169912602;0.121603689;-0.871035686;-0.299725983;0.086170964;0.555260754;"SS2U001017;SS2U001129;SS2U001222;SS2U004012;SS2U005215;SS2U005677;SS2U012452;SS2U028677;SS2U029617;SS2U029956;SS2U030015;SS2U030178;SS2U052351;SS2U059182 172;40S ribosomal protein S25;14;11.71240098;-0.826317538;0.036346673;-0.943810767;-1.152731321;0.181797936;0.143163093;"SS2U001009;SS2U001019;SS2U008283;SS2U013619;SS2U016816;SS2U019939;SS2U019940;SS2U028548;SS2U030986;SS2U030992;SS2U042904;SS2U045466;SS2U058771;SS2U059219 173;40S ribosomal protein S26;9;11.41527369;-0.575362998;-0.151154802;-0.864244981;-1.146714206;0.375915742;0.262527727;"SS2U000816;SS2U001158;SS2U006441;SS2U006442;SS2U009452;SS2U021415;SS2U026934;SS2U047178;SS2U057120 174;40S ribosomal protein S27;11;11.17324708;-0.764529982;0.093747649;-0.882059351;-0.893653496;0.218528292;0.267892782;"SS2U007314;SS2U017540;SS2U026091;SS2U041457;SS2U043906;SS2U050402;SS2U057917;SS2U058259;SS2U058404;SS2U058885;SS2U058968 175;40S ribosomal protein S27-like;1;4.587840422;-0.824602768;-0.090280476;-0.411274883;1.892297561;0.228831776;0.000928931;SS2U057231 176;40S ribosomal protein S27a;8;11.56338817;-0.267112495;-0.116238839;-0.513869872;-0.622574573;0.674158364;0.730477039;"SS2U001433;SS2U004552;SS2U006860;SS2U013941;SS2U026930;SS2U031015;SS2U058980;SS2U059069 177;40S ribosomal protein S28;6;10.04924555;-0.685973088;0.018269227;-0.895847397;-1.170987091;0.281203681;0.17274397;"SS2U004476;SS2U005237;SS2U006868;SS2U013735;SS2U039927;SS2U057533 178;40S ribosomal protein S29;5;8.400378395;-0.486077579;0.083067968;-0.93534242;-1.034503195;0.499379778;0.242774046;"SS2U018512;SS2U027553;SS2U029914;SS2U042705;SS2U057853 179;40S ribosomal protein S3;14;11.60495338;-0.247658451;-0.214130542;-0.643792538;-0.859085889;0.694284351;0.488188213;"SS2U015493;SS2U016401;SS2U016417;SS2U017934;SS2U024171;SS2U024176;SS2U026025;SS2U033044;SS2U043900;SS2U054205;SS2U056275;SS2U058313;SS2U059092;SS2U059150 180;40S ribosomal protein S3-B;2;8.670060843;-5.306182618;-0.21914068;-3.9385762;-5.364323587;0.004981796;0.000497716;"SS2U024172;SS2U024242 181;40S ribosomal protein S30;6;10.32704619;-0.900386129;0.13805772;-0.847054792;-1.14204532;0.148006414;0.142776116;"SS2U004199;SS2U008473;SS2U009203;SS2U058163;SS2U058348;SS2U058857 182;40S ribosomal protein S3a;12;12.11875446;-0.323021095;-0.150127867;-0.651026828;-0.891754628;0.607795262;0.4503829;"SS2U000581;SS2U000914;SS2U001005;SS2U001074;SS2U021468;SS2U026568;SS2U026578;SS2U029723;SS2U029903;SS2U029962;SS2U041592;SS2U059300 183;40S ribosomal protein S4;10;11.81082293;-0.129648843;-0.10193833;-0.487885183;-0.616590274;0.845018766;0.723157116;"SS2U000435;SS2U000538;SS2U000921;SS2U000946;SS2U012729;SS2U027223;SS2U034080;SS2U047630;SS2U059246;SS2U059273 184;40S ribosomal protein S4 X isoform;3;8.250730069;0.338488034;-0.738463477;-0.305519637;-0.202058701;0.732107145;0.904496173;"SS2U013110;SS2U019290;SS2U036013 185;40S ribosomal protein S5;9;11.84212606;-0.307513461;-0.090244577;-0.531700762;-0.764342416;0.600901576;0.544437508;"SS2U000891;SS2U005512;SS2U025584;SS2U026048;SS2U026119;SS2U037589;SS2U053399;SS2U059230;SS2U059285 186;40S ribosomal protein S5a;7;7.634660321;-1.370832398;0.22819518;-2.011796235;-1.755119853;0.142001566;0.021179957;"SS2U001172;SS2U001205;SS2U003625;SS2U004951;SS2U010119;SS2U029949;SS2U049428 187;40S ribosomal protein S6;18;11.682397;-0.530257435;0.057048154;-0.81526032;-1.000421449;0.373338492;0.213371025;"SS2U000643;SS2U000644;SS2U000990;SS2U001115;SS2U001175;SS2U017514;SS2U020757;SS2U022066;SS2U023709;SS2U025536;SS2U026532;SS2U031843;SS2U033480;SS2U040727;SS2U040728;SS2U042826;SS2U059014;SS2U059029 188;40S ribosomal protein S7;10;10.83095385;-1.147907883;0.169981044;-1.105844186;-1.331483239;0.095429058;0.075697682;"SS2U000929;SS2U001085;SS2U001086;SS2U004603;SS2U017677;SS2U021443;SS2U022969;SS2U024798;SS2U045464;SS2U059278 189;40S ribosomal protein S8;11;11.60920508;-0.722684205;0.014943405;-0.817533938;-0.976004299;0.228831776;0.26544142;"SS2U001103;SS2U005588;SS2U005694;SS2U005892;SS2U019368;SS2U024534;SS2U029942;SS2U030975;SS2U030981;SS2U058718;SS2U059167 190;40S ribosomal protein S9;11;11.26481092;-0.62540428;-0.031252622;-0.791210032;-1.249100646;0.279233281;0.121333019;"SS2U001140;SS2U007081;SS2U012819;SS2U017419;SS2U019001;SS2U019002;SS2U030908;SS2U037501;SS2U048823;SS2U051332;SS2U059144 191;40S ribosomal protein SA;18;12.35384398;-0.206283857;-0.222297206;-0.728850128;-0.987533501;0.761525808;0.372771604;"SS2U010721;SS2U017034;SS2U023665;SS2U026750;SS2U026916;SS2U028646;SS2U028757;SS2U028805;SS2U030605;SS2U032474;SS2U035469;SS2U040825;SS2U042018;SS2U045438;SS2U050706;SS2U051930;SS2U059016;SS2U059073 192;45 kDa calcium-binding protein;3;4.552469539;0.380431642;-0.100520917;0.323487542;0.478385078;0.480260573;0.756485728;"SS2U036129;SS2U040294;SS2U050497 193;4F2 cell-surface antigen heavy chain;6;6.586196068;-0.285362074;0.3519648;0.338529986;1.377429948;0.618092793;0.046251512;"SS2U003595;SS2U015795;SS2U052061;SS2U055316;SS2U057504;SS2U057936 194;5'-3' exoribonuclease 1;2;4.436416467;0.407581376;-0.199090334;0.150193469;-0.189220305;0.486565928;0.957589653;"SS2U025174;SS2U041460 195;5'-3' exoribonuclease 2;6;7.803360787;0.377284377;-0.231776892;-0.17399318;-0.987660924;0.47893134;0.336932189;"SS2U011779;SS2U041060;SS2U047319;SS2U047944;SS2U052560;SS2U053918 196;5'-AMP-activated protein kinase catalytic subunit alpha-1;3;4.039945533;0.517418396;-0.194877035;0.185694805;0.139632619;0.350173999;0.955733822;"SS2U028935;SS2U028953;SS2U038415 197;5'-AMP-activated protein kinase catalytic subunit alpha-2;1;1.899343997;1.148852501;0.32207873;2.290360785;4.515671059;0.159590239;1.46E-10;SS2U004328 198;5'-AMP-activated protein kinase subunit beta-1;5;6.429101503;-0.248146707;0.045858315;-0.445789558;-0.775663619;0.682145835;0.476871638;"SS2U016017;SS2U017648;SS2U017924;SS2U054450;SS2U055631 199;5'-AMP-activated protein kinase subunit gamma-1;3;4.130341499;0.702971288;-0.263262424;0.317340069;0.146820115;0.246955196;0.890770079;"SS2U032396;SS2U043720;SS2U046156 This is a portion of the data; to view all the data, please download the file. Dataset 1. Parameters and multiple testing corrected p-values for expression analysis. The file is tab-delimited and the columns are; “Unigene.Description”: the annotation for that gene/paralog group. “NR.contigs”: number of contigs with this annotation. “logCPM”: count per million, log-scale. "logFC.morph": Mean fold change between the morphs, log-scale. "logFC.T163", "logFC.T200", "logFC.T433": Mean fold change for each timepoints compared to timepoint 141, log-scale. "FDR.morph": P-value for morph difference, multiple testing corrected. "FDR.time": P-value for time differences, multiple testing corrected. "Contigs": SalmonDB id for the contigs with the specific annotation 109 . Gene_Type;Gene;Morph;Relative_age;Biological_replicate;cDNA_No;Ct_value;Sample;Batch Reference;Actb;AC;161;1;3;15.96261883;Whole_embryo;c Reference;Actb;AC;161;2;3;16.32308578;Whole_embryo;c Reference;Actb;AC;200;1;3;16.3116312;Whole_embryo;c Reference;Actb;AC;200;2;3;16.69984245;Whole_embryo;c Reference;Actb;SB;161;1;3;15.91931581;Whole_embryo;c Reference;Actb;SB;161;2;3;15.95784521;Whole_embryo;c Reference;Actb;SB;200;1;3;17.22946262;Whole_embryo;c Reference;Actb;SB;200;2;3;16.48554039;Whole_embryo;c Reference;Ub2l3;AC;161;1;3;19.2323761;Whole_embryo;c Reference;Ub2l3;AC;161;2;3;19.53557777;Whole_embryo;c Reference;Ub2l3;AC;200;1;3;19.7100153;Whole_embryo;c Reference;Ub2l3;AC;200;2;3;20.06556892;Whole_embryo;c Reference;Ub2l3;SB;161;1;3;18.85280609;Whole_embryo;c Reference;Ub2l3;SB;161;2;3;18.97263432;Whole_embryo;c Reference;Ub2l3;SB;200;1;3;20.86740685;Whole_embryo;c Reference;Ub2l3;SB;200;2;3;19.65790176;Whole_embryo;c Reference;Ef1a;AC;161;1;3;16.76741409;Whole_embryo;c Reference;Ef1a;AC;161;2;3;16.88599777;Whole_embryo;c Reference;Ef1a;AC;200;1;3;17.02689171;Whole_embryo;c Reference;Ef1a;AC;200;2;3;17.05676842;Whole_embryo;c Reference;Ef1a;SB;161;1;3;16.1616497;Whole_embryo;c Reference;Ef1a;SB;161;2;3;16.08823395;Whole_embryo;c Reference;Ef1a;SB;200;1;3;17.47857857;Whole_embryo;c Reference;Ef1a;SB;200;2;3;16.80790234;Whole_embryo;c Reference;Actb;AC;161;1;4;15.44944715;Whole_embryo;c Reference;Actb;AC;161;2;4;15.68050861;Whole_embryo;c Reference;Actb;AC;200;1;4;15.74295759;Whole_embryo;c Reference;Actb;AC;200;2;4;15.98812437;Whole_embryo;c Reference;Actb;SB;161;1;4;15.04642916;Whole_embryo;c Reference;Actb;SB;161;2;4;15.25384712;Whole_embryo;c Reference;Actb;SB;200;1;4;16.69416809;Whole_embryo;c Reference;Actb;SB;200;2;4;15.51182985;Whole_embryo;c Reference;Ub2l3;AC;161;1;4;18.98914528;Whole_embryo;c Reference;Ub2l3;AC;161;2;4;19.06344986;Whole_embryo;c Reference;Ub2l3;AC;200;1;4;19.48450947;Whole_embryo;c Reference;Ub2l3;AC;200;2;4;19.50106907;Whole_embryo;c Reference;Ub2l3;SB;161;1;4;18.56895733;Whole_embryo;c Reference;Ub2l3;SB;161;2;4;18.72522068;Whole_embryo;c Reference;Ub2l3;SB;200;1;4;20.6594038;Whole_embryo;c Reference;Ub2l3;SB;200;2;4;19.26242065;Whole_embryo;c Reference;Ef1a;AC;161;1;4;16.48565102;Whole_embryo;c Reference;Ef1a;AC;161;2;4;16.47541046;Whole_embryo;c Reference;Ef1a;AC;200;1;4;16.72178936;Whole_embryo;c Reference;Ef1a;AC;200;2;4;16.8230648;Whole_embryo;c Reference;Ef1a;SB;161;1;4;15.88469839;Whole_embryo;c Reference;Ef1a;SB;161;2;4;15.93828535;Whole_embryo;c Reference;Ef1a;SB;200;1;4;17.21857929;Whole_embryo;c Reference;Ef1a;SB;200;2;4;16.40386295;Whole_embryo;c Candidate;Nattl;AC;161;1;3;24.30113316;Whole_embryo;c Candidate;Nattl;AC;161;2;3;24.95383644;Whole_embryo;c Candidate;Nattl;AC;200;1;3;23.1154232;Whole_embryo;c Candidate;Nattl;AC;200;2;3;23.16105175;Whole_embryo;c Candidate;Nattl;SB;161;1;3;21.88699722;Whole_embryo;c Candidate;Nattl;SB;161;2;3;23.86600208;Whole_embryo;c Candidate;Nattl;SB;200;1;3;21.4312315;Whole_embryo;c Candidate;Nattl;SB;200;2;3;21.58208561;Whole_embryo;c Candidate;Alp;AC;161;1;3;24.61887646;Whole_embryo;c Candidate;Alp;AC;161;2;3;24.90855503;Whole_embryo;c Candidate;Alp;AC;200;1;3;24.64563656;Whole_embryo;c Candidate;Alp;AC;200;2;3;24.88164043;Whole_embryo;c Candidate;Alp;SB;161;1;3;23.86744976;Whole_embryo;c Candidate;Alp;SB;161;2;3;24.1090517;Whole_embryo;c Candidate;Alp;SB;200;1;3;24.03513622;Whole_embryo;c Candidate;Alp;SB;200;2;3;23.89897919;Whole_embryo;c Candidate;Cgat2;AC;161;1;3;25.23556042;Whole_embryo;c Candidate;Cgat2;AC;161;2;3;25.13300419;Whole_embryo;c Candidate;Cgat2;AC;200;1;3;25.34113216;Whole_embryo;c Candidate;Cgat2;AC;200;2;3;25.33448505;Whole_embryo;c Candidate;Cgat2;SB;161;1;3;24.65633106;Whole_embryo;c Candidate;Cgat2;SB;161;2;3;24.73155785;Whole_embryo;c Candidate;Cgat2;SB;200;1;3;26.19965458;Whole_embryo;c Candidate;Cgat2;SB;200;2;3;25.16437054;Whole_embryo;c Candidate;Cox6b1;AC;161;1;4;26.7786026;Whole_embryo;c Candidate;Cox6b1;AC;161;2;4;27.34981537;Whole_embryo;c Candidate;Cox6b1;AC;200;1;4;26.94288731;Whole_embryo;c Candidate;Cox6b1;AC;200;2;4;27.02507305;Whole_embryo;c Candidate;Cox6b1;SB;161;1;4;26.77010155;Whole_embryo;c Candidate;Cox6b1;SB;161;2;4;26.87522507;Whole_embryo;c Candidate;Cox6b1;SB;200;1;4;26.58977795;Whole_embryo;c Candidate;Cox6b1;SB;200;2;4;26.95159721;Whole_embryo;c Candidate;Krtap4-3;AC;161;1;4;24.45112514;Whole_embryo;c Candidate;Krtap4-3;AC;161;2;4;24.41178513;Whole_embryo;c Candidate;Krtap4-3;AC;200;1;4;24.57724857;Whole_embryo;c Candidate;Krtap4-3;AC;200;2;4;24.6350317;Whole_embryo;c Candidate;Krtap4-3;SB;161;1;4;24.18867779;Whole_embryo;c Candidate;Krtap4-3;SB;161;2;4;24.52984619;Whole_embryo;c Candidate;Krtap4-3;SB;200;1;4;26.09012604;Whole_embryo;c Candidate;Krtap4-3;SB;200;2;4;25.35136032;Whole_embryo;c Candidate;Lyz;AC;161;1;3;25.649189;Whole_embryo;c Candidate;Lyz;AC;161;2;3;25.93523693;Whole_embryo;c Candidate;Lyz;AC;200;1;3;25.97491074;Whole_embryo;c Candidate;Lyz;AC;200;2;3;26.36354256;Whole_embryo;c Candidate;Lyz;SB;161;1;3;23.10821247;Whole_embryo;c Candidate;Lyz;SB;161;2;3;23.59787178;Whole_embryo;c Candidate;Lyz;SB;200;1;3;23.74123478;Whole_embryo;c Candidate;Lyz;SB;200;2;3;23.9212904;Whole_embryo;c Candidate;Ndub6;AC;161;1;4;20.99980164;Whole_embryo;c Candidate;Ndub6;AC;161;2;4;21.25080109;Whole_embryo;c Candidate;Ndub6;AC;200;1;4;21.03236389;Whole_embryo;c Candidate;Ndub6;AC;200;2;4;21.15505123;Whole_embryo;c Candidate;Ndub6;SB;161;1;4;20.56019783;Whole_embryo;c Candidate;Ndub6;SB;161;2;4;20.64115429;Whole_embryo;c Candidate;Ndub6;SB;200;1;4;21.33388996;Whole_embryo;c Candidate;Ndub6;SB;200;2;4;20.87278366;Whole_embryo;c Candidate;Parp6;AC;161;1;4;23.82729721;Whole_embryo;c Candidate;Parp6;AC;161;2;4;23.62686062;Whole_embryo;c Candidate;Parp6;AC;200;1;4;23.44495487;Whole_embryo;c Candidate;Parp6;AC;200;2;4;24.04651356;Whole_embryo;c Candidate;Parp6;SB;161;1;4;23.95689011;Whole_embryo;c Candidate;Parp6;SB;161;2;4;23.98719311;Whole_embryo;c Candidate;Parp6;SB;200;1;4;25.88320351;Whole_embryo;c Candidate;Parp6;SB;200;2;4;23.96511269;Whole_embryo;c Candidate;Ubl5;AC;161;1;3;20.95245171;Whole_embryo;c Candidate;Ubl5;AC;161;2;3;21.51314068;Whole_embryo;c Candidate;Ubl5;AC;200;1;3;21.69692421;Whole_embryo;c Candidate;Ubl5;AC;200;2;3;21.88066006;Whole_embryo;c Candidate;Ubl5;SB;161;1;3;20.72139168;Whole_embryo;c Candidate;Ubl5;SB;161;2;3;20.6724329;Whole_embryo;c Candidate;Ubl5;SB;200;1;3;21.8654623;Whole_embryo;c Candidate;Ubl5;SB;200;2;3;21.64883995;Whole_embryo;c Reference;Actb;AC;161;1;1;18.93461307;Whole_embryo;a Reference;Actb;AC;161;2;1;17.01329973;Whole_embryo;a Reference;Actb;AC;200;1;1;18.74925942;Whole_embryo;a Reference;Actb;AC;200;2;1;17.38162289;Whole_embryo;a Reference;Actb;AC;256;1;1;18.32057422;Whole_embryo;a Reference;Actb;AC;256;2;1;18.25436369;Whole_embryo;a Reference;Actb;AC;256;3;1;19.35329826;Whole_embryo;a Reference;Actb;AC;315;1;1;17.83992698;Whole_embryo;a Reference;Actb;AC;315;2;1;16.75994191;Whole_embryo;a Reference;Actb;AC;315;3;1;17.49895169;Whole_embryo;a Reference;Actb;PL;161;1;1;16.53899002;Whole_embryo;a Reference;Actb;PL;161;2;1;16.014712;Whole_embryo;a Reference;Actb;PL;161;3;1;16.62383843;Whole_embryo;a Reference;Actb;PL;200;1;1;16.97808864;Whole_embryo;a Reference;Actb;PL;200;2;1;17.30882522;Whole_embryo;a Reference;Actb;PL;256;1;1;17.33051229;Whole_embryo;a Reference;Actb;PL;256;2;1;18.05678958;Whole_embryo;a Reference;Actb;PL;315;1;1;16.8715216;Whole_embryo;a Reference;Actb;PL;315;2;1;17.2627397;Whole_embryo;a Reference;Actb;PL;315;3;1;18.4954958;Whole_embryo;a Reference;Actb;SB;161;1;1;18.10572111;Whole_embryo;a Reference;Actb;SB;161;2;1;18.47548703;Whole_embryo;a Reference;Actb;SB;200;1;1;19.51686616;Whole_embryo;a Reference;Actb;SB;200;2;1;16.81035177;Whole_embryo;a Reference;Actb;SB;256;1;1;16.57099441;Whole_embryo;a Reference;Actb;SB;256;2;1;16.65354854;Whole_embryo;a Reference;Actb;SB;256;3;1;16.74384742;Whole_embryo;a Reference;Actb;SB;315;1;1;18.36778707;Whole_embryo;a Reference;Actb;SB;315;2;1;16.24909757;Whole_embryo;a Reference;Actb;SB;315;3;1;17.70580329;Whole_embryo;a Reference;Actb;AC;161;1;1;17.90917178;Whole_embryo;b Reference;Actb;AC;161;2;1;17.07442431;Whole_embryo;b Reference;Actb;AC;200;1;1;18.11745198;Whole_embryo;b Reference;Actb;AC;200;2;1;17.60052731;Whole_embryo;b Reference;Actb;AC;315;1;1;16.81914311;Whole_embryo;b Reference;Actb;AC;315;2;1;16.71034441;Whole_embryo;b Reference;Actb;PL;161;1;1;16.66228787;Whole_embryo;b Reference;Actb;PL;161;2;1;15.9647415;Whole_embryo;b Reference;Actb;PL;161;3;1;16.5319537;Whole_embryo;b Reference;Actb;PL;200;1;1;16.43611547;Whole_embryo;b Reference;Actb;PL;200;2;1;16.13770159;Whole_embryo;b Reference;Actb;PL;256;1;1;17.09882909;Whole_embryo;b Reference;Actb;PL;256;2;1;17.18234183;Whole_embryo;b Reference;Actb;PL;315;1;1;15.81195874;Whole_embryo;b Reference;Actb;PL;315;2;1;16.2744054;Whole_embryo;b Reference;Actb;SB;161;1;1;17.00400802;Whole_embryo;b Reference;Actb;SB;161;2;1;16.56781516;Whole_embryo;b Reference;Actb;SB;200;1;1;19.06980825;Whole_embryo;b Reference;Actb;SB;200;2;1;17.14548894;Whole_embryo;b Reference;Actb;SB;256;1;1;16.26166826;Whole_embryo;b Reference;Actb;SB;256;2;1;16.40651416;Whole_embryo;b Reference;Actb;SB;256;3;1;16.58124201;Whole_embryo;b Reference;Actb;SB;315;1;1;17.83687183;Whole_embryo;b Reference;Actb;SB;315;2;1;16.24142187;Whole_embryo;b Reference;If5a1;AC;161;1;1;24.75698158;Whole_embryo;a Reference;If5a1;AC;161;2;1;22.82441777;Whole_embryo;a Reference;If5a1;AC;200;1;1;23.87932034;Whole_embryo;a Reference;If5a1;AC;200;2;1;22.32171113;Whole_embryo;a Reference;If5a1;AC;256;1;1;22.10594451;Whole_embryo;a Reference;If5a1;AC;256;2;1;22.04629998;Whole_embryo;a Reference;If5a1;AC;256;3;1;21.68510554;Whole_embryo;a Reference;If5a1;AC;315;1;1;22.9018425;Whole_embryo;a Reference;If5a1;AC;315;2;1;21.96694438;Whole_embryo;a Reference;If5a1;AC;315;3;1;24.27062399;Whole_embryo;a Reference;If5a1;PL;161;1;1;22.80112612;Whole_embryo;a Reference;If5a1;PL;161;2;1;22.57955528;Whole_embryo;a Reference;If5a1;PL;161;3;1;23.54804685;Whole_embryo;a Reference;If5a1;PL;200;1;1;22.08827499;Whole_embryo;a Reference;If5a1;PL;200;2;1;21.72163439;Whole_embryo;a Reference;If5a1;PL;256;1;1;22.48477022;Whole_embryo;a Reference;If5a1;PL;256;2;1;21.57767856;Whole_embryo;a Reference;If5a1;PL;315;1;1;23.0637937;Whole_embryo;a Reference;If5a1;PL;315;2;1;21.41490463;Whole_embryo;a Reference;If5a1;PL;315;3;1;23.79193518;Whole_embryo;a Reference;If5a1;SB;161;1;1;24.33332821;Whole_embryo;a Reference;If5a1;SB;161;2;1;25.15546513;Whole_embryo;a Reference;If5a1;SB;200;1;1;25.90748262;Whole_embryo;a Reference;If5a1;SB;200;2;1;22.28793459;Whole_embryo;a Reference;If5a1;SB;256;1;1;22.05466539;Whole_embryo;a This is a portion of the data; to view all the data, please download the file. Dataset 2. qPCR data for tests of expression in charr developing embryos and adult tissues. “Gene Type”: Designates the reference and candidate genes. “Gene”: Name of the gene. “Morph”: Which charr type the sample came from. "Relative age": Developmental timepoint, and also indicates the samples from adult fish. "Biological replicate": The two or more biological replicates used. "cDNA No": Marks the cDNA isolation used. "Ct value": Estimate of gene expression. "Sample": Indicates the material used, whole embryos or distinct tissues. "Batch": Demarcates distinct collections of cDNA, applies only to nattl 110 . Gene_Type;Gene;Morph;Relative_age;Biological_replicate;Ct_value;cDNA_No;Tissue Reference;If5a1;AC;178;1;23.67264175;1;Pooled_heads Reference;If5a1;SB;178;1;23.89719925;1;Pooled_heads Reference;If5a1;PL;178;1;23.43337822;1;Pooled_heads Reference;If5a1;LB;178;1;23.76682129;1;Pooled_heads Reference;If5a1;AC;178;2;23.90053482;1;Pooled_heads Reference;If5a1;SB;178;2;23.75086136;1;Pooled_heads Reference;If5a1;PL;178;2;23.53201408;1;Pooled_heads Reference;If5a1;LB;178;2;23.74039001;1;Pooled_heads Reference;If5a1;AC;216;1;23.70517731;1;Pooled_heads Reference;If5a1;SB;216;1;22.64871025;1;Pooled_heads Reference;If5a1;PL;216;1;22.88567162;1;Pooled_heads Reference;If5a1;LB;216;1;22.64291;1;Pooled_heads Reference;If5a1;AC;216;2;23.10091476;1;Pooled_heads Reference;If5a1;SB;216;2;22.47453308;1;Pooled_heads Reference;If5a1;PL;216;2;22.3912973;1;Pooled_heads Reference;If5a1;LB;216;2;22.60772491;1;Pooled_heads Reference;Actb;AC;178;1;15.91521549;1;Pooled_heads Reference;Actb;SB;178;1;15.83242464;1;Pooled_heads Reference;Actb;PL;178;1;15.36369801;1;Pooled_heads Reference;Actb;LB;178;1;15.46041203;1;Pooled_heads Reference;Actb;AC;178;2;16.16721382;1;Pooled_heads Reference;Actb;SB;178;2;15.57671585;1;Pooled_heads Reference;Actb;PL;178;2;15.35849991;1;Pooled_heads Reference;Actb;LB;178;2;15.59954109;1;Pooled_heads Reference;Actb;AC;216;1;16.65059662;1;Pooled_heads Reference;Actb;SB;216;1;15.450243;1;Pooled_heads Reference;Actb;PL;216;1;15.78530502;1;Pooled_heads Reference;Actb;LB;216;1;15.58950424;1;Pooled_heads Reference;Actb;AC;216;2;15.99549103;1;Pooled_heads Reference;Actb;SB;216;2;15.21378136;1;Pooled_heads Reference;Actb;PL;216;2;15.38698196;1;Pooled_heads Reference;Actb;LB;216;2;15.51268959;1;Pooled_heads Candidate;Mvp;AC;178;1;22.9414444;1;Pooled_heads Candidate;Mvp;SB;178;1;21.91384125;1;Pooled_heads Candidate;Mvp;PL;178;1;22.16065979;1;Pooled_heads Candidate;Mvp;LB;178;1;21.95458603;1;Pooled_heads Candidate;Mvp;AC;178;2;23.06663322;1;Pooled_heads Candidate;Mvp;SB;178;2;21.76210022;1;Pooled_heads Candidate;Mvp;PL;178;2;22.28490448;1;Pooled_heads Candidate;Mvp;LB;178;2;21.67274857;1;Pooled_heads Candidate;Mvp;AC;216;1;23.39944839;1;Pooled_heads Candidate;Mvp;SB;216;1;21.63397598;1;Pooled_heads Candidate;Mvp;PL;216;1;22.65891266;1;Pooled_heads Candidate;Mvp;LB;216;1;21.62683868;1;Pooled_heads Candidate;Mvp;AC;216;2;23.40564346;1;Pooled_heads Candidate;Mvp;SB;216;2;21.86926651;1;Pooled_heads Candidate;Mvp;PL;216;2;22.54525375;1;Pooled_heads Candidate;Mvp;LB;216;2;21.84273911;1;Pooled_heads Candidate;Jup;AC;178;1;21.92790604;1;Pooled_heads Candidate;Jup;SB;178;1;21.33343506;1;Pooled_heads Candidate;Jup;PL;178;1;21.1556282;1;Pooled_heads Candidate;Jup;LB;178;1;21.10895157;1;Pooled_heads Candidate;Jup;AC;178;2;22.24541092;1;Pooled_heads Candidate;Jup;SB;178;2;21.60882187;1;Pooled_heads Candidate;Jup;PL;178;2;21.35161018;1;Pooled_heads Candidate;Jup;LB;178;2;21.14461136;1;Pooled_heads Candidate;Jup;AC;216;1;22.64751434;1;Pooled_heads Candidate;Jup;SB;216;1;20.92277527;1;Pooled_heads Candidate;Jup;PL;216;1;21.7883091;1;Pooled_heads Candidate;Jup;LB;216;1;21.11831665;1;Pooled_heads Candidate;Jup;AC;216;2;22.39131927;1;Pooled_heads Candidate;Jup;SB;216;2;21.19046783;1;Pooled_heads Candidate;Jup;PL;216;2;21.52971077;1;Pooled_heads Candidate;Jup;LB;216;2;21.01268768;1;Pooled_heads Candidate;Lsr;AC;178;1;25.98275757;1;Pooled_heads Candidate;Lsr;SB;178;1;24.9202652;1;Pooled_heads Candidate;Lsr;PL;178;1;25.09762383;1;Pooled_heads Candidate;Lsr;LB;178;1;24.9207077;1;Pooled_heads Candidate;Lsr;AC;178;2;26.35001755;1;Pooled_heads Candidate;Lsr;SB;178;2;25.15621948;1;Pooled_heads Candidate;Lsr;PL;178;2;25.43023682;1;Pooled_heads Candidate;Lsr;LB;178;2;24.90939903;1;Pooled_heads Candidate;Lsr;AC;216;1;26.66849518;1;Pooled_heads Candidate;Lsr;SB;216;1;24.91859436;1;Pooled_heads Candidate;Lsr;PL;216;1;25.9844017;1;Pooled_heads Candidate;Lsr;LB;216;1;24.82223892;1;Pooled_heads Candidate;Lsr;AC;216;2;26.38882828;1;Pooled_heads Candidate;Lsr;SB;216;2;24.8479538;1;Pooled_heads Candidate;Lsr;PL;216;2;25.85172272;1;Pooled_heads Candidate;Lsr;LB;216;2;24.95061493;1;Pooled_heads Candidate;Rarg;AC;178;1;22.7040844;1;Pooled_heads Candidate;Rarg;SB;178;1;23.38894272;1;Pooled_heads Candidate;Rarg;PL;178;1;22.54174995;1;Pooled_heads Candidate;Rarg;LB;178;1;22.86488724;1;Pooled_heads Candidate;Rarg;AC;178;2;22.75419617;1;Pooled_heads Candidate;Rarg;SB;178;2;23.29712677;1;Pooled_heads Candidate;Rarg;PL;178;2;22.55051804;1;Pooled_heads Candidate;Rarg;LB;178;2;22.38739395;1;Pooled_heads Candidate;Rarg;AC;216;1;23.37831879;1;Pooled_heads Candidate;Rarg;SB;216;1;22.82614708;1;Pooled_heads Candidate;Rarg;PL;216;1;23.13128662;1;Pooled_heads Candidate;Rarg;LB;216;1;22.88451385;1;Pooled_heads Candidate;Rarg;AC;216;2;23.13782501;1;Pooled_heads Candidate;Rarg;SB;216;2;22.66239929;1;Pooled_heads Candidate;Rarg;PL;216;2;22.64619446;1;Pooled_heads Candidate;Rarg;LB;216;2;22.82075882;1;Pooled_heads Candidate;Vdra;AC;178;1;23.1421032;1;Pooled_heads Candidate;Vdra;SB;178;1;23.39729309;1;Pooled_heads Candidate;Vdra;PL;178;1;23.35404778;1;Pooled_heads Candidate;Vdra;LB;178;1;22.97281265;1;Pooled_heads Candidate;Vdra;AC;178;2;23.66833305;1;Pooled_heads Candidate;Vdra;SB;178;2;22.62556458;1;Pooled_heads Candidate;Vdra;PL;178;2;23.14506531;1;Pooled_heads Candidate;Vdra;LB;178;2;23.04750061;1;Pooled_heads Candidate;Vdra;AC;216;1;24.61454391;1;Pooled_heads Candidate;Vdra;SB;216;1;23.10774994;1;Pooled_heads Candidate;Vdra;PL;216;1;23.93480873;1;Pooled_heads Candidate;Vdra;LB;216;1;23.15319443;1;Pooled_heads Candidate;Vdra;AC;216;2;24.06957245;1;Pooled_heads Candidate;Vdra;SB;216;2;23.00084305;1;Pooled_heads Candidate;Vdra;PL;216;2;23.61406326;1;Pooled_heads Candidate;Vdra;LB;216;2;23.3383255;1;Pooled_heads Candidate;Tgfbr2;AC;178;1;25.73878098;1;Pooled_heads Candidate;Tgfbr2;SB;178;1;25.08353806;1;Pooled_heads Candidate;Tgfbr2;PL;178;1;24.9316082;1;Pooled_heads Candidate;Tgfbr2;LB;178;1;24.94442558;1;Pooled_heads Candidate;Tgfbr2;AC;178;2;26.16744232;1;Pooled_heads Candidate;Tgfbr2;SB;178;2;24.9635582;1;Pooled_heads Candidate;Tgfbr2;PL;178;2;25.13551331;1;Pooled_heads Candidate;Tgfbr2;LB;178;2;25.28170395;1;Pooled_heads Candidate;Tgfbr2;AC;216;1;26.54673386;1;Pooled_heads Candidate;Tgfbr2;SB;216;1;25.25802994;1;Pooled_heads Candidate;Tgfbr2;PL;216;1;25.86025238;1;Pooled_heads Candidate;Tgfbr2;LB;216;1;25.04957581;1;Pooled_heads Candidate;Tgfbr2;AC;216;2;26.1955204;1;Pooled_heads Candidate;Tgfbr2;SB;216;2;25.22771835;1;Pooled_heads Candidate;Tgfbr2;PL;216;2;25.63209915;1;Pooled_heads Candidate;Tgfbr2;LB;216;2;25.05401039;1;Pooled_heads Reference;If5a1;AC;200;1;23.47161865;2;Pooled_heads Reference;If5a1;SB;200;1;23.07133942;2;Pooled_heads Reference;If5a1;PL;200;1;24.18017319;2;Pooled_heads Reference;If5a1;LB;200;1;23.15392525;2;Pooled_heads Reference;If5a1;AC;200;2;22.9918045;2;Pooled_heads Reference;If5a1;SB;200;2;22.64175262;2;Pooled_heads Reference;If5a1;PL;200;2;24.18965607;2;Pooled_heads Reference;If5a1;LB;200;2;22.71433945;2;Pooled_heads Reference;Actb;AC;200;1;15.71403885;2;Pooled_heads Reference;Actb;SB;200;1;15.22663116;2;Pooled_heads Reference;Actb;PL;200;1;16.10777283;2;Pooled_heads Reference;Actb;LB;200;1;15.57907486;2;Pooled_heads Reference;Actb;AC;200;2;15.3622036;2;Pooled_heads Reference;Actb;SB;200;2;14.8659539;2;Pooled_heads Reference;Actb;PL;200;2;16.25887726;2;Pooled_heads Reference;Actb;LB;200;2;15.21011208;2;Pooled_heads Candidate;Mvp;AC;200;1;22.99713516;2;Pooled_heads Candidate;Mvp;SB;200;1;21.39568901;2;Pooled_heads Candidate;Mvp;PL;200;1;23.18587112;2;Pooled_heads Candidate;Mvp;LB;200;1;21.54834557;2;Pooled_heads Candidate;Mvp;AC;200;2;22.25123024;2;Pooled_heads Candidate;Mvp;SB;200;2;21.20922089;2;Pooled_heads Candidate;Mvp;PL;200;2;23.15605545;2;Pooled_heads Candidate;Mvp;LB;200;2;21.26544952;2;Pooled_heads Candidate;Jup;AC;200;1;21.87226295;2;Pooled_heads Candidate;Jup;SB;200;1;20.7249527;2;Pooled_heads Candidate;Jup;PL;200;1;22.22583961;2;Pooled_heads Candidate;Jup;LB;200;1;20.83753967;2;Pooled_heads Candidate;Jup;AC;200;2;21.48207474;2;Pooled_heads Candidate;Jup;SB;200;2;20.41962051;2;Pooled_heads Candidate;Jup;PL;200;2;22.50665092;2;Pooled_heads Candidate;Jup;LB;200;2;20.57951736;2;Pooled_heads Candidate;Lsr;AC;200;1;26.31171608;2;Pooled_heads Candidate;Lsr;SB;200;1;24.93918991;2;Pooled_heads Candidate;Lsr;PL;200;1;26.90993881;2;Pooled_heads Candidate;Lsr;LB;200;1;24.93612099;2;Pooled_heads Candidate;Lsr;AC;200;2;25.51472092;2;Pooled_heads Candidate;Lsr;SB;200;2;24.41723633;2;Pooled_heads Candidate;Lsr;PL;200;2;26.69015884;2;Pooled_heads Candidate;Lsr;LB;200;2;24.50409126;2;Pooled_heads Candidate;Rarg;AC;200;1;22.87320137;2;Pooled_heads Candidate;Rarg;SB;200;1;22.46928978;2;Pooled_heads Candidate;Rarg;PL;200;1;23.59035873;2;Pooled_heads Candidate;Rarg;LB;200;1;22.55023003;2;Pooled_heads Candidate;Rarg;AC;200;2;22.26335907;2;Pooled_heads Candidate;Rarg;SB;200;2;21.93601608;2;Pooled_heads Candidate;Rarg;PL;200;2;22.93943024;2;Pooled_heads Candidate;Rarg;LB;200;2;22.08405304;2;Pooled_heads Candidate;Vdra;AC;200;1;23.80267143;2;Pooled_heads Candidate;Vdra;SB;200;1;22.66882515;2;Pooled_heads Candidate;Vdra;PL;200;1;24.12267303;2;Pooled_heads Candidate;Vdra;LB;200;1;22.74294662;2;Pooled_heads Candidate;Vdra;AC;200;2;23.35219193;2;Pooled_heads Candidate;Vdra;SB;200;2;22.05793114;2;Pooled_heads Candidate;Vdra;PL;200;2;24.28331299;2;Pooled_heads Candidate;Vdra;LB;200;2;22.67995148;2;Pooled_heads Candidate;Tgfbr2;AC;200;1;25.97596741;2;Pooled_heads Candidate;Tgfbr2;SB;200;1;25.18391418;2;Pooled_heads Candidate;Tgfbr2;PL;200;1;26.6747036;2;Pooled_heads Candidate;Tgfbr2;LB;200;1;24.9662075;2;Pooled_heads Candidate;Tgfbr2;AC;200;2;25.64708328;2;Pooled_heads Candidate;Tgfbr2;SB;200;2;24.92686844;2;Pooled_heads Candidate;Tgfbr2;PL;200;2;26.92587662;2;Pooled_heads Candidate;Tgfbr2;LB;200;2;24.89536095;2;Pooled_heads Reference;If5a1;AC;178;1;23.35324097;3;Pooled_heads Reference;If5a1;SB;178;1;23.25496864;3;Pooled_heads Reference;If5a1;PL;178;1;23.09447479;3;Pooled_heads Reference;If5a1;LB;178;1;22.99862671;3;Pooled_heads Reference;If5a1;AC;178;2;23.20590973;3;Pooled_heads Reference;If5a1;SB;178;2;23.23107544;3;Pooled_heads Reference;If5a1;PL;178;2;23.07160378;3;Pooled_heads This is a portion of the data; to view all the data, please download the file. Dataset 3. qPCR data for tests of expression in charr developing embryo heads. “Gene Type”: Designates the reference and candidate genes. “Gene”: Name of the gene. “Morph”: Which charr type the sample came from. "Relative age": Developmental timepoint. "Biological replicate": The two or more biological replicates used. "cDNA No": Marks the cDNA isolation used. "Ct value": Estimate of gene expression. "Tissue": Indicates the material used 111 . Discussion We are interested in the predictability of evolution at the molecular level, especially whether there exist principles that influence the rewiring of developmental and regulatory systems 4 , 76 . One way to study this is to identify genetic and developmental effects affecting key traits in species or populations which exhibit parallel evolution. The aim of this study was to find expression and genetic differences separating the small benthic morph in Lake Thingvallavatn and aquaculture charr, with the long term objective being to reveal the genetic and molecular systems that associate with benthic morphology in charr. The transcriptome reflects the biology of these two morphs, their different histories and ecology. AC-charr will also be shaped by domestication, which may explain for instance the higher expression of metabolic genes in AC-charr. Developmental transcriptome of Arctic charr morphs As no reference genome is available for Arctic charr, we mapped reads to S. salar EST-contigs 57 in order to estimate expression and identify candidate genetic polymorphisms. As many of the contigs are short or have overlapping annotations, we collapsed genes into paralogous genes when appropriate for the expression analysis. The main advantage was the reduced number of statistical tests (and hence an increase in statistical power). The downside is that paralog-specific expression patterns are masked, as the qPCR results of the natterin like gene family show ( Figure 5 and S1 Figure ). Recent rainbow trout data shows about 1/4 of paralogs from the latest whole genome duplication event retain the very similar expression patterns 16 indicating that distinct expression patterns of paralogs is quite common 77 . In their analysis of the Arctic charr gill transcriptome, Norman et al. (2014) 23 , 24 also used Illumina sequencing technology to evaluate expression. Their reads were longer (2x100 bp) than in this study (36 bp) enabling them to assemble contigs. They did not consider distinct paralogs in their approach and merged contigs based on sequence identity. Thus the complexity of Arctic charr transcriptome still remains unsolved. The data reflected differential deployment of several gene classes during Arctic charr development, which is most probably genetic in origin. We raised the embryos in a common garden, but their parents were wild so parental environments and transgenerational plasticity may also have contributed. Studies in salmonids and other fish have demonstrated large changes in expression during early development, including coordinated changes in many cellular and developmental systems 19 , 78 – 81 . Several blood coagulation factors genes showed significant changes during charr development, and were also more highly expressed in the SB-charr. This might reflect differences in the rate of development of blood composition, or tissue composition, in the two morphs. While our main interest is on the derived and repeatedly evolved small benthic charr, the data can also reflect differences due to breeding. As was reasoned in the introduction we chose to compare SB to AC-charr. This proved useful, as the data revealed differential expression of several developmental genes and regulators with differential expression between benthic and limnetic charr 51 , 52 . Previously we found tight correlation of RNA-seq expression and qPCR estimates - using data from this very transcriptome 51 . Furthermore, we actually used the same morphs (AC and SB) and samples in a comparison of the developmental miRNA transcriptome – which reveal that expression of several miRNAs correlates with morph differences 56 . Higher expression of lysozyme II C and natterin-like in SB-charr Natural selection can shape variation in immunological genes. We decided to study further Lyz2 and the putative immunological nattl genes that had higher expression in SB. Note, because only two charr transcriptomes studied, it was impossible to polarize the changes. It was not possible to say that these genes are upregulated in SB or downregulated in AC charr. The substrate of lysozyme 82 is the bacterial cell wall peptidoglycan and it acts directly on Gram-positive bacteria 83 . Lysozyme also promotes the degradation of the outer membrane and therefore indirectly acts also on Gram-negative bacteria 84 . Another gene that caught our attention was natterin-like . Natterins were first discovered from the venom gland of the tropical toxic fish species Thalassophryne nattereri 70 , 71 , and are found by sequence similarity in e.g. zebrafish, Atlantic salmon and here in Arctic charr. The Natterin proteins contain a mannose-binding lectin-like domain (Jacalin-domain). Mannose-binding lectins are pathogen recognition proteins (antibodies) and therefore are important for the acute phase response of fish 85 , 86 , thus we hypothesized that nattl genes in charr may have immune related functions. The data are consistent with this as the highest expression was found in skin and kidney. This putative immune functions needs to be verified. One can speculate that higher expression of Lyz2 and some Nattl paralogs in SB-charr reflect preparation of juveniles for bottom dwelling habitats, which may be rich in bacteria and challenging for immune systems. It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens or in families of Aquaculture charr breed for pathogen resistance. An evolutionary question is, whether immunological genes are expected to show similar or less parallelism than others genes shaped by natural selection? The current data does not reflect on this question, but our population genetic work shows genetic variation in immunological genes ( MHCIIα and cath2 ) does not correlate with the SB-charr ecotype in Iceland 45 . In this study we collapsed contigs into paralog groups for the transcriptome analyses. The disadvantage of this approach is that differential expression of a paralog, can be masked by related genes that do not differ between groups. We looked at this by studying the expression of three paralogs of the natterin like genes in different morphs during Arctic charr development, and among tissues of adult AC-charr. The data suggest that the three nattl genes are expressed differentially between the morphs, thus it is not divergence in the expression of one paralog that explains the general nattl expression disparity in the transcriptome. Certainly, other scenarios could apply to other genes in the transcriptome. Expression divergence in craniofacial genes in benthic morphs A study of the skulls of post-hatching embryos and juveniles from Lake Thingvallavatn, showed that some elements of the developing head ossified earlier in SB than in PL-charr 87 . Morphometric analyses of developing heads (same stages as studied here) demonstrate differences in craniofacial elements between AC and SB-charr, along a limnetic vs. benthic axis 74 . Based on those developmental phenotypes we investigated further genes with roles in craniofacial development that were differentially expressed in the transcriptome. Our published data 51 , 52 and the current data (7 out of 8 craniofacial candidates were confirmed by qPCR) demonstrate the utility of the SB and AC-charr developmental transcriptomes for identifying candidate genes with differential expression, even within specific structures like the head. All seven of the verified genes had consistently higher expression in the developing head of two benthic morphs (SB and LB), and lower in more limnetic fish (AC and PL). We must highlight the fact that three of these morphs (SB, LB and PL) are closely related and live in sympatry in Lake Thingvallavatn 44 . We focused on a several craniofacial candidate genes including a few within, or transcriptionally connected to, the Tgf- β and Ahr signaling pathways 88 – 90 . These are the Lsr , Cldn4, Jup, Scin, Vdra, Mvp and Tgfbr2 , here described briefly . Adseverin (Scin) has roles in rearrangements of the actin cytoskeleton, chondrocyte differentiation and skeletal formation 91 , 92 . Lsr encodes a component of tri-cellular tight junctions 93 and has been shown to be suppressed upon Tgf- β 1 stimulation 94 in a human cell line. Similarly, Cldn4 , a tight junction protein with unknown role during embryonic morphogenesis, is a target of the Tgf- β and Ahr signaling pathways 95 , 96 . The Tgfbr2 , encoding a receptor of Tgf- β, i involved in craniofacial morphogenesis 97 . Mvp is the predominant component of cytoplasmic ribonucleoprotein structures called vaults 98 , which is highly conserved across eukaryotes and are implicated in several processes from signal transmission and immune response 99 . Finally, higher expression of Vdra , encoding the vitamin D receptor A, was found in the heads of benthic charr. The receptor regulates mineral homeostasis, osteoblast differentiation and bone metabolism 100 . A related study from our group, building on this transcriptome, described in more detail the differential expression of these and other coexpressed genes in limnetic and benthic charr 53 . To summarize, the results show that RNA-sequencing of Aquaculture charr with limnetic craniofacial morphology and small benthic charr can implicate candidate genes for qPCR analyses. Those studies 52 , 53 have revealed genes that associate with limnetic and benthic divergence in craniofacial elements in sympatric charr morphs. It would be interesting if expression of these genes associates with benthic morphology in independently evolved charr populations, as was seen for certain mTOR-pathway genes in muscle of adult SB-charr 47 , or even in other species with similar trophic diversity. Genetics differences between the AC and SB-morphs - possibly in mtDNA function Previous studies on microsatellite markers documented the history of charr populations in Iceland and in particular the parallel evolution of SB-charr 44 . The data confirm genetic differences between SB and AC-charr. By comparing AC and SB-charr, that represents a small benthic resource morph that has evolved repeatedly in Icelandic stream and pond habitats 44 , we hoped to implicate genes and pathways involved in adaptation to these special habitats. The allele frequency differences and expression divergence observed in the transcriptome reflect neutral population genetic processes and/or selection during AC charr domestication or adaptation of SB-charr. Changes in the AC-charr are interesting as domestication over several decades led to rapid growth and increased size 50 . Morphometrics have not been used to compare the body or craniofacial shape of AC to other charr morphs, but domestication of O. mykiss has affected body shape and fin structure in particular 101 . By studying expression and allele frequencies in limnetic and benthic morphs from more locations, it may be possible to disentangle the role of drift and selection. We attempted to verify several SNPs, and focused mostly on variants in mtDNA because to us the data suggest interesting divergence between AC and SB charr in systems related to energy metabolism. First, there is 2X higher expression of respiratory electron transport chain components in AC compared to SB-charr and 100% more mitochondrial derived reads are found in the AC-charr samples. Note that the direction of divergence is unknown, i.e. whether expression was up in AC or down in SB. Second, many derived candidate-SNPs in genes related to mitochondrial function were at high frequency on the AC branch. For instance in S100A1 , which has been implicated in mitochondrial regulation in cardiac tissue in humans 102 , but its expression is probably not exclusive to this tissue. Third, while the mitochondrial ribosomal genes generally evolve slowly, we do see derived variants at high frequency in the SB and large benthic charr in Lake Thingvallavatn. Specifically, m3411C>T in SB affects a position that is highly conserved among fish, and could affect function of the 16s rRNA. Earlier studies of mitochondrial markers in S. alpinus did not find large signals of divergence within Iceland 40 , 42 , 45 , probably because they studied other genes. The mitochondrion is more than a powerhouse, it integrates metabolism, cell cycle and apoptosis 103 . The number of mitochondria and its functions are known to correlate with environmental attributes. For instance in Antarctic fishes under extreme cold, higher numbers of mitochondria are found in muscle and heart cells 104 . Our data suggest an expression difference between morphs that could reflect differences in total number of mitochondrion, the number of mtDNA copies per mitochondrion or cell, or difference in RNA expression from the mtDNA, possibly due to evolution of mtDNA related to diet and/or temperature 105 . The results suggest divergence (adaptive or neutral) in mitochondrial function due to the domestication of aquaculture charr and/or adaptation of the small benthic charr to its habitat. Increase in mitochondrial function in AC charr embryos could reflect higher basal metabolic rate in this aquaculture stock. Alternatively, lower metabolic rate in the SB charr would also be curious in the context of their ecology. Clearly further work is needed to map out the functional differences of mitochondrial related genes in AC charr, more SB populations and hopefully anadromous charr morphs (representing the ancestral state). The mtDNA signals could also be investigated in populations along ecological clines (e.g. temperature) or with respect to life history 106 . Conclusions The charr developmental transcriptome provides a starting point to investigate the molecular systems that associate with artificial selection during aquaculture breeding of charr or divergence among the highly polymorphic and rapidly evolving Arctic charr in Iceland. The data reveal differential expression of two immunological genes between morphs and of several craniofacial developmental genes, that may help sculpture benthic vs. limnetic heads. The genetic data suggest among other things differentiation in the charr mtDNA between the SB and AC-charr morphs. It must be acknowledged that it is not trivial to identify genes affecting variation in ecologically important phenotypes, like shape 107 , 108 . Our broad interest is in how natural selection tweaks genetic regulatory systems, for instance via genetic changes in regulatory sequences or post transcriptional modifiers relating to adaptations. Genetic changes affecting gene expression can be raw material for adaptation, but could also rise in frequency due to reverberations in regulatory cascades 76 . We plan to study the degree of developmental and population genetics parallelism of the small benthic charr, typically found in cold springs and small pond habitats in Iceland with lava substratum 29 , 44 . The availability of charr populations at different stages of divergence sets the stage for future genomic studies of the roles of genes, environment and plasticity for shaping this polymorphic species. Data availability The sequencing reads were deposited into the NCBI SRA archive under BioProject identifier PRJNA239766 and with accession numbers: SRX761559, SRX761571, SRX761575, SRX761577, SRX761451, SRX761461, SRX761490 and SRX761501. All DNA sequences where deposited to Genbank as popsets under the accession numbers KP019972-KP020026. F1000Research : Dataset 1. Parameters and multiple testing corrected p-values for expression analysis, 10.5256/f1000research.6402.d48005 109 F1000Research : Dataset 2. qPCR data for tests of expression in charr developing embryos and adult tissues., 10.5256/f1000research.6402.d48006 110 F1000Research : Dataset 3. qPCR data for tests of expression in charr developing embryo heads., 10.5256/f1000research.6402.d48007 111 Author contributions • Conceived and designed the study: JG, AP, ZOJ, SSS, SRF, VHM, EPA. • Sampling, crosses and rearing: SSS, BKK, ZOJ, KHK, VHM, AP. • RNA extraction and RNA sequencing: SRF. • Analyses of RNA sequencing data: JG, AP. • qPCR work: EPA, SSS2, VHM. • SNP analyses: JG, AP. • SNP confirmation: IMJ, KHK, AP. • Comparative genomic analysis: AP. • Writing: AP, JG, EPA, VHM, SSS. • Analyses: JG, AP, EPA, SSS2. • Gathered the data: ZOJ, SRF, EPA, IAJ, KHK, SSS2. Competing interests No competing interests were disclosed. Grant information This project was supported by The Icelandic Center for Research (grant number: 100204011) to SSS, AP, ZOJ and BKK, The University of Iceland Research/Doctoral Fund to JG and KHK and University of Iceland research fund to AP, SSS and ZOJ. I confirm that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgements We would like to thank Baldur Kristjansson for help with genomic alignments. We are very grateful to Droplaug N. Magnusdottir, Gudbjorg Th. Orlygsdottir, Steinunn Snorradottir and Olafur Th. Magnusson at deCODE Genetics for help with the Illumina sequencing. Special thanks to Fredrik Holm for assembling Figure 1 and the three reviewers for their thoughtful comments and corrections. Supporting Information S1 Figure. Relative expression of nattl and nattl 1–3 in tissues of adult AC-charr. Relative expression of Natterin ( A ) & Natterin paralogs 1–3 ( B–D ) within different tissues (skin, heart, liver, gill, spleen, intestine & kidney) of adult aquaculture charr (RT-qPCR); expression plotted for different tissues, relative to heart tissue (lowest expression levels). S2 Figure. Relative expression of selected craniofacial candidate genes. Relative expression of 12 candidate genes with characterized craniofacial expression during zebrafish development (ZFIN website) in the head of SB, LB, PL and AC at three time points in development. In the transcriptome data all of the genes had shown higher expression in SB at 200 τ s. The expression is normalized to the geometric means of two craniofacial reference genes ( ACTB and IF5A1 ). Expression is relative to a replicate of AC morph at 200 (τs), set to one. Error bars represent standard deviation calculated from two biological replicates and each biological replicate contains homogenate of six heads. Supplemental Table S1 A. qPCR primers used in this study. Gene Description Primer Sequence (5’- 3’) Product Size (bp) PCR Efficiency Melting Temperature (°C) Exon Boundary Actb Beta Cytoskeletal Actin F-GAAGATCAAGATCATCGCCC R-CAGACTCGTCGTACTCCTGCT 122 1.95 80.5 ± 0.7 Yes Alp Alkaline phosphatase F-ACAGCATACCTCTGTGGGG R-GGTGGCATGGTTCACACG 177 1.90 85.12 ± 0.5 Yes Cldn4 Claudin-4 F-GTGCTGTGC CATCCCAAG R-CACCACACAGGTCATCCACA 100 1.98 80.4 ± 0.6 Yes Cgat2 Chondroitin beta-1,4-N- acetylgalactosaminyltransferase 2 F-GAGAGCCACTTTACTGAGGGG R-GAATGGACGGAAAAGAGTAACG 120 1.98 81.86 ± 0.3 Yes Cox6b1 Cytochrome c oxidase subunit VIb isoform 1 F-GAGGGTCTACAAATCACTGTGC R-CCTGGAGTCCTACTCATACAAACAT 147 1.93 82.22 ± 0.7 Yes Ef1α Eukaryotic Translation Elongation Factor 1 Alpha F-GAAGATCGGCTATAACCCTGC R-ACCTTCCATCCCTTGAACC 111 1.94 81.36 ± 0.4 Yes If5a1 Eukaryotic Translation Initiation Factor 5A F-GGCTTCGTGGTGCTGAAG R-CCATGTGGACCTTAGCGTG 91 1.91 80.76 ± 0.6 Yes Jup Junction plakoglobin F-CACAGCAGACATACCAGGATG G R-CTGGCGATCTCTCCCCTGTT 109 1.97 81.0 ± 0.3 Yes Krtap4–3 Keratin-associated protein 4–3 F-GCGGGACATCTACACTGCTTA R-AGAAGGCTAAAGTCTTAGTGACTATC 151 1.89 81.88 ± 0.6 Yes Lsr Lipolysis-stimulated lipoprotein receptor F-TGCTGTCACTCTGGGCGA R-CCGTCTGGGCAAGGTTCA G 80 1.91 80.77 ± 0.5 Yes Lyz Lysozyme F-TTCCAGATCAACAGCCGCTA R-GATCGCCACTGTGATGTCAT 111 1.94 81.87 ± 0.7 Yes Mvp Major vault protein F-ACCAACTCCCAGGAGGCT R-CCTCTCCAGACGACCACG 75 1.97 78.93 ± 0.3 Yes Nattl Natterin-like protein F-GTGAAAGTCACCTGCATGAATG R-CATCTCTCCTTTGTGGATACCC 104 1.98 78.81 ± 0.8 No Nattl-1 Natterin-like protein paralog-1 F-AATCCGTGTCCTACCACAATGA R-GGTGTGTCGGTCAAAGCA 135 1.77 78.03 ± 0.1 No Nattl-2 Natterin-like protein paralog-1 F-TGAAATVTVTGTCTCATCACAAC R-GGATCTGGTCGAGGTGGC 163 1.72 80.50 ± 0.2 No Nattl-3 Natterin-like protein paralog-1 F-GTGACATCCGTTTCTCACCAG R-GATGTGTCGGTCAAAGCG 138 1.77 79.12 ± 0.2 No Ndub6 NADH dehydrogenase 1 beta subcomplex subunit 6 F-TGGTGGAGTGTTCGCCTT R-CTCTCTGGGAGGTCTGGAA 171 1.89 82.40 ± 0.3 Yes Parp6 Poly (ADP-Ribose) Polymerase Family, Member 6 F-CCGTATGAATACCGTTCCACAGG R-CACCCAGATGTTGCCGTGCTT 147 1.93 81.87 ± 0.7 Yes Supplemental Table S1 B. Gene Description Primer Sequence (5’- 3’) Product Size (bp) PCR Efficiency Melting Temperature (°C) Exon Boundary Rarg Retinoic acid receptor gamma-A F-AAGGCGAGCCCCTTCTTC R-TGCTCTGGGTCTCCACCG 82 1.92 78.62 ± 0.3 Yes Scin Scinderin/Adseverin F-CACCTGATCCCAGACATCCAA R-CCTCACTCAACAACCTCGC 136 1.90 83.24 ± 0.7 No Tgfbr2 TGF-beta receptor type-2 F-CTGCTCCGAGGACGAGTG R-ACCGACACCACCTGGGAG 72 1.93 79.02 ± 0.5 Yes Ubl5 Ubiquitin-like protein 5 F-AATAAGGATGATTGAGGTGGTTTG R-ATGAGCTTCTTCAGGTCTCC 99 1.95 78.44 ± 0.3 Yes Ub2l3 Ubiquitin-Conjugating Enzyme E2L 3 F-CGAGAAGGGACAGGTGTGTC R-ACCAACGCAATCAGGGACT 96 1.93 79.62 ± 0.3 Yes Vdra Vitamin D3 receptor A F-CGTCACCAAGGCGGGTCA R-TGGAGCTTG AGTTTCTTCAGGC 81 1.93 78.12 ± 0.3 Yes Supplemental Table S2 A. Verification of candidate polymorphisms. Primer sequences, melting temperatures and primary data. Sequence Position Forward primer Reverse primer Tm forward Tm reverse Paralogs NC_000861.1 1829 GTGCCTCAGACCCACCTAGA TCTGTCGCCCGTACTAAGGT 60.26 59.76 No NC_000861.1 3119 GGCCAGAGTAAACACCGAGA CCTGGATTACTCCGGTCTGA 60.25 60.07 No NC_000861.1 3411 GGCCAGAGTAAACACCGAGA CCTGGATTACTCCGGTCTGA 60.25 60.07 No NC_000861.1 8876 GACGTCCTTCACTCCTGAGC GGGCTCATAAACTGGTCGAA 59.99 60.07 No NC_000861.1 15240 ACCCTAAAACCGAACGATCC TGGCTAGGAAGAGTCCGGTA 60.19 59.83 No SS2U034121 233 CTCAACGTGCTTGACCAGTG CCCTTACCCTCCAGGATCTC 60.5 59.89 Yes SS2U054644 1037 AAGGACGGCCACTATGGTCT GGGGCATAGAGTGCACAGG 60.9 61.65 Yes SS2U054644 1188 TCAGAGATAGTGAAGAAGATGCTG CGTACTTGATAAGACCTGTCGGTA 57.92 59.62 No SS2U054644 1283 TCAGAGATAGTGAAGAAGATGCTG CGTACTTGATAAGACCTGTCGGTA 57.92 59.62 No SS2U055283 1822 TGTGTGAGGTGGTTGAGGAG GGGTCATTGCTCCCTACAGA 59.7 60.07 No SS2U055923 615 GTGGACCCAGAGGATGAGAA AGAACCTGCTCCCAGTTTGA 60.05 59.84 No SS2U058906 350 GCCAAAACCTCCACAATGAT AACTGGCCTTCCAGATCAGA 59.8 59.8 Yes/No Paralogs: indicates whether the PCR and sequencing yielded mixed products, indicative of paralogous genes. Supplemental Table S2 B. Sequence Genome contig Gene name Position Ref Var Freq_AC Freq_SB FreqP_PL FreqP_SB FreqP_LB NC_000861.1 n.a. 12S ribosomal RNA 1829 G A 0/53 77/81 0/6 3/4 1/8 NC_000861.1 n.a. 16S ribosomal RNA 3119 A T 46/87 18/28 0/8 0/8 0/8 NC_000861.1 n.a. 16S ribosomal RNA 3411 C T 0/119 26/33 0/8 5/8 1/8 NC_000861.1 n.a. tRNA-Lys 8876 C A 73/779 74/352 0/4 0/4 n.a. NC_000861.1 n.a. NADH dehydrogenase 6 15240 G A 2/3608 137/2702 2/4 0/4 n.a. SS2U034121 AGKD01052493.1 Eukaryotic translation initiation factor 4 gamma 2 233 C T 0/95 22/40 2/4 2/2 n.a. SS2U054644 AGKD01031893.1 Uroporphyrinogen decarboxylase 1037 G A 28/33 0/56 n.a. n.a. n.a. SS2U054644 AGKD01031893.1 Uroporphyrinogen decarboxylase 1188 C T 0/53 19/25 4/4 4/4 n.a. SS2U054644 AGKD01031893.1 Uroporphyrinogen decarboxylase 1283 G A 4/60 12/15 4/4 4/4 n.a. SS2U055283 AGKD01013777.1 DNA2-like helicase 1822 G A 1/65 25/50 3/4 n.a. n.a. SS2U055923 AGKD01022586.1 Bystin 615 G A 106/109 7/190 0/4 n.a. n.a. SS2U058906 AGKD01005918.1 Mid1-interacting protein 1-like 350 G T 0/49 67/68 4/4 4/4 n.a. Sequence: name of the genebank sequence or EST-contig used as reference for mapped reads. Genome contig: name of salmon genome (ICSASG_v1) contig with best sequence match to the respective EST-contig. Ref: Reference variant. Var: The derived variant. Freq_AC and Freq_SB: Frequency of variant reads as fraction of total numbers of reads mapped in Aquaculture (AC) or Small benthic (SB). FreqP: The frequency of variant in genotyping by PCR and direct sequencing, as a fraction of total number of chromosomes sequenced. Supplemental Table S3. Mapping of Illumina reads to S. salar EST data. Numbers of reads aligning to salmon reference for each sample. Alignment per read SB 141 SB 163 SB 200 SB 433 AC 141 AC 163 AC 200 AC 433 0 33088778 30492314 27175901 25569628 32159386 30051365 31267710 28563169 1 6979368 11791558 11449549 11058555 11599602 11320997 11027195 10650748 2 2742358 4021683 3814418 3734404 4328402 4523686 3959198 3655786 3 2099068 2964994 2748108 2651522 3111277 3332577 2878729 2515303 4 1228292 1777846 1720902 1968251 1977738 2182392 1929818 1980420 5 914704 1317556 1284262 1434314 1471739 1679277 1447604 1426744 6 645264 946579 938290 1087959 1001350 1083025 1045157 1081063 7 425856 595785 578175 726290 657220 750523 690286 735351 8 293065 428003 424426 590100 530040 591332 527821 579860 9 206205 319401 334861 455838 296169 334264 387901 485653 10+ 749074 1419362 1761275 3041930 1092980 1189781 1967857 3294222 Total reads 49372032 56075081 52230167 52318791 58225903 57039219 57129276 54968319 Supplemental Table S4. ANOVAs on qPCR data. Expression of nine genes was analyzed in whole SB- and AC-charr embryos, at two developmental timepoints (161 and 200 τs ). Gene Term Df F value p value Significance FDR RNA-seq Alp Morph 1 13.4797 0.0214 * 0.0697 Time 1 14.9526 0.0180 * 0.0012 M x T 1 3.9519 0.1177 . Cgat2 Morph 1 0.0257 0.8804 . 0.0035 Time 1 1.5141 0.2859 . 0.3312 M x T 1 0.1866 0.6880 . Cox6B1 Morph 1 0.0898 0.7793 . 0.0580 Time 1 3.8312 0.1219 . 0.6320 M x T 1 0.7359 0.4393 . Krtap4–3 Morph 1 30.0255 0.0054 ** 0.0121 Time 1 0.3902 0.5661 . 0.2784 M x T 1 4.5225 0.1006 . Lyz Morph 1 64.1566 0.0013 ** 0.0406 Time 1 1.0390 0.3657 . 0.0005 M x T 1 1.2026 0.3344 . Nattl Morph 1 8.1148 0.0465 * 7.718e-07 Time 1 14.6659 0.0186 * 6.714e-14 M x T 1 0.2958 0.6154 . Ndub6 Morph 1 0.7447 0.4368 . 0.0982 Time 1 7.3316 0.0537 . 0.6698 M x T 1 0.2269 0.6587 . Parp6 Morph 1 11.2682 0.0284 * 0.1076 Time 1 0.7393 0.4384 . 0.3789 M x T 1 0.2343 0.6537 . Ubl5 Morph 1 1.1420 0.3454 . 0.0587 Time 1 0.2434 0.6476 . 0.0025 M x T 1 0.3974 0.5627 . Significance: p > 0.05; * p < 0.05; ** p < 0.01. FDR RNA-seq: indicates significance of Morph and Time effects in the transcriptome data. Supplemental Table S5. ANOVAs on Natterin-like qPCR on adults. Studied were levels of Natterin-like and Natterin-like Paralogs 1– 3 in Arctic charr whole embryos (among SB, AC and PL morphs) and tissues from adult AC-charr. Gene Term Df F value p value Significance Nattl Morph 2 11.5515 0.0002 *** Time 5 8.3202 3.99e-05 *** M x T 9 4.4758 0.0007 *** Nattl1 Morph 2 19.4070 0.0001 *** Time 3 5.9346 0.0089 ** M x T 5 4.5761 0.0126 * Nattl2 Morph 2 14.2921 0.0005 *** Time 3 15.0463 0.0001 *** M x T 5 3.2462 0.0404 * Nattl3 Morph 2 34.4888 6.33e-06 *** Time 3 4.4204 0.0238 * M x T 5 4.1843 0.0174 * Nattl Tissue 6 15.468 1.42e-08 *** Nattl1 Tissue 6 12.022 0.0002 *** Nattl2 Tissue 6 7.6811 0.0011 ** Nattl3 Tissue 6 46.182 8.89e-06 *** Significance: p > 0.05; * p < 0.05; ** p < 0.01. Supplemental Table S6. Gene Ontology analyses of derived SNPs in SB-charr. Category Observed In category TERM FDR adjusted p-value GO:0006412 24 189 translation 4.34E-006 GO:0006396 8 32 RNA processing 0.0016 GO:0006414 6 19 translational elongation 0.0038 GO:0006313 5 20 transposition, DNA-mediated 0.0498 GO:0015074 5 21 DNA integration 0.0510 GO:0006260 6 35 DNA replication 0.0679 GO:0055114 20 285 oxidation-reduction process 0.0679 Supplemental Table S7. Predicted effect of SNP-candidates differing in frequency between charr morphs. Effect on transcribed region Uni_SB Uni_AC Rep_SB Rep_AC 5´prime 32 19 35 24 Synonymous 232 179 176 113 Non-synonymous 112 72 81 72 3´prime 147 123 59 74 From RNA-reads that mapped to one (Uni) or more (Rep) S. salar ESTs. The candidate SNPs frequencies differ more than 50% between SB and AC-charr, summarized by which morph with higher frequency of the derived allele. Faculty Opinions recommended References 1. Gould SJ: Ontogeny and Phylogeny. Harvard University Press, 1977. Reference Source 2. Jacob F: Evolution and tinkering. Science. 1977; 196 (4295): 1161–1166. PubMed Abstract | Publisher Full Text 3. Stern DL, Orgogozo V: The loci of evolution: how predictable is genetic evolution? Evolution. 2008; 62 (9): 2155–77. PubMed Abstract | Publisher Full Text | Free Full Text 4. Stern DL, Orgogozo V: Is genetic evolution predictable? Science. 2009; 323 (5915): 746–751. PubMed Abstract | Publisher Full Text | Free Full Text 5. Rockman MV: The QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution. 2012; 66 (1): 1–17. PubMed Abstract | Publisher Full Text | Free Full Text 6. Abzhanov A, Protas M, Grant BR, et al. : Bmp4 and morphological variation of beaks in Darwin’s finches. Science. 2004; 305 (5689): 1462–1465. PubMed Abstract | Publisher Full Text 7. Albertson RC, Kocher TD: Genetic and developmental basis of cichlid trophic diversity. Heredity (Edinb). 2006; 97 (3): 211–221. PubMed Abstract | Publisher Full Text 8. Cresko WA, McGuigan KL, Phillips PC, et al. : Studies of threespine stickleback developmental evolution: progress and promise. Genetica. 2007; 129 (1): 105–126. PubMed Abstract | Publisher Full Text 9. Skúlason S, Smith TB: Resource polymorphisms in vertebrates. Trends Ecol Evol. 1995; 10 (9): 366–370. PubMed Abstract | Publisher Full Text 10. Snorrason SS, Skúlason S: Adaptive Speciation in Northern Freshwater Fishes. In Ulf Dieckmann, Michael Doebeli, Johan A. J. Metz, and Diethard Tautz, editors, Adaptive speciation . Cambridge University Press, Cambridge, chapter 10, 2004; 210–228. Publisher Full Text 11. Klemetsen A: The Charr Problem Revisited: Exceptional Phenotypic Plasticity Promotes Ecological Speciation in Postglacial Lakes. Freshwater Rev. 2010; 3 (1): 49–74. Publisher Full Text 12. Bernatchez L, Renaut S, Whiteley AR, et al. : On the origin of species: insights from the ecological genomics of lake whitefish. Philos Trans R Soc Lond B Biol Sci. 2010; 365 (1547): 1783–800. PubMed Abstract | Publisher Full Text | Free Full Text 13. Merilä J: Nine-spined stickleback ( Pungitius pungitius ): an emerging model for evolutionary biology research. Ann N Y Acad Sci. 2013; 1289 : 18–35. PubMed Abstract | Publisher Full Text 14. Fraser DJ, Weir LK, Bernatchez L, et al. : Extent and scale of local adaptation in salmonid fishes: review and meta-analysis. Heredity (Edinb). 2011; 106 (3): 404–20. PubMed Abstract | Publisher Full Text | Free Full Text 15. Macqueen DJ, Johnston IA: A well-constrained estimate for the timing of the salmonid whole genome duplication reveals major decoupling from species diversification. Proc Biol Sci. 2014; 281 (1778): 20132881. PubMed Abstract | Publisher Full Text | Free Full Text 16. Berthelot C, Brunet F, Chalopin D, et al. : The rainbow trout genome provides novel insights into evolution after whole-genome duplication in vertebrates. Nat Commun. 2014; 5 : 3657. PubMed Abstract | Publisher Full Text | Free Full Text 17. Davidson WS, Koop BF, Jones SJ, et al. : Sequencing the genome of the Atlantic salmon ( Salmo salar ). Genome Biol. 2010; 11 (9): 403. PubMed Abstract | Free Full Text 18. Moghadam HK, Ferguson MM, Danzmann RG: Whole genome duplication: challenges and considerations associated with sequence orthology assignment in Salmoninae. J Fish Biol. 2011; 79 (3): 561–574. PubMed Abstract | Publisher Full Text 19. Domazet-Lošo T, Tautz D: A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns. Nature. 2010; 468 (7325): 815–8. PubMed Abstract | Publisher Full Text 20. Bozinovic G, Sit TL, Hinton DE, et al. : Gene expression throughout a vertebrate’s embryogenesis. BMC Genomics. 2011; 12 : 132. PubMed Abstract | Publisher Full Text | Free Full Text 21. Giger T, Excoffier L, Day PJ, et al. : Life history shapes gene expression in salmonids. Curr Biol. 2006; 16 (8): R281–2. PubMed Abstract | Publisher Full Text 22. Filteau M, Pavey SA, St-Cyr J, et al. : Gene coexpression networks reveal key drivers of phenotypic divergence in lake whitefish. Mol Biol Evol. 2013; 30 (6): 1384–96. PubMed Abstract | Publisher Full Text 23. Norman JD, Ferguson MM, Danzmann RG: Transcriptomics of salinity tolerance capacity in Arctic charr ( Salvelinus alpinus ): a comparison of gene expression profiles between divergent QTL genotypes. Physiol Genomics. 2014; 46 (4): 123–37. PubMed Abstract | Publisher Full Text | Free Full Text 24. Norman JD, Ferguson MM, Danzmann RG: An integrated transcriptomic and comparative genomic analysis of differential gene expression in Arctic charr ( Salvelinus alpinus ) following seawater exposure. J Exp Biol. 2014; 217 (Pt 22): 4029–4042. PubMed Abstract | Publisher Full Text 25. Vijay N, Poelstra JW, Künstner A, et al. : Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments. Mol Ecol. 2013; 22 (3): 620–34. PubMed Abstract | Publisher Full Text 26. Noakes DLG: Charr truth: Sympatric differentiation in Salvelinus species. Environ Biol Fishes. 2008; 83 (1): 7–15. Publisher Full Text 27. Ghalambor CK, Hoke KL, Ruell EW, et al. : Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature. 2015; 525 (7569): 372–5. PubMed Abstract | Publisher Full Text 28. Adams CE, Fraser D, Wilson AJ, et al. : Patterns of phenotypic and genetic variability show hidden diversity in Scottish Arctic charr. Ecol Freshwater Fish. 2007; 16 (1): 78–86. Publisher Full Text 29. Kristjánsson BK, Malmquist HJ, Ingimarsson F, et al. : Relationships between lake ecology and morphological characters in Icelandic Arctic charr, Salvelinus alpinus . Biol J Linn Soc. 2011; 103 (4): 761–771. Publisher Full Text 30. Woods PJ, Skúlason S, Snorrason SS, et al. : Intraspecific diversity in Arctic charr, Salvelinus alpinus , in Iceland: II. Which environmental factors influence resource polymorphism in lakes? Evolutionary Ecol Res. 2012; 14 (8): 993–1013. Reference Source 31. Kristjánsson BK, Skúlason S, Snorrason SS, et al. : Fine-scale parallel patterns in diversity of small benthic Arctic charr ( Salvelinus alpinus ) in relation to the ecology of lava/groundwater habitats. Ecol Evol. 2012; 2 (6): 1099–112. PubMed Abstract | Publisher Full Text | Free Full Text 32. Skúlason S, Noakes DL, Snorrason SS: Ontogeny of trophic morphology in four sympatric morphs of Arctic charr Salvelinus alpinus in Thingvallavatn, Iceland. Biol J Linn Soc. 1989; 38 (3): 281–301. Publisher Full Text 33. Skúlason S, Snorrason SS, Ota D, et al. : Genetically based differences in foraging behaviour among sympatric morphs of Arctic charr (Pisces: Salmonidae). Anim Behav. 1993; 45 (6): 1179–1192. Publisher Full Text 34. Skúlason S, Snorrason SS, Noakes DLG, et al. : Genetic basis of life history variations among sympatric morphs of Arctic char Salvelinus alpinus . Can J Fish Aquat Sci. 1996; 53 (8): 1807–1813. Publisher Full Text 35. Parsons KJ, Skúlason S, Ferguson M: Morphological variation over ontogeny and environments in resource polymorphic Arctic charr ( Salvelinus alpinus ). Evol Dev. 2010; 12 (3): 246–257. PubMed Abstract | Publisher Full Text 36. Parsons KJ, Sheets HD, Skúlason S, et al. : Phenotypic plasticity, heterochrony and ontogenetic repatterning during juvenile development of divergent Arctic charr ( Salvelinus alpinus ). J Evol Biol. 2011; 24 (8): 1640–1652. PubMed Abstract | Publisher Full Text 37. Sandlund TO, Gunnarsson K, Jónasson PM, et al. : The Arctic charr Salvelinus alpinus in Thingvallavatn. Oikos. 1992; 64 (1/2): 305–351. Publisher Full Text 38. Snorrason SS, Skúlason S, Jonsson B, et al. : Trophic specialization in Arctic charr Salvelinus alpinus (Pisces; Salmonidae): morphological divergence and ontogenetic niche shifts. Biol J Linn Soc. 1994; 52 (1): 1–18. Publisher Full Text 39. Magnusson KP, Ferguson MM: Genetic analysis of four sympatric morphs of Arctic charr, Salvelinus alpinus , from Thingvallavatn, Iceland. Environ Biol Fishes. 1987; 20 (1): 67–73. Publisher Full Text 40. Danzmann RG, Ferguson MM, Skulason S, et al. : Mitochondrial DNA diversity among four sympatric morphs of Arctic charr, Salvelinus alpinus L., from Thingvallavatn, Iceland. J Fish Biol. 1991; 39 (5): 649–659. Publisher Full Text 41. Pálsson S, Árnason E: Sequence variation for cytochrome b genes of three salmonid species from Iceland. Aquaculture. 1994; 128 (1–2): 29–39. Publisher Full Text 42. Volpe JP, Ferguson MM: Molecular genetic examination of the polymorphic Arctic charr Salvelinus alpinus of Thingvallavatn, Iceland. Mol Ecol. 1996; 5 (6): 763–72. PubMed Abstract | Publisher Full Text 43. Wilson AJ, Gíslason D, Skúlason S, et al. : Population genetic structure of Arctic charr, Salvelinus alpinus from northwest Europe on large and small spatial scales. Mol Ecol. 2004; 13 (5): 1129–42. PubMed Abstract | Publisher Full Text 44. Kapralova KH, Morrissey MB, Kristjánsson BK, et al. : Evolution of adaptive diversity and genetic connectivity in Arctic charr ( Salvelinus alpinus ) in Iceland. Heredity (Edinb). 2011; 106 (3): 472–487. PubMed Abstract | Publisher Full Text | Free Full Text 45. Kapralova KH, Gudbrandsson J, Reynisdottir S, et al. : Differentiation at the MHCIIα and Cath2 loci in sympatric Salvelinus alpinus resource morphs in Lake Thingvallavatn. PLoS One. 2013; 8 (7): e69402. PubMed Abstract | Publisher Full Text | Free Full Text 46. Saemundsson K: Geology of the Thingvallavatn Area. Oikos. 1992; 64 (1/2): 40–68. Publisher Full Text 47. Macqueen DJ, Kristjánsson BK, Paxton CG, et al. : The parallel evolution of dwarfism in Arctic charr is accompanied by adaptive divergence in mTOR-pathway gene expression. Mol Ecol. 2011; 20 (15): 3167–84. PubMed Abstract | Publisher Full Text 48. Doiron S, Bernatchez L, Blier PU: A comparative mitogenomic analysis of the potential adaptive value of Arctic charr mtDNA introgression in brook charr populations ( Salvelinus fontinalis Mitchill). Mol Biol Evol. 2002; 19 (11): 1902–9. PubMed Abstract | Publisher Full Text 49. Miller W, Schuster SC, Welch AJ, et al. : Polar and brown bear genomes reveal ancient admixture and demographic footprints of past climate change. Proc Natl Acad Sci U S A. 2012; 109 (36): E2382–90. PubMed Abstract | Publisher Full Text | Free Full Text 50. Svavarsson E: Árangur í kynbótum á bleikju og næstu skref [reference in Icelandic]. In Fræðaþing landbúnaðarsins 4 . 2007; 121–125. Reference Source 51. Ahi EP, Guðbrandsson J, Kapralova KH, et al. : Validation of reference genes for expression studies during craniofacial development in arctic charr. PLoS One. 2013; 8 (6): e66389. PubMed Abstract | Publisher Full Text | Free Full Text 52. Ahi EP, Kapralova KH, Pálsson A, et al. : Transcriptional dynamics of a conserved gene expression network associated with craniofacial divergence in Arctic charr. Evodevo. 2014; 5 (1): 40. PubMed Abstract | Publisher Full Text | Free Full Text 53. Ahi EP, Steinhäuser SS, Pálsson A, et al. : Differential expression of the aryl hydrocarbon receptor pathway associates with craniofacial polymorphism in sympatric Arctic charr. Evodevo. 2015; 6 (1): 27. PubMed Abstract | Publisher Full Text | Free Full Text 54. Snorrason SS, Skúlason S, Sandlund OT, et al. : Shape polymorphism in Arctic charr, Salvelinus alpinus . Physiol Ecol Japan. 1989; 1 : 393–404. Reference Source 55. Gorodilov YN: Description of the early ontogeny of the Atlantic salmon, Salmo salar , with a novel system of interval (state) identification. Environ Biol Fishes. 1996; 47 (2): 109–127. Publisher Full Text 56. Kapralova KH, Franzdóttir SR, Jónsson H, et al. : Patterns of miRNA expression in Arctic charr development. PLoS One. 2014; 9 (8): e106084. PubMed Abstract | Publisher Full Text | Free Full Text 57. Di Génova A, Aravena A, Zapata L, et al. : SalmonDB: a bioinformatics resource for Salmo salar and Oncorhynchus mykiss . Database (Oxford). 2011; 2011 : bar050. PubMed Abstract | Publisher Full Text | Free Full Text 58. Li B, Dewey CN: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011; 12 (1): 323. PubMed Abstract | Publisher Full Text | Free Full Text 59. Robinson MD, McCarthy DJ, Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26 (1): 139–40. PubMed Abstract | Publisher Full Text | Free Full Text 60. R Core Team: R: A Language and Environment for Statistical Computing. 2014. 61. Young MD, Wakefield MJ, Smyth GK, et al. : Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010; 11 (2): R14. PubMed Abstract | Publisher Full Text | Free Full Text 62. Untergasser A, Cutcutache I, Koressaar T, et al. : Primer3--new capabilities and interfaces. Nucleic Acids Res. 2012; 40 (15): e115. PubMed Abstract | Publisher Full Text | Free Full Text 63. Bustin SA, Benes V, Garson JA, et al. : The MIQE guidelines: M inimum I nformation for publication of Q uantitative real-time PCR E xperiments. Clin Chem. 2009; 55 (4): 611–22. PubMed Abstract | Publisher Full Text 64. Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2 -ΔΔ C T Method. Methods. 2001; 25 (4): 402–8. PubMed Abstract | Publisher Full Text 65. Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25 (14): 1754–60. PubMed Abstract | Publisher Full Text | Free Full Text 66. Koboldt DC, Zhang Q, Larson DE, et al. : VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012; 22 (3): 568–76. PubMed Abstract | Publisher Full Text | Free Full Text 67. Piskol R, Ramaswami G, Li JB: Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet. 2013; 93 (4): 641–651. PubMed Abstract | Publisher Full Text | Free Full Text 68. Larkin MA, Blackshields G, Brown NP, et al. : Clustal W and Clustal X version 2.0. Bioinformatics. 2007; 23 (21): 2947–2948. PubMed Abstract | Publisher Full Text 69. Nicholas KB, Nicholas HB, Deerfield DW: GeneDoc: analysis and visualization of genetic variation. EMBNEW.NEWS. 1997; 4 (14). Reference Source 70. Magalhães GS, Lopes-Ferreira M, Junqueira-de Azevedo IL, et al. : Natterins, a new class of proteins with kininogenase activity characterized from Thalassophryne nattereri fish venom. Biochimie. 2005; 87 (8): 687–99. PubMed Abstract | Publisher Full Text 71. Magalhães GS, Junqueira-de-Azevedo IL, Lopes-Ferreira M, et al. : Transcriptome analysis of expressed sequence tags from the venom glands of the fish Thalassophryne nattereri . Biochimie. 2006; 88 (6): 693–9. PubMed Abstract | Publisher Full Text 72. Sprague J, Bayraktaroglu L, Clements D, et al. : The Zebrafish Information Network: the zebrafish model organism database. Nucleic Acids Res. 2006; 34 (Database issue): D581–D585. PubMed Abstract | Publisher Full Text | Free Full Text 73. Eiríksson GM, Skúlason S, Snorrason SS: Heterochrony in skeletal development and body size in progeny of two morphs of Arctic charr from Thingvallavatn, Iceland. J Fish Biol. 1999; 55 (sa): 175–185. Publisher Full Text 74. Kapralova KH, Jónsson ZO, Pálsson A, et al. : Bones in motion: Ontogeny of craniofacial development in sympatric arctic charr morphs. Dev Dyn. 2015; 244 (9): 1168–1178. PubMed Abstract | Publisher Full Text 75. Jonsson B, Skúlason S, Snorrason SS, et al. : Life History Variation of Polymorphic Arctic Charr ( Salvelinus alpinus ) in Thingvallavatn, Iceland. Can J Fish Aquat Sci. 1988; 45 (9): 1537–1547. Publisher Full Text 76. Palsson A, Wesolowska N, Reynisdóttir S, et al. : Naturally occurring deletions of hunchback binding sites in the even-skipped stripe 3+7 enhancer. PLoS One. 2014; 9 (5); e91924. PubMed Abstract | Publisher Full Text | Free Full Text 77. Zou C, Lehti-Shiu MD, Thomashow M, et al. : Evolution of stress-regulated gene expression in duplicate genes of Arabidopsis thaliana . PLoS Genet. 2009; 5 (7): e1000581. PubMed Abstract | Publisher Full Text | Free Full Text 78. Jantzen SG, Sanderson DS, von Schalburg KR, et al. : A 44K microarray dataset of the changing transcriptome in developing Atlantic salmon ( Salmo salar L.). BMC Res Notes. 2011; 4 : 88. PubMed Abstract | Publisher Full Text | Free Full Text 79. Drivenes Ø, Taranger GL, Edvardsen RB: Gene expression profiling of Atlantic cod ( Gadus morhua ) embryogenesis using microarray. Mar Biotechnol (NY). 2012; 14 (2): 167–76. PubMed Abstract | Publisher Full Text 80. Bougas B, Audet C, Bernatchez L: The influence of parental effects on transcriptomic landscape during early development in brook charr ( Salvelinus fontinalis , Mitchill). Heredity (Edinb). 2013; 110 (5): 484–491. PubMed Abstract | Publisher Full Text | Free Full Text 81. Piasecka B, Lichocki P, Moretti S, et al. : The hourglass and the early conservation models--co-existing patterns of developmental constraints in vertebrates. PLoS Genet. 2013; 9 (4): e1003476. PubMed Abstract | Publisher Full Text | Free Full Text 82. Fleming A: On a Remarkable Bacteriolytic Element Found in Tissues and Secretions. Proc R Soc Lond B. 1922; 93 (653): 306–317. Publisher Full Text 83. Chipman DM, Sharon N: Mechanism of lysozyme action. Science. 1969; 165 (3892): 454–465. PubMed Abstract | Publisher Full Text 84. Subramanian S, MacKinnon SL, Ross NW: A comparative study on innate immune parameters in the epidermal mucus of various fish species. Comp Biochem Physiol B Biochem Mol Biol. 2007; 148 (3): 256–63. PubMed Abstract | Publisher Full Text 85. Magnadóttir B: Innate immunity of fish (overview). Fish Shellfish Immunol. 2006; 20 (2): 137–51. PubMed Abstract | Publisher Full Text 86. Magnadottir B: Immunological control of fish diseases. Mar Biotechnol (NY). 2010; 12 (4): 361–79. PubMed Abstract | Publisher Full Text 87. Eiriksson GM: Heterochrony in bone development and growth in two morphs of Arctic charr ( Salvelinus alpinus ) from Thingvallavatn , Iceland . Master’s thesis, University of Iceland, 1999. Reference Source 88. Chai Y, Ito Y, Han J: TGF-beta signaling and its functional significance in regulating the fate of cranial neural crest cells. Crit Rev Oral Biol Med. 2003; 14 (2): 78–88. PubMed Abstract | Publisher Full Text 89. Puga A, Tomlinson CR, Xia Y: Ah receptor signals cross-talk with multiple developmental pathways. Biochem Pharmacol. 2005; 69 (2): 199–207. PubMed Abstract | Publisher Full Text 90. Goodale BC, La Du JK, Bisson WH, et al. : AHR2 mutant reveals functional diversity of aryl hydrocarbon receptors in zebrafish. PLoS One. 2012; 7 (1): e29346. PubMed Abstract | Publisher Full Text | Free Full Text 91. Nurminsky D, Magee C, Faverman L, et al. : Regulation of chondrocyte differentiation by actin-severing protein adseverin. Dev Biol. 2007; 302 (2): 427–37. PubMed Abstract | Publisher Full Text | Free Full Text 92. Vieira FA, Thorne MA, Stueber K, et al. : Comparative analysis of a teleost skeleton transcriptome provides insight into its regulation. Gen Comp Endocrinol. 2013; 191 : 45–58. PubMed Abstract | Publisher Full Text 93. Furuse M, Oda Y, Higashi T, et al. : Lipolysis-stimulated lipoprotein receptor: a novel membrane protein of tricellular tight junctions. Ann N Y Acad Sci. 2012; 1257 : 54–8. PubMed Abstract | Publisher Full Text 94. Jazag A, Ijichi H, Kanai F, et al. : Smad4 silencing in pancreatic cancer cell lines using stable RNA interference and gene expression profiles induced by transforming growth factor-beta. Oncogene. 2005; 24 (4): 662–71. PubMed Abstract | Publisher Full Text 95. Planchart A, Mattingly CJ: 2,3,7,8-Tetrachlorodibenzo- p -dioxin upregulates FoxQ1b in zebrafish jaw primordium. Chem Res Toxicol. 2010; 23 (3): 480–7. PubMed Abstract | Publisher Full Text | Free Full Text 96. Hering NA, Andres S, Fromm A, et al. : Transforming growth factor- β , a whey protein component, strengthens the intestinal barrier by upregulating claudin-4 in HT-29/B6 cells. J Nutr. 2011; 141 (5): 783–9. PubMed Abstract | Publisher Full Text 97. Ito Y, Yeo JY, Chytil A, et al. : Conditional inactivation of Tgfbr2 in cranial neural crest causes cleft palate and calvaria defects. Development. 2003; 130 (21): 5269–80. PubMed Abstract | Publisher Full Text 98. Kedersha NL, Miquel MC, Bittner D, et al. : Vaults. II. Ribonucleoprotein structures are highly conserved among higher and lower eukaryotes. J Cell Biol. 1990; 110 (4): 895–901. PubMed Abstract | Publisher Full Text | Free Full Text 99. Berger W, Steiner E, Grusch M, et al. : Vaults and the major vault protein: novel roles in signal pathway regulation and immunity. Cell Mol Life Sci. 2009; 66 (1): 43–61. PubMed Abstract | Publisher Full Text 100. van Driel M, Pols HA, van Leeuwen JP: Osteoblast differentiation and control by vitamin D and vitamin D metabolites. Curr Pharm Des. 2004; 10 (21): 2535–55. PubMed Abstract | Publisher Full Text 101. Pulcini D, Wheeler PA, Cataudella S, et al. : Domestication shapes morphology in rainbow trout Oncorhynchus mykiss . J Fish Biol. 2013; 82 (2): 390–407. PubMed Abstract | Publisher Full Text 102. Völkers M, Rohde D, Goodman C, et al. : S100A1: a regulator of striated muscle sarcoplasmic reticulum Ca 2+ handling, sarcomeric, and mitochondrial function. J Biomed Biotechnol. 2010; 2010 : 178614. PubMed Abstract | Publisher Full Text | Free Full Text 103. McBride HM, Neuspiel M, Wasiak S: Mitochondria: more than just a powerhouse. Curr Biol. 2006; 16 (14): R551–60. PubMed Abstract | Publisher Full Text 104. O’Brien KM, Mueller IA: The unique mitochondrial form and function of Antarctic channichthyid icefishes. Integr Comp Biol. 2010; 50 (6): 993–1008. PubMed Abstract | Publisher Full Text 105. William J, Ballard O, Pichaud N: Mitochondrial DNA: more than an evolutionary bystander. Functional Ecol. 2014; 28 (1): 218–231. Publisher Full Text 106. Teacher AG, André C, Merilä J, et al. : Whole mitochondrial genome scan for population structure and selection in the Atlantic herring. BMC Evol Biol. 2012; 12 : 248. PubMed Abstract | Publisher Full Text | Free Full Text 107. Palsson A, Gibson G: Association between nucleotide variation in Egfr and wing shape in Drosophila melanogaster . Genetics. 2004; 167 (3): 1187–1198. PubMed Abstract | Publisher Full Text | Free Full Text 108. Palsson A, Dodgson J, Dworkin I, et al. : Tests for the replication of an association between Egfr and natural variation in Drosophila melanogaster wing morphology. BMC Genet. 2005; 6 : 44. PubMed Abstract | Publisher Full Text | Free Full Text 109. Gudbrandsson J, Ahi E, Franzdottir S, et al. : Dataset 1 in: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs. F1000Research. 2015. Data Source 110. Gudbrandsson J, Ahi E, Franzdottir S, et al. : Dataset 2 in: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs. F1000Research. 2015. Data Source 111. Gudbrandsson J, Ahi E, Franzdottir S, et al. : Dataset 3 in: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs. F1000Research. 2015. Data Source Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 01 Jun 2015 ADD YOUR COMMENT Comment Author details Author details 1 Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 2 Holar University College, Saudarkrokur, 551, Iceland Competing interests No competing interests were disclosed. Grant information This project was supported by The Icelandic Center for Research (grant number: 100204011) to SSS, AP, ZOJ and BKK, The University of Iceland Research/Doctoral Fund to JG and KHK and University of Iceland research fund to AP, SSS and ZOJ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (3) version 3 Revised Published: 02 Dec 2016, 4:136 https://doi.org/10.12688/f1000research.6402.3 version 2 Revised Published: 25 Apr 2016, 4:136 https://doi.org/10.12688/f1000research.6402.2 version 1 Published: 01 Jun 2015, 4:136 https://doi.org/10.12688/f1000research.6402.1 Copyright © 2016 Gudbrandsson J et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Gudbrandsson J, Ahi EP, Franzdottir SR et al. The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.12688/f1000research.6402.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 3 VERSION 3 PUBLISHED 02 Dec 2016 Revised Views 0 Cite How to cite this report: Östman Ö. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.10982.r18642 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v3#referee-response-18642 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 20 Dec 2016 Örjan Östman , Department of Aquatic Resources, Swedish University of Agricultural Sciences, Uppsala, Sweden Approved VIEWS 0 https://doi.org/10.5256/f1000research.10982.r18642 The authors have addressed all my comments and incorporated them into the manuscript. So my opinion of the study ... Continue reading READ ALL The authors have addressed all my comments and incorporated them into the manuscript. So my opinion of the study is it will be an important contribution for further work, and thus, I approve this version of the manuscript. Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Östman Ö. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.10982.r18642 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v3#referee-response-18642 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 25 Apr 2016 Revised Views 0 Cite How to cite this report: Östman Ö. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.9044.r15587 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v2#referee-response-15587 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Aug 2016 Örjan Östman , Department of Aquatic Resources, Swedish University of Agricultural Sciences, Uppsala, Sweden Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.9044.r15587 The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome of eggs and larvae from a natural population of small ... Continue reading READ ALL The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome of eggs and larvae from a natural population of small benthic charr (SB) and Icelandic aquaculture charr (AC), which is fast growing and have a ‘limnic-like’ morphology. They find a list of potential candidate genes involved in the ecological differentiation of artic charr (and during the embryonic development). In addition they studied the transcriptome from different tissues of adult AC-charr. From the transcriptome of these populations two populations they developed 12 SNP-markers applied to other sympathric (Lake Thingvallavatn) wild morphs to study if these genes differed between other morphs. Finally they also study mtDNA expression between morphs to find they mainly differ between the benthic morphs and a limnic. The search for genes involved in the ecological divergence of species is an important topic that has exploded the last decade with the new generation of sequencing. I find this study to be an important contribution because of the study system with artic charr is an example of relative recent and rapid divergence into many different morphs/ecological, and the extensive and thorough investigation of the differences in the transcriptome between morphs. However, I think the authors try to stretch their conclusions a bit too far. The study is great as a base for further research in the topic, which I guess is in the pipeline. The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion). The cultivated population has been selected for fast body growth and they are not from Lake Thingvallavatn and little can therefore be said about the genetics of the ecological differentiation of sympatric species. Not very surprising genes related to metabolism seemed upregulated in AC and immunogens upregulated in SB. What does that actually tells us about the genetics of ecological differentiation of natural populations?? Although the aims on p. 4 feels valid, they are not contingent with the previous text in the introduction. Thus, I suggest that the much of the earlier part of the introduction is rewritten to actually address the differences in gene expression between a cultivated morph and its extreme opposite small benthic artic charr. As far as I understand it is only egg that are kept in the same environment, but the parents have been raised in different environments and transgenerational plasticity cannot be ruled out. This is not a major criticism (the ideal case would be to have had them in lines in a common environment of course) but needs to be addressed in the text. The ‘Nattl’ paralogs provide an interesting case where the expression of different paralogs has been studied. But again, are the result difficult to interpret from an ecological niche differentiation perspective. Often is the natural small limnic morph (PL) in between SB and AC (Fig. 5A), but for Nattl1 and Nattl2 AC and SB seem most similar? The connection to the original question is weak and the authors do not conclude more than “…it is not divergence in the expression of one paralog that explains the general nattl expression disparity in the transcriptome.” Fair enough, but that is more about the genetic architecture than the genetics of ecological divergence. In the validation of the transcriptome differences with qPCR of 9 genes/paralogs only 5 was still significant. What conclusions should one make out of that, that around half of the 296 paralogs differing between SB and AC are false detection (despite FDR < 5%). I support the use of qPCR but please comment on the implication of this. I think Fig. 6 should be converted into a bar-graph plot instead (this feels more like a table). To conclude, I think this study is a great contribution for suggesting putative differences in the transcriptome of a fish species. However, the importance for understanding ecological differentiation of sympatric species it is, however, so far limited as it that would require more using natural morphs, replicated populations, back-crosses, investigation of plasticity and how reproductive isolation is maintained etc., which is likely to come. But until that, I suggest the this text should mainly focus on the differences between SB and AC arctic charr, and not try to squeeze in everything in one paper. Minor comments: In the equation on p. 6 I guess M & T is ‘Morph’ and ‘Time’? If so spell out or use M & T consistently. Note that on p. 8 the qPCR of 8 paralogs in embryonic heads is metioned but the results do not come until “Expression differences in the developing heads of benthic and limnetic charr morphs”. Use “.” instead of “,” as decimal sign in Fig. 4. Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Östman Ö. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.9044.r15587 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v2#referee-response-15587 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 02 Dec 2016 Arnar Palsson , Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 02 Dec 2016 Author Response The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome ... Continue reading The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome of eggs and larvae from a natural population of small benthic charr (SB) and Icelandic aquaculture charr (AC), which is fast growing and have a ‘limnic-like’ morphology. They find a list of potential candidate genes involved in the ecological differentiation of artic charr (and during the embryonic development). In addition they studied the transcriptome from different tissues of adult AC-charr. From the transcriptome of these populations two populations they developed 12 SNP-markers applied to other sympathric (Lake Thingvallavatn) wild morphs to study if these genes differed between other morphs. Finally they also study mtDNA expression between morphs to find they mainly differ between the benthic morphs and a limnic. The search for genes involved in the ecological divergence of species is an important topic that has exploded the last decade with the new generation of sequencing. I find this study to be an important contribution because of the study system with artic charr is an example of relative recent and rapid divergence into many different morphs/ecological, and the extensive and thorough investigation of the differences in the transcriptome between morphs. However, I think the authors try to stretch their conclusions a bit too far. The study is great as a base for further research in the topic, which I guess is in the pipeline. The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion). The cultivated population has been selected for fast body growth and they are not from Lake Thingvallavatn and little can therefore be said about the genetics of the ecological differentiation of sympatric species. Not very surprising genes related to metabolism seemed upregulated in AC and immunogens upregulated in SB. What does that actually tells us about the genetics of ecological differentiation of natural populations?? Although the aims on p. 4 feels valid, they are not contingent with the previous text in the introduction. Thus, I suggest that the much of the earlier part of the introduction is rewritten to actually address the differences in gene expression between a cultivated morph and its extreme opposite small benthic arctic charr. Reply: We thank the reviewer for his comments, we have now carefully reviewed the introduction, results, discussion and conclusions to address his concerns. We provide below an excerpt of most of the changes made. “However, I think the authors try to stretch their conclusions a bit too far” Reply: We acknowledge that the discussion in particular, worded the conclusions about ecological effects too strongly and have toned those down, for example: “The charr developmental transcriptome provides a starting point to investigate the molecular systems that associate with divergence among the highly polymorphic and rapidly evolving Arctic charr in Iceland.” “The embryos were reared in a common garden setting, which minimizes the impact of environmental factors, as we are interested in genes showing expression differences between the two morphs. Those genes might implicate pathways involved in the ecological divergence among charr populations and of course adaptation of the AC charr during breeding 50 “ The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion) … Reply: We agree with the reviewer’s remarks, the flow of the introduction and partly the discussion was not optimal, with the interpretations overreaching in some places. We have now restructured the introduction, added a separate section on Aquaculture charr, and improved the description of the results. For each result section we tried to make clear where the data support conclusions about the difference between SB and AC only or more general about benthic - limnetic differences (like where the follow up qPCR or SNP validation involved also samples of Lake Thingvallavatn morphs). Also, throughout the manuscript we also brought the contrast of AC and SB charr into sharper focus, and the fact that many patterns can reflect the AC charr domestication, for example: “The aim of this study was to find expression and genetic differences separating the small benthic morph in Lake Thingvallavatn and aquaculture charr, with the long term objective being to reveal the genetic and molecular systems that associate with benthic morphology in charr. The transcriptome reflects the biology of these two morphs, their different histories and ecology. AC-charr will also be shaped by domestication, which may explain for instance the higher expression of metabolic genes in AC-charr.” “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens or in families of Aquaculture charr breed for pathogen resistance.” “The results suggest divergence (adaptive or neutral) in mitochondrial function due to the domestication of aquaculture charr and/or adaptation of the small benthic charr to its habitat. Increase in mitochondrial function in AC charr embryos could reflect higher basal metabolic rate in this aquaculture stock. Alternatively, lower metabolic rate in the SB charr would also be curious in the context of their ecology. Clearly further work is needed to map out the functional differences of mitochondrial related genes in AC charr, more SB populations and hopefully anadromous charr morphs (representing the ancestral state).” As far as I understand it is only egg that are kept in the same environment, but the parents have been raised in different environments and transgenerational plasticity cannot be ruled out. This is not a major criticism (the ideal case would be to have had them in lines in a common environment of course) but needs to be addressed in the text. Reply: We add a sentence about transgenerational plasticity in the discussion. “We raised the embryos in a common garden, but their parents were wild so parental environments and transgenerational plasticity may also have contributed.” The ‘Nattl’ paralogs provide an interesting case where the expression of different paralogs has been studied. But again, are the result difficult to interpret from an ecological niche differentiation perspective. Often is the natural small limnic morph (PL) in between SB and AC (Fig. 5A), but for Nattl1 and Nattl2 AC and SB seem most similar? The connection to the original question is weak and the authors do not conclude more than “…it is not divergence in the expression of one paralog that explains the general nattl expression disparity in the transcriptome.” Fair enough, but that is more about the genetic architecture than the genetics of ecological divergence. Reply: This issue is of interest to us. We wanted to work more on the Nattl genes and confess that the paragraph did not summarize the data properly. We paraphrased it, toning down the interpretation and added a more forward looking statement – about how to build on this dataset “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens.” In the validation of the transcriptome differences with qPCR of 9 genes/paralogs only 5 was still significant. What conclusions should one make out of that, that around half of the 296 paralogs differing between SB and AC are false detection (despite FDR < 5%). I support the use of qPCR but please comment on the implication of this. Reply: The transcriptome was done on 4 timepoints, but only 2 of those were used for the qPCR verification. Thus we expect incomplete correlation, because of the contribution of the earliest or latest (in particular) timepoints. We explain this clarification to the results on qPCR verification (Table 3) and added a caveat, “Thus this transcriptome should not be taken at face value, because substantial fraction of signals were false positives. ” I think Fig. 6 should be converted into a bar-graph plot instead (this feels more like a table). Reply: We opted for this heatmap-table representation, as we feel it emphasizes the benthic – limnetic separation most clearly. The other option we explored was indeed a bar-graph (Supplemental figure 2), with extra lines and stars indicating the significance of the post-hoc tests, but felt the heatmap-table captured best the pattern. To conclude, I think this study is a great contribution for suggesting putative differences in the transcriptome of a fish species. However, the importance for understanding ecological differentiation of sympatric species it is, however, so far limited as it that would require more using natural morphs, replicated populations, back-crosses, investigation of plasticity and how reproductive isolation is maintained etc., which is likely to come. But until that, I suggest the this text should mainly focus on the differences between SB and AC arctic charr, and not try to squeeze in everything in one paper. Reply: We acknowledge that with respect to our long term research goals, this can be viewed as a pilot study as the contrast is between the SB and AC charr. In this version we tried to focus more on describing the differences between SB and AC charr, and highlight also results that may reflect the AC-charr biology (see some sentences listed above, and more in the manuscript). By validating differential gene expression and some of the SNPs also on samples from more wild populations, the study also revealed interesting candidates for follow up studies addressing the long term objectives of the group. Minor comments: In the equation on p. 6 I guess M & T is ‘Morph’ and ‘Time’? If so spell out or use M & T consistently. Reply: We opted for spelling out Morph and Time – its more transparent. Note that on p. 8 the qPCR of 8 paralogs in embryonic heads is mentioned but the results do not come until “Expression differences in the developing heads of benthic and limnetic charr morphs”. Reply: Good point, now the next section is referenced “… and 8 in embryonic heads (see next section)...” Use “.” instead of “,” as decimal sign in Fig. 4. Reply: Fixed. The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome of eggs and larvae from a natural population of small benthic charr (SB) and Icelandic aquaculture charr (AC), which is fast growing and have a ‘limnic-like’ morphology. They find a list of potential candidate genes involved in the ecological differentiation of artic charr (and during the embryonic development). In addition they studied the transcriptome from different tissues of adult AC-charr. From the transcriptome of these populations two populations they developed 12 SNP-markers applied to other sympathric (Lake Thingvallavatn) wild morphs to study if these genes differed between other morphs. Finally they also study mtDNA expression between morphs to find they mainly differ between the benthic morphs and a limnic. The search for genes involved in the ecological divergence of species is an important topic that has exploded the last decade with the new generation of sequencing. I find this study to be an important contribution because of the study system with artic charr is an example of relative recent and rapid divergence into many different morphs/ecological, and the extensive and thorough investigation of the differences in the transcriptome between morphs. However, I think the authors try to stretch their conclusions a bit too far. The study is great as a base for further research in the topic, which I guess is in the pipeline. The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion). The cultivated population has been selected for fast body growth and they are not from Lake Thingvallavatn and little can therefore be said about the genetics of the ecological differentiation of sympatric species. Not very surprising genes related to metabolism seemed upregulated in AC and immunogens upregulated in SB. What does that actually tells us about the genetics of ecological differentiation of natural populations?? Although the aims on p. 4 feels valid, they are not contingent with the previous text in the introduction. Thus, I suggest that the much of the earlier part of the introduction is rewritten to actually address the differences in gene expression between a cultivated morph and its extreme opposite small benthic arctic charr. Reply: We thank the reviewer for his comments, we have now carefully reviewed the introduction, results, discussion and conclusions to address his concerns. We provide below an excerpt of most of the changes made. “However, I think the authors try to stretch their conclusions a bit too far” Reply: We acknowledge that the discussion in particular, worded the conclusions about ecological effects too strongly and have toned those down, for example: “The charr developmental transcriptome provides a starting point to investigate the molecular systems that associate with divergence among the highly polymorphic and rapidly evolving Arctic charr in Iceland.” “The embryos were reared in a common garden setting, which minimizes the impact of environmental factors, as we are interested in genes showing expression differences between the two morphs. Those genes might implicate pathways involved in the ecological divergence among charr populations and of course adaptation of the AC charr during breeding 50 “ The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion) … Reply: We agree with the reviewer’s remarks, the flow of the introduction and partly the discussion was not optimal, with the interpretations overreaching in some places. We have now restructured the introduction, added a separate section on Aquaculture charr, and improved the description of the results. For each result section we tried to make clear where the data support conclusions about the difference between SB and AC only or more general about benthic - limnetic differences (like where the follow up qPCR or SNP validation involved also samples of Lake Thingvallavatn morphs). Also, throughout the manuscript we also brought the contrast of AC and SB charr into sharper focus, and the fact that many patterns can reflect the AC charr domestication, for example: “The aim of this study was to find expression and genetic differences separating the small benthic morph in Lake Thingvallavatn and aquaculture charr, with the long term objective being to reveal the genetic and molecular systems that associate with benthic morphology in charr. The transcriptome reflects the biology of these two morphs, their different histories and ecology. AC-charr will also be shaped by domestication, which may explain for instance the higher expression of metabolic genes in AC-charr.” “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens or in families of Aquaculture charr breed for pathogen resistance.” “The results suggest divergence (adaptive or neutral) in mitochondrial function due to the domestication of aquaculture charr and/or adaptation of the small benthic charr to its habitat. Increase in mitochondrial function in AC charr embryos could reflect higher basal metabolic rate in this aquaculture stock. Alternatively, lower metabolic rate in the SB charr would also be curious in the context of their ecology. Clearly further work is needed to map out the functional differences of mitochondrial related genes in AC charr, more SB populations and hopefully anadromous charr morphs (representing the ancestral state).” As far as I understand it is only egg that are kept in the same environment, but the parents have been raised in different environments and transgenerational plasticity cannot be ruled out. This is not a major criticism (the ideal case would be to have had them in lines in a common environment of course) but needs to be addressed in the text. Reply: We add a sentence about transgenerational plasticity in the discussion. “We raised the embryos in a common garden, but their parents were wild so parental environments and transgenerational plasticity may also have contributed.” The ‘Nattl’ paralogs provide an interesting case where the expression of different paralogs has been studied. But again, are the result difficult to interpret from an ecological niche differentiation perspective. Often is the natural small limnic morph (PL) in between SB and AC (Fig. 5A), but for Nattl1 and Nattl2 AC and SB seem most similar? The connection to the original question is weak and the authors do not conclude more than “…it is not divergence in the expression of one paralog that explains the general nattl expression disparity in the transcriptome.” Fair enough, but that is more about the genetic architecture than the genetics of ecological divergence. Reply: This issue is of interest to us. We wanted to work more on the Nattl genes and confess that the paragraph did not summarize the data properly. We paraphrased it, toning down the interpretation and added a more forward looking statement – about how to build on this dataset “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens.” In the validation of the transcriptome differences with qPCR of 9 genes/paralogs only 5 was still significant. What conclusions should one make out of that, that around half of the 296 paralogs differing between SB and AC are false detection (despite FDR < 5%). I support the use of qPCR but please comment on the implication of this. Reply: The transcriptome was done on 4 timepoints, but only 2 of those were used for the qPCR verification. Thus we expect incomplete correlation, because of the contribution of the earliest or latest (in particular) timepoints. We explain this clarification to the results on qPCR verification (Table 3) and added a caveat, “Thus this transcriptome should not be taken at face value, because substantial fraction of signals were false positives. ” I think Fig. 6 should be converted into a bar-graph plot instead (this feels more like a table). Reply: We opted for this heatmap-table representation, as we feel it emphasizes the benthic – limnetic separation most clearly. The other option we explored was indeed a bar-graph (Supplemental figure 2), with extra lines and stars indicating the significance of the post-hoc tests, but felt the heatmap-table captured best the pattern. To conclude, I think this study is a great contribution for suggesting putative differences in the transcriptome of a fish species. However, the importance for understanding ecological differentiation of sympatric species it is, however, so far limited as it that would require more using natural morphs, replicated populations, back-crosses, investigation of plasticity and how reproductive isolation is maintained etc., which is likely to come. But until that, I suggest the this text should mainly focus on the differences between SB and AC arctic charr, and not try to squeeze in everything in one paper. Reply: We acknowledge that with respect to our long term research goals, this can be viewed as a pilot study as the contrast is between the SB and AC charr. In this version we tried to focus more on describing the differences between SB and AC charr, and highlight also results that may reflect the AC-charr biology (see some sentences listed above, and more in the manuscript). By validating differential gene expression and some of the SNPs also on samples from more wild populations, the study also revealed interesting candidates for follow up studies addressing the long term objectives of the group. Minor comments: In the equation on p. 6 I guess M & T is ‘Morph’ and ‘Time’? If so spell out or use M & T consistently. Reply: We opted for spelling out Morph and Time – its more transparent. Note that on p. 8 the qPCR of 8 paralogs in embryonic heads is mentioned but the results do not come until “Expression differences in the developing heads of benthic and limnetic charr morphs”. Reply: Good point, now the next section is referenced “… and 8 in embryonic heads (see next section)...” Use “.” instead of “,” as decimal sign in Fig. 4. Reply: Fixed. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 02 Dec 2016 Arnar Palsson , Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 02 Dec 2016 Author Response The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome ... Continue reading The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome of eggs and larvae from a natural population of small benthic charr (SB) and Icelandic aquaculture charr (AC), which is fast growing and have a ‘limnic-like’ morphology. They find a list of potential candidate genes involved in the ecological differentiation of artic charr (and during the embryonic development). In addition they studied the transcriptome from different tissues of adult AC-charr. From the transcriptome of these populations two populations they developed 12 SNP-markers applied to other sympathric (Lake Thingvallavatn) wild morphs to study if these genes differed between other morphs. Finally they also study mtDNA expression between morphs to find they mainly differ between the benthic morphs and a limnic. The search for genes involved in the ecological divergence of species is an important topic that has exploded the last decade with the new generation of sequencing. I find this study to be an important contribution because of the study system with artic charr is an example of relative recent and rapid divergence into many different morphs/ecological, and the extensive and thorough investigation of the differences in the transcriptome between morphs. However, I think the authors try to stretch their conclusions a bit too far. The study is great as a base for further research in the topic, which I guess is in the pipeline. The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion). The cultivated population has been selected for fast body growth and they are not from Lake Thingvallavatn and little can therefore be said about the genetics of the ecological differentiation of sympatric species. Not very surprising genes related to metabolism seemed upregulated in AC and immunogens upregulated in SB. What does that actually tells us about the genetics of ecological differentiation of natural populations?? Although the aims on p. 4 feels valid, they are not contingent with the previous text in the introduction. Thus, I suggest that the much of the earlier part of the introduction is rewritten to actually address the differences in gene expression between a cultivated morph and its extreme opposite small benthic arctic charr. Reply: We thank the reviewer for his comments, we have now carefully reviewed the introduction, results, discussion and conclusions to address his concerns. We provide below an excerpt of most of the changes made. “However, I think the authors try to stretch their conclusions a bit too far” Reply: We acknowledge that the discussion in particular, worded the conclusions about ecological effects too strongly and have toned those down, for example: “The charr developmental transcriptome provides a starting point to investigate the molecular systems that associate with divergence among the highly polymorphic and rapidly evolving Arctic charr in Iceland.” “The embryos were reared in a common garden setting, which minimizes the impact of environmental factors, as we are interested in genes showing expression differences between the two morphs. Those genes might implicate pathways involved in the ecological divergence among charr populations and of course adaptation of the AC charr during breeding 50 “ The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion) … Reply: We agree with the reviewer’s remarks, the flow of the introduction and partly the discussion was not optimal, with the interpretations overreaching in some places. We have now restructured the introduction, added a separate section on Aquaculture charr, and improved the description of the results. For each result section we tried to make clear where the data support conclusions about the difference between SB and AC only or more general about benthic - limnetic differences (like where the follow up qPCR or SNP validation involved also samples of Lake Thingvallavatn morphs). Also, throughout the manuscript we also brought the contrast of AC and SB charr into sharper focus, and the fact that many patterns can reflect the AC charr domestication, for example: “The aim of this study was to find expression and genetic differences separating the small benthic morph in Lake Thingvallavatn and aquaculture charr, with the long term objective being to reveal the genetic and molecular systems that associate with benthic morphology in charr. The transcriptome reflects the biology of these two morphs, their different histories and ecology. AC-charr will also be shaped by domestication, which may explain for instance the higher expression of metabolic genes in AC-charr.” “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens or in families of Aquaculture charr breed for pathogen resistance.” “The results suggest divergence (adaptive or neutral) in mitochondrial function due to the domestication of aquaculture charr and/or adaptation of the small benthic charr to its habitat. Increase in mitochondrial function in AC charr embryos could reflect higher basal metabolic rate in this aquaculture stock. Alternatively, lower metabolic rate in the SB charr would also be curious in the context of their ecology. Clearly further work is needed to map out the functional differences of mitochondrial related genes in AC charr, more SB populations and hopefully anadromous charr morphs (representing the ancestral state).” As far as I understand it is only egg that are kept in the same environment, but the parents have been raised in different environments and transgenerational plasticity cannot be ruled out. This is not a major criticism (the ideal case would be to have had them in lines in a common environment of course) but needs to be addressed in the text. Reply: We add a sentence about transgenerational plasticity in the discussion. “We raised the embryos in a common garden, but their parents were wild so parental environments and transgenerational plasticity may also have contributed.” The ‘Nattl’ paralogs provide an interesting case where the expression of different paralogs has been studied. But again, are the result difficult to interpret from an ecological niche differentiation perspective. Often is the natural small limnic morph (PL) in between SB and AC (Fig. 5A), but for Nattl1 and Nattl2 AC and SB seem most similar? The connection to the original question is weak and the authors do not conclude more than “…it is not divergence in the expression of one paralog that explains the general nattl expression disparity in the transcriptome.” Fair enough, but that is more about the genetic architecture than the genetics of ecological divergence. Reply: This issue is of interest to us. We wanted to work more on the Nattl genes and confess that the paragraph did not summarize the data properly. We paraphrased it, toning down the interpretation and added a more forward looking statement – about how to build on this dataset “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens.” In the validation of the transcriptome differences with qPCR of 9 genes/paralogs only 5 was still significant. What conclusions should one make out of that, that around half of the 296 paralogs differing between SB and AC are false detection (despite FDR < 5%). I support the use of qPCR but please comment on the implication of this. Reply: The transcriptome was done on 4 timepoints, but only 2 of those were used for the qPCR verification. Thus we expect incomplete correlation, because of the contribution of the earliest or latest (in particular) timepoints. We explain this clarification to the results on qPCR verification (Table 3) and added a caveat, “Thus this transcriptome should not be taken at face value, because substantial fraction of signals were false positives. ” I think Fig. 6 should be converted into a bar-graph plot instead (this feels more like a table). Reply: We opted for this heatmap-table representation, as we feel it emphasizes the benthic – limnetic separation most clearly. The other option we explored was indeed a bar-graph (Supplemental figure 2), with extra lines and stars indicating the significance of the post-hoc tests, but felt the heatmap-table captured best the pattern. To conclude, I think this study is a great contribution for suggesting putative differences in the transcriptome of a fish species. However, the importance for understanding ecological differentiation of sympatric species it is, however, so far limited as it that would require more using natural morphs, replicated populations, back-crosses, investigation of plasticity and how reproductive isolation is maintained etc., which is likely to come. But until that, I suggest the this text should mainly focus on the differences between SB and AC arctic charr, and not try to squeeze in everything in one paper. Reply: We acknowledge that with respect to our long term research goals, this can be viewed as a pilot study as the contrast is between the SB and AC charr. In this version we tried to focus more on describing the differences between SB and AC charr, and highlight also results that may reflect the AC-charr biology (see some sentences listed above, and more in the manuscript). By validating differential gene expression and some of the SNPs also on samples from more wild populations, the study also revealed interesting candidates for follow up studies addressing the long term objectives of the group. Minor comments: In the equation on p. 6 I guess M & T is ‘Morph’ and ‘Time’? If so spell out or use M & T consistently. Reply: We opted for spelling out Morph and Time – its more transparent. Note that on p. 8 the qPCR of 8 paralogs in embryonic heads is mentioned but the results do not come until “Expression differences in the developing heads of benthic and limnetic charr morphs”. Reply: Good point, now the next section is referenced “… and 8 in embryonic heads (see next section)...” Use “.” instead of “,” as decimal sign in Fig. 4. Reply: Fixed. The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome of eggs and larvae from a natural population of small benthic charr (SB) and Icelandic aquaculture charr (AC), which is fast growing and have a ‘limnic-like’ morphology. They find a list of potential candidate genes involved in the ecological differentiation of artic charr (and during the embryonic development). In addition they studied the transcriptome from different tissues of adult AC-charr. From the transcriptome of these populations two populations they developed 12 SNP-markers applied to other sympathric (Lake Thingvallavatn) wild morphs to study if these genes differed between other morphs. Finally they also study mtDNA expression between morphs to find they mainly differ between the benthic morphs and a limnic. The search for genes involved in the ecological divergence of species is an important topic that has exploded the last decade with the new generation of sequencing. I find this study to be an important contribution because of the study system with artic charr is an example of relative recent and rapid divergence into many different morphs/ecological, and the extensive and thorough investigation of the differences in the transcriptome between morphs. However, I think the authors try to stretch their conclusions a bit too far. The study is great as a base for further research in the topic, which I guess is in the pipeline. The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion). The cultivated population has been selected for fast body growth and they are not from Lake Thingvallavatn and little can therefore be said about the genetics of the ecological differentiation of sympatric species. Not very surprising genes related to metabolism seemed upregulated in AC and immunogens upregulated in SB. What does that actually tells us about the genetics of ecological differentiation of natural populations?? Although the aims on p. 4 feels valid, they are not contingent with the previous text in the introduction. Thus, I suggest that the much of the earlier part of the introduction is rewritten to actually address the differences in gene expression between a cultivated morph and its extreme opposite small benthic arctic charr. Reply: We thank the reviewer for his comments, we have now carefully reviewed the introduction, results, discussion and conclusions to address his concerns. We provide below an excerpt of most of the changes made. “However, I think the authors try to stretch their conclusions a bit too far” Reply: We acknowledge that the discussion in particular, worded the conclusions about ecological effects too strongly and have toned those down, for example: “The charr developmental transcriptome provides a starting point to investigate the molecular systems that associate with divergence among the highly polymorphic and rapidly evolving Arctic charr in Iceland.” “The embryos were reared in a common garden setting, which minimizes the impact of environmental factors, as we are interested in genes showing expression differences between the two morphs. Those genes might implicate pathways involved in the ecological divergence among charr populations and of course adaptation of the AC charr during breeding 50 “ The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion) … Reply: We agree with the reviewer’s remarks, the flow of the introduction and partly the discussion was not optimal, with the interpretations overreaching in some places. We have now restructured the introduction, added a separate section on Aquaculture charr, and improved the description of the results. For each result section we tried to make clear where the data support conclusions about the difference between SB and AC only or more general about benthic - limnetic differences (like where the follow up qPCR or SNP validation involved also samples of Lake Thingvallavatn morphs). Also, throughout the manuscript we also brought the contrast of AC and SB charr into sharper focus, and the fact that many patterns can reflect the AC charr domestication, for example: “The aim of this study was to find expression and genetic differences separating the small benthic morph in Lake Thingvallavatn and aquaculture charr, with the long term objective being to reveal the genetic and molecular systems that associate with benthic morphology in charr. The transcriptome reflects the biology of these two morphs, their different histories and ecology. AC-charr will also be shaped by domestication, which may explain for instance the higher expression of metabolic genes in AC-charr.” “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens or in families of Aquaculture charr breed for pathogen resistance.” “The results suggest divergence (adaptive or neutral) in mitochondrial function due to the domestication of aquaculture charr and/or adaptation of the small benthic charr to its habitat. Increase in mitochondrial function in AC charr embryos could reflect higher basal metabolic rate in this aquaculture stock. Alternatively, lower metabolic rate in the SB charr would also be curious in the context of their ecology. Clearly further work is needed to map out the functional differences of mitochondrial related genes in AC charr, more SB populations and hopefully anadromous charr morphs (representing the ancestral state).” As far as I understand it is only egg that are kept in the same environment, but the parents have been raised in different environments and transgenerational plasticity cannot be ruled out. This is not a major criticism (the ideal case would be to have had them in lines in a common environment of course) but needs to be addressed in the text. Reply: We add a sentence about transgenerational plasticity in the discussion. “We raised the embryos in a common garden, but their parents were wild so parental environments and transgenerational plasticity may also have contributed.” The ‘Nattl’ paralogs provide an interesting case where the expression of different paralogs has been studied. But again, are the result difficult to interpret from an ecological niche differentiation perspective. Often is the natural small limnic morph (PL) in between SB and AC (Fig. 5A), but for Nattl1 and Nattl2 AC and SB seem most similar? The connection to the original question is weak and the authors do not conclude more than “…it is not divergence in the expression of one paralog that explains the general nattl expression disparity in the transcriptome.” Fair enough, but that is more about the genetic architecture than the genetics of ecological divergence. Reply: This issue is of interest to us. We wanted to work more on the Nattl genes and confess that the paragraph did not summarize the data properly. We paraphrased it, toning down the interpretation and added a more forward looking statement – about how to build on this dataset “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens.” In the validation of the transcriptome differences with qPCR of 9 genes/paralogs only 5 was still significant. What conclusions should one make out of that, that around half of the 296 paralogs differing between SB and AC are false detection (despite FDR < 5%). I support the use of qPCR but please comment on the implication of this. Reply: The transcriptome was done on 4 timepoints, but only 2 of those were used for the qPCR verification. Thus we expect incomplete correlation, because of the contribution of the earliest or latest (in particular) timepoints. We explain this clarification to the results on qPCR verification (Table 3) and added a caveat, “Thus this transcriptome should not be taken at face value, because substantial fraction of signals were false positives. ” I think Fig. 6 should be converted into a bar-graph plot instead (this feels more like a table). Reply: We opted for this heatmap-table representation, as we feel it emphasizes the benthic – limnetic separation most clearly. The other option we explored was indeed a bar-graph (Supplemental figure 2), with extra lines and stars indicating the significance of the post-hoc tests, but felt the heatmap-table captured best the pattern. To conclude, I think this study is a great contribution for suggesting putative differences in the transcriptome of a fish species. However, the importance for understanding ecological differentiation of sympatric species it is, however, so far limited as it that would require more using natural morphs, replicated populations, back-crosses, investigation of plasticity and how reproductive isolation is maintained etc., which is likely to come. But until that, I suggest the this text should mainly focus on the differences between SB and AC arctic charr, and not try to squeeze in everything in one paper. Reply: We acknowledge that with respect to our long term research goals, this can be viewed as a pilot study as the contrast is between the SB and AC charr. In this version we tried to focus more on describing the differences between SB and AC charr, and highlight also results that may reflect the AC-charr biology (see some sentences listed above, and more in the manuscript). By validating differential gene expression and some of the SNPs also on samples from more wild populations, the study also revealed interesting candidates for follow up studies addressing the long term objectives of the group. Minor comments: In the equation on p. 6 I guess M & T is ‘Morph’ and ‘Time’? If so spell out or use M & T consistently. Reply: We opted for spelling out Morph and Time – its more transparent. Note that on p. 8 the qPCR of 8 paralogs in embryonic heads is mentioned but the results do not come until “Expression differences in the developing heads of benthic and limnetic charr morphs”. Reply: Good point, now the next section is referenced “… and 8 in embryonic heads (see next section)...” Use “.” instead of “,” as decimal sign in Fig. 4. Reply: Fixed. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Macqueen D. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.9044.r13702 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v2#referee-response-13702 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 May 2016 Daniel Macqueen , Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK Approved VIEWS 0 https://doi.org/10.5256/f1000research.9044.r13702 Second review of Gudbrandsson et al . “ The developmental transcriptome of contrasting Arctic charr (Salvelinus alpinus) morphs”. Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range ... Continue reading READ ALL Second review of Gudbrandsson et al . “ The developmental transcriptome of contrasting Arctic charr (Salvelinus alpinus) morphs”. Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range of associated changes into version 2 of their paper. Readers will find these changes, along with several clarifications provided in the published response to reviewers section, to facilitate transparent interpretation of this large and diverse study, including its strengths and caveats. My overall opinion of the study remains unchanged – it is interesting and reports findings of merit that will be followed up on in future work. I am thus happy to approve version 2 of the paper . I did spot a few typos or grammatical issues that the authors might address and had some final comments that might be addressed – all of a minor nature and easy to address. Abstract – “ energy metabolism and blood coagulation genes ” remove “ genes ” Abstract - “ Comparison of single nucleotide polymorphism (SNP) frequencies reveals ” change “ reveals ” to “ revealed ” (for accurate use of tense) Introduction – “ Examples of such species complexes are provided finches of the Galapagos island” should be “ Examples of such species complexes are provided by finches of the Galapagos island ” Introduction: “ Thus we were quite keen to apply RNA-sequencing to analyze ecomorphs in our study system, Arctic charr ”. The authors should add the Latin name for charr here, rather than in the next paragraph. Introduction: “ The family is estimated to be between 88–103 million years old 21,22 . A whole genome duplication event occurred before the radiation of the salmonid family 21–24 which has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event ” It would be simpler to just state that the common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago. The actual age of the salmonid family depends on whether one considers the (extinct) direct ancestors to salmonids that didn’t experience genome duplication to be salmonids. Introduction: “ Furthermore, recent estimates from the rainbow trout (Oncorhynchus mykiss) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that on the order of 4500 were retained in rainbow trout 22 ” This information is inaccurate. Firstly, based on the paper cited (Berthalot et al . 2014), this information should state that around 4,500 pairs of ohnologous genes were retained from Ss4R (i.e. around 9,000 separate genes). More importantly, without going into detail, the stated data represents a non-comprehensive fraction of the genome. I suggest the authors update this part of the text with accurate estimates, since the number of retained Ss4R ohnologue pairs in much larger than what is stated. The authors might also draw in more comprehensive data from the recent publication of the Atlantic salmon genome (Lien et al. Nature, 533, 200–205) 1 . The simplest way to present the information is to state that around half of the original Ss4R ohnologue pairs are still functionally retained (both stated papers are in agreement about that). Figure 1: It would be easier for the reader to link the text and images if the authors updated with ‘a’, ‘b’, ‘c’ and ‘d’ panels for each of the different charr morphs. Introduction: “ In this study, we compare SB-charr from ” should be “ In this study, we compared SB-charr from ” (again, it is correct here to use past tense – the authors should check the rest of the manuscript for similar tense issues). Figure 2: Minor comments – the text “ Map on salmon genes ” is vague and open to several interpretations. Better: “ Map on Atlantic salmon expressed sequence tags ”? References 1. Lien S, Koop BF, Sandve SR, Miller JR, et al.: The Atlantic salmon genome provides insights into rediploidization. Nature . 2016; 533 (7602): 200-5 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Macqueen D. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.9044.r13702 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v2#referee-response-13702 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 02 Dec 2016 Arnar Palsson , Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 02 Dec 2016 Author Response Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range of associated changes into version 2 of their paper. Readers ... Continue reading Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range of associated changes into version 2 of their paper. Readers will find these changes, along with several clarifications provided in the published response to reviewers section, to facilitate transparent interpretation of this large and diverse study, including its strengths and caveats. My overall opinion of the study remains unchanged – it is interesting and reports findings of merit that will be followed up on in future work. I am thus happy to approve version 2 of the paper . I did spot a few typos or grammatical issues that the authors might address and had some final comments that might be addressed – all of a minor nature and easy to address. Abstract – “ energy metabolism and blood coagulation genes ” remove “ genes ” Abstract - “ Comparison of single nucleotide polymorphism (SNP) frequencies reveals ” change “ reveals ” to “ revealed ” (for accurate use of tense) Introduction – “ Examples of such species complexes are provided finches of the Galapagos island” should be “ Examples of such species complexes are provided by finches of the Galapagos island ” Introduction: “ Thus we were quite keen to apply RNA-sequencing to analyze ecomorphs in our study system, Arctic charr ”. The authors should add the Latin name for charr here, rather than in the next paragraph. Reply: They have all been fixed. Introduction: “ The family is estimated to be between 88–103 million years old 21,22 . A whole genome duplication event occurred before the radiation of the salmonid family 21–24 which has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event ” It would be simpler to just state that the common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago. The actual age of the salmonid family depends on whether one considers the (extinct) direct ancestors to salmonids that didn’t experience genome duplication to be salmonids. Reply: Good suggestion, we now use this wording. Introduction: “ Furthermore, recent estimates from the rainbow trout (Oncorhynchus mykiss) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that on the order of 4500 were retained in rainbow trout 22 ” This information is inaccurate. Firstly, based on the paper cited (Berthalot et al . 2014), this information should state that around 4,500 pairs of ohnologous genes were retained from Ss4R (i.e. around 9,000 separate genes). More importantly, without going into detail, the stated data represents a non-comprehensive fraction of the genome. I suggest the authors update this part of the text with accurate estimates, since the number of retained Ss4R ohnologue pairs in much larger than what is stated. The authors might also draw in more comprehensive data from the recent publication of the Atlantic salmon genome (Lien et al. Nature, 533, 200–205) 1 . The simplest way to present the information is to state that around half of the original Ss4R ohnologue pairs are still functionally retained (both stated papers are in agreement about that). Reply: We thank the reviewer for a good point and clarification. We adopt the wording and rewrote part of this paragraph, it now reads: “The common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago, the fourth vertebrate whole- genome duplication (Ss4R) 21 – 24 . This has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event) in salmonid lineages. Estimates from the rainbow trout ( Oncorhynchus mykiss ) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that around half of the original Ss4R ohnologue pairs are still functionally retained in rainbow trout 22 .” Figure 1: It would be easier for the reader to link the text and images if the authors updated with ‘a’, ‘b’, ‘c’ and ‘d’ panels for each of the different charr morphs. Reply: This has been fixed. Introduction: “ In this study, we compare SB-charr from ” should be “ In this study, we compared SB-charr from ” (again, it is correct here to use past tense – the authors should check the rest of the manuscript for similar tense issues). Reply: Fixed, we went through the manuscript and corrected a few more errors of this type. Figure 2: Minor comments – the text “ Map on salmon genes ” is vague and open to several interpretations. Better: “ Map on Atlantic salmon expressed sequence tags ”? Reply: Fixed, put “ Map on Atlantic salmon ESTs” in the figure and “ESTs = expressed sequence tags ” into legend. Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range of associated changes into version 2 of their paper. Readers will find these changes, along with several clarifications provided in the published response to reviewers section, to facilitate transparent interpretation of this large and diverse study, including its strengths and caveats. My overall opinion of the study remains unchanged – it is interesting and reports findings of merit that will be followed up on in future work. I am thus happy to approve version 2 of the paper . I did spot a few typos or grammatical issues that the authors might address and had some final comments that might be addressed – all of a minor nature and easy to address. Abstract – “ energy metabolism and blood coagulation genes ” remove “ genes ” Abstract - “ Comparison of single nucleotide polymorphism (SNP) frequencies reveals ” change “ reveals ” to “ revealed ” (for accurate use of tense) Introduction – “ Examples of such species complexes are provided finches of the Galapagos island” should be “ Examples of such species complexes are provided by finches of the Galapagos island ” Introduction: “ Thus we were quite keen to apply RNA-sequencing to analyze ecomorphs in our study system, Arctic charr ”. The authors should add the Latin name for charr here, rather than in the next paragraph. Reply: They have all been fixed. Introduction: “ The family is estimated to be between 88–103 million years old 21,22 . A whole genome duplication event occurred before the radiation of the salmonid family 21–24 which has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event ” It would be simpler to just state that the common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago. The actual age of the salmonid family depends on whether one considers the (extinct) direct ancestors to salmonids that didn’t experience genome duplication to be salmonids. Reply: Good suggestion, we now use this wording. Introduction: “ Furthermore, recent estimates from the rainbow trout (Oncorhynchus mykiss) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that on the order of 4500 were retained in rainbow trout 22 ” This information is inaccurate. Firstly, based on the paper cited (Berthalot et al . 2014), this information should state that around 4,500 pairs of ohnologous genes were retained from Ss4R (i.e. around 9,000 separate genes). More importantly, without going into detail, the stated data represents a non-comprehensive fraction of the genome. I suggest the authors update this part of the text with accurate estimates, since the number of retained Ss4R ohnologue pairs in much larger than what is stated. The authors might also draw in more comprehensive data from the recent publication of the Atlantic salmon genome (Lien et al. Nature, 533, 200–205) 1 . The simplest way to present the information is to state that around half of the original Ss4R ohnologue pairs are still functionally retained (both stated papers are in agreement about that). Reply: We thank the reviewer for a good point and clarification. We adopt the wording and rewrote part of this paragraph, it now reads: “The common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago, the fourth vertebrate whole- genome duplication (Ss4R) 21 – 24 . This has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event) in salmonid lineages. Estimates from the rainbow trout ( Oncorhynchus mykiss ) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that around half of the original Ss4R ohnologue pairs are still functionally retained in rainbow trout 22 .” Figure 1: It would be easier for the reader to link the text and images if the authors updated with ‘a’, ‘b’, ‘c’ and ‘d’ panels for each of the different charr morphs. Reply: This has been fixed. Introduction: “ In this study, we compare SB-charr from ” should be “ In this study, we compared SB-charr from ” (again, it is correct here to use past tense – the authors should check the rest of the manuscript for similar tense issues). Reply: Fixed, we went through the manuscript and corrected a few more errors of this type. Figure 2: Minor comments – the text “ Map on salmon genes ” is vague and open to several interpretations. Better: “ Map on Atlantic salmon expressed sequence tags ”? Reply: Fixed, put “ Map on Atlantic salmon ESTs” in the figure and “ESTs = expressed sequence tags ” into legend. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 02 Dec 2016 Arnar Palsson , Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 02 Dec 2016 Author Response Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range of associated changes into version 2 of their paper. Readers ... Continue reading Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range of associated changes into version 2 of their paper. Readers will find these changes, along with several clarifications provided in the published response to reviewers section, to facilitate transparent interpretation of this large and diverse study, including its strengths and caveats. My overall opinion of the study remains unchanged – it is interesting and reports findings of merit that will be followed up on in future work. I am thus happy to approve version 2 of the paper . I did spot a few typos or grammatical issues that the authors might address and had some final comments that might be addressed – all of a minor nature and easy to address. Abstract – “ energy metabolism and blood coagulation genes ” remove “ genes ” Abstract - “ Comparison of single nucleotide polymorphism (SNP) frequencies reveals ” change “ reveals ” to “ revealed ” (for accurate use of tense) Introduction – “ Examples of such species complexes are provided finches of the Galapagos island” should be “ Examples of such species complexes are provided by finches of the Galapagos island ” Introduction: “ Thus we were quite keen to apply RNA-sequencing to analyze ecomorphs in our study system, Arctic charr ”. The authors should add the Latin name for charr here, rather than in the next paragraph. Reply: They have all been fixed. Introduction: “ The family is estimated to be between 88–103 million years old 21,22 . A whole genome duplication event occurred before the radiation of the salmonid family 21–24 which has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event ” It would be simpler to just state that the common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago. The actual age of the salmonid family depends on whether one considers the (extinct) direct ancestors to salmonids that didn’t experience genome duplication to be salmonids. Reply: Good suggestion, we now use this wording. Introduction: “ Furthermore, recent estimates from the rainbow trout (Oncorhynchus mykiss) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that on the order of 4500 were retained in rainbow trout 22 ” This information is inaccurate. Firstly, based on the paper cited (Berthalot et al . 2014), this information should state that around 4,500 pairs of ohnologous genes were retained from Ss4R (i.e. around 9,000 separate genes). More importantly, without going into detail, the stated data represents a non-comprehensive fraction of the genome. I suggest the authors update this part of the text with accurate estimates, since the number of retained Ss4R ohnologue pairs in much larger than what is stated. The authors might also draw in more comprehensive data from the recent publication of the Atlantic salmon genome (Lien et al. Nature, 533, 200–205) 1 . The simplest way to present the information is to state that around half of the original Ss4R ohnologue pairs are still functionally retained (both stated papers are in agreement about that). Reply: We thank the reviewer for a good point and clarification. We adopt the wording and rewrote part of this paragraph, it now reads: “The common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago, the fourth vertebrate whole- genome duplication (Ss4R) 21 – 24 . This has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event) in salmonid lineages. Estimates from the rainbow trout ( Oncorhynchus mykiss ) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that around half of the original Ss4R ohnologue pairs are still functionally retained in rainbow trout 22 .” Figure 1: It would be easier for the reader to link the text and images if the authors updated with ‘a’, ‘b’, ‘c’ and ‘d’ panels for each of the different charr morphs. Reply: This has been fixed. Introduction: “ In this study, we compare SB-charr from ” should be “ In this study, we compared SB-charr from ” (again, it is correct here to use past tense – the authors should check the rest of the manuscript for similar tense issues). Reply: Fixed, we went through the manuscript and corrected a few more errors of this type. Figure 2: Minor comments – the text “ Map on salmon genes ” is vague and open to several interpretations. Better: “ Map on Atlantic salmon expressed sequence tags ”? Reply: Fixed, put “ Map on Atlantic salmon ESTs” in the figure and “ESTs = expressed sequence tags ” into legend. Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range of associated changes into version 2 of their paper. Readers will find these changes, along with several clarifications provided in the published response to reviewers section, to facilitate transparent interpretation of this large and diverse study, including its strengths and caveats. My overall opinion of the study remains unchanged – it is interesting and reports findings of merit that will be followed up on in future work. I am thus happy to approve version 2 of the paper . I did spot a few typos or grammatical issues that the authors might address and had some final comments that might be addressed – all of a minor nature and easy to address. Abstract – “ energy metabolism and blood coagulation genes ” remove “ genes ” Abstract - “ Comparison of single nucleotide polymorphism (SNP) frequencies reveals ” change “ reveals ” to “ revealed ” (for accurate use of tense) Introduction – “ Examples of such species complexes are provided finches of the Galapagos island” should be “ Examples of such species complexes are provided by finches of the Galapagos island ” Introduction: “ Thus we were quite keen to apply RNA-sequencing to analyze ecomorphs in our study system, Arctic charr ”. The authors should add the Latin name for charr here, rather than in the next paragraph. Reply: They have all been fixed. Introduction: “ The family is estimated to be between 88–103 million years old 21,22 . A whole genome duplication event occurred before the radiation of the salmonid family 21–24 which has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event ” It would be simpler to just state that the common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago. The actual age of the salmonid family depends on whether one considers the (extinct) direct ancestors to salmonids that didn’t experience genome duplication to be salmonids. Reply: Good suggestion, we now use this wording. Introduction: “ Furthermore, recent estimates from the rainbow trout (Oncorhynchus mykiss) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that on the order of 4500 were retained in rainbow trout 22 ” This information is inaccurate. Firstly, based on the paper cited (Berthalot et al . 2014), this information should state that around 4,500 pairs of ohnologous genes were retained from Ss4R (i.e. around 9,000 separate genes). More importantly, without going into detail, the stated data represents a non-comprehensive fraction of the genome. I suggest the authors update this part of the text with accurate estimates, since the number of retained Ss4R ohnologue pairs in much larger than what is stated. The authors might also draw in more comprehensive data from the recent publication of the Atlantic salmon genome (Lien et al. Nature, 533, 200–205) 1 . The simplest way to present the information is to state that around half of the original Ss4R ohnologue pairs are still functionally retained (both stated papers are in agreement about that). Reply: We thank the reviewer for a good point and clarification. We adopt the wording and rewrote part of this paragraph, it now reads: “The common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago, the fourth vertebrate whole- genome duplication (Ss4R) 21 – 24 . This has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event) in salmonid lineages. Estimates from the rainbow trout ( Oncorhynchus mykiss ) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that around half of the original Ss4R ohnologue pairs are still functionally retained in rainbow trout 22 .” Figure 1: It would be easier for the reader to link the text and images if the authors updated with ‘a’, ‘b’, ‘c’ and ‘d’ panels for each of the different charr morphs. Reply: This has been fixed. Introduction: “ In this study, we compare SB-charr from ” should be “ In this study, we compared SB-charr from ” (again, it is correct here to use past tense – the authors should check the rest of the manuscript for similar tense issues). Reply: Fixed, we went through the manuscript and corrected a few more errors of this type. Figure 2: Minor comments – the text “ Map on salmon genes ” is vague and open to several interpretations. Better: “ Map on Atlantic salmon expressed sequence tags ”? Reply: Fixed, put “ Map on Atlantic salmon ESTs” in the figure and “ESTs = expressed sequence tags ” into legend. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 01 Jun 2015 Views 0 Cite How to cite this report: Dalziel A. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.6869.r9419 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v1#referee-response-9419 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Jul 2015 Anne Dalziel , Institute for Systems and Integrative Biology (IBIS), Department of Biology, Laval University, Quebec City, QC, Canada Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.6869.r9419 In this paper “The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs” Gudbrandsson et al . have tested for differential gene expression at multiple developmental time-points among a number of Artic charr morpho-types from Lake Thingvallavatn (3 wild morphs, 1 ... Continue reading READ ALL In this paper “The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs” Gudbrandsson et al . have tested for differential gene expression at multiple developmental time-points among a number of Artic charr morpho-types from Lake Thingvallavatn (3 wild morphs, 1 studied with RNA-seq and qPCR, the others with qPCR only) and Holar aquaculture (1 domesticated morph, RNA-seq and qPCR). They have also studied multiple tissues/body regions for a subset of the differentially expressed genes found with RNA-seq. The goal of the paper was to find candidate genes that may underlie variation in morphology, with a focus on craniofacial morphology related to benthic vs. limnetic feeding. In general, I think this goal was met and this paper contributes to our understanding of the mechanisms contributing to morphological evolution in a non-genetic model organism. The authors provide an extensive, multi-time point comparison of two morphologically divergent groups of charr reared in a common environment (reducing the influence of phenotypic plasticity) and have collected a tremendous amount of data. This information will help them to hone in on the genetic loci contributing to phenotypic evolution in this very interesting system, and on the effects of domestication. However, there are a number of major issues that do need to be more clearly addressed in the manuscript prior to final publication. I have outlined these comments below. Major Comments Introduction : Requires some reorganization, clarification of what phenotypes have evolved in parallel among morphs, and how the authors separate the effects of domestication (SB vs. AC) from benthic/limnetic evolution (SB/LB vs. PL/AC). a) At present, the introduction focuses upon the utility of instances of parallel evolution to help us determine how repeatable evolutionary change may be. This is definitely true, and the repeated evolution of the dwarf, benthic morph (SB; the focus of the introduction/abstract/discussion) in many lakes strongly argues that this phenotype has evolved via natural selection. However, it is not clear to me if true ‘parallelism’ seen among the SB (small benthic) and LB (large benthivorous) vs. AC (Holar aquaculture) and PL (small planktivorous) morphs because not enough information is provided for me to assess this. To support the argument for parallelism the specific traits that have evolved in parallel among morphs must be displayed and the evolutionary history of these morphs should be clarified (e.g. in paragraph 6 and Figure 1). As well, any related non-parallelism in traits should also be discussed (i.e. how are the domesticated AC and wild PL different?). At present Figure 1 only shows the AC and SB morphs, and does not point out the specific traits they are interested in. This is critical background information for readers who are not familiar with this system. b) The comparison of AC (domestic, limnetic-like head) vs. LB (wild, benthic like head) looks at two confounded variables: domestication and the benthic/limnetic morphology. This should be clearly stated in the introduction, and the use of the additional morphs (PL, LB) in detangling domestication vs. benthic/limnetic evolution should be noted. c) The use of the AC morph is still a bit unclear to me. The argument for point ‘ii) of the availability of abundant AC material’ could be expanded by providing more information on the ‘limnetic’ like features of this morph and why it is an appropriate comparison to a benthic morph, the genetic divergence from the lake Thingvallavatn fish, and also the selection regime it has experienced (selection for limnetic features? What other traits vary with domestication?). d) Paragraph 2 – Much of this paragraph, including discussing the ability to measure gene expression and relate to phenotype in fishes, is unnecessary as fish are no different from other vertebrates in this respect. Instead, the final sentence “One approach to identify pathways related to function or morphological differences is to study gene expression during development” should become the ‘topic sentence’ and expanded upon to explain why gene expression studies are especially relevant ways to link genotype to phenotype in evo-devo studies. e) Better highlight the strengths – The authors have done a wonderful job of assessing multiple developmental time points and rearing fish in a common garden environment. However, they do not highlight these strengths. Some small notes on the importance of controlling for phenotypic plasticity in these traits (which are known to be quite plastic) to better study genetic differentiation would be a nice addition. Methods: a) Page 4 paragraph 1 - Clarify the number of fish used to make the crosses (this will help us determine the likelihood of selecting a full or half-sib for sequencing/qPCR). b) I should note that I am not an expert in the analysis of RNA-seq data, but luckily the first reviewer has done an excellent job of commenting upon these aspects of the project. I fully agree with their comments and suggestions. I would also like to see more information on the methods used to pool samples and how RNA-seq data was normalized among samples, developmental times and morphs. I will also note that the authors often use S.salar for comparisions, not O.mykiss , which is a closer relative to S.alpinus . The reasons for this approach should be discussed. c) I am also not trained as a population geneticist. However, from my experience studying paralogous genes in salmonids, and with respect to the author’s own findings for the Nattl paralogs (Fig 4), I do not think it is prudent to “assume that the expression of paralogous genes is stable… ” in the methods (page 12). In fact, Berthelot et al . (2014) find the opposite (see my comments for the discussion). d) The authors should use their genetic information to test if the fish chosen are siblings with each other (full or half-sibs). This may have important implications for the population genetic analyses. e) Page 5 - It is not appropriate to change the meaning of the word ‘gene’. I think it is much clearer to use the term ‘paralog group’ or ‘gene family’ when referring to the fact that the authors do not study single genes, but instead groups of paralogs. f) Selection of genes for qPCR – the methods by which genes for the qPCR studies (Fig 3) were selected should be clearly noted. From my reading, it seems that most of these genes do not significantly vary among SB and AC at the 1% FDR level (Tables 1 and 2; only Natterin?). Thus, I am assuming these genes are only significant at the 5% FDR level (S1 file) – why focus upon these and not those significant at 1%? As well, it would be good to include information on why different genes were selected for Figure 3 (qPCR validation of whole fish) and Figure 4 (candidate genes-qPCR validation in just the head). Finally, the abbreviations used for qPCR validation should also be listed in Table 1 for easy comparisons among figures/tables. Results & Figures: a) Include an experimental design figure - At present, it is difficult to keep track of all of the morphotypes, tissues, and developmental time points used without referring to the methods. Thus, an experimental design figure summarizing the samples used (morphotype, population, sample size, developmental time point), how they were pooled and which techniques were used to measure gene expression on each sample (RNA-seq and/or qPCR) is needed. b) Include the LB and PL morphs in Figure 1 and clarify traits of interest – The legend states that “differences in size, coloration and head morphology are apparent”, but it would be better to specifically point out the differences they are referring to. F1000 is for a general audience, and this would help non-ichthylogists better understand what ecologically-important traits the authors are interested in (e.g. those related to benthic/limnetic feeding). In addition, the two other morphs used in the qPCR studies should also be displayed (large benthivorous and small planktivorous) to facilitate phenotypic comparisons and assess parallelism in benthic/limnetic feeding and/or the effects of domestication on AC. c) Figure 5- this is actually a table not a figure (?) and is a bit confusing. I think it is much easier to interpret Figure S2 (displaying the data as in Fig 3 and 4), and that Fig 5 and S2 should be switched. It would be great to show significant differences in mRNA content in this, and all other figures, by including symbols. Also, full gene names should be listed in all figure legends. Discussion: The discussion focuses on the SB morph (page 17 – “The objective of this study were to get a handle on genetic and molecular systems that associate with benthic morphology in charr by mainly focusing on the small benthic morph in Lake Thingvallavatn, Iceland”), while the introduction discusses parallel evolution (indicating that the comparisons should be among many morphs). These are two different topics i) mRNA content differences among benthic vs. limnetic morphs changing in parallel or ii) linking mRNA content to phenotype in SB (benthic, wild) vs. AC (limnetic head, domesticated) morphs. In particular, the role of domestication vs. wild fish divergence needs to be addressed. At present these two topics/questions are mixed in the introduction/discussion and should be addressed separately. a) Paragraph on Immune Defences - Is immunity also expected to evolve in parallel in all benthic morphs? Is this predicted to be unique to SB vs. AC? Whatever the case, the parallelism (or not) in these genes should also be discussed, and whether this relates more to domestication in AC or differences between limnetic vs. benthic fish. Much of the functional discussion can also be cut. b) Page 18 – The information about genes found to be differentially expressed among morphs in your prior work should also be in the introduction, as it is background work that explains why you took this transcriptomic approach. This can also be used to explain why you focused in on particular qPCR genes. c) A discussion of domestication related differences vs. benthic/limnetic differences should be included. I think the data from head gene expression is very interesting (Figs 5, S2) and really speaks to this question. d) In general, the role of stochastic evolutionary processes, and not just selection (artificial and natural) should be noted. For example, if the AC charr were simply taken from a stock with a different mtDNA haplotype then these differences in the mtDNA genome might not be adaptive, just random. If the AC fish has much higher mtDNA expression might this be simply a domestication issue and not indicative of selection in SB as stated? Finally, you find that not all mitochondrial transcripts (which are transcribed as a polycistronic transcript) are found at similar levels (Table 1) – what does this tell you about differential degradation/post-transcriptional processes? e) There is no discussion about the “Analyses of polymorphism in Arctic charr transcriptome” (Table 3, 4, 5), except for the mtDNA. Minor Comments Introduction: a) Paragraph 3 – “Furthermore, recent estimates from the rainbow trout….by utilizing multiple data sources the genome assembly problem of this family can be solved”. I am not sure how this statement is relevant to this particular study. This and the following statement seem more appropriate for the methods/discussion to me. b) The morphs being discussed should be clarified throughout the paper. For example, the authors often state “among morphs/among charr populations” but it is not clear which of the many morphs they are referring to (e.g. Paragraph 5, first sentence on allozymes and mtDNA and later sentence on MCHIIa – do you mean all 4 morphs of specific 2-way comparisons? Are some morphs more differentiated than others?) Methods: a) The authors should note why they did not use the PI (large piscivorous) morph in any qPCR studies (in the methods or discussion) as this would be a nice morph to use in their tests for parallelism. b) Page 5 (last paragraph) – the methods used to remove particular variants needs to be clarified. In particular, why the assumptions used to remove variants are valid by referencing past studies. Figures & Results: a) Figure 2. The key for Figure 2 should include a specific heading for morph and time-point with the abbreviations restated [e.g. Timepoint: 141 dpf, Morph: Small Benthic (SB)]. b) Figure 6 – would be helpful to label the protein coding genes in this figure as well as the 12s and 16s RNAs. c) Figure 7 – It is not clear to me which variant is present in which morph. Adding the nucleotide to the x-axis (i.e. frequency of m1829G for B) would make this figure easier to quickly interpret. The “A.charr_WT” and “A.charr_M” should also be defined in the legend and it would be more appropriate to use scientific names for all species. Discussion: a) Discussion of reference 32 – The discussion of reference 32 is not put into the proper context. Figure 6 of this paper (Berthelot et al . 2014) shows that there are many genes that have no correlation among expression patterns and/or differences in expression levels (1573, 1248, and 1895=4716 paralog pairs), and that together these represent more than the 1,407 correlated/similar expression level paralogs. This section of the discussion needs to be modified. b) The Norman et al . (2014) paper should be mentioned earlier – if this is available why was it not used for their analyses? As well, the last sentence in this paragraph can be cut as it is evident. c) Page 18 – “Our new data also demonstrate differences in craniofacial elements between AC- and SB-charr, along a limnetic vs. benthic axis 79 ”. Are you referring to ref 79 or data from this study? If you are referring to 79, clarify and note what you found. This occurs a few times in the discussion General grammatical errors There are a number of grammatical errors throughout this paper (e.g. “31 genes were higher expressed in SB and 40 genes higher in AC-charr”; “that may help sculpture benthic vs. limnetic heads” pg 19). Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Dalziel A. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.6869.r9419 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v1#referee-response-9419 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 25 Apr 2016 Arnar Palsson , Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 25 Apr 2016 Author Response Major Comments Introduction:‭ ‬Requires some reorganization,‭ ‬clarification of what phenotypes have evolved in parallel among morphs,‭ ‬and how the authors separate the effects of domestication‭ (‬SB vs.‭ ‬AC‭) ‬from benthic/limnetic evolution‭ ... Continue reading Major Comments Introduction:‭ ‬Requires some reorganization,‭ ‬clarification of what phenotypes have evolved in parallel among morphs,‭ ‬and how the authors separate the effects of domestication‭ (‬SB vs.‭ ‬AC‭) ‬from benthic/limnetic evolution‭ (‬SB/LB vs.‭ ‬PL/AC‭)‬.‭ ‬a‭) ‬At present,‭ ‬the introduction focuses upon the utility of instances of parallel evolution to help us determine how repeatable evolutionary change may be.‭ ‬This is definitely true,‭ ‬and the repeated evolution of the dwarf,‭ ‬benthic morph‭ (‬SB‭; ‬the focus of the introduction/abstract/discussion‭) ‬in many lakes strongly argues that this phenotype has evolved via natural selection.‭ ‬However,‭ ‬it is not clear to me if true‭ ‘‬parallelism‭’ ‬seen among the SB‭ (‬small benthic‭) ‬and LB‭ (‬large benthivorous‭) ‬vs.‭ ‬AC‭ (‬Holar aquaculture‭) ‬and PL‭ (‬small planktivorous‭) ‬morphs because not enough information is provided for me to assess this.‭ ‬To support the argument for parallelism the specific traits that have evolved in parallel among morphs must be displayed and the evolutionary history of these morphs should be clarified‭ (‬e.g.‭ ‬in paragraph‭ ‬6‭ ‬and Figure‭ ‬1‭)‬.‭ ‬As well,‭ ‬any related non-parallelism in traits should also be discussed‭ (‬i.e.‭ ‬how are the domesticated AC and wild PL different‭?)‬.‭ ‬At present Figure‭ ‬1‭ ‬only shows the AC and SB morphs,‭ ‬and does not point out the specific traits they are interested in.‭ ‬This is critical background information for readers who are not familiar with this system. Reply: ‭ ‬These are excellent suggestions.‭ ‬At the end of the intro we stress the difference between the aims of our research program‭ (‬study the genetics of parallel evolution‭) ‬and the aims of this study‭ (‬get a handle on differences between sympatric morphs,‭ ‬with the AC as possible outgroup‭)‬.‭ ‬The morphs studied here do not represent parallel evolution of benthic phenotypes‭ (‬SB and LB are both from the same lake and appear to be closely related‭ ‬-‭ ‬Kapralova et al‭ ‬2011‭)‬.‭ ‬Analyses of that question requires further studies.‭ ‬This data can implicate genes that separate PL/AC and SB/LB and may be studied in such follow up analyses of more populations.‭ ‬We have updated figure‭ ‬1‭ ‬as advised‭ ‬-‭ ‬including the‭ ‬4‭ ‬morphs studied,‭ ‬expanded on the legend and also provide an overview of research approach‭ (‬part B‭)‬.‭ ‬ b‭) ‬The comparison of AC‭ (‬domestic,‭ ‬limnetic-like head‭) ‬vs.‭ ‬LB‭ (‬wild,‭ ‬benthic like head‭) ‬looks at two confounded variables:‭ ‬domestication and the benthic/limnetic morphology.‭ ‬This should be clearly stated in the introduction,‭ ‬and the use of the additional morphs‭ (‬PL,‭ ‬LB‭) ‬in detangling domestication vs.‭ ‬benthic/limnetic evolution should be noted.‭ ‬c‭) ‬The use of the AC morph is still a bit unclear to me.‭ ‬The argument for point‭ ‘‬ii‭) ‬of the availability of abundant AC material‭’ ‬could be expanded by providing more information on the‭ ‘‬limnetic‭’ ‬like features of this morph and why it is an appropriate comparison to a benthic morph,‭ ‬the genetic divergence from the lake Thingvallavatn fish,‭ ‬and also the selection regime it has experienced‭ (‬selection for limnetic features‭? ‬What other traits vary with domestication‭?)‬.‭ Reply (‬b and c‭)‬:‭ ‬The reviewer is correct,‭ ‬AC and SB are separated by multiple traits,‭ ‬and the data probably reveal signals associating with most of them.‭ ‬Unfortunately the AC charr is not well characterized phenotypically,‭ ‬thus we can not address the question of other traits.‭ ‬We focus mainly on the head and jaw morphology,‭ ‬as these attributes distinguish benthic and limnetic morphs.‭ T‬he revised intro elaborates on the choice of AC,‭ ‬and how the follow up work on the morphs from Lake Thingvallavatn can help us sort this out.‭ ‬This point is also picked up in the discussion. d‭) ‬Paragraph‭ ‬2‭ – ‬Much of this paragraph,‭ ‬including discussing the ability to measure gene expression and relate to phenotype in fishes,‭ ‬is unnecessary as fish are no different from other vertebrates in this respect.‭ ‬Instead,‭ ‬the final sentence‭ “‬One approach to identify pathways related to function or morphological differences is to study gene expression during development‭” ‬should become the‭ ‘‬topic sentence‭’ ‬and expanded upon to explain why gene expression studies are especially relevant ways to link genotype to phenotype in evo-devo studies.‭ Reply : ‬We restructured and shortened this paragraph around this topic sentence‭ ‬-‭ ‬and gave more room for the previous RNAseq study on Arctic charr. ‬ e‭) ‬Better highlight the strengths‭ – ‬The authors have done a wonderful job of assessing multiple developmental time points and rearing fish in a common garden environment.‭ ‬However,‭ ‬they do not highlight these strengths.‭ ‬Some small notes on the importance of controlling for phenotypic plasticity in these traits‭ (‬which are known to be quite plastic‭) ‬to better study genetic differentiation would be a nice addition. Reply : ‬Great advice,‭ ‬we tried to integrate this into the last paragraph of the intro.‭ ‬ Methods:‭ ‬a‭) ‬Page‭ ‬4‭ ‬paragraph‭ ‬1‭ ‬-‭ ‬Clarify the number of fish used to make the crosses‭ (‬this will help us determine the likelihood of selecting a full or half-sib for sequencing/qPCR‭)‬. Reply : ‬We did bulk crosses,‭ ‬joining eggs from‭ ‬5-10‭ ‬females in a can and sperm from‭ ‬3-5‭ ‬males‭ (‬SB,‭ ‬PL,‭ ‬LB‭) ‬and single parent cross for AC.‭ ‬Each sample included RNA pooled from‭ ‬3‭ ‬embryos,‭ ‬so there is a chance that full sibs were sequenced,‭ ‬but unlikely.‭ ‬The embryos/samples for qPCR are from similar pools. Now described better in methods.‭ ‬ b‭) ‬I should note that I am not an expert in the analysis of RNA-seq data,‭ ‬but luckily the first reviewer has done an excellent job of commenting upon these aspects of the project.‭ ‬I fully agree with their comments and suggestions.‭ ‬I would also like to see more information on the methods used to pool samples and how RNA-seq data was normalized among samples,‭ ‬developmental times and morphs.‭ ‬I will also note that the authors often use‭ ‬S.salar for comparisions,‭ ‬not‭ ‬O.mykiss,‭ ‬which is a closer relative to S.alpinus.‭ ‬The reasons for this approach should be discussed.‭ Reply : ‬The RNA was isolated from individual embryos,‭ ‬quantified and then united‭ (‬in equal concentrations‭) ‬prior to cDNA synthesis.‭ The read counts per gene are normalized per million reads in sample. Not normalized with other variables. ‬ c‭) ‬ I am also not trained as a population geneticist.‭ ‬However,‭ ‬from my experience studying paralogous genes in salmonids,‭ ‬and with respect to the author’s own findings for the Nattl paralogs‭ (‬Fig‭ ‬4‭)‬,‭ ‬I do not think it is prudent to‭ “‬assume that the expression of paralogous genes is stable‭… ” ‬in the methods‭ (‬page‭ ‬12‭)‬. ‭ ‬In fact,‭ ‬Berthelot‭ ‬et al.‭ (‬2014‭) ‬find the opposite‭ (‬see my comments for the discussion‭)‬.‭ ‬ Reply :‭ ‬Excellent suggestion.‭ ‬We corrected our misunderstanding,‭ ‬added this fact into the intro and discussion,‭ ‬and reinterpreted our data in this light. d‭) ‬The authors should use their genetic information to test if the fish chosen are siblings with each other‭ (‬full or half-sibs‭)‬.‭ ‬This may have important implications for the population genetic analyses.‭ Reply :‬The fish chosen for pop-gen work are random sample from spawning grounds‭ ‬-‭ ‬assumed to be not sibling groups.‭ ‬Our earlier study‭ (K‬apralova‭ ‬2011‭) ‬showed no family structure in charr collected this way from the lake. ‬e‭) ‬Page‭ ‬5‭ ‬-‭ ‬It is not appropriate to change the meaning of the word‭ ‘‬gene‭’‬.‭ ‬I think it is much clearer to use the term‭ ‘‬paralog group‭’ ‬or‭ ‘‬gene family‭’ ‬when referring to the fact that the authors do not study single genes,‭ ‬but instead groups of paralogs. ‬ Reply : ‬Excellent suggestion.‭ ‬We amended this., and use paralog group throughout. f‭) ‬Selection of genes for qPCR‭ – ‬the methods by which genes for the qPCR studies‭ (‬Fig‭ ‬3‭) ‬were selected should be clearly noted.‭ ‬From my reading,‭ ‬it seems that most of these genes do not significantly vary among SB and AC at the‭ ‬1%‭ ‬FDR level‭ (‬Tables‭ ‬1‭ ‬and‭ ‬2‭; ‬only Natterin‭?)‬.‭ ‬Thus,‭ ‬I am assuming these genes are only significant at the‭ ‬5%‭ ‬FDR level‭ (‬S1‭ ‬file‭) – ‬why focus upon these and not those significant at‭ ‬1%‭?‬ ‭ ‬As well,‭ ‬it would be good to include information on why different genes were selected for Figure‭ ‬3‭ (‬qPCR validation of whole fish‭) ‬and Figure‭ ‬4‭ (‬candidate genes-qPCR validation in just the head‭)‬.‭ ‬Finally,‭ ‬the abbreviations used for qPCR validation should also be listed in Table‭ ‬1‭ ‬for easy comparisons among figures/tables.‭ Reply : ‬Very important point.‭ ‬We deliberately studied some genes with less statistical support‭ (‬FDR between‭ ‬5%‭ ‬and‭ ‬10%‭)‬,‭ ‬to gauge the differences in the genes with less support and in particular to have a bigger pool of candidates that may relate to the specific developmental process‭ (‬like head and jaw formation‭)‬.‭ ‬Of course we can not assert that all the genes with strongest DE signal in the transcriptome are true positives,‭ ‬but the data can be used for hypothesis generation.‭ ‬We also amended table‭ ‬1‭ ‬and the figure legends accordingly. Results‭ & ‬Figures:‭ ‬ a‭) ‬Include an experimental design figure‭ ‬-‭ ‬At present,‭ ‬it is difficult to keep track of all of the morphotypes,‭ ‬tissues,‭ ‬and developmental time points used without referring to the methods.‭ ‬Thus,‭ ‬an experimental design figure summarizing the samples used‭ (‬morphotype,‭ ‬population,‭ ‬sample size,‭ ‬developmental time point‭)‬,‭ ‬how they were pooled and which techniques were used to measure gene expression on each sample‭ (‬RNA-seq and/or qPCR‭)‬ ‭ ‬is needed. b‭) ‬Include the LB and PL morphs in Figure‭ ‬1‭ ‬and clarify traits of interest‭ – ‬The legend states that‭ “‬differences in size,‭ ‬coloration and head morphology are apparent‭”‬,‭ ‬but it would be better to specifically point out the differences they are referring to.‭ ‬F1000‭ ‬is for a general audience,‭ ‬and this would help non-ichthylogists better understand what ecologically-important traits the authors are interested in‭ (‬e.g.‭ ‬those related to benthic/limnetic feeding‭)‬. ‭ ‬In addition,‭ ‬the two other morphs used in the qPCR studies should also be displayed‭ (‬large benthivorous and small planktivorous‭) ‬to facilitate phenotypic comparisons and assess parallelism in benthic/limnetic feeding and/or the effects of domestication on AC. Reply : (‬a and b‭) ‬Excellent suggestions.‭ ‬Now picture‭ ‬1‭ ‬has all‭ ‬4‭ ‬morphs,‭ ‬and a schematic describing the work flow and samples.‭ ‬c‭) ‬Figure‭ ‬5-‭ ‬this is actually a table not a figure‭ (?) ‬and is a bit confusing.‭ ‬I think it is much easier to interpret Figure S2‭ (‬displaying the data as in Fig‭ ‬3‭ ‬and‭ ‬4‭)‬,‭ ‬and that Fig‭ ‬5‭ ‬and S2‭ ‬should be switched.‭ ‬It would be great to show significant differences in mRNA content in this,‭ ‬and all other figures,‭ ‬by including symbols.‭ ‬Also,‭ ‬full gene names should be listed in all figure legends.‭ Reply : ‬We acknowledge that this graph is not the simplest,‭ ‬but would like to keep it over Figure S2.‭ ‬Our reasoning is that this graph illustrates the sharp differences between the limnetic‭ (‬AC-PL‭) ‬and benthic‭ (‬SB-LB‭)‬,‭ ‬which are the main result in this section.‭ ‬But we will of course switch them,‭ ‬or possibly join both in a single figure ‭?? ‬if the reviewer insists or the editors recommend it. Discussion:‭ ‬The discussion focuses on the SB morph‭ (‬page‭ ‬17‭ – “‬The objective of this study were to get a handle on genetic and molecular systems that associate with benthic morphology in charr by mainly focusing on the small benthic morph in Lake Thingvallavatn,‭ ‬Iceland‭”)‬,‭ ‬while the introduction discusses parallel evolution‭ (‬indicating that the comparisons should be among many morphs‭)‬.‭ ‬These are two different topics i‭) ‬mRNA content differences among benthic vs.‭ ‬limnetic morphs changing in parallel or ii‭) ‬linking mRNA content to phenotype in SB‭ (‬benthic,‭ ‬wild‭) ‬vs.‭ ‬AC‭ (‬limnetic head,‭ ‬domesticated‭) ‬morphs.‭ ‬In particular,‭ ‬the role of domestication vs.‭ ‬wild fish divergence needs to be addressed.‭ ‬At present these two topics/questions are mixed in the introduction/discussion and should be addressed separately.‭ Reply : ‬We tried to separate these two aims more clearly in the revised discussion.‭ ‬The strategy was to use the AC vs SB contrast for hypothesis generation,‭ ‬as the first aim is central to our program.‭ ‬We have now added sentences on the domestication in two parts of the discussion. ‬a‭) ‬Paragraph on Immune Defenses‭ ‬-‭ ‬Is immunity also expected to evolve in parallel in all benthic morphs‭? ‬Is this predicted to be unique to SB vs.‭ ‬AC‭? ‬Whatever the case,‭ ‬the parallelism‭ (‬or not‭) ‬in these genes should also be discussed,‭ ‬and whether this relates more to domestication in AC or differences between limnetic vs.‭ ‬benthic fish. ‭ ‬Much of the functional discussion can also be cut. Reply : ‬Good question,‭ ‬we assume it to be so,‭ ‬but that may be wrong.‭ ‬We moved the discussion towards this question and away from functional description.‭ ‬b‭) ‬Page‭ ‬18‭ – ‬The information about genes found to be differentially expressed among morphs in your prior work should also be in the introduction,‭ ‬as it is background work that explains why you took this transcriptomic approach.‭ ‬This can also be used to explain why you focused in on particular qPCR genes. Reply: ‬We added a sentence in the intro about the published papers,‭ ‬that this transcriptome made available.‭ ‬In those papers we focused on genes with putative craniofacial effects,‭ ‬though the focus in this study was broader.‭ ‬ c‭) ‬A discussion of domestication related differences vs.‭ ‬benthic/limnetic differences should be included.‭ ‬I think the data from head gene expression is very interesting‭ (‬Figs‭ ‬5,‭ ‬S2‭) ‬and really speaks to this question.‭ ‬d‭) ‬In general,‭ ‬the role of stochastic evolutionary processes,‭ ‬and not just selection‭ (‬artificial and natural‭) ‬should be noted.‭ ‬For example,‭ ‬if the AC charr were simply taken from a stock with a different mtDNA haplotype then these differences in the mtDNA genome might not be adaptive,‭ ‬just random. ‭ ‬If the AC fish has much higher mtDNA expression might this be simply a domestication issue and not indicative of selection in SB as stated‭?‬ ‭ ‬Finally,‭ ‬you find that not all mitochondrial transcripts‭ (‬which are transcribed as a polycistronic transcript‭) ‬are found at similar levels‭ (‬Table‭ ‬1‭) – ‬what does this tell you about differential degradation/post-transcriptional processes‭?‬ e‭) ‬There is no discussion about the‭ “‬Analyses of polymorphism in Arctic charr transcriptome‭” (‬Table‭ ‬3,‭ ‬4,‭ ‬5‭)‬,‭ ‬except for the mtDNA. Reply: (‬c,d,e‭) Excellent suggestions. ‬We added in the final discussion section few sentences on domesticated charr vs Benthic/limnetic.‭ ‬Unfortunately we do not have quantitative data on the phenotypes‭ (‬head shape,‭ ‬and jaw‭) ‬of the AC charr and acknowledge that we categorize it as limnetic based on general features.‭ We gladly added a sentence citing neutral forces,‭ ‬and are acutely aware that much of the divergence is likely due to history,‭ ‬drift etc.‭ ‬The domestication can certainly be the driver for the higher expression in AC‭ ‬-‭ ‬but we need transcriptomes from more populations/morphs to address that point.‭ ‬And yes,‭ ‬the variance in RNA levels from different parts of the mtDNA do indeed suggest differential half life of the various RNA species.‭ ‬Some are certainly degraded and others most probably actively utilized‭ ‬/‭ ‬protected.‭ ‬We decided not to follow that thought further though,‭ ‬as the MS already consists of quite a few threads already. We also added sentences on the genetic polymorphism,‭ ‬before focusing more on the mtDNA.‭ ‬The main reason we dont want to elaborate to much on the SNPs is that we feel these data are mainly for generating hypotheses,‭ ‬and that more work is needed to substantiate SNPs and study their distribution in other populations. ‭ ‬Minor Comments‭ ‬Introduction:‭ ‬ a‭) ‬Paragraph‭ ‬3‭ – “‬Furthermore,‭ ‬recent estimates from the rainbow trout‭…‬.by utilizing multiple data sources the genome assembly problem of this family can be solved‭”‬.‭ ‬I am not sure how this statement is relevant to this particular study.‭ ‬This and the following statement seem more appropriate for the methods/discussion to me. ‬ Reply: ‬We deleted this sentence and simplified the paragraph. ‬ b‭) ‬The morphs being discussed should be clarified throughout the paper.‭ ‬For example,‭ ‬the authors often state‭ “‬among morphs/among charr populations‭” ‬but it is not clear which of the many morphs they are referring to‭ (‬e.g.‭ ‬Paragraph‭ ‬5,‭ ‬first sentence on allozymes and mtDNA and later sentence on MCHIIa‭ – ‬do you mean all‭ ‬4‭ ‬morphs of specific‭ ‬2-way comparisons‭? ‬Are some morphs more differentiated than others‭?) Reply: ‬We tried to clarify this in various places in the manuscript,‭ ‬but in some cases we refer to morphs in general.‭ ‬Genetic separation can be estimated with Fst values either between pairs or over a larger set of groups‭ (‬populations,‭ ‬morphs‭)‬.‭ ‬In the intro we cite the work done to date in Iceland,‭ ‬which highlights the need for more pop.‭ ‬genetic analyses.‭ ‬ ‭ ‬ Methods:‭ ‬ a‭) ‬The authors should note why they did not use the PI‭ (‬large piscivorous‭) ‬morph in any qPCR studies‭ (‬in the methods or discussion‭) ‬as this would be a nice morph to use in their tests for parallelism.‭ Reply: The PI charr is very rare in the lake and hard to catch.‭ ‬We later captured few sexually mature individuals,‭ ‬and generated couple of families,‭ ‬that were used for one study‭ (‬Ahi et al‭ ‬Evodevo 2015‭)‬. ‬b‭) ‬Page‭ ‬5‭ (‬last paragraph‭) – ‬the methods used to remove particular variants needs to be clarified.‭ ‬In particular,‭ ‬why the assumptions used to remove variants are valid by referencing past studies. ‬ Reply: ‬Many of the principles are common to most pipelines for removing spurious variants.‭ ‬In addition we applied filters necessitated by the properties of our dataset‭ (‬pool of individuals‭)‬,‭ ‬the mapping to an outgroup and paralogs due to salmonid genome complexity. ‬ Figures‭ & ‬Results:‭ ‬ a‭) ‬Figure‭ ‬2.‭ ‬The key for Figure‭ ‬2‭ ‬should include a specific heading for morph and time-point with the abbreviations restated‭ [‬e.g.‭ ‬Timepoint:‭ ‬141‭ ‬dpf,‭ ‬Morph:‭ ‬Small Benthic‭ (‬SB‭)]‬. Reply: ‬Now fixed.‭ ‬ ‭ ‬ b‭) ‬Figure‭ ‬6‭ – ‬would be helpful to label the protein coding genes in this figure as well as the‭ ‬12s and‭ ‬16s RNAs.‭ ‬ Reply: ‬Now fixed.‭ ‬ ‭ ‬ c‭) ‬Figure‭ ‬7‭ – ‬It is not clear to me which variant is present in which morph.‭ ‬Adding the nucleotide to the x-axis‭ (‬i.e.‭ ‬frequency of m1829G for B‭) ‬would make this figure easier to quickly interpret.‭ ‬The‭ “‬A.charr_WT‭” ‬and‭ “‬A.charr_M‭” ‬should also be defined in the legend and it would be more appropriate to use scientific names for all species.‭ ‬ Reply: ‬Now fixed‭ ‬ Discussion:‭ ‬ a‭) ‬Discussion of reference‭ ‬32‭ – ‬The discussion of reference‭ ‬32‭ ‬is not put into the proper context.‭ ‬Figure‭ ‬6‭ ‬of this paper‭ (‬Berthelot‭ ‬et al.‭ ‬2014‭) ‬shows that there are many genes that have no correlation among expression patterns and/or differences in expression levels‭ (‬1573,‭ ‬1248,‭ ‬and‭ ‬1895‭=‬4716‭ ‬paralog pairs‭)‬,‭ ‬and that together these represent more than the‭ ‬1,407‭ ‬correlated/similar expression level paralogs.‭ ‬This section of the discussion needs to be modified.‭ Reply: ‬Really valuable point,‭ ‬that we are especially grateful for.‭ ‬That we have added this fact to the intro and altered our interpretations in the discussion. ‬ b‭) ‬The Norman‭ ‬et al.‭ (‬2014‭) ‬paper should be mentioned earlier‭ – ‬if this is available why was it not used for their analyses‭? ‬As well,‭ ‬the last sentence in this paragraph can be cut as it is evident. Reply: ‬The Norman papers are now presented more clearly in the intro.‭ ‬There are historical reasons for not including their data in our analyses,‭ ‬we had completed the analyses for this manuscript when they became available and have since then focused our data analyses efforts on another transcriptome generated in the lab‭ (‬with longer reads‭)‬.‭ c‭) ‬Page‭ ‬18‭ – “‬Our new data also demonstrate differences in craniofacial elements between AC-‭ ‬and SB-charr,‭ ‬along a limnetic vs.‭ ‬benthic axis79‭”‬.‭ ‬Are you referring to ref‭ ‬79‭ ‬or data from this study‭? ‬If you are referring to‭ ‬79,‭ ‬clarify and note what you found.‭ ‬This occurs a few times in the discussion‭ Reply: ‬Ref‭ ‬79‭ ‬is a related study that built in part on the data presented here.‭ ‬We have now rephrased this in the manuscript,‭ ‬hopefully to the better. Major Comments Introduction:‭ ‬Requires some reorganization,‭ ‬clarification of what phenotypes have evolved in parallel among morphs,‭ ‬and how the authors separate the effects of domestication‭ (‬SB vs.‭ ‬AC‭) ‬from benthic/limnetic evolution‭ (‬SB/LB vs.‭ ‬PL/AC‭)‬.‭ ‬a‭) ‬At present,‭ ‬the introduction focuses upon the utility of instances of parallel evolution to help us determine how repeatable evolutionary change may be.‭ ‬This is definitely true,‭ ‬and the repeated evolution of the dwarf,‭ ‬benthic morph‭ (‬SB‭; ‬the focus of the introduction/abstract/discussion‭) ‬in many lakes strongly argues that this phenotype has evolved via natural selection.‭ ‬However,‭ ‬it is not clear to me if true‭ ‘‬parallelism‭’ ‬seen among the SB‭ (‬small benthic‭) ‬and LB‭ (‬large benthivorous‭) ‬vs.‭ ‬AC‭ (‬Holar aquaculture‭) ‬and PL‭ (‬small planktivorous‭) ‬morphs because not enough information is provided for me to assess this.‭ ‬To support the argument for parallelism the specific traits that have evolved in parallel among morphs must be displayed and the evolutionary history of these morphs should be clarified‭ (‬e.g.‭ ‬in paragraph‭ ‬6‭ ‬and Figure‭ ‬1‭)‬.‭ ‬As well,‭ ‬any related non-parallelism in traits should also be discussed‭ (‬i.e.‭ ‬how are the domesticated AC and wild PL different‭?)‬.‭ ‬At present Figure‭ ‬1‭ ‬only shows the AC and SB morphs,‭ ‬and does not point out the specific traits they are interested in.‭ ‬This is critical background information for readers who are not familiar with this system. Reply: ‭ ‬These are excellent suggestions.‭ ‬At the end of the intro we stress the difference between the aims of our research program‭ (‬study the genetics of parallel evolution‭) ‬and the aims of this study‭ (‬get a handle on differences between sympatric morphs,‭ ‬with the AC as possible outgroup‭)‬.‭ ‬The morphs studied here do not represent parallel evolution of benthic phenotypes‭ (‬SB and LB are both from the same lake and appear to be closely related‭ ‬-‭ ‬Kapralova et al‭ ‬2011‭)‬.‭ ‬Analyses of that question requires further studies.‭ ‬This data can implicate genes that separate PL/AC and SB/LB and may be studied in such follow up analyses of more populations.‭ ‬We have updated figure‭ ‬1‭ ‬as advised‭ ‬-‭ ‬including the‭ ‬4‭ ‬morphs studied,‭ ‬expanded on the legend and also provide an overview of research approach‭ (‬part B‭)‬.‭ ‬ b‭) ‬The comparison of AC‭ (‬domestic,‭ ‬limnetic-like head‭) ‬vs.‭ ‬LB‭ (‬wild,‭ ‬benthic like head‭) ‬looks at two confounded variables:‭ ‬domestication and the benthic/limnetic morphology.‭ ‬This should be clearly stated in the introduction,‭ ‬and the use of the additional morphs‭ (‬PL,‭ ‬LB‭) ‬in detangling domestication vs.‭ ‬benthic/limnetic evolution should be noted.‭ ‬c‭) ‬The use of the AC morph is still a bit unclear to me.‭ ‬The argument for point‭ ‘‬ii‭) ‬of the availability of abundant AC material‭’ ‬could be expanded by providing more information on the‭ ‘‬limnetic‭’ ‬like features of this morph and why it is an appropriate comparison to a benthic morph,‭ ‬the genetic divergence from the lake Thingvallavatn fish,‭ ‬and also the selection regime it has experienced‭ (‬selection for limnetic features‭? ‬What other traits vary with domestication‭?)‬.‭ Reply (‬b and c‭)‬:‭ ‬The reviewer is correct,‭ ‬AC and SB are separated by multiple traits,‭ ‬and the data probably reveal signals associating with most of them.‭ ‬Unfortunately the AC charr is not well characterized phenotypically,‭ ‬thus we can not address the question of other traits.‭ ‬We focus mainly on the head and jaw morphology,‭ ‬as these attributes distinguish benthic and limnetic morphs.‭ T‬he revised intro elaborates on the choice of AC,‭ ‬and how the follow up work on the morphs from Lake Thingvallavatn can help us sort this out.‭ ‬This point is also picked up in the discussion. d‭) ‬Paragraph‭ ‬2‭ – ‬Much of this paragraph,‭ ‬including discussing the ability to measure gene expression and relate to phenotype in fishes,‭ ‬is unnecessary as fish are no different from other vertebrates in this respect.‭ ‬Instead,‭ ‬the final sentence‭ “‬One approach to identify pathways related to function or morphological differences is to study gene expression during development‭” ‬should become the‭ ‘‬topic sentence‭’ ‬and expanded upon to explain why gene expression studies are especially relevant ways to link genotype to phenotype in evo-devo studies.‭ Reply : ‬We restructured and shortened this paragraph around this topic sentence‭ ‬-‭ ‬and gave more room for the previous RNAseq study on Arctic charr. ‬ e‭) ‬Better highlight the strengths‭ – ‬The authors have done a wonderful job of assessing multiple developmental time points and rearing fish in a common garden environment.‭ ‬However,‭ ‬they do not highlight these strengths.‭ ‬Some small notes on the importance of controlling for phenotypic plasticity in these traits‭ (‬which are known to be quite plastic‭) ‬to better study genetic differentiation would be a nice addition. Reply : ‬Great advice,‭ ‬we tried to integrate this into the last paragraph of the intro.‭ ‬ Methods:‭ ‬a‭) ‬Page‭ ‬4‭ ‬paragraph‭ ‬1‭ ‬-‭ ‬Clarify the number of fish used to make the crosses‭ (‬this will help us determine the likelihood of selecting a full or half-sib for sequencing/qPCR‭)‬. Reply : ‬We did bulk crosses,‭ ‬joining eggs from‭ ‬5-10‭ ‬females in a can and sperm from‭ ‬3-5‭ ‬males‭ (‬SB,‭ ‬PL,‭ ‬LB‭) ‬and single parent cross for AC.‭ ‬Each sample included RNA pooled from‭ ‬3‭ ‬embryos,‭ ‬so there is a chance that full sibs were sequenced,‭ ‬but unlikely.‭ ‬The embryos/samples for qPCR are from similar pools. Now described better in methods.‭ ‬ b‭) ‬I should note that I am not an expert in the analysis of RNA-seq data,‭ ‬but luckily the first reviewer has done an excellent job of commenting upon these aspects of the project.‭ ‬I fully agree with their comments and suggestions.‭ ‬I would also like to see more information on the methods used to pool samples and how RNA-seq data was normalized among samples,‭ ‬developmental times and morphs.‭ ‬I will also note that the authors often use‭ ‬S.salar for comparisions,‭ ‬not‭ ‬O.mykiss,‭ ‬which is a closer relative to S.alpinus.‭ ‬The reasons for this approach should be discussed.‭ Reply : ‬The RNA was isolated from individual embryos,‭ ‬quantified and then united‭ (‬in equal concentrations‭) ‬prior to cDNA synthesis.‭ The read counts per gene are normalized per million reads in sample. Not normalized with other variables. ‬ c‭) ‬ I am also not trained as a population geneticist.‭ ‬However,‭ ‬from my experience studying paralogous genes in salmonids,‭ ‬and with respect to the author’s own findings for the Nattl paralogs‭ (‬Fig‭ ‬4‭)‬,‭ ‬I do not think it is prudent to‭ “‬assume that the expression of paralogous genes is stable‭… ” ‬in the methods‭ (‬page‭ ‬12‭)‬. ‭ ‬In fact,‭ ‬Berthelot‭ ‬et al.‭ (‬2014‭) ‬find the opposite‭ (‬see my comments for the discussion‭)‬.‭ ‬ Reply :‭ ‬Excellent suggestion.‭ ‬We corrected our misunderstanding,‭ ‬added this fact into the intro and discussion,‭ ‬and reinterpreted our data in this light. d‭) ‬The authors should use their genetic information to test if the fish chosen are siblings with each other‭ (‬full or half-sibs‭)‬.‭ ‬This may have important implications for the population genetic analyses.‭ Reply :‬The fish chosen for pop-gen work are random sample from spawning grounds‭ ‬-‭ ‬assumed to be not sibling groups.‭ ‬Our earlier study‭ (K‬apralova‭ ‬2011‭) ‬showed no family structure in charr collected this way from the lake. ‬e‭) ‬Page‭ ‬5‭ ‬-‭ ‬It is not appropriate to change the meaning of the word‭ ‘‬gene‭’‬.‭ ‬I think it is much clearer to use the term‭ ‘‬paralog group‭’ ‬or‭ ‘‬gene family‭’ ‬when referring to the fact that the authors do not study single genes,‭ ‬but instead groups of paralogs. ‬ Reply : ‬Excellent suggestion.‭ ‬We amended this., and use paralog group throughout. f‭) ‬Selection of genes for qPCR‭ – ‬the methods by which genes for the qPCR studies‭ (‬Fig‭ ‬3‭) ‬were selected should be clearly noted.‭ ‬From my reading,‭ ‬it seems that most of these genes do not significantly vary among SB and AC at the‭ ‬1%‭ ‬FDR level‭ (‬Tables‭ ‬1‭ ‬and‭ ‬2‭; ‬only Natterin‭?)‬.‭ ‬Thus,‭ ‬I am assuming these genes are only significant at the‭ ‬5%‭ ‬FDR level‭ (‬S1‭ ‬file‭) – ‬why focus upon these and not those significant at‭ ‬1%‭?‬ ‭ ‬As well,‭ ‬it would be good to include information on why different genes were selected for Figure‭ ‬3‭ (‬qPCR validation of whole fish‭) ‬and Figure‭ ‬4‭ (‬candidate genes-qPCR validation in just the head‭)‬.‭ ‬Finally,‭ ‬the abbreviations used for qPCR validation should also be listed in Table‭ ‬1‭ ‬for easy comparisons among figures/tables.‭ Reply : ‬Very important point.‭ ‬We deliberately studied some genes with less statistical support‭ (‬FDR between‭ ‬5%‭ ‬and‭ ‬10%‭)‬,‭ ‬to gauge the differences in the genes with less support and in particular to have a bigger pool of candidates that may relate to the specific developmental process‭ (‬like head and jaw formation‭)‬.‭ ‬Of course we can not assert that all the genes with strongest DE signal in the transcriptome are true positives,‭ ‬but the data can be used for hypothesis generation.‭ ‬We also amended table‭ ‬1‭ ‬and the figure legends accordingly. Results‭ & ‬Figures:‭ ‬ a‭) ‬Include an experimental design figure‭ ‬-‭ ‬At present,‭ ‬it is difficult to keep track of all of the morphotypes,‭ ‬tissues,‭ ‬and developmental time points used without referring to the methods.‭ ‬Thus,‭ ‬an experimental design figure summarizing the samples used‭ (‬morphotype,‭ ‬population,‭ ‬sample size,‭ ‬developmental time point‭)‬,‭ ‬how they were pooled and which techniques were used to measure gene expression on each sample‭ (‬RNA-seq and/or qPCR‭)‬ ‭ ‬is needed. b‭) ‬Include the LB and PL morphs in Figure‭ ‬1‭ ‬and clarify traits of interest‭ – ‬The legend states that‭ “‬differences in size,‭ ‬coloration and head morphology are apparent‭”‬,‭ ‬but it would be better to specifically point out the differences they are referring to.‭ ‬F1000‭ ‬is for a general audience,‭ ‬and this would help non-ichthylogists better understand what ecologically-important traits the authors are interested in‭ (‬e.g.‭ ‬those related to benthic/limnetic feeding‭)‬. ‭ ‬In addition,‭ ‬the two other morphs used in the qPCR studies should also be displayed‭ (‬large benthivorous and small planktivorous‭) ‬to facilitate phenotypic comparisons and assess parallelism in benthic/limnetic feeding and/or the effects of domestication on AC. Reply : (‬a and b‭) ‬Excellent suggestions.‭ ‬Now picture‭ ‬1‭ ‬has all‭ ‬4‭ ‬morphs,‭ ‬and a schematic describing the work flow and samples.‭ ‬c‭) ‬Figure‭ ‬5-‭ ‬this is actually a table not a figure‭ (?) ‬and is a bit confusing.‭ ‬I think it is much easier to interpret Figure S2‭ (‬displaying the data as in Fig‭ ‬3‭ ‬and‭ ‬4‭)‬,‭ ‬and that Fig‭ ‬5‭ ‬and S2‭ ‬should be switched.‭ ‬It would be great to show significant differences in mRNA content in this,‭ ‬and all other figures,‭ ‬by including symbols.‭ ‬Also,‭ ‬full gene names should be listed in all figure legends.‭ Reply : ‬We acknowledge that this graph is not the simplest,‭ ‬but would like to keep it over Figure S2.‭ ‬Our reasoning is that this graph illustrates the sharp differences between the limnetic‭ (‬AC-PL‭) ‬and benthic‭ (‬SB-LB‭)‬,‭ ‬which are the main result in this section.‭ ‬But we will of course switch them,‭ ‬or possibly join both in a single figure ‭?? ‬if the reviewer insists or the editors recommend it. Discussion:‭ ‬The discussion focuses on the SB morph‭ (‬page‭ ‬17‭ – “‬The objective of this study were to get a handle on genetic and molecular systems that associate with benthic morphology in charr by mainly focusing on the small benthic morph in Lake Thingvallavatn,‭ ‬Iceland‭”)‬,‭ ‬while the introduction discusses parallel evolution‭ (‬indicating that the comparisons should be among many morphs‭)‬.‭ ‬These are two different topics i‭) ‬mRNA content differences among benthic vs.‭ ‬limnetic morphs changing in parallel or ii‭) ‬linking mRNA content to phenotype in SB‭ (‬benthic,‭ ‬wild‭) ‬vs.‭ ‬AC‭ (‬limnetic head,‭ ‬domesticated‭) ‬morphs.‭ ‬In particular,‭ ‬the role of domestication vs.‭ ‬wild fish divergence needs to be addressed.‭ ‬At present these two topics/questions are mixed in the introduction/discussion and should be addressed separately.‭ Reply : ‬We tried to separate these two aims more clearly in the revised discussion.‭ ‬The strategy was to use the AC vs SB contrast for hypothesis generation,‭ ‬as the first aim is central to our program.‭ ‬We have now added sentences on the domestication in two parts of the discussion. ‬a‭) ‬Paragraph on Immune Defenses‭ ‬-‭ ‬Is immunity also expected to evolve in parallel in all benthic morphs‭? ‬Is this predicted to be unique to SB vs.‭ ‬AC‭? ‬Whatever the case,‭ ‬the parallelism‭ (‬or not‭) ‬in these genes should also be discussed,‭ ‬and whether this relates more to domestication in AC or differences between limnetic vs.‭ ‬benthic fish. ‭ ‬Much of the functional discussion can also be cut. Reply : ‬Good question,‭ ‬we assume it to be so,‭ ‬but that may be wrong.‭ ‬We moved the discussion towards this question and away from functional description.‭ ‬b‭) ‬Page‭ ‬18‭ – ‬The information about genes found to be differentially expressed among morphs in your prior work should also be in the introduction,‭ ‬as it is background work that explains why you took this transcriptomic approach.‭ ‬This can also be used to explain why you focused in on particular qPCR genes. Reply: ‬We added a sentence in the intro about the published papers,‭ ‬that this transcriptome made available.‭ ‬In those papers we focused on genes with putative craniofacial effects,‭ ‬though the focus in this study was broader.‭ ‬ c‭) ‬A discussion of domestication related differences vs.‭ ‬benthic/limnetic differences should be included.‭ ‬I think the data from head gene expression is very interesting‭ (‬Figs‭ ‬5,‭ ‬S2‭) ‬and really speaks to this question.‭ ‬d‭) ‬In general,‭ ‬the role of stochastic evolutionary processes,‭ ‬and not just selection‭ (‬artificial and natural‭) ‬should be noted.‭ ‬For example,‭ ‬if the AC charr were simply taken from a stock with a different mtDNA haplotype then these differences in the mtDNA genome might not be adaptive,‭ ‬just random. ‭ ‬If the AC fish has much higher mtDNA expression might this be simply a domestication issue and not indicative of selection in SB as stated‭?‬ ‭ ‬Finally,‭ ‬you find that not all mitochondrial transcripts‭ (‬which are transcribed as a polycistronic transcript‭) ‬are found at similar levels‭ (‬Table‭ ‬1‭) – ‬what does this tell you about differential degradation/post-transcriptional processes‭?‬ e‭) ‬There is no discussion about the‭ “‬Analyses of polymorphism in Arctic charr transcriptome‭” (‬Table‭ ‬3,‭ ‬4,‭ ‬5‭)‬,‭ ‬except for the mtDNA. Reply: (‬c,d,e‭) Excellent suggestions. ‬We added in the final discussion section few sentences on domesticated charr vs Benthic/limnetic.‭ ‬Unfortunately we do not have quantitative data on the phenotypes‭ (‬head shape,‭ ‬and jaw‭) ‬of the AC charr and acknowledge that we categorize it as limnetic based on general features.‭ We gladly added a sentence citing neutral forces,‭ ‬and are acutely aware that much of the divergence is likely due to history,‭ ‬drift etc.‭ ‬The domestication can certainly be the driver for the higher expression in AC‭ ‬-‭ ‬but we need transcriptomes from more populations/morphs to address that point.‭ ‬And yes,‭ ‬the variance in RNA levels from different parts of the mtDNA do indeed suggest differential half life of the various RNA species.‭ ‬Some are certainly degraded and others most probably actively utilized‭ ‬/‭ ‬protected.‭ ‬We decided not to follow that thought further though,‭ ‬as the MS already consists of quite a few threads already. We also added sentences on the genetic polymorphism,‭ ‬before focusing more on the mtDNA.‭ ‬The main reason we dont want to elaborate to much on the SNPs is that we feel these data are mainly for generating hypotheses,‭ ‬and that more work is needed to substantiate SNPs and study their distribution in other populations. ‭ ‬Minor Comments‭ ‬Introduction:‭ ‬ a‭) ‬Paragraph‭ ‬3‭ – “‬Furthermore,‭ ‬recent estimates from the rainbow trout‭…‬.by utilizing multiple data sources the genome assembly problem of this family can be solved‭”‬.‭ ‬I am not sure how this statement is relevant to this particular study.‭ ‬This and the following statement seem more appropriate for the methods/discussion to me. ‬ Reply: ‬We deleted this sentence and simplified the paragraph. ‬ b‭) ‬The morphs being discussed should be clarified throughout the paper.‭ ‬For example,‭ ‬the authors often state‭ “‬among morphs/among charr populations‭” ‬but it is not clear which of the many morphs they are referring to‭ (‬e.g.‭ ‬Paragraph‭ ‬5,‭ ‬first sentence on allozymes and mtDNA and later sentence on MCHIIa‭ – ‬do you mean all‭ ‬4‭ ‬morphs of specific‭ ‬2-way comparisons‭? ‬Are some morphs more differentiated than others‭?) Reply: ‬We tried to clarify this in various places in the manuscript,‭ ‬but in some cases we refer to morphs in general.‭ ‬Genetic separation can be estimated with Fst values either between pairs or over a larger set of groups‭ (‬populations,‭ ‬morphs‭)‬.‭ ‬In the intro we cite the work done to date in Iceland,‭ ‬which highlights the need for more pop.‭ ‬genetic analyses.‭ ‬ ‭ ‬ Methods:‭ ‬ a‭) ‬The authors should note why they did not use the PI‭ (‬large piscivorous‭) ‬morph in any qPCR studies‭ (‬in the methods or discussion‭) ‬as this would be a nice morph to use in their tests for parallelism.‭ Reply: The PI charr is very rare in the lake and hard to catch.‭ ‬We later captured few sexually mature individuals,‭ ‬and generated couple of families,‭ ‬that were used for one study‭ (‬Ahi et al‭ ‬Evodevo 2015‭)‬. ‬b‭) ‬Page‭ ‬5‭ (‬last paragraph‭) – ‬the methods used to remove particular variants needs to be clarified.‭ ‬In particular,‭ ‬why the assumptions used to remove variants are valid by referencing past studies. ‬ Reply: ‬Many of the principles are common to most pipelines for removing spurious variants.‭ ‬In addition we applied filters necessitated by the properties of our dataset‭ (‬pool of individuals‭)‬,‭ ‬the mapping to an outgroup and paralogs due to salmonid genome complexity. ‬ Figures‭ & ‬Results:‭ ‬ a‭) ‬Figure‭ ‬2.‭ ‬The key for Figure‭ ‬2‭ ‬should include a specific heading for morph and time-point with the abbreviations restated‭ [‬e.g.‭ ‬Timepoint:‭ ‬141‭ ‬dpf,‭ ‬Morph:‭ ‬Small Benthic‭ (‬SB‭)]‬. Reply: ‬Now fixed.‭ ‬ ‭ ‬ b‭) ‬Figure‭ ‬6‭ – ‬would be helpful to label the protein coding genes in this figure as well as the‭ ‬12s and‭ ‬16s RNAs.‭ ‬ Reply: ‬Now fixed.‭ ‬ ‭ ‬ c‭) ‬Figure‭ ‬7‭ – ‬It is not clear to me which variant is present in which morph.‭ ‬Adding the nucleotide to the x-axis‭ (‬i.e.‭ ‬frequency of m1829G for B‭) ‬would make this figure easier to quickly interpret.‭ ‬The‭ “‬A.charr_WT‭” ‬and‭ “‬A.charr_M‭” ‬should also be defined in the legend and it would be more appropriate to use scientific names for all species.‭ ‬ Reply: ‬Now fixed‭ ‬ Discussion:‭ ‬ a‭) ‬Discussion of reference‭ ‬32‭ – ‬The discussion of reference‭ ‬32‭ ‬is not put into the proper context.‭ ‬Figure‭ ‬6‭ ‬of this paper‭ (‬Berthelot‭ ‬et al.‭ ‬2014‭) ‬shows that there are many genes that have no correlation among expression patterns and/or differences in expression levels‭ (‬1573,‭ ‬1248,‭ ‬and‭ ‬1895‭=‬4716‭ ‬paralog pairs‭)‬,‭ ‬and that together these represent more than the‭ ‬1,407‭ ‬correlated/similar expression level paralogs.‭ ‬This section of the discussion needs to be modified.‭ Reply: ‬Really valuable point,‭ ‬that we are especially grateful for.‭ ‬That we have added this fact to the intro and altered our interpretations in the discussion. ‬ b‭) ‬The Norman‭ ‬et al.‭ (‬2014‭) ‬paper should be mentioned earlier‭ – ‬if this is available why was it not used for their analyses‭? ‬As well,‭ ‬the last sentence in this paragraph can be cut as it is evident. Reply: ‬The Norman papers are now presented more clearly in the intro.‭ ‬There are historical reasons for not including their data in our analyses,‭ ‬we had completed the analyses for this manuscript when they became available and have since then focused our data analyses efforts on another transcriptome generated in the lab‭ (‬with longer reads‭)‬.‭ c‭) ‬Page‭ ‬18‭ – “‬Our new data also demonstrate differences in craniofacial elements between AC-‭ ‬and SB-charr,‭ ‬along a limnetic vs.‭ ‬benthic axis79‭”‬.‭ ‬Are you referring to ref‭ ‬79‭ ‬or data from this study‭? ‬If you are referring to‭ ‬79,‭ ‬clarify and note what you found.‭ ‬This occurs a few times in the discussion‭ Reply: ‬Ref‭ ‬79‭ ‬is a related study that built in part on the data presented here.‭ ‬We have now rephrased this in the manuscript,‭ ‬hopefully to the better. Competing Interests: No competing interests were disclosed.No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 25 Apr 2016 Arnar Palsson , Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 25 Apr 2016 Author Response Major Comments Introduction:‭ ‬Requires some reorganization,‭ ‬clarification of what phenotypes have evolved in parallel among morphs,‭ ‬and how the authors separate the effects of domestication‭ (‬SB vs.‭ ‬AC‭) ‬from benthic/limnetic evolution‭ ... Continue reading Major Comments Introduction:‭ ‬Requires some reorganization,‭ ‬clarification of what phenotypes have evolved in parallel among morphs,‭ ‬and how the authors separate the effects of domestication‭ (‬SB vs.‭ ‬AC‭) ‬from benthic/limnetic evolution‭ (‬SB/LB vs.‭ ‬PL/AC‭)‬.‭ ‬a‭) ‬At present,‭ ‬the introduction focuses upon the utility of instances of parallel evolution to help us determine how repeatable evolutionary change may be.‭ ‬This is definitely true,‭ ‬and the repeated evolution of the dwarf,‭ ‬benthic morph‭ (‬SB‭; ‬the focus of the introduction/abstract/discussion‭) ‬in many lakes strongly argues that this phenotype has evolved via natural selection.‭ ‬However,‭ ‬it is not clear to me if true‭ ‘‬parallelism‭’ ‬seen among the SB‭ (‬small benthic‭) ‬and LB‭ (‬large benthivorous‭) ‬vs.‭ ‬AC‭ (‬Holar aquaculture‭) ‬and PL‭ (‬small planktivorous‭) ‬morphs because not enough information is provided for me to assess this.‭ ‬To support the argument for parallelism the specific traits that have evolved in parallel among morphs must be displayed and the evolutionary history of these morphs should be clarified‭ (‬e.g.‭ ‬in paragraph‭ ‬6‭ ‬and Figure‭ ‬1‭)‬.‭ ‬As well,‭ ‬any related non-parallelism in traits should also be discussed‭ (‬i.e.‭ ‬how are the domesticated AC and wild PL different‭?)‬.‭ ‬At present Figure‭ ‬1‭ ‬only shows the AC and SB morphs,‭ ‬and does not point out the specific traits they are interested in.‭ ‬This is critical background information for readers who are not familiar with this system. Reply: ‭ ‬These are excellent suggestions.‭ ‬At the end of the intro we stress the difference between the aims of our research program‭ (‬study the genetics of parallel evolution‭) ‬and the aims of this study‭ (‬get a handle on differences between sympatric morphs,‭ ‬with the AC as possible outgroup‭)‬.‭ ‬The morphs studied here do not represent parallel evolution of benthic phenotypes‭ (‬SB and LB are both from the same lake and appear to be closely related‭ ‬-‭ ‬Kapralova et al‭ ‬2011‭)‬.‭ ‬Analyses of that question requires further studies.‭ ‬This data can implicate genes that separate PL/AC and SB/LB and may be studied in such follow up analyses of more populations.‭ ‬We have updated figure‭ ‬1‭ ‬as advised‭ ‬-‭ ‬including the‭ ‬4‭ ‬morphs studied,‭ ‬expanded on the legend and also provide an overview of research approach‭ (‬part B‭)‬.‭ ‬ b‭) ‬The comparison of AC‭ (‬domestic,‭ ‬limnetic-like head‭) ‬vs.‭ ‬LB‭ (‬wild,‭ ‬benthic like head‭) ‬looks at two confounded variables:‭ ‬domestication and the benthic/limnetic morphology.‭ ‬This should be clearly stated in the introduction,‭ ‬and the use of the additional morphs‭ (‬PL,‭ ‬LB‭) ‬in detangling domestication vs.‭ ‬benthic/limnetic evolution should be noted.‭ ‬c‭) ‬The use of the AC morph is still a bit unclear to me.‭ ‬The argument for point‭ ‘‬ii‭) ‬of the availability of abundant AC material‭’ ‬could be expanded by providing more information on the‭ ‘‬limnetic‭’ ‬like features of this morph and why it is an appropriate comparison to a benthic morph,‭ ‬the genetic divergence from the lake Thingvallavatn fish,‭ ‬and also the selection regime it has experienced‭ (‬selection for limnetic features‭? ‬What other traits vary with domestication‭?)‬.‭ Reply (‬b and c‭)‬:‭ ‬The reviewer is correct,‭ ‬AC and SB are separated by multiple traits,‭ ‬and the data probably reveal signals associating with most of them.‭ ‬Unfortunately the AC charr is not well characterized phenotypically,‭ ‬thus we can not address the question of other traits.‭ ‬We focus mainly on the head and jaw morphology,‭ ‬as these attributes distinguish benthic and limnetic morphs.‭ T‬he revised intro elaborates on the choice of AC,‭ ‬and how the follow up work on the morphs from Lake Thingvallavatn can help us sort this out.‭ ‬This point is also picked up in the discussion. d‭) ‬Paragraph‭ ‬2‭ – ‬Much of this paragraph,‭ ‬including discussing the ability to measure gene expression and relate to phenotype in fishes,‭ ‬is unnecessary as fish are no different from other vertebrates in this respect.‭ ‬Instead,‭ ‬the final sentence‭ “‬One approach to identify pathways related to function or morphological differences is to study gene expression during development‭” ‬should become the‭ ‘‬topic sentence‭’ ‬and expanded upon to explain why gene expression studies are especially relevant ways to link genotype to phenotype in evo-devo studies.‭ Reply : ‬We restructured and shortened this paragraph around this topic sentence‭ ‬-‭ ‬and gave more room for the previous RNAseq study on Arctic charr. ‬ e‭) ‬Better highlight the strengths‭ – ‬The authors have done a wonderful job of assessing multiple developmental time points and rearing fish in a common garden environment.‭ ‬However,‭ ‬they do not highlight these strengths.‭ ‬Some small notes on the importance of controlling for phenotypic plasticity in these traits‭ (‬which are known to be quite plastic‭) ‬to better study genetic differentiation would be a nice addition. Reply : ‬Great advice,‭ ‬we tried to integrate this into the last paragraph of the intro.‭ ‬ Methods:‭ ‬a‭) ‬Page‭ ‬4‭ ‬paragraph‭ ‬1‭ ‬-‭ ‬Clarify the number of fish used to make the crosses‭ (‬this will help us determine the likelihood of selecting a full or half-sib for sequencing/qPCR‭)‬. Reply : ‬We did bulk crosses,‭ ‬joining eggs from‭ ‬5-10‭ ‬females in a can and sperm from‭ ‬3-5‭ ‬males‭ (‬SB,‭ ‬PL,‭ ‬LB‭) ‬and single parent cross for AC.‭ ‬Each sample included RNA pooled from‭ ‬3‭ ‬embryos,‭ ‬so there is a chance that full sibs were sequenced,‭ ‬but unlikely.‭ ‬The embryos/samples for qPCR are from similar pools. Now described better in methods.‭ ‬ b‭) ‬I should note that I am not an expert in the analysis of RNA-seq data,‭ ‬but luckily the first reviewer has done an excellent job of commenting upon these aspects of the project.‭ ‬I fully agree with their comments and suggestions.‭ ‬I would also like to see more information on the methods used to pool samples and how RNA-seq data was normalized among samples,‭ ‬developmental times and morphs.‭ ‬I will also note that the authors often use‭ ‬S.salar for comparisions,‭ ‬not‭ ‬O.mykiss,‭ ‬which is a closer relative to S.alpinus.‭ ‬The reasons for this approach should be discussed.‭ Reply : ‬The RNA was isolated from individual embryos,‭ ‬quantified and then united‭ (‬in equal concentrations‭) ‬prior to cDNA synthesis.‭ The read counts per gene are normalized per million reads in sample. Not normalized with other variables. ‬ c‭) ‬ I am also not trained as a population geneticist.‭ ‬However,‭ ‬from my experience studying paralogous genes in salmonids,‭ ‬and with respect to the author’s own findings for the Nattl paralogs‭ (‬Fig‭ ‬4‭)‬,‭ ‬I do not think it is prudent to‭ “‬assume that the expression of paralogous genes is stable‭… ” ‬in the methods‭ (‬page‭ ‬12‭)‬. ‭ ‬In fact,‭ ‬Berthelot‭ ‬et al.‭ (‬2014‭) ‬find the opposite‭ (‬see my comments for the discussion‭)‬.‭ ‬ Reply :‭ ‬Excellent suggestion.‭ ‬We corrected our misunderstanding,‭ ‬added this fact into the intro and discussion,‭ ‬and reinterpreted our data in this light. d‭) ‬The authors should use their genetic information to test if the fish chosen are siblings with each other‭ (‬full or half-sibs‭)‬.‭ ‬This may have important implications for the population genetic analyses.‭ Reply :‬The fish chosen for pop-gen work are random sample from spawning grounds‭ ‬-‭ ‬assumed to be not sibling groups.‭ ‬Our earlier study‭ (K‬apralova‭ ‬2011‭) ‬showed no family structure in charr collected this way from the lake. ‬e‭) ‬Page‭ ‬5‭ ‬-‭ ‬It is not appropriate to change the meaning of the word‭ ‘‬gene‭’‬.‭ ‬I think it is much clearer to use the term‭ ‘‬paralog group‭’ ‬or‭ ‘‬gene family‭’ ‬when referring to the fact that the authors do not study single genes,‭ ‬but instead groups of paralogs. ‬ Reply : ‬Excellent suggestion.‭ ‬We amended this., and use paralog group throughout. f‭) ‬Selection of genes for qPCR‭ – ‬the methods by which genes for the qPCR studies‭ (‬Fig‭ ‬3‭) ‬were selected should be clearly noted.‭ ‬From my reading,‭ ‬it seems that most of these genes do not significantly vary among SB and AC at the‭ ‬1%‭ ‬FDR level‭ (‬Tables‭ ‬1‭ ‬and‭ ‬2‭; ‬only Natterin‭?)‬.‭ ‬Thus,‭ ‬I am assuming these genes are only significant at the‭ ‬5%‭ ‬FDR level‭ (‬S1‭ ‬file‭) – ‬why focus upon these and not those significant at‭ ‬1%‭?‬ ‭ ‬As well,‭ ‬it would be good to include information on why different genes were selected for Figure‭ ‬3‭ (‬qPCR validation of whole fish‭) ‬and Figure‭ ‬4‭ (‬candidate genes-qPCR validation in just the head‭)‬.‭ ‬Finally,‭ ‬the abbreviations used for qPCR validation should also be listed in Table‭ ‬1‭ ‬for easy comparisons among figures/tables.‭ Reply : ‬Very important point.‭ ‬We deliberately studied some genes with less statistical support‭ (‬FDR between‭ ‬5%‭ ‬and‭ ‬10%‭)‬,‭ ‬to gauge the differences in the genes with less support and in particular to have a bigger pool of candidates that may relate to the specific developmental process‭ (‬like head and jaw formation‭)‬.‭ ‬Of course we can not assert that all the genes with strongest DE signal in the transcriptome are true positives,‭ ‬but the data can be used for hypothesis generation.‭ ‬We also amended table‭ ‬1‭ ‬and the figure legends accordingly. Results‭ & ‬Figures:‭ ‬ a‭) ‬Include an experimental design figure‭ ‬-‭ ‬At present,‭ ‬it is difficult to keep track of all of the morphotypes,‭ ‬tissues,‭ ‬and developmental time points used without referring to the methods.‭ ‬Thus,‭ ‬an experimental design figure summarizing the samples used‭ (‬morphotype,‭ ‬population,‭ ‬sample size,‭ ‬developmental time point‭)‬,‭ ‬how they were pooled and which techniques were used to measure gene expression on each sample‭ (‬RNA-seq and/or qPCR‭)‬ ‭ ‬is needed. b‭) ‬Include the LB and PL morphs in Figure‭ ‬1‭ ‬and clarify traits of interest‭ – ‬The legend states that‭ “‬differences in size,‭ ‬coloration and head morphology are apparent‭”‬,‭ ‬but it would be better to specifically point out the differences they are referring to.‭ ‬F1000‭ ‬is for a general audience,‭ ‬and this would help non-ichthylogists better understand what ecologically-important traits the authors are interested in‭ (‬e.g.‭ ‬those related to benthic/limnetic feeding‭)‬. ‭ ‬In addition,‭ ‬the two other morphs used in the qPCR studies should also be displayed‭ (‬large benthivorous and small planktivorous‭) ‬to facilitate phenotypic comparisons and assess parallelism in benthic/limnetic feeding and/or the effects of domestication on AC. Reply : (‬a and b‭) ‬Excellent suggestions.‭ ‬Now picture‭ ‬1‭ ‬has all‭ ‬4‭ ‬morphs,‭ ‬and a schematic describing the work flow and samples.‭ ‬c‭) ‬Figure‭ ‬5-‭ ‬this is actually a table not a figure‭ (?) ‬and is a bit confusing.‭ ‬I think it is much easier to interpret Figure S2‭ (‬displaying the data as in Fig‭ ‬3‭ ‬and‭ ‬4‭)‬,‭ ‬and that Fig‭ ‬5‭ ‬and S2‭ ‬should be switched.‭ ‬It would be great to show significant differences in mRNA content in this,‭ ‬and all other figures,‭ ‬by including symbols.‭ ‬Also,‭ ‬full gene names should be listed in all figure legends.‭ Reply : ‬We acknowledge that this graph is not the simplest,‭ ‬but would like to keep it over Figure S2.‭ ‬Our reasoning is that this graph illustrates the sharp differences between the limnetic‭ (‬AC-PL‭) ‬and benthic‭ (‬SB-LB‭)‬,‭ ‬which are the main result in this section.‭ ‬But we will of course switch them,‭ ‬or possibly join both in a single figure ‭?? ‬if the reviewer insists or the editors recommend it. Discussion:‭ ‬The discussion focuses on the SB morph‭ (‬page‭ ‬17‭ – “‬The objective of this study were to get a handle on genetic and molecular systems that associate with benthic morphology in charr by mainly focusing on the small benthic morph in Lake Thingvallavatn,‭ ‬Iceland‭”)‬,‭ ‬while the introduction discusses parallel evolution‭ (‬indicating that the comparisons should be among many morphs‭)‬.‭ ‬These are two different topics i‭) ‬mRNA content differences among benthic vs.‭ ‬limnetic morphs changing in parallel or ii‭) ‬linking mRNA content to phenotype in SB‭ (‬benthic,‭ ‬wild‭) ‬vs.‭ ‬AC‭ (‬limnetic head,‭ ‬domesticated‭) ‬morphs.‭ ‬In particular,‭ ‬the role of domestication vs.‭ ‬wild fish divergence needs to be addressed.‭ ‬At present these two topics/questions are mixed in the introduction/discussion and should be addressed separately.‭ Reply : ‬We tried to separate these two aims more clearly in the revised discussion.‭ ‬The strategy was to use the AC vs SB contrast for hypothesis generation,‭ ‬as the first aim is central to our program.‭ ‬We have now added sentences on the domestication in two parts of the discussion. ‬a‭) ‬Paragraph on Immune Defenses‭ ‬-‭ ‬Is immunity also expected to evolve in parallel in all benthic morphs‭? ‬Is this predicted to be unique to SB vs.‭ ‬AC‭? ‬Whatever the case,‭ ‬the parallelism‭ (‬or not‭) ‬in these genes should also be discussed,‭ ‬and whether this relates more to domestication in AC or differences between limnetic vs.‭ ‬benthic fish. ‭ ‬Much of the functional discussion can also be cut. Reply : ‬Good question,‭ ‬we assume it to be so,‭ ‬but that may be wrong.‭ ‬We moved the discussion towards this question and away from functional description.‭ ‬b‭) ‬Page‭ ‬18‭ – ‬The information about genes found to be differentially expressed among morphs in your prior work should also be in the introduction,‭ ‬as it is background work that explains why you took this transcriptomic approach.‭ ‬This can also be used to explain why you focused in on particular qPCR genes. Reply: ‬We added a sentence in the intro about the published papers,‭ ‬that this transcriptome made available.‭ ‬In those papers we focused on genes with putative craniofacial effects,‭ ‬though the focus in this study was broader.‭ ‬ c‭) ‬A discussion of domestication related differences vs.‭ ‬benthic/limnetic differences should be included.‭ ‬I think the data from head gene expression is very interesting‭ (‬Figs‭ ‬5,‭ ‬S2‭) ‬and really speaks to this question.‭ ‬d‭) ‬In general,‭ ‬the role of stochastic evolutionary processes,‭ ‬and not just selection‭ (‬artificial and natural‭) ‬should be noted.‭ ‬For example,‭ ‬if the AC charr were simply taken from a stock with a different mtDNA haplotype then these differences in the mtDNA genome might not be adaptive,‭ ‬just random. ‭ ‬If the AC fish has much higher mtDNA expression might this be simply a domestication issue and not indicative of selection in SB as stated‭?‬ ‭ ‬Finally,‭ ‬you find that not all mitochondrial transcripts‭ (‬which are transcribed as a polycistronic transcript‭) ‬are found at similar levels‭ (‬Table‭ ‬1‭) – ‬what does this tell you about differential degradation/post-transcriptional processes‭?‬ e‭) ‬There is no discussion about the‭ “‬Analyses of polymorphism in Arctic charr transcriptome‭” (‬Table‭ ‬3,‭ ‬4,‭ ‬5‭)‬,‭ ‬except for the mtDNA. Reply: (‬c,d,e‭) Excellent suggestions. ‬We added in the final discussion section few sentences on domesticated charr vs Benthic/limnetic.‭ ‬Unfortunately we do not have quantitative data on the phenotypes‭ (‬head shape,‭ ‬and jaw‭) ‬of the AC charr and acknowledge that we categorize it as limnetic based on general features.‭ We gladly added a sentence citing neutral forces,‭ ‬and are acutely aware that much of the divergence is likely due to history,‭ ‬drift etc.‭ ‬The domestication can certainly be the driver for the higher expression in AC‭ ‬-‭ ‬but we need transcriptomes from more populations/morphs to address that point.‭ ‬And yes,‭ ‬the variance in RNA levels from different parts of the mtDNA do indeed suggest differential half life of the various RNA species.‭ ‬Some are certainly degraded and others most probably actively utilized‭ ‬/‭ ‬protected.‭ ‬We decided not to follow that thought further though,‭ ‬as the MS already consists of quite a few threads already. We also added sentences on the genetic polymorphism,‭ ‬before focusing more on the mtDNA.‭ ‬The main reason we dont want to elaborate to much on the SNPs is that we feel these data are mainly for generating hypotheses,‭ ‬and that more work is needed to substantiate SNPs and study their distribution in other populations. ‭ ‬Minor Comments‭ ‬Introduction:‭ ‬ a‭) ‬Paragraph‭ ‬3‭ – “‬Furthermore,‭ ‬recent estimates from the rainbow trout‭…‬.by utilizing multiple data sources the genome assembly problem of this family can be solved‭”‬.‭ ‬I am not sure how this statement is relevant to this particular study.‭ ‬This and the following statement seem more appropriate for the methods/discussion to me. ‬ Reply: ‬We deleted this sentence and simplified the paragraph. ‬ b‭) ‬The morphs being discussed should be clarified throughout the paper.‭ ‬For example,‭ ‬the authors often state‭ “‬among morphs/among charr populations‭” ‬but it is not clear which of the many morphs they are referring to‭ (‬e.g.‭ ‬Paragraph‭ ‬5,‭ ‬first sentence on allozymes and mtDNA and later sentence on MCHIIa‭ – ‬do you mean all‭ ‬4‭ ‬morphs of specific‭ ‬2-way comparisons‭? ‬Are some morphs more differentiated than others‭?) Reply: ‬We tried to clarify this in various places in the manuscript,‭ ‬but in some cases we refer to morphs in general.‭ ‬Genetic separation can be estimated with Fst values either between pairs or over a larger set of groups‭ (‬populations,‭ ‬morphs‭)‬.‭ ‬In the intro we cite the work done to date in Iceland,‭ ‬which highlights the need for more pop.‭ ‬genetic analyses.‭ ‬ ‭ ‬ Methods:‭ ‬ a‭) ‬The authors should note why they did not use the PI‭ (‬large piscivorous‭) ‬morph in any qPCR studies‭ (‬in the methods or discussion‭) ‬as this would be a nice morph to use in their tests for parallelism.‭ Reply: The PI charr is very rare in the lake and hard to catch.‭ ‬We later captured few sexually mature individuals,‭ ‬and generated couple of families,‭ ‬that were used for one study‭ (‬Ahi et al‭ ‬Evodevo 2015‭)‬. ‬b‭) ‬Page‭ ‬5‭ (‬last paragraph‭) – ‬the methods used to remove particular variants needs to be clarified.‭ ‬In particular,‭ ‬why the assumptions used to remove variants are valid by referencing past studies. ‬ Reply: ‬Many of the principles are common to most pipelines for removing spurious variants.‭ ‬In addition we applied filters necessitated by the properties of our dataset‭ (‬pool of individuals‭)‬,‭ ‬the mapping to an outgroup and paralogs due to salmonid genome complexity. ‬ Figures‭ & ‬Results:‭ ‬ a‭) ‬Figure‭ ‬2.‭ ‬The key for Figure‭ ‬2‭ ‬should include a specific heading for morph and time-point with the abbreviations restated‭ [‬e.g.‭ ‬Timepoint:‭ ‬141‭ ‬dpf,‭ ‬Morph:‭ ‬Small Benthic‭ (‬SB‭)]‬. Reply: ‬Now fixed.‭ ‬ ‭ ‬ b‭) ‬Figure‭ ‬6‭ – ‬would be helpful to label the protein coding genes in this figure as well as the‭ ‬12s and‭ ‬16s RNAs.‭ ‬ Reply: ‬Now fixed.‭ ‬ ‭ ‬ c‭) ‬Figure‭ ‬7‭ – ‬It is not clear to me which variant is present in which morph.‭ ‬Adding the nucleotide to the x-axis‭ (‬i.e.‭ ‬frequency of m1829G for B‭) ‬would make this figure easier to quickly interpret.‭ ‬The‭ “‬A.charr_WT‭” ‬and‭ “‬A.charr_M‭” ‬should also be defined in the legend and it would be more appropriate to use scientific names for all species.‭ ‬ Reply: ‬Now fixed‭ ‬ Discussion:‭ ‬ a‭) ‬Discussion of reference‭ ‬32‭ – ‬The discussion of reference‭ ‬32‭ ‬is not put into the proper context.‭ ‬Figure‭ ‬6‭ ‬of this paper‭ (‬Berthelot‭ ‬et al.‭ ‬2014‭) ‬shows that there are many genes that have no correlation among expression patterns and/or differences in expression levels‭ (‬1573,‭ ‬1248,‭ ‬and‭ ‬1895‭=‬4716‭ ‬paralog pairs‭)‬,‭ ‬and that together these represent more than the‭ ‬1,407‭ ‬correlated/similar expression level paralogs.‭ ‬This section of the discussion needs to be modified.‭ Reply: ‬Really valuable point,‭ ‬that we are especially grateful for.‭ ‬That we have added this fact to the intro and altered our interpretations in the discussion. ‬ b‭) ‬The Norman‭ ‬et al.‭ (‬2014‭) ‬paper should be mentioned earlier‭ – ‬if this is available why was it not used for their analyses‭? ‬As well,‭ ‬the last sentence in this paragraph can be cut as it is evident. Reply: ‬The Norman papers are now presented more clearly in the intro.‭ ‬There are historical reasons for not including their data in our analyses,‭ ‬we had completed the analyses for this manuscript when they became available and have since then focused our data analyses efforts on another transcriptome generated in the lab‭ (‬with longer reads‭)‬.‭ c‭) ‬Page‭ ‬18‭ – “‬Our new data also demonstrate differences in craniofacial elements between AC-‭ ‬and SB-charr,‭ ‬along a limnetic vs.‭ ‬benthic axis79‭”‬.‭ ‬Are you referring to ref‭ ‬79‭ ‬or data from this study‭? ‬If you are referring to‭ ‬79,‭ ‬clarify and note what you found.‭ ‬This occurs a few times in the discussion‭ Reply: ‬Ref‭ ‬79‭ ‬is a related study that built in part on the data presented here.‭ ‬We have now rephrased this in the manuscript,‭ ‬hopefully to the better. Major Comments Introduction:‭ ‬Requires some reorganization,‭ ‬clarification of what phenotypes have evolved in parallel among morphs,‭ ‬and how the authors separate the effects of domestication‭ (‬SB vs.‭ ‬AC‭) ‬from benthic/limnetic evolution‭ (‬SB/LB vs.‭ ‬PL/AC‭)‬.‭ ‬a‭) ‬At present,‭ ‬the introduction focuses upon the utility of instances of parallel evolution to help us determine how repeatable evolutionary change may be.‭ ‬This is definitely true,‭ ‬and the repeated evolution of the dwarf,‭ ‬benthic morph‭ (‬SB‭; ‬the focus of the introduction/abstract/discussion‭) ‬in many lakes strongly argues that this phenotype has evolved via natural selection.‭ ‬However,‭ ‬it is not clear to me if true‭ ‘‬parallelism‭’ ‬seen among the SB‭ (‬small benthic‭) ‬and LB‭ (‬large benthivorous‭) ‬vs.‭ ‬AC‭ (‬Holar aquaculture‭) ‬and PL‭ (‬small planktivorous‭) ‬morphs because not enough information is provided for me to assess this.‭ ‬To support the argument for parallelism the specific traits that have evolved in parallel among morphs must be displayed and the evolutionary history of these morphs should be clarified‭ (‬e.g.‭ ‬in paragraph‭ ‬6‭ ‬and Figure‭ ‬1‭)‬.‭ ‬As well,‭ ‬any related non-parallelism in traits should also be discussed‭ (‬i.e.‭ ‬how are the domesticated AC and wild PL different‭?)‬.‭ ‬At present Figure‭ ‬1‭ ‬only shows the AC and SB morphs,‭ ‬and does not point out the specific traits they are interested in.‭ ‬This is critical background information for readers who are not familiar with this system. Reply: ‭ ‬These are excellent suggestions.‭ ‬At the end of the intro we stress the difference between the aims of our research program‭ (‬study the genetics of parallel evolution‭) ‬and the aims of this study‭ (‬get a handle on differences between sympatric morphs,‭ ‬with the AC as possible outgroup‭)‬.‭ ‬The morphs studied here do not represent parallel evolution of benthic phenotypes‭ (‬SB and LB are both from the same lake and appear to be closely related‭ ‬-‭ ‬Kapralova et al‭ ‬2011‭)‬.‭ ‬Analyses of that question requires further studies.‭ ‬This data can implicate genes that separate PL/AC and SB/LB and may be studied in such follow up analyses of more populations.‭ ‬We have updated figure‭ ‬1‭ ‬as advised‭ ‬-‭ ‬including the‭ ‬4‭ ‬morphs studied,‭ ‬expanded on the legend and also provide an overview of research approach‭ (‬part B‭)‬.‭ ‬ b‭) ‬The comparison of AC‭ (‬domestic,‭ ‬limnetic-like head‭) ‬vs.‭ ‬LB‭ (‬wild,‭ ‬benthic like head‭) ‬looks at two confounded variables:‭ ‬domestication and the benthic/limnetic morphology.‭ ‬This should be clearly stated in the introduction,‭ ‬and the use of the additional morphs‭ (‬PL,‭ ‬LB‭) ‬in detangling domestication vs.‭ ‬benthic/limnetic evolution should be noted.‭ ‬c‭) ‬The use of the AC morph is still a bit unclear to me.‭ ‬The argument for point‭ ‘‬ii‭) ‬of the availability of abundant AC material‭’ ‬could be expanded by providing more information on the‭ ‘‬limnetic‭’ ‬like features of this morph and why it is an appropriate comparison to a benthic morph,‭ ‬the genetic divergence from the lake Thingvallavatn fish,‭ ‬and also the selection regime it has experienced‭ (‬selection for limnetic features‭? ‬What other traits vary with domestication‭?)‬.‭ Reply (‬b and c‭)‬:‭ ‬The reviewer is correct,‭ ‬AC and SB are separated by multiple traits,‭ ‬and the data probably reveal signals associating with most of them.‭ ‬Unfortunately the AC charr is not well characterized phenotypically,‭ ‬thus we can not address the question of other traits.‭ ‬We focus mainly on the head and jaw morphology,‭ ‬as these attributes distinguish benthic and limnetic morphs.‭ T‬he revised intro elaborates on the choice of AC,‭ ‬and how the follow up work on the morphs from Lake Thingvallavatn can help us sort this out.‭ ‬This point is also picked up in the discussion. d‭) ‬Paragraph‭ ‬2‭ – ‬Much of this paragraph,‭ ‬including discussing the ability to measure gene expression and relate to phenotype in fishes,‭ ‬is unnecessary as fish are no different from other vertebrates in this respect.‭ ‬Instead,‭ ‬the final sentence‭ “‬One approach to identify pathways related to function or morphological differences is to study gene expression during development‭” ‬should become the‭ ‘‬topic sentence‭’ ‬and expanded upon to explain why gene expression studies are especially relevant ways to link genotype to phenotype in evo-devo studies.‭ Reply : ‬We restructured and shortened this paragraph around this topic sentence‭ ‬-‭ ‬and gave more room for the previous RNAseq study on Arctic charr. ‬ e‭) ‬Better highlight the strengths‭ – ‬The authors have done a wonderful job of assessing multiple developmental time points and rearing fish in a common garden environment.‭ ‬However,‭ ‬they do not highlight these strengths.‭ ‬Some small notes on the importance of controlling for phenotypic plasticity in these traits‭ (‬which are known to be quite plastic‭) ‬to better study genetic differentiation would be a nice addition. Reply : ‬Great advice,‭ ‬we tried to integrate this into the last paragraph of the intro.‭ ‬ Methods:‭ ‬a‭) ‬Page‭ ‬4‭ ‬paragraph‭ ‬1‭ ‬-‭ ‬Clarify the number of fish used to make the crosses‭ (‬this will help us determine the likelihood of selecting a full or half-sib for sequencing/qPCR‭)‬. Reply : ‬We did bulk crosses,‭ ‬joining eggs from‭ ‬5-10‭ ‬females in a can and sperm from‭ ‬3-5‭ ‬males‭ (‬SB,‭ ‬PL,‭ ‬LB‭) ‬and single parent cross for AC.‭ ‬Each sample included RNA pooled from‭ ‬3‭ ‬embryos,‭ ‬so there is a chance that full sibs were sequenced,‭ ‬but unlikely.‭ ‬The embryos/samples for qPCR are from similar pools. Now described better in methods.‭ ‬ b‭) ‬I should note that I am not an expert in the analysis of RNA-seq data,‭ ‬but luckily the first reviewer has done an excellent job of commenting upon these aspects of the project.‭ ‬I fully agree with their comments and suggestions.‭ ‬I would also like to see more information on the methods used to pool samples and how RNA-seq data was normalized among samples,‭ ‬developmental times and morphs.‭ ‬I will also note that the authors often use‭ ‬S.salar for comparisions,‭ ‬not‭ ‬O.mykiss,‭ ‬which is a closer relative to S.alpinus.‭ ‬The reasons for this approach should be discussed.‭ Reply : ‬The RNA was isolated from individual embryos,‭ ‬quantified and then united‭ (‬in equal concentrations‭) ‬prior to cDNA synthesis.‭ The read counts per gene are normalized per million reads in sample. Not normalized with other variables. ‬ c‭) ‬ I am also not trained as a population geneticist.‭ ‬However,‭ ‬from my experience studying paralogous genes in salmonids,‭ ‬and with respect to the author’s own findings for the Nattl paralogs‭ (‬Fig‭ ‬4‭)‬,‭ ‬I do not think it is prudent to‭ “‬assume that the expression of paralogous genes is stable‭… ” ‬in the methods‭ (‬page‭ ‬12‭)‬. ‭ ‬In fact,‭ ‬Berthelot‭ ‬et al.‭ (‬2014‭) ‬find the opposite‭ (‬see my comments for the discussion‭)‬.‭ ‬ Reply :‭ ‬Excellent suggestion.‭ ‬We corrected our misunderstanding,‭ ‬added this fact into the intro and discussion,‭ ‬and reinterpreted our data in this light. d‭) ‬The authors should use their genetic information to test if the fish chosen are siblings with each other‭ (‬full or half-sibs‭)‬.‭ ‬This may have important implications for the population genetic analyses.‭ Reply :‬The fish chosen for pop-gen work are random sample from spawning grounds‭ ‬-‭ ‬assumed to be not sibling groups.‭ ‬Our earlier study‭ (K‬apralova‭ ‬2011‭) ‬showed no family structure in charr collected this way from the lake. ‬e‭) ‬Page‭ ‬5‭ ‬-‭ ‬It is not appropriate to change the meaning of the word‭ ‘‬gene‭’‬.‭ ‬I think it is much clearer to use the term‭ ‘‬paralog group‭’ ‬or‭ ‘‬gene family‭’ ‬when referring to the fact that the authors do not study single genes,‭ ‬but instead groups of paralogs. ‬ Reply : ‬Excellent suggestion.‭ ‬We amended this., and use paralog group throughout. f‭) ‬Selection of genes for qPCR‭ – ‬the methods by which genes for the qPCR studies‭ (‬Fig‭ ‬3‭) ‬were selected should be clearly noted.‭ ‬From my reading,‭ ‬it seems that most of these genes do not significantly vary among SB and AC at the‭ ‬1%‭ ‬FDR level‭ (‬Tables‭ ‬1‭ ‬and‭ ‬2‭; ‬only Natterin‭?)‬.‭ ‬Thus,‭ ‬I am assuming these genes are only significant at the‭ ‬5%‭ ‬FDR level‭ (‬S1‭ ‬file‭) – ‬why focus upon these and not those significant at‭ ‬1%‭?‬ ‭ ‬As well,‭ ‬it would be good to include information on why different genes were selected for Figure‭ ‬3‭ (‬qPCR validation of whole fish‭) ‬and Figure‭ ‬4‭ (‬candidate genes-qPCR validation in just the head‭)‬.‭ ‬Finally,‭ ‬the abbreviations used for qPCR validation should also be listed in Table‭ ‬1‭ ‬for easy comparisons among figures/tables.‭ Reply : ‬Very important point.‭ ‬We deliberately studied some genes with less statistical support‭ (‬FDR between‭ ‬5%‭ ‬and‭ ‬10%‭)‬,‭ ‬to gauge the differences in the genes with less support and in particular to have a bigger pool of candidates that may relate to the specific developmental process‭ (‬like head and jaw formation‭)‬.‭ ‬Of course we can not assert that all the genes with strongest DE signal in the transcriptome are true positives,‭ ‬but the data can be used for hypothesis generation.‭ ‬We also amended table‭ ‬1‭ ‬and the figure legends accordingly. Results‭ & ‬Figures:‭ ‬ a‭) ‬Include an experimental design figure‭ ‬-‭ ‬At present,‭ ‬it is difficult to keep track of all of the morphotypes,‭ ‬tissues,‭ ‬and developmental time points used without referring to the methods.‭ ‬Thus,‭ ‬an experimental design figure summarizing the samples used‭ (‬morphotype,‭ ‬population,‭ ‬sample size,‭ ‬developmental time point‭)‬,‭ ‬how they were pooled and which techniques were used to measure gene expression on each sample‭ (‬RNA-seq and/or qPCR‭)‬ ‭ ‬is needed. b‭) ‬Include the LB and PL morphs in Figure‭ ‬1‭ ‬and clarify traits of interest‭ – ‬The legend states that‭ “‬differences in size,‭ ‬coloration and head morphology are apparent‭”‬,‭ ‬but it would be better to specifically point out the differences they are referring to.‭ ‬F1000‭ ‬is for a general audience,‭ ‬and this would help non-ichthylogists better understand what ecologically-important traits the authors are interested in‭ (‬e.g.‭ ‬those related to benthic/limnetic feeding‭)‬. ‭ ‬In addition,‭ ‬the two other morphs used in the qPCR studies should also be displayed‭ (‬large benthivorous and small planktivorous‭) ‬to facilitate phenotypic comparisons and assess parallelism in benthic/limnetic feeding and/or the effects of domestication on AC. Reply : (‬a and b‭) ‬Excellent suggestions.‭ ‬Now picture‭ ‬1‭ ‬has all‭ ‬4‭ ‬morphs,‭ ‬and a schematic describing the work flow and samples.‭ ‬c‭) ‬Figure‭ ‬5-‭ ‬this is actually a table not a figure‭ (?) ‬and is a bit confusing.‭ ‬I think it is much easier to interpret Figure S2‭ (‬displaying the data as in Fig‭ ‬3‭ ‬and‭ ‬4‭)‬,‭ ‬and that Fig‭ ‬5‭ ‬and S2‭ ‬should be switched.‭ ‬It would be great to show significant differences in mRNA content in this,‭ ‬and all other figures,‭ ‬by including symbols.‭ ‬Also,‭ ‬full gene names should be listed in all figure legends.‭ Reply : ‬We acknowledge that this graph is not the simplest,‭ ‬but would like to keep it over Figure S2.‭ ‬Our reasoning is that this graph illustrates the sharp differences between the limnetic‭ (‬AC-PL‭) ‬and benthic‭ (‬SB-LB‭)‬,‭ ‬which are the main result in this section.‭ ‬But we will of course switch them,‭ ‬or possibly join both in a single figure ‭?? ‬if the reviewer insists or the editors recommend it. Discussion:‭ ‬The discussion focuses on the SB morph‭ (‬page‭ ‬17‭ – “‬The objective of this study were to get a handle on genetic and molecular systems that associate with benthic morphology in charr by mainly focusing on the small benthic morph in Lake Thingvallavatn,‭ ‬Iceland‭”)‬,‭ ‬while the introduction discusses parallel evolution‭ (‬indicating that the comparisons should be among many morphs‭)‬.‭ ‬These are two different topics i‭) ‬mRNA content differences among benthic vs.‭ ‬limnetic morphs changing in parallel or ii‭) ‬linking mRNA content to phenotype in SB‭ (‬benthic,‭ ‬wild‭) ‬vs.‭ ‬AC‭ (‬limnetic head,‭ ‬domesticated‭) ‬morphs.‭ ‬In particular,‭ ‬the role of domestication vs.‭ ‬wild fish divergence needs to be addressed.‭ ‬At present these two topics/questions are mixed in the introduction/discussion and should be addressed separately.‭ Reply : ‬We tried to separate these two aims more clearly in the revised discussion.‭ ‬The strategy was to use the AC vs SB contrast for hypothesis generation,‭ ‬as the first aim is central to our program.‭ ‬We have now added sentences on the domestication in two parts of the discussion. ‬a‭) ‬Paragraph on Immune Defenses‭ ‬-‭ ‬Is immunity also expected to evolve in parallel in all benthic morphs‭? ‬Is this predicted to be unique to SB vs.‭ ‬AC‭? ‬Whatever the case,‭ ‬the parallelism‭ (‬or not‭) ‬in these genes should also be discussed,‭ ‬and whether this relates more to domestication in AC or differences between limnetic vs.‭ ‬benthic fish. ‭ ‬Much of the functional discussion can also be cut. Reply : ‬Good question,‭ ‬we assume it to be so,‭ ‬but that may be wrong.‭ ‬We moved the discussion towards this question and away from functional description.‭ ‬b‭) ‬Page‭ ‬18‭ – ‬The information about genes found to be differentially expressed among morphs in your prior work should also be in the introduction,‭ ‬as it is background work that explains why you took this transcriptomic approach.‭ ‬This can also be used to explain why you focused in on particular qPCR genes. Reply: ‬We added a sentence in the intro about the published papers,‭ ‬that this transcriptome made available.‭ ‬In those papers we focused on genes with putative craniofacial effects,‭ ‬though the focus in this study was broader.‭ ‬ c‭) ‬A discussion of domestication related differences vs.‭ ‬benthic/limnetic differences should be included.‭ ‬I think the data from head gene expression is very interesting‭ (‬Figs‭ ‬5,‭ ‬S2‭) ‬and really speaks to this question.‭ ‬d‭) ‬In general,‭ ‬the role of stochastic evolutionary processes,‭ ‬and not just selection‭ (‬artificial and natural‭) ‬should be noted.‭ ‬For example,‭ ‬if the AC charr were simply taken from a stock with a different mtDNA haplotype then these differences in the mtDNA genome might not be adaptive,‭ ‬just random. ‭ ‬If the AC fish has much higher mtDNA expression might this be simply a domestication issue and not indicative of selection in SB as stated‭?‬ ‭ ‬Finally,‭ ‬you find that not all mitochondrial transcripts‭ (‬which are transcribed as a polycistronic transcript‭) ‬are found at similar levels‭ (‬Table‭ ‬1‭) – ‬what does this tell you about differential degradation/post-transcriptional processes‭?‬ e‭) ‬There is no discussion about the‭ “‬Analyses of polymorphism in Arctic charr transcriptome‭” (‬Table‭ ‬3,‭ ‬4,‭ ‬5‭)‬,‭ ‬except for the mtDNA. Reply: (‬c,d,e‭) Excellent suggestions. ‬We added in the final discussion section few sentences on domesticated charr vs Benthic/limnetic.‭ ‬Unfortunately we do not have quantitative data on the phenotypes‭ (‬head shape,‭ ‬and jaw‭) ‬of the AC charr and acknowledge that we categorize it as limnetic based on general features.‭ We gladly added a sentence citing neutral forces,‭ ‬and are acutely aware that much of the divergence is likely due to history,‭ ‬drift etc.‭ ‬The domestication can certainly be the driver for the higher expression in AC‭ ‬-‭ ‬but we need transcriptomes from more populations/morphs to address that point.‭ ‬And yes,‭ ‬the variance in RNA levels from different parts of the mtDNA do indeed suggest differential half life of the various RNA species.‭ ‬Some are certainly degraded and others most probably actively utilized‭ ‬/‭ ‬protected.‭ ‬We decided not to follow that thought further though,‭ ‬as the MS already consists of quite a few threads already. We also added sentences on the genetic polymorphism,‭ ‬before focusing more on the mtDNA.‭ ‬The main reason we dont want to elaborate to much on the SNPs is that we feel these data are mainly for generating hypotheses,‭ ‬and that more work is needed to substantiate SNPs and study their distribution in other populations. ‭ ‬Minor Comments‭ ‬Introduction:‭ ‬ a‭) ‬Paragraph‭ ‬3‭ – “‬Furthermore,‭ ‬recent estimates from the rainbow trout‭…‬.by utilizing multiple data sources the genome assembly problem of this family can be solved‭”‬.‭ ‬I am not sure how this statement is relevant to this particular study.‭ ‬This and the following statement seem more appropriate for the methods/discussion to me. ‬ Reply: ‬We deleted this sentence and simplified the paragraph. ‬ b‭) ‬The morphs being discussed should be clarified throughout the paper.‭ ‬For example,‭ ‬the authors often state‭ “‬among morphs/among charr populations‭” ‬but it is not clear which of the many morphs they are referring to‭ (‬e.g.‭ ‬Paragraph‭ ‬5,‭ ‬first sentence on allozymes and mtDNA and later sentence on MCHIIa‭ – ‬do you mean all‭ ‬4‭ ‬morphs of specific‭ ‬2-way comparisons‭? ‬Are some morphs more differentiated than others‭?) Reply: ‬We tried to clarify this in various places in the manuscript,‭ ‬but in some cases we refer to morphs in general.‭ ‬Genetic separation can be estimated with Fst values either between pairs or over a larger set of groups‭ (‬populations,‭ ‬morphs‭)‬.‭ ‬In the intro we cite the work done to date in Iceland,‭ ‬which highlights the need for more pop.‭ ‬genetic analyses.‭ ‬ ‭ ‬ Methods:‭ ‬ a‭) ‬The authors should note why they did not use the PI‭ (‬large piscivorous‭) ‬morph in any qPCR studies‭ (‬in the methods or discussion‭) ‬as this would be a nice morph to use in their tests for parallelism.‭ Reply: The PI charr is very rare in the lake and hard to catch.‭ ‬We later captured few sexually mature individuals,‭ ‬and generated couple of families,‭ ‬that were used for one study‭ (‬Ahi et al‭ ‬Evodevo 2015‭)‬. ‬b‭) ‬Page‭ ‬5‭ (‬last paragraph‭) – ‬the methods used to remove particular variants needs to be clarified.‭ ‬In particular,‭ ‬why the assumptions used to remove variants are valid by referencing past studies. ‬ Reply: ‬Many of the principles are common to most pipelines for removing spurious variants.‭ ‬In addition we applied filters necessitated by the properties of our dataset‭ (‬pool of individuals‭)‬,‭ ‬the mapping to an outgroup and paralogs due to salmonid genome complexity. ‬ Figures‭ & ‬Results:‭ ‬ a‭) ‬Figure‭ ‬2.‭ ‬The key for Figure‭ ‬2‭ ‬should include a specific heading for morph and time-point with the abbreviations restated‭ [‬e.g.‭ ‬Timepoint:‭ ‬141‭ ‬dpf,‭ ‬Morph:‭ ‬Small Benthic‭ (‬SB‭)]‬. Reply: ‬Now fixed.‭ ‬ ‭ ‬ b‭) ‬Figure‭ ‬6‭ – ‬would be helpful to label the protein coding genes in this figure as well as the‭ ‬12s and‭ ‬16s RNAs.‭ ‬ Reply: ‬Now fixed.‭ ‬ ‭ ‬ c‭) ‬Figure‭ ‬7‭ – ‬It is not clear to me which variant is present in which morph.‭ ‬Adding the nucleotide to the x-axis‭ (‬i.e.‭ ‬frequency of m1829G for B‭) ‬would make this figure easier to quickly interpret.‭ ‬The‭ “‬A.charr_WT‭” ‬and‭ “‬A.charr_M‭” ‬should also be defined in the legend and it would be more appropriate to use scientific names for all species.‭ ‬ Reply: ‬Now fixed‭ ‬ Discussion:‭ ‬ a‭) ‬Discussion of reference‭ ‬32‭ – ‬The discussion of reference‭ ‬32‭ ‬is not put into the proper context.‭ ‬Figure‭ ‬6‭ ‬of this paper‭ (‬Berthelot‭ ‬et al.‭ ‬2014‭) ‬shows that there are many genes that have no correlation among expression patterns and/or differences in expression levels‭ (‬1573,‭ ‬1248,‭ ‬and‭ ‬1895‭=‬4716‭ ‬paralog pairs‭)‬,‭ ‬and that together these represent more than the‭ ‬1,407‭ ‬correlated/similar expression level paralogs.‭ ‬This section of the discussion needs to be modified.‭ Reply: ‬Really valuable point,‭ ‬that we are especially grateful for.‭ ‬That we have added this fact to the intro and altered our interpretations in the discussion. ‬ b‭) ‬The Norman‭ ‬et al.‭ (‬2014‭) ‬paper should be mentioned earlier‭ – ‬if this is available why was it not used for their analyses‭? ‬As well,‭ ‬the last sentence in this paragraph can be cut as it is evident. Reply: ‬The Norman papers are now presented more clearly in the intro.‭ ‬There are historical reasons for not including their data in our analyses,‭ ‬we had completed the analyses for this manuscript when they became available and have since then focused our data analyses efforts on another transcriptome generated in the lab‭ (‬with longer reads‭)‬.‭ c‭) ‬Page‭ ‬18‭ – “‬Our new data also demonstrate differences in craniofacial elements between AC-‭ ‬and SB-charr,‭ ‬along a limnetic vs.‭ ‬benthic axis79‭”‬.‭ ‬Are you referring to ref‭ ‬79‭ ‬or data from this study‭? ‬If you are referring to‭ ‬79,‭ ‬clarify and note what you found.‭ ‬This occurs a few times in the discussion‭ Reply: ‬Ref‭ ‬79‭ ‬is a related study that built in part on the data presented here.‭ ‬We have now rephrased this in the manuscript,‭ ‬hopefully to the better. Competing Interests: No competing interests were disclosed.No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Macqueen D. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.6869.r8970 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v1#referee-response-8970 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Jul 2015 Daniel Macqueen , Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.6869.r8970 Review of Gudbrandsson et al . “The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs”. The work is founded on the solid premise that rapidly evolving phenotypes in nature can be underpinned by changes at the transcriptome level. The model system ... Continue reading READ ALL Review of Gudbrandsson et al . “The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs”. The work is founded on the solid premise that rapidly evolving phenotypes in nature can be underpinned by changes at the transcriptome level. The model system here is Arctic charr populations that have evolved (since the last ice age) major differences in phenotypes along the ‘benthic’ - ‘limnetic’ axis, with strong differences in head morphology linked to feeding specializations. The work provides an extensive analysis of transcriptome and genetic differences between different morphs and populations. It is interesting, generally well-written and has merit on many levels. It is also rather hard going, since so much ground is covered on diverse areas. The study also comes with a large number of caveats, of which the authors are undoubtedly aware. Overall though, I am supportive of this work, as it represents one of the most detailed analyses of molecular mechanisms linked to rapid phenotypic evolution in Arctic charr. I see it as a great start point for future work and a source of several new findings and hypotheses. I suggest that the paper be indexed in F1000 Research as long as its caveats are transparent and the authors address my comments. I list below a number of suggestions that may help the authors improve the work, or that at least highlight study limitations for the benefit of interested readers. I also provide a number of minor comments and suggestions, which should help improve the manuscript more incrementally. Main comments & caveats RNAseq study design. I sympathize with the fact that the authors are trying to publish Illumina data that was generated in 2009, since (obviously) the technology has moved on greatly in the last 6 years, while its costs have been reduced dramatically. Adding to this is the fact that the authors are using a particularly complex transcriptome in terms of high content of similar paralogues (and expressed transposable elements), without a reference sequence for mapping in their species. I accept the author’s argument that it is more sensible to map against a closely related species with the sequence data rather than to try and create a de novo assembly from 36bp reads. I also believe it is sensible to pool read counts for putative paralogous contigs in this study, since the short read length ablates any ability to separate paralogous differences in expression (yet does not preclude the generation of useful hypotheses about putative gene expression differences among morphs). However, I do question whether the use of Atlantic salmon EST contigs is the best approach here. Firstly, reference assemblies for both Atlantic salmon and rainbow trout are now available, which distinguish paralogous variation. More importantly, using these reference genome data would provide certainty that reads are being mapped to exons from single genes, whereas many of the ESTs will provide a fragmented representation of exon sequences, presumably relying on annotation to piece them back into ‘genes’ post hoc . In addition, paired 100bp Ilumina reads are available at high coverage for Arctic charr (e.g. Norman et al . 2014), which could also be used to generate a specific reference transcriptome to map against in this study, although this might be underrepresented in terms of developmental genes as it is a gill study. Overall, I do wonder how much more information might have been gleaned from this dataset with a different mapping strategy? With all the above said, I understand that the authors have built up a large study based around the original mapping to the salmon ESTs and that it would not be routine for them to repeat the study using better reference data. Furthermore, the approach used has definitely led to the generation of several valid hypotheses concerning the nature of gene expression and genetic differences among charr morphs, which have been followed up using independent approaches. Methods “Biological Replication in RNAseq” – a general comment: obviously the design of the study is not optimal because biological variation within developmental stages is not considered in the statistics. Thus, the approach lacks power to detect differences when morph variation is restricted to different developmental stages. I wanted to explain my opinion (for the record) that the study design is nonetheless useful for identifying constitutive differences between morphs. This is especially true because gene expression variability is likely to be relatively low in embryonic stages (compared to a similar study design in adults at least). Further, the pooling of individuals will have helped to at least recapture some biological variation at different stages. Thus, as mentioned above, I see the author’s use of RNAseq as a hypothesis-generating approach, which has been quite fruitful in identifying putative differences between different morphs. Methods “QPCR study design”. The authors adhere to the MIQE guidelines, but do not always follow the best approaches. Most pertinently, the authors use the 2 −∆∆Ct method (assuming PCR efficiency of 2.0) despite having gone to the effort of gaining and reporting efficiencies for each assay, which can be as low as 1.72 for some genes. The effect of failing to incorporate differences in efficiency are highly established and this is likely to have affected the author’s results. The authors should consider incorporating the effect of differences in efficiency into their analyses. This is likely to have some impact on the study conclusions in my opinion. Methods “ Polymorphisms in charr transcriptome”. While this is not exactly my area of expertise, I struggled to understand the methods behind filtering paralogous variants from SNPs in the data. The authors state “ As the SNP analysis was done on individual contigs, differences among paralogs appear in the data. However, since each sample is a pool of few individuals, it is very unlikely that we have the same frequency of true SNPs in the samples. This property was used to remove variants that are most likely due to expressed paralogs ”. Can the authors please try to re-explain this in even simpler terms to help me get it? I don’t see how this description leads to a robust identification of paralogous variation. Is there an underlying assumption of equal expression among paralogues? If so, this is likely to be routinely invalidated. Methods “ Verification of candidate SNPs”. While it is good that the authors have attempted to verify SNPs identified from their RNAseq data, I don’t believe the data is particularly well incorporated in the results section. It needs to be stated up front the extent to which the SNPs predicted from the RNAseq were independently verified. Also, the methods for this section can be improved, especially “ we conducted genomic comparisons of the Salmon genome, ESTs and short contigs from the preliminary assembly of the Arctic charr transcriptome ”. None of this information is elaborated on – what is the preliminary assembly of the Arctic charr transcriptome? Which version of the salmon genome was used and how? Moreover, it would be useful to actually explain in the methods that the genotyping was done on a small number of SB, PL and PI morphs, rather than relying on the reader to extract all the required information from Table S2. I guess overall, the way this section is incorporated into the manuscript needs some thought in terms of improving the reader’s experience. I struggled after reading it several times and am still not sure I have all the information I need. Results . “ Analyses of those reads require an Arctic charr genome sequence or transcriptome assembly from longer and paired end reads .” As mentioned already, the latter is available to generate an Arctic charr transcriptome assembly to map against. Results ; Figure 3 and 4. The authors found that around half the genes studied were not differentially expressed among morphs by qPCR. Obviously this is quite a large number, but on closer inspection, I noticed that Ndub6 , Ubl5 and parp6 were not even differentially expressed according to RNAseq. Thus, I am confused at the selection of genes from the RNAseq analysis for verification by qPCR. The authors should explain this selection more transparently and provide clearer indices of the correlation between RNAseq and qPCR results and associated discussion. Minor comments, typos and suggested changes Abstract: “ Species and populations with parallel evolution of specific traits can help illuminate how predictable adaptations and divergence are at the molecular and developmental level . Grammatically – his reads better: “ ….. can help illuminate the predictability of adaptations and divergence at the molecular and developmental level” Introduction: “ Examples of such a species complex are the finches of the Galapagos islands, cichlids in the African great lakes are exciting multi-species systems in this respect” . Grammatically – reads better: “ Examples of such species complexes are provided by finches of the Galapagos islands, while cichlids of the African great lakes also provide an exciting multi-species system in the same respect” Introduction: “ Some northern freshwater fish species exhibit frequent parallelism in trophic structures and life history and in several cases are they found as distinct resource morphs ” change to “ …. are found as distinct resource morphs ” Introduction: “ in the development of ecological differences in tropic morphology ” change to “… trophic morphology ”. Introduction: “ The family is estimated to be between 63.2 and 58.1 million years old ”. This information is not correct – it is correct to state that the age of the salmonid crown (based on the cited paper; different estimates exist in the literature, e.g. Macqueen and Johnston, 2014; Campbell et al . 2013 ) is estimated at 63.2 and 58.1 million years old, but the family dates back much further – to the origin of the WGD event in fact, which occurred more like 88-103 Ma (Macqueen and Johnston, 2014; Berthelot et al . 2014). Thus, the last common ancestor to extant salmonid species is what the authors are actually referring to in this sentence. Introduction: “ Furthermore, for data with short reads, mapping to a related reference genome/transcriptome is recommended over de novo assembly ”. While this sentence is technically correct in the context of the work cited, I feel it is being used slightly out of context. For a start, what comprises a ‘short read’ is undefined. 36bp is short, but it is possible to get a sold reference transcriptome using 2*100bp, assuming the appropriate diversity of transcripts is represented and suitable depth is attained. Introduction: “ nuclear genes, reveled both subtle ” change to “ nuclear genes, revealed both subtle ” Minor comment – AC, PL, LB and SB were already defined in introduction. Methods: “ Fishing in Lake Thingvallavatn was with permissions ” changed to “ Fishing in Lake Thingvallavatn was done with permissions ”. Methods: “ of differently expressed genes, we preformed clustering analyses ” change to “ …we performed clustering analyses ” Results: “ The most drastic changes were seen in processes related to glycolysis (GO:0006096, FDR = 0.0009), were the expression of 19 out of 25 genes ” change to “…. where the expression ”. Figure 7. What does the charr_WT vs. charr_M signify in the alignment data? Discussion “ We are interested in how predictable evolution is a the molecular level and if there certain principles influence the rewiring of developmental and regulatory systems during evolution ” consider changing to “ We are interested in the predictability of evolution at the molecular level, especially whether there exist principles that influence the rewiring of developmental and regulatory systems” . Discussion. “ Recent rainbow trout data shows most paralogs from the latest whole genome duplication event retain the same expression pattern 32 indicating that this scenario is probably uncommon; hence it is of considerable interest when two paralogs show distinct expression patterns”. I do not agree that it is of considerable interest when two paralogs show distinct expression patterns – I could list tens of examples for salmonids. Conclusions “ The results suggest genetic and expression changes in multiple systems relate to divergence among populations .” Change to “ … associated with divergence among populations .” Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Macqueen D. Reviewer Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.6869.r8970 ) The direct URL for this report is: https://f1000research.com/articles/4-136/v1#referee-response-8970 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 25 Apr 2016 Arnar Palsson , Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 25 Apr 2016 Author Response Main comments‭ & ‬caveats RNAseq study design.‭ ‬ I sympathize with the fact that the authors are trying to publish Illumina data that was generated in‭ ‬2009,‭ ‬since‭ (‬obviously‭) ‬the technology ... Continue reading Main comments‭ & ‬caveats RNAseq study design.‭ ‬ I sympathize with the fact that the authors are trying to publish Illumina data that was generated in‭ ‬2009,‭ ‬since‭ (‬obviously‭) ‬the technology has moved on greatly in the last‭ ‬6‭ ‬years,‭ ‬while its costs have been reduced dramatically.‭ ‬Adding to this is the fact that the authors are using a particularly complex transcriptome in terms of high content of similar paralogues‭ (‬and expressed transposable elements‭)‬,‭ ‬without a reference sequence for mapping in their species.‭ ‬I accept the author’s argument that it is more sensible to map against a closely related species with the sequence data rather than to try and create a‭ ‬de novo‭ ‬assembly from‭ ‬36bp reads.‭ ‬I also believe it is sensible to pool read counts for putative paralogous contigs in this study,‭ ‬since the short read length ablates any ability to separate paralogous differences in expression‭ (‬yet does not preclude the generation of useful hypotheses about putative gene expression differences among morphs‭)‬.‭ ‬However,‭ ‬I do question whether the use of Atlantic salmon EST contigs is the best approach here.‭ ‬Firstly,‭ ‬reference assemblies for both Atlantic salmon and rainbow trout are now available,‭ ‬which distinguish paralogous variation.‭ ‬More importantly,‭ ‬using these reference genome data would provide certainty that reads are being mapped to exons from single genes,‭ ‬whereas many of the ESTs will provide a fragmented representation of exon sequences,‭ ‬presumably relying on annotation to piece them back into‭ ‘‬genes‭’ ‬post hoc‭ ‬.‭ ‬In addition,‭ ‬paired‭ ‬100bp Ilumina reads are available at high coverage for Arctic charr‭ (‬e.g.‭ ‬Norman‭ ‬et al.‭ ‬2014‭)‬,‭ ‬which could also be used to generate a specific reference transcriptome to map against in this study,‭ ‬although this might be underrepresented in terms of developmental genes as it is a gill study.‭ ‬Overall,‭ ‬I do wonder how much more information might have been gleaned from this dataset with a different mapping strategy‭? With all the above said,‭ ‬I understand that the authors have built up a large study based around the original mapping to the salmon ESTs and that it would not be routine for them to repeat the study using better reference data.‭ ‬Furthermore,‭ ‬the approach used has definitely led to the generation of several valid hypotheses concerning the nature of gene expression and genetic differences among charr morphs,‭ ‬which have been followed up using independent approaches.‭ ‬ Reply :‭ ‬We thank the reviewer for excellent diagnosis and suggestions.‭ ‬The paper describes the‭ (‬in our humble opinion‭) ‬most sensible summary of the data,‭ ‬as the writing of the paper started‭ ‬2‭ ‬years ago.‭ ‬We did map on the‭ ‬O.mykiss‭ ‬cDNA collection also,‭ ‬got similar results,‭ ‬but opted for reporting on the salmon data to avoid further extending an already long manuscript.‭ ‬We are currently analyzing DE and SNPs on a new assembly‭ (‬100‭ ‬bp PE reads‭ ‬-‭ ‬48‭ ‬samples‭ ‬-‭ ‬3‭ ‬morphs‭ ‬-‭ ‬development‭)‬,‭ ‬and may include a remapping of this dataset in that.‭ ‭ ‬ Methods‭ “‬Biological Replication in RNAseq‭” – ‬a general comment:‭ ‬obviously the design of the study is not optimal because biological variation within developmental stages is not considered in the statistics.‭ ‬Thus,‭ ‬the approach lacks power to detect differences when morph variation is restricted to different developmental stages.‭ ‬I wanted to explain my opinion‭ (‬for the record‭) ‬that the study design is nonetheless useful for identifying constitutive differences between morphs.‭ ‬This is especially true because gene expression variability is likely to be relatively low in embryonic stages‭ (‬compared to a similar study design in adults at least‭)‬.‭ ‬Further,‭ ‬the pooling of individuals will have helped to at least recapture some biological variation at different stages.‭ ‬Thus,‭ ‬as mentioned above,‭ ‬I see the author’s use of RNAseq as a hypothesis-generating approach,‭ ‬which has been quite fruitful in identifying putative differences between different morphs. ‬Reply :‭ ‬We appreciate the reviewers careful analyses of the study and approach.‭ ‬We tried to emphasize the‭ “‬hypothesis-generation‭” ‬aspect during the rewrite. Methods‭ “‬QPCR study design‭”‬.‭ ‬The authors adhere to the MIQE guidelines,‭ ‬but do not always follow the best approaches.‭ ‬Most pertinently,‭ ‬the authors use the‭ ‬2−‭∆∆‬Ct method‭ (‬assuming PCR efficiency of‭ ‬2.0‭) ‬despite having gone to the effort of gaining and reporting efficiencies for each assay,‭ ‬which can be as low as‭ ‬1.72‭ ‬for some genes.‭ ‬The effect of failing to incorporate differences in efficiency are highly established and this is likely to have affected the author’s results.‭ ‬The authors should consider incorporating the effect of differences in efficiency into their analyses.‭ ‬This is likely to have some impact on the study conclusions in my opinion. Reply : ‬Great point.‭ ‬The qPCR primer efficiencies more than‭ ‬1.90‭ ‬can be easily assumed as‭ ‬2‭ ‬because of the negligible effects.‭ ‬Since we used LinReg software for efficiencies not the traditional method,‭ ‬it takes into account the efficiencies for each test for a given primer pair and discard those have different and lower efficiencies.‭ ‬However,‭ ‬the Natterin-like paralogues were below the cut-off.‭ The statistical analyses were done on deltaCt values, prior to transformation based on efficiencies used for visualization. We ‬now report the graphs of their expression adjusting for the lower efficiency, and state in the results “Note however, the efficiency of the primers for the nattl genes ranged from 1.72 to 1.77, which suggests this data should be interpreted with caution.”‭ ‬ Methods‭ “‬Polymorphisms in charr transcriptome‭”‬.‭ ‬While this is not exactly my area of expertise,‭ ‬I struggled to understand the methods behind filtering paralogous variants from SNPs in the data.‭ ‬The authors state‭ “‬As the SNP analysis was done on individual contigs,‭ ‬differences among paralogs appear in the data.‭ ‬However,‭ ‬since each sample is a pool of few individuals,‭ ‬it is very unlikely that we have the same frequency of true SNPs in the samples.‭ ‬This property was used to remove variants that are most likely due to expressed paralogs‭”‬.‭ ‬Can the authors please try to re-explain this in even simpler terms to help me get it‭? ‬I don’t see how this description leads to a robust identification of paralogous variation.‭ ‬Is there an underlying assumption of equal expression among paralogues‭? ‬If so,‭ ‬this is likely to be routinely invalidated.‭ ‬ Reply : ‬We acknowledge this part is a hard read.‭ ‬We rewrote this part of the methods. Here is another summary.‭ ‬Reads from regions that are very similar in paralogous genes can map to both of them.‭ ‬Because we consider also reads that map to many contigs,‭ ‬some of the candidate variants will reflect sequence differences between paralogs,‭ ‬not polymorphism in either paralog.‭ ‬Next we deploy the population genetic argument,‭ ‬since we are sequencing RNA from‭ ‬6‭ ‬chromosomes in each‭ ‬sample,‭ ‬then it is very unlikely that a TRUE SNP will be at the same frequency in all of the‭ ‬8‭ ‬samples.‭ ‬But variants‭ ‬-‭ ‬that are due to differences bwn paralogs‭ ‬-‭ ‬are likely to be similar in frequency because they are unaffected by the population sampling.‭ ‬This filter is designed to toss those out. To emphasize‭ ‬the objective is not‭ ‬to find differences between paralogs,‭ ‬but rather to enrich for true SNPs.‭ ‬This method will toss out many sites separating paralogous genes (but not all because some paralogous genes are differentially expressed between morphs or time points).‭ ‭ ‬Methods‭ “‬Verification of candidate SNPs‭”‬.‭ ‬While it is good that the authors have attempted to verify SNPs identified from their RNAseq data,‭ ‬I don’t believe the data is particularly well incorporated in the results section.‭ ‬It needs to be stated up front the extent to which the SNPs predicted from the RNAseq were independently verified.‭ ‬Also,‭ ‬the methods for this section can be improved,‭ ‬especially‭ “‬we conducted genomic comparisons of the Salmon genome,‭ ‬ESTs and short contigs from the preliminary assembly of the Arctic charr transcriptome‭”‬.‭ ‬None of this information is elaborated on‭ – ‬what is the preliminary assembly of the Arctic charr transcriptome‭? ‬Which version of the salmon genome was used and how‭? ‬Moreover,‭ ‬it would be useful to actually explain in the methods that the genotyping was done on a small number of SB,‭ ‬PL and PI morphs,‭ ‬rather than relying on the reader to extract all the required information from Table S2.‭ ‬I guess overall,‭ ‬the way this section is incorporated into the manuscript needs some thought in terms of improving the reader’s experience.‭ ‬I struggled after reading it several times and am still not sure I have all the information I need.‭ Reply :‭ ‬We fixed the methods section to accommodate both reviewers which brought up similar points.‭ ‬We highlight the sampling‭ (‬8‭ ‬individuals of‭ ‬3‭ ‬morphs‭)‬,‭ ‬and extend the description of the genomic comparisons.‭ ‬We also extend the discussion of those results. Results.‭ “‬Analyses of those reads require an Arctic charr genome sequence or transcriptome assembly from longer and paired end reads.‭” ‬As mentioned already,‭ ‬the latter is available to generate an Arctic charr transcriptome assembly to map against.‭ ‬ Reply : ‬Unfortunately the great Norman et al‭. ‬2014‭ ‬data‭ (‬http://www.ncbi.nlm.nih.gov/pubmed/24368751‭) ‬came to our attention after we had done these analyses,‭ ‬and started working on our new data‭ (‬see above‭)‬.‭ ‬Thus we opted for not redoing the whole analyses for this manuscript,‭ ‬but focus on the verification‭ ‬-‭ ‬and of course working on a new assembly using longer reads. Results‭; ‬Figure‭ ‬3‭ ‬and‭ ‬4.‭ ‬The authors found that around half the genes studied were not differentially expressed among morphs by qPCR.‭ ‬Obviously this is quite a large number,‭ ‬but on closer inspection,‭ ‬I noticed that‭ ‬Ndub6,‭ ‬Ubl5‭ ‬and‭ ‬parp6‭ ‬were not even differentially expressed according to RNAseq.‭ ‬Thus,‭ ‬I am confused at the selection of genes from the RNAseq analysis for verification by qPCR.‭ ‬The authors should explain this selection more transparently and provide clearer indices of the correlation between RNAseq and qPCR results and associated discussion. Reply : ‬This reflects the history of the project,‭ ‬and the difference between the preliminary and final analyses.‭ ‬We decided to report on all the data‭ ‬-‭ ‬but explain better in the manuscript the classification of genes tested with qPCR,‭ ‬at‭ ‬1%,‭ ‬5%‭ ‬and‭ ‬10%‭ ‬FDR.‭ ‬In summary,‭ ‬some of the genes tested were above‭ ‬5%‭ ‬and one even just above‭ ‬10%‭ ‬FDR.‭ ‬Some of those were not corroborated by qPCR.‭ ‬The number of genes is insufficient to do a statistical comparison of the verification rate at the different FDR levels.‭ ‬A table‭ (‬new Table‭ ‬3‭) ‬-‭ ‬supported with few sentences in the results,‭ ‬hopefully clarifies this. ‬ Minor comments,‭ ‬typos and suggested changes Abstract:‭ “‬Species and populations with parallel evolution of specific traits can help illuminate how predictable adaptations and divergence are at the molecular and developmental level.‭ ‬Grammatically‭ – ‬his reads better:‭ “‬…..‭ ‬can help illuminate the predictability of adaptations and divergence at the molecular and developmental level‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Examples of such a species complex are the finches of the Galapagos islands,‭ ‬cichlids in the African great lakes are exciting multi-species systems in this respect‭”‬.‭ ‬Grammatically‭ – ‬reads better:‭ “‬Examples of such species complexes are provided by finches of the Galapagos islands,‭ ‬while cichlids of the African great lakes also provide an exciting multi-species system in the same respect‭”‬ Reply :‭ ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Some northern freshwater fish species exhibit frequent parallelism in trophic structures and life history and in several cases are they found as distinct resource morphs‭” ‬change to‭ “‬….‭ ‬are found as distinct resource morphs‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬in the development of ecological differences in tropic morphology‭” ‬change to‭ “… ‬trophic morphology‭”‬.‭ ‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬The family is estimated to be between‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old‭”‬.‭ ‬This information is not correct‭ – ‬it is correct to state that the age of the salmonid crown‭ (‬based on the cited paper‭; ‬different estimates exist in the literature,‭ ‬e.g.‭ ‬Macqueen and Johnston,‭ ‬2014‭; ‬Campbell‭ ‬et al.‭ ‬2013‭) ‬is estimated at‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old,‭ ‬but the family dates back much further‭ – ‬to the origin of the WGD event in fact,‭ ‬which occurred more like‭ ‬88-103‭ ‬Ma‭ (‬Macqueen and Johnston,‭ ‬2014‭; ‬Berthelot‭ ‬et al.‭ ‬2014‭)‬.‭ ‬Thus,‭ ‬the last common ancestor to extant salmonid species is what the authors are actually referring to in this sentence. Reply : ‬Thanks so for pointing this out.‭ ‬We changed the text to‭ “‬local adaptation has been extensively studied in the salmonid family,‭ ‬to which Arctic charr belongs‭ {‬Fraser2011‭}‬.‭ ‬The family is estimated to be between‭ ‬88-103‭ ‬million years old‭ {‬Macqueen2014,Berthelot2014c‭}‬.‭ ‬A whole genome duplication event occurred before the radiation of the salmonid family‭ {‬Davidson2010,Moghadam2011,Macqueen2014,Berthelot2014c‭} ‬which has provided time for divergence of ohnologous genes‭ (‬paralogous genes originated by whole genome duplication event‭)‬.‭ ” ‬ Introduction:‭ “‬Furthermore,‭ ‬for data with short reads,‭ ‬mapping to a related reference genome/transcriptome is recommended over de novo assembly‭”‬.‭ ‬While this sentence is technically correct in the context of the work cited,‭ ‬I feel it is being used slightly out of context.‭ ‬For a start,‭ ‬what comprises a‭ ‘‬short read‭’ ‬is undefined.‭ ‬36bp is short,‭ ‬but it is possible to get a sold reference transcriptome using‭ ‬2‭*‬100bp,‭ ‬assuming the appropriate diversity of transcripts is represented and suitable depth is attained.‭ ‬ Reply : ‬Great point,‭ ‬we opted for keeping the point‭ (‬at this place in the ms‭) ‬but changing the wording to:‭ ‬In this study we opted to map the reads‭ (‬36‭ ‬bp‭) ‬to a related reference genome/transcriptome‭ {‬Vijay2013a‭}‬,‭ ‬instead of conducting de novo assembly.‭ Introduction:‭ “‬nuclear genes,‭ ‬reveled both subtle‭” ‬change to‭ “‬nuclear genes,‭ ‬revealed both subtle‭” Reply : ‬Thanks fixed.‭ ‬ Minor comment‭ – ‬AC,‭ ‬PL,‭ ‬LB and SB were already defined in introduction.‭ ‬ Reply : ‬Thanks, removed this. Methods:‭ “‬Fishing in Lake Thingvallavatn was with permissions‭” ‬changed to‭ “‬Fishing in Lake Thingvallavatn was done with permissions‭”‬.‭ ‬ Reply : Ammended. Methods:‭ “‬of differently expressed genes,‭ ‬we preformed clustering analyses‭” ‬change to‭ “‬…we performed clustering analyses‭”‬ Reply : ‬Thanks,‭ ‬fixed. Results:‭ “‬The most drastic changes were seen in processes related to glycolysis‭ (‬GO:0006096,‭ ‬FDR‭ = ‬0.0009‭)‬,‭ ‬were the expression of‭ ‬19‭ ‬out of‭ ‬25‭ ‬genes‭” ‬change to‭ “…‬.‭ ‬where the expression‭”‬.‭ ‬ Reply :‭ ‬Thanks,‭ ‬fixed. Figure‭ ‬7.‭ ‬What does the charr_WT vs.‭ ‬charr_M signify in the alignment data‭?‬ Reply : ‬Designates the two alleles,‭ ‬the legend now makes this explicit.‭ ‭ ‬Discussion‭ “‬We are interested in how predictable evolution is a the molecular level and if there certain principles influence the rewiring of developmental and regulatory systems during evolution‭” ‬consider changing to‭ “‬We are interested in the predictability of evolution at the molecular level,‭ ‬especially whether there exist principles that influence the rewiring of developmental and regulatory systems‭”‬.‭ ‬ Reply : ‬Thanks,‭ ‬excellent suggestion,‭ ‬included Discussion.‭ “‬Recent rainbow trout data shows most paralogs from the latest whole genome duplication event retain the same expression pattern32‭ ‬indicating that this scenario is probably uncommon‭; ‬hence it is of considerable interest when two paralogs show distinct expression patterns‭”‬.‭ ‬I do not agree that it is of considerable interest when two paralogs show distinct expression patterns‭ – ‬I could list tens of examples for salmonids.‭ ‬ Reply :‭ ‬Good point,‭ ‬we have revisited this interpretation‭ (‬see also point by rev.‭ ‬1‭)‬.‭ ‭ ‬ Conclusions‭ “‬The results suggest genetic and expression changes in multiple systems relate to divergence among populations.‭” ‬Change to‭ “‬… associated with divergence among populations.‭” Reply : ‬Thanks,‭ ‬fixed. Main comments‭ & ‬caveats RNAseq study design.‭ ‬ I sympathize with the fact that the authors are trying to publish Illumina data that was generated in‭ ‬2009,‭ ‬since‭ (‬obviously‭) ‬the technology has moved on greatly in the last‭ ‬6‭ ‬years,‭ ‬while its costs have been reduced dramatically.‭ ‬Adding to this is the fact that the authors are using a particularly complex transcriptome in terms of high content of similar paralogues‭ (‬and expressed transposable elements‭)‬,‭ ‬without a reference sequence for mapping in their species.‭ ‬I accept the author’s argument that it is more sensible to map against a closely related species with the sequence data rather than to try and create a‭ ‬de novo‭ ‬assembly from‭ ‬36bp reads.‭ ‬I also believe it is sensible to pool read counts for putative paralogous contigs in this study,‭ ‬since the short read length ablates any ability to separate paralogous differences in expression‭ (‬yet does not preclude the generation of useful hypotheses about putative gene expression differences among morphs‭)‬.‭ ‬However,‭ ‬I do question whether the use of Atlantic salmon EST contigs is the best approach here.‭ ‬Firstly,‭ ‬reference assemblies for both Atlantic salmon and rainbow trout are now available,‭ ‬which distinguish paralogous variation.‭ ‬More importantly,‭ ‬using these reference genome data would provide certainty that reads are being mapped to exons from single genes,‭ ‬whereas many of the ESTs will provide a fragmented representation of exon sequences,‭ ‬presumably relying on annotation to piece them back into‭ ‘‬genes‭’ ‬post hoc‭ ‬.‭ ‬In addition,‭ ‬paired‭ ‬100bp Ilumina reads are available at high coverage for Arctic charr‭ (‬e.g.‭ ‬Norman‭ ‬et al.‭ ‬2014‭)‬,‭ ‬which could also be used to generate a specific reference transcriptome to map against in this study,‭ ‬although this might be underrepresented in terms of developmental genes as it is a gill study.‭ ‬Overall,‭ ‬I do wonder how much more information might have been gleaned from this dataset with a different mapping strategy‭? With all the above said,‭ ‬I understand that the authors have built up a large study based around the original mapping to the salmon ESTs and that it would not be routine for them to repeat the study using better reference data.‭ ‬Furthermore,‭ ‬the approach used has definitely led to the generation of several valid hypotheses concerning the nature of gene expression and genetic differences among charr morphs,‭ ‬which have been followed up using independent approaches.‭ ‬ Reply :‭ ‬We thank the reviewer for excellent diagnosis and suggestions.‭ ‬The paper describes the‭ (‬in our humble opinion‭) ‬most sensible summary of the data,‭ ‬as the writing of the paper started‭ ‬2‭ ‬years ago.‭ ‬We did map on the‭ ‬O.mykiss‭ ‬cDNA collection also,‭ ‬got similar results,‭ ‬but opted for reporting on the salmon data to avoid further extending an already long manuscript.‭ ‬We are currently analyzing DE and SNPs on a new assembly‭ (‬100‭ ‬bp PE reads‭ ‬-‭ ‬48‭ ‬samples‭ ‬-‭ ‬3‭ ‬morphs‭ ‬-‭ ‬development‭)‬,‭ ‬and may include a remapping of this dataset in that.‭ ‭ ‬ Methods‭ “‬Biological Replication in RNAseq‭” – ‬a general comment:‭ ‬obviously the design of the study is not optimal because biological variation within developmental stages is not considered in the statistics.‭ ‬Thus,‭ ‬the approach lacks power to detect differences when morph variation is restricted to different developmental stages.‭ ‬I wanted to explain my opinion‭ (‬for the record‭) ‬that the study design is nonetheless useful for identifying constitutive differences between morphs.‭ ‬This is especially true because gene expression variability is likely to be relatively low in embryonic stages‭ (‬compared to a similar study design in adults at least‭)‬.‭ ‬Further,‭ ‬the pooling of individuals will have helped to at least recapture some biological variation at different stages.‭ ‬Thus,‭ ‬as mentioned above,‭ ‬I see the author’s use of RNAseq as a hypothesis-generating approach,‭ ‬which has been quite fruitful in identifying putative differences between different morphs. ‬Reply :‭ ‬We appreciate the reviewers careful analyses of the study and approach.‭ ‬We tried to emphasize the‭ “‬hypothesis-generation‭” ‬aspect during the rewrite. Methods‭ “‬QPCR study design‭”‬.‭ ‬The authors adhere to the MIQE guidelines,‭ ‬but do not always follow the best approaches.‭ ‬Most pertinently,‭ ‬the authors use the‭ ‬2−‭∆∆‬Ct method‭ (‬assuming PCR efficiency of‭ ‬2.0‭) ‬despite having gone to the effort of gaining and reporting efficiencies for each assay,‭ ‬which can be as low as‭ ‬1.72‭ ‬for some genes.‭ ‬The effect of failing to incorporate differences in efficiency are highly established and this is likely to have affected the author’s results.‭ ‬The authors should consider incorporating the effect of differences in efficiency into their analyses.‭ ‬This is likely to have some impact on the study conclusions in my opinion. Reply : ‬Great point.‭ ‬The qPCR primer efficiencies more than‭ ‬1.90‭ ‬can be easily assumed as‭ ‬2‭ ‬because of the negligible effects.‭ ‬Since we used LinReg software for efficiencies not the traditional method,‭ ‬it takes into account the efficiencies for each test for a given primer pair and discard those have different and lower efficiencies.‭ ‬However,‭ ‬the Natterin-like paralogues were below the cut-off.‭ The statistical analyses were done on deltaCt values, prior to transformation based on efficiencies used for visualization. We ‬now report the graphs of their expression adjusting for the lower efficiency, and state in the results “Note however, the efficiency of the primers for the nattl genes ranged from 1.72 to 1.77, which suggests this data should be interpreted with caution.”‭ ‬ Methods‭ “‬Polymorphisms in charr transcriptome‭”‬.‭ ‬While this is not exactly my area of expertise,‭ ‬I struggled to understand the methods behind filtering paralogous variants from SNPs in the data.‭ ‬The authors state‭ “‬As the SNP analysis was done on individual contigs,‭ ‬differences among paralogs appear in the data.‭ ‬However,‭ ‬since each sample is a pool of few individuals,‭ ‬it is very unlikely that we have the same frequency of true SNPs in the samples.‭ ‬This property was used to remove variants that are most likely due to expressed paralogs‭”‬.‭ ‬Can the authors please try to re-explain this in even simpler terms to help me get it‭? ‬I don’t see how this description leads to a robust identification of paralogous variation.‭ ‬Is there an underlying assumption of equal expression among paralogues‭? ‬If so,‭ ‬this is likely to be routinely invalidated.‭ ‬ Reply : ‬We acknowledge this part is a hard read.‭ ‬We rewrote this part of the methods. Here is another summary.‭ ‬Reads from regions that are very similar in paralogous genes can map to both of them.‭ ‬Because we consider also reads that map to many contigs,‭ ‬some of the candidate variants will reflect sequence differences between paralogs,‭ ‬not polymorphism in either paralog.‭ ‬Next we deploy the population genetic argument,‭ ‬since we are sequencing RNA from‭ ‬6‭ ‬chromosomes in each‭ ‬sample,‭ ‬then it is very unlikely that a TRUE SNP will be at the same frequency in all of the‭ ‬8‭ ‬samples.‭ ‬But variants‭ ‬-‭ ‬that are due to differences bwn paralogs‭ ‬-‭ ‬are likely to be similar in frequency because they are unaffected by the population sampling.‭ ‬This filter is designed to toss those out. To emphasize‭ ‬the objective is not‭ ‬to find differences between paralogs,‭ ‬but rather to enrich for true SNPs.‭ ‬This method will toss out many sites separating paralogous genes (but not all because some paralogous genes are differentially expressed between morphs or time points).‭ ‭ ‬Methods‭ “‬Verification of candidate SNPs‭”‬.‭ ‬While it is good that the authors have attempted to verify SNPs identified from their RNAseq data,‭ ‬I don’t believe the data is particularly well incorporated in the results section.‭ ‬It needs to be stated up front the extent to which the SNPs predicted from the RNAseq were independently verified.‭ ‬Also,‭ ‬the methods for this section can be improved,‭ ‬especially‭ “‬we conducted genomic comparisons of the Salmon genome,‭ ‬ESTs and short contigs from the preliminary assembly of the Arctic charr transcriptome‭”‬.‭ ‬None of this information is elaborated on‭ – ‬what is the preliminary assembly of the Arctic charr transcriptome‭? ‬Which version of the salmon genome was used and how‭? ‬Moreover,‭ ‬it would be useful to actually explain in the methods that the genotyping was done on a small number of SB,‭ ‬PL and PI morphs,‭ ‬rather than relying on the reader to extract all the required information from Table S2.‭ ‬I guess overall,‭ ‬the way this section is incorporated into the manuscript needs some thought in terms of improving the reader’s experience.‭ ‬I struggled after reading it several times and am still not sure I have all the information I need.‭ Reply :‭ ‬We fixed the methods section to accommodate both reviewers which brought up similar points.‭ ‬We highlight the sampling‭ (‬8‭ ‬individuals of‭ ‬3‭ ‬morphs‭)‬,‭ ‬and extend the description of the genomic comparisons.‭ ‬We also extend the discussion of those results. Results.‭ “‬Analyses of those reads require an Arctic charr genome sequence or transcriptome assembly from longer and paired end reads.‭” ‬As mentioned already,‭ ‬the latter is available to generate an Arctic charr transcriptome assembly to map against.‭ ‬ Reply : ‬Unfortunately the great Norman et al‭. ‬2014‭ ‬data‭ (‬http://www.ncbi.nlm.nih.gov/pubmed/24368751‭) ‬came to our attention after we had done these analyses,‭ ‬and started working on our new data‭ (‬see above‭)‬.‭ ‬Thus we opted for not redoing the whole analyses for this manuscript,‭ ‬but focus on the verification‭ ‬-‭ ‬and of course working on a new assembly using longer reads. Results‭; ‬Figure‭ ‬3‭ ‬and‭ ‬4.‭ ‬The authors found that around half the genes studied were not differentially expressed among morphs by qPCR.‭ ‬Obviously this is quite a large number,‭ ‬but on closer inspection,‭ ‬I noticed that‭ ‬Ndub6,‭ ‬Ubl5‭ ‬and‭ ‬parp6‭ ‬were not even differentially expressed according to RNAseq.‭ ‬Thus,‭ ‬I am confused at the selection of genes from the RNAseq analysis for verification by qPCR.‭ ‬The authors should explain this selection more transparently and provide clearer indices of the correlation between RNAseq and qPCR results and associated discussion. Reply : ‬This reflects the history of the project,‭ ‬and the difference between the preliminary and final analyses.‭ ‬We decided to report on all the data‭ ‬-‭ ‬but explain better in the manuscript the classification of genes tested with qPCR,‭ ‬at‭ ‬1%,‭ ‬5%‭ ‬and‭ ‬10%‭ ‬FDR.‭ ‬In summary,‭ ‬some of the genes tested were above‭ ‬5%‭ ‬and one even just above‭ ‬10%‭ ‬FDR.‭ ‬Some of those were not corroborated by qPCR.‭ ‬The number of genes is insufficient to do a statistical comparison of the verification rate at the different FDR levels.‭ ‬A table‭ (‬new Table‭ ‬3‭) ‬-‭ ‬supported with few sentences in the results,‭ ‬hopefully clarifies this. ‬ Minor comments,‭ ‬typos and suggested changes Abstract:‭ “‬Species and populations with parallel evolution of specific traits can help illuminate how predictable adaptations and divergence are at the molecular and developmental level.‭ ‬Grammatically‭ – ‬his reads better:‭ “‬…..‭ ‬can help illuminate the predictability of adaptations and divergence at the molecular and developmental level‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Examples of such a species complex are the finches of the Galapagos islands,‭ ‬cichlids in the African great lakes are exciting multi-species systems in this respect‭”‬.‭ ‬Grammatically‭ – ‬reads better:‭ “‬Examples of such species complexes are provided by finches of the Galapagos islands,‭ ‬while cichlids of the African great lakes also provide an exciting multi-species system in the same respect‭”‬ Reply :‭ ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Some northern freshwater fish species exhibit frequent parallelism in trophic structures and life history and in several cases are they found as distinct resource morphs‭” ‬change to‭ “‬….‭ ‬are found as distinct resource morphs‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬in the development of ecological differences in tropic morphology‭” ‬change to‭ “… ‬trophic morphology‭”‬.‭ ‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬The family is estimated to be between‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old‭”‬.‭ ‬This information is not correct‭ – ‬it is correct to state that the age of the salmonid crown‭ (‬based on the cited paper‭; ‬different estimates exist in the literature,‭ ‬e.g.‭ ‬Macqueen and Johnston,‭ ‬2014‭; ‬Campbell‭ ‬et al.‭ ‬2013‭) ‬is estimated at‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old,‭ ‬but the family dates back much further‭ – ‬to the origin of the WGD event in fact,‭ ‬which occurred more like‭ ‬88-103‭ ‬Ma‭ (‬Macqueen and Johnston,‭ ‬2014‭; ‬Berthelot‭ ‬et al.‭ ‬2014‭)‬.‭ ‬Thus,‭ ‬the last common ancestor to extant salmonid species is what the authors are actually referring to in this sentence. Reply : ‬Thanks so for pointing this out.‭ ‬We changed the text to‭ “‬local adaptation has been extensively studied in the salmonid family,‭ ‬to which Arctic charr belongs‭ {‬Fraser2011‭}‬.‭ ‬The family is estimated to be between‭ ‬88-103‭ ‬million years old‭ {‬Macqueen2014,Berthelot2014c‭}‬.‭ ‬A whole genome duplication event occurred before the radiation of the salmonid family‭ {‬Davidson2010,Moghadam2011,Macqueen2014,Berthelot2014c‭} ‬which has provided time for divergence of ohnologous genes‭ (‬paralogous genes originated by whole genome duplication event‭)‬.‭ ” ‬ Introduction:‭ “‬Furthermore,‭ ‬for data with short reads,‭ ‬mapping to a related reference genome/transcriptome is recommended over de novo assembly‭”‬.‭ ‬While this sentence is technically correct in the context of the work cited,‭ ‬I feel it is being used slightly out of context.‭ ‬For a start,‭ ‬what comprises a‭ ‘‬short read‭’ ‬is undefined.‭ ‬36bp is short,‭ ‬but it is possible to get a sold reference transcriptome using‭ ‬2‭*‬100bp,‭ ‬assuming the appropriate diversity of transcripts is represented and suitable depth is attained.‭ ‬ Reply : ‬Great point,‭ ‬we opted for keeping the point‭ (‬at this place in the ms‭) ‬but changing the wording to:‭ ‬In this study we opted to map the reads‭ (‬36‭ ‬bp‭) ‬to a related reference genome/transcriptome‭ {‬Vijay2013a‭}‬,‭ ‬instead of conducting de novo assembly.‭ Introduction:‭ “‬nuclear genes,‭ ‬reveled both subtle‭” ‬change to‭ “‬nuclear genes,‭ ‬revealed both subtle‭” Reply : ‬Thanks fixed.‭ ‬ Minor comment‭ – ‬AC,‭ ‬PL,‭ ‬LB and SB were already defined in introduction.‭ ‬ Reply : ‬Thanks, removed this. Methods:‭ “‬Fishing in Lake Thingvallavatn was with permissions‭” ‬changed to‭ “‬Fishing in Lake Thingvallavatn was done with permissions‭”‬.‭ ‬ Reply : Ammended. Methods:‭ “‬of differently expressed genes,‭ ‬we preformed clustering analyses‭” ‬change to‭ “‬…we performed clustering analyses‭”‬ Reply : ‬Thanks,‭ ‬fixed. Results:‭ “‬The most drastic changes were seen in processes related to glycolysis‭ (‬GO:0006096,‭ ‬FDR‭ = ‬0.0009‭)‬,‭ ‬were the expression of‭ ‬19‭ ‬out of‭ ‬25‭ ‬genes‭” ‬change to‭ “…‬.‭ ‬where the expression‭”‬.‭ ‬ Reply :‭ ‬Thanks,‭ ‬fixed. Figure‭ ‬7.‭ ‬What does the charr_WT vs.‭ ‬charr_M signify in the alignment data‭?‬ Reply : ‬Designates the two alleles,‭ ‬the legend now makes this explicit.‭ ‭ ‬Discussion‭ “‬We are interested in how predictable evolution is a the molecular level and if there certain principles influence the rewiring of developmental and regulatory systems during evolution‭” ‬consider changing to‭ “‬We are interested in the predictability of evolution at the molecular level,‭ ‬especially whether there exist principles that influence the rewiring of developmental and regulatory systems‭”‬.‭ ‬ Reply : ‬Thanks,‭ ‬excellent suggestion,‭ ‬included Discussion.‭ “‬Recent rainbow trout data shows most paralogs from the latest whole genome duplication event retain the same expression pattern32‭ ‬indicating that this scenario is probably uncommon‭; ‬hence it is of considerable interest when two paralogs show distinct expression patterns‭”‬.‭ ‬I do not agree that it is of considerable interest when two paralogs show distinct expression patterns‭ – ‬I could list tens of examples for salmonids.‭ ‬ Reply :‭ ‬Good point,‭ ‬we have revisited this interpretation‭ (‬see also point by rev.‭ ‬1‭)‬.‭ ‭ ‬ Conclusions‭ “‬The results suggest genetic and expression changes in multiple systems relate to divergence among populations.‭” ‬Change to‭ “‬… associated with divergence among populations.‭” Reply : ‬Thanks,‭ ‬fixed. Competing Interests: No competing interests were disclosed.No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 25 Apr 2016 Arnar Palsson , Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland 25 Apr 2016 Author Response Main comments‭ & ‬caveats RNAseq study design.‭ ‬ I sympathize with the fact that the authors are trying to publish Illumina data that was generated in‭ ‬2009,‭ ‬since‭ (‬obviously‭) ‬the technology ... Continue reading Main comments‭ & ‬caveats RNAseq study design.‭ ‬ I sympathize with the fact that the authors are trying to publish Illumina data that was generated in‭ ‬2009,‭ ‬since‭ (‬obviously‭) ‬the technology has moved on greatly in the last‭ ‬6‭ ‬years,‭ ‬while its costs have been reduced dramatically.‭ ‬Adding to this is the fact that the authors are using a particularly complex transcriptome in terms of high content of similar paralogues‭ (‬and expressed transposable elements‭)‬,‭ ‬without a reference sequence for mapping in their species.‭ ‬I accept the author’s argument that it is more sensible to map against a closely related species with the sequence data rather than to try and create a‭ ‬de novo‭ ‬assembly from‭ ‬36bp reads.‭ ‬I also believe it is sensible to pool read counts for putative paralogous contigs in this study,‭ ‬since the short read length ablates any ability to separate paralogous differences in expression‭ (‬yet does not preclude the generation of useful hypotheses about putative gene expression differences among morphs‭)‬.‭ ‬However,‭ ‬I do question whether the use of Atlantic salmon EST contigs is the best approach here.‭ ‬Firstly,‭ ‬reference assemblies for both Atlantic salmon and rainbow trout are now available,‭ ‬which distinguish paralogous variation.‭ ‬More importantly,‭ ‬using these reference genome data would provide certainty that reads are being mapped to exons from single genes,‭ ‬whereas many of the ESTs will provide a fragmented representation of exon sequences,‭ ‬presumably relying on annotation to piece them back into‭ ‘‬genes‭’ ‬post hoc‭ ‬.‭ ‬In addition,‭ ‬paired‭ ‬100bp Ilumina reads are available at high coverage for Arctic charr‭ (‬e.g.‭ ‬Norman‭ ‬et al.‭ ‬2014‭)‬,‭ ‬which could also be used to generate a specific reference transcriptome to map against in this study,‭ ‬although this might be underrepresented in terms of developmental genes as it is a gill study.‭ ‬Overall,‭ ‬I do wonder how much more information might have been gleaned from this dataset with a different mapping strategy‭? With all the above said,‭ ‬I understand that the authors have built up a large study based around the original mapping to the salmon ESTs and that it would not be routine for them to repeat the study using better reference data.‭ ‬Furthermore,‭ ‬the approach used has definitely led to the generation of several valid hypotheses concerning the nature of gene expression and genetic differences among charr morphs,‭ ‬which have been followed up using independent approaches.‭ ‬ Reply :‭ ‬We thank the reviewer for excellent diagnosis and suggestions.‭ ‬The paper describes the‭ (‬in our humble opinion‭) ‬most sensible summary of the data,‭ ‬as the writing of the paper started‭ ‬2‭ ‬years ago.‭ ‬We did map on the‭ ‬O.mykiss‭ ‬cDNA collection also,‭ ‬got similar results,‭ ‬but opted for reporting on the salmon data to avoid further extending an already long manuscript.‭ ‬We are currently analyzing DE and SNPs on a new assembly‭ (‬100‭ ‬bp PE reads‭ ‬-‭ ‬48‭ ‬samples‭ ‬-‭ ‬3‭ ‬morphs‭ ‬-‭ ‬development‭)‬,‭ ‬and may include a remapping of this dataset in that.‭ ‭ ‬ Methods‭ “‬Biological Replication in RNAseq‭” – ‬a general comment:‭ ‬obviously the design of the study is not optimal because biological variation within developmental stages is not considered in the statistics.‭ ‬Thus,‭ ‬the approach lacks power to detect differences when morph variation is restricted to different developmental stages.‭ ‬I wanted to explain my opinion‭ (‬for the record‭) ‬that the study design is nonetheless useful for identifying constitutive differences between morphs.‭ ‬This is especially true because gene expression variability is likely to be relatively low in embryonic stages‭ (‬compared to a similar study design in adults at least‭)‬.‭ ‬Further,‭ ‬the pooling of individuals will have helped to at least recapture some biological variation at different stages.‭ ‬Thus,‭ ‬as mentioned above,‭ ‬I see the author’s use of RNAseq as a hypothesis-generating approach,‭ ‬which has been quite fruitful in identifying putative differences between different morphs. ‬Reply :‭ ‬We appreciate the reviewers careful analyses of the study and approach.‭ ‬We tried to emphasize the‭ “‬hypothesis-generation‭” ‬aspect during the rewrite. Methods‭ “‬QPCR study design‭”‬.‭ ‬The authors adhere to the MIQE guidelines,‭ ‬but do not always follow the best approaches.‭ ‬Most pertinently,‭ ‬the authors use the‭ ‬2−‭∆∆‬Ct method‭ (‬assuming PCR efficiency of‭ ‬2.0‭) ‬despite having gone to the effort of gaining and reporting efficiencies for each assay,‭ ‬which can be as low as‭ ‬1.72‭ ‬for some genes.‭ ‬The effect of failing to incorporate differences in efficiency are highly established and this is likely to have affected the author’s results.‭ ‬The authors should consider incorporating the effect of differences in efficiency into their analyses.‭ ‬This is likely to have some impact on the study conclusions in my opinion. Reply : ‬Great point.‭ ‬The qPCR primer efficiencies more than‭ ‬1.90‭ ‬can be easily assumed as‭ ‬2‭ ‬because of the negligible effects.‭ ‬Since we used LinReg software for efficiencies not the traditional method,‭ ‬it takes into account the efficiencies for each test for a given primer pair and discard those have different and lower efficiencies.‭ ‬However,‭ ‬the Natterin-like paralogues were below the cut-off.‭ The statistical analyses were done on deltaCt values, prior to transformation based on efficiencies used for visualization. We ‬now report the graphs of their expression adjusting for the lower efficiency, and state in the results “Note however, the efficiency of the primers for the nattl genes ranged from 1.72 to 1.77, which suggests this data should be interpreted with caution.”‭ ‬ Methods‭ “‬Polymorphisms in charr transcriptome‭”‬.‭ ‬While this is not exactly my area of expertise,‭ ‬I struggled to understand the methods behind filtering paralogous variants from SNPs in the data.‭ ‬The authors state‭ “‬As the SNP analysis was done on individual contigs,‭ ‬differences among paralogs appear in the data.‭ ‬However,‭ ‬since each sample is a pool of few individuals,‭ ‬it is very unlikely that we have the same frequency of true SNPs in the samples.‭ ‬This property was used to remove variants that are most likely due to expressed paralogs‭”‬.‭ ‬Can the authors please try to re-explain this in even simpler terms to help me get it‭? ‬I don’t see how this description leads to a robust identification of paralogous variation.‭ ‬Is there an underlying assumption of equal expression among paralogues‭? ‬If so,‭ ‬this is likely to be routinely invalidated.‭ ‬ Reply : ‬We acknowledge this part is a hard read.‭ ‬We rewrote this part of the methods. Here is another summary.‭ ‬Reads from regions that are very similar in paralogous genes can map to both of them.‭ ‬Because we consider also reads that map to many contigs,‭ ‬some of the candidate variants will reflect sequence differences between paralogs,‭ ‬not polymorphism in either paralog.‭ ‬Next we deploy the population genetic argument,‭ ‬since we are sequencing RNA from‭ ‬6‭ ‬chromosomes in each‭ ‬sample,‭ ‬then it is very unlikely that a TRUE SNP will be at the same frequency in all of the‭ ‬8‭ ‬samples.‭ ‬But variants‭ ‬-‭ ‬that are due to differences bwn paralogs‭ ‬-‭ ‬are likely to be similar in frequency because they are unaffected by the population sampling.‭ ‬This filter is designed to toss those out. To emphasize‭ ‬the objective is not‭ ‬to find differences between paralogs,‭ ‬but rather to enrich for true SNPs.‭ ‬This method will toss out many sites separating paralogous genes (but not all because some paralogous genes are differentially expressed between morphs or time points).‭ ‭ ‬Methods‭ “‬Verification of candidate SNPs‭”‬.‭ ‬While it is good that the authors have attempted to verify SNPs identified from their RNAseq data,‭ ‬I don’t believe the data is particularly well incorporated in the results section.‭ ‬It needs to be stated up front the extent to which the SNPs predicted from the RNAseq were independently verified.‭ ‬Also,‭ ‬the methods for this section can be improved,‭ ‬especially‭ “‬we conducted genomic comparisons of the Salmon genome,‭ ‬ESTs and short contigs from the preliminary assembly of the Arctic charr transcriptome‭”‬.‭ ‬None of this information is elaborated on‭ – ‬what is the preliminary assembly of the Arctic charr transcriptome‭? ‬Which version of the salmon genome was used and how‭? ‬Moreover,‭ ‬it would be useful to actually explain in the methods that the genotyping was done on a small number of SB,‭ ‬PL and PI morphs,‭ ‬rather than relying on the reader to extract all the required information from Table S2.‭ ‬I guess overall,‭ ‬the way this section is incorporated into the manuscript needs some thought in terms of improving the reader’s experience.‭ ‬I struggled after reading it several times and am still not sure I have all the information I need.‭ Reply :‭ ‬We fixed the methods section to accommodate both reviewers which brought up similar points.‭ ‬We highlight the sampling‭ (‬8‭ ‬individuals of‭ ‬3‭ ‬morphs‭)‬,‭ ‬and extend the description of the genomic comparisons.‭ ‬We also extend the discussion of those results. Results.‭ “‬Analyses of those reads require an Arctic charr genome sequence or transcriptome assembly from longer and paired end reads.‭” ‬As mentioned already,‭ ‬the latter is available to generate an Arctic charr transcriptome assembly to map against.‭ ‬ Reply : ‬Unfortunately the great Norman et al‭. ‬2014‭ ‬data‭ (‬http://www.ncbi.nlm.nih.gov/pubmed/24368751‭) ‬came to our attention after we had done these analyses,‭ ‬and started working on our new data‭ (‬see above‭)‬.‭ ‬Thus we opted for not redoing the whole analyses for this manuscript,‭ ‬but focus on the verification‭ ‬-‭ ‬and of course working on a new assembly using longer reads. Results‭; ‬Figure‭ ‬3‭ ‬and‭ ‬4.‭ ‬The authors found that around half the genes studied were not differentially expressed among morphs by qPCR.‭ ‬Obviously this is quite a large number,‭ ‬but on closer inspection,‭ ‬I noticed that‭ ‬Ndub6,‭ ‬Ubl5‭ ‬and‭ ‬parp6‭ ‬were not even differentially expressed according to RNAseq.‭ ‬Thus,‭ ‬I am confused at the selection of genes from the RNAseq analysis for verification by qPCR.‭ ‬The authors should explain this selection more transparently and provide clearer indices of the correlation between RNAseq and qPCR results and associated discussion. Reply : ‬This reflects the history of the project,‭ ‬and the difference between the preliminary and final analyses.‭ ‬We decided to report on all the data‭ ‬-‭ ‬but explain better in the manuscript the classification of genes tested with qPCR,‭ ‬at‭ ‬1%,‭ ‬5%‭ ‬and‭ ‬10%‭ ‬FDR.‭ ‬In summary,‭ ‬some of the genes tested were above‭ ‬5%‭ ‬and one even just above‭ ‬10%‭ ‬FDR.‭ ‬Some of those were not corroborated by qPCR.‭ ‬The number of genes is insufficient to do a statistical comparison of the verification rate at the different FDR levels.‭ ‬A table‭ (‬new Table‭ ‬3‭) ‬-‭ ‬supported with few sentences in the results,‭ ‬hopefully clarifies this. ‬ Minor comments,‭ ‬typos and suggested changes Abstract:‭ “‬Species and populations with parallel evolution of specific traits can help illuminate how predictable adaptations and divergence are at the molecular and developmental level.‭ ‬Grammatically‭ – ‬his reads better:‭ “‬…..‭ ‬can help illuminate the predictability of adaptations and divergence at the molecular and developmental level‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Examples of such a species complex are the finches of the Galapagos islands,‭ ‬cichlids in the African great lakes are exciting multi-species systems in this respect‭”‬.‭ ‬Grammatically‭ – ‬reads better:‭ “‬Examples of such species complexes are provided by finches of the Galapagos islands,‭ ‬while cichlids of the African great lakes also provide an exciting multi-species system in the same respect‭”‬ Reply :‭ ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Some northern freshwater fish species exhibit frequent parallelism in trophic structures and life history and in several cases are they found as distinct resource morphs‭” ‬change to‭ “‬….‭ ‬are found as distinct resource morphs‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬in the development of ecological differences in tropic morphology‭” ‬change to‭ “… ‬trophic morphology‭”‬.‭ ‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬The family is estimated to be between‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old‭”‬.‭ ‬This information is not correct‭ – ‬it is correct to state that the age of the salmonid crown‭ (‬based on the cited paper‭; ‬different estimates exist in the literature,‭ ‬e.g.‭ ‬Macqueen and Johnston,‭ ‬2014‭; ‬Campbell‭ ‬et al.‭ ‬2013‭) ‬is estimated at‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old,‭ ‬but the family dates back much further‭ – ‬to the origin of the WGD event in fact,‭ ‬which occurred more like‭ ‬88-103‭ ‬Ma‭ (‬Macqueen and Johnston,‭ ‬2014‭; ‬Berthelot‭ ‬et al.‭ ‬2014‭)‬.‭ ‬Thus,‭ ‬the last common ancestor to extant salmonid species is what the authors are actually referring to in this sentence. Reply : ‬Thanks so for pointing this out.‭ ‬We changed the text to‭ “‬local adaptation has been extensively studied in the salmonid family,‭ ‬to which Arctic charr belongs‭ {‬Fraser2011‭}‬.‭ ‬The family is estimated to be between‭ ‬88-103‭ ‬million years old‭ {‬Macqueen2014,Berthelot2014c‭}‬.‭ ‬A whole genome duplication event occurred before the radiation of the salmonid family‭ {‬Davidson2010,Moghadam2011,Macqueen2014,Berthelot2014c‭} ‬which has provided time for divergence of ohnologous genes‭ (‬paralogous genes originated by whole genome duplication event‭)‬.‭ ” ‬ Introduction:‭ “‬Furthermore,‭ ‬for data with short reads,‭ ‬mapping to a related reference genome/transcriptome is recommended over de novo assembly‭”‬.‭ ‬While this sentence is technically correct in the context of the work cited,‭ ‬I feel it is being used slightly out of context.‭ ‬For a start,‭ ‬what comprises a‭ ‘‬short read‭’ ‬is undefined.‭ ‬36bp is short,‭ ‬but it is possible to get a sold reference transcriptome using‭ ‬2‭*‬100bp,‭ ‬assuming the appropriate diversity of transcripts is represented and suitable depth is attained.‭ ‬ Reply : ‬Great point,‭ ‬we opted for keeping the point‭ (‬at this place in the ms‭) ‬but changing the wording to:‭ ‬In this study we opted to map the reads‭ (‬36‭ ‬bp‭) ‬to a related reference genome/transcriptome‭ {‬Vijay2013a‭}‬,‭ ‬instead of conducting de novo assembly.‭ Introduction:‭ “‬nuclear genes,‭ ‬reveled both subtle‭” ‬change to‭ “‬nuclear genes,‭ ‬revealed both subtle‭” Reply : ‬Thanks fixed.‭ ‬ Minor comment‭ – ‬AC,‭ ‬PL,‭ ‬LB and SB were already defined in introduction.‭ ‬ Reply : ‬Thanks, removed this. Methods:‭ “‬Fishing in Lake Thingvallavatn was with permissions‭” ‬changed to‭ “‬Fishing in Lake Thingvallavatn was done with permissions‭”‬.‭ ‬ Reply : Ammended. Methods:‭ “‬of differently expressed genes,‭ ‬we preformed clustering analyses‭” ‬change to‭ “‬…we performed clustering analyses‭”‬ Reply : ‬Thanks,‭ ‬fixed. Results:‭ “‬The most drastic changes were seen in processes related to glycolysis‭ (‬GO:0006096,‭ ‬FDR‭ = ‬0.0009‭)‬,‭ ‬were the expression of‭ ‬19‭ ‬out of‭ ‬25‭ ‬genes‭” ‬change to‭ “…‬.‭ ‬where the expression‭”‬.‭ ‬ Reply :‭ ‬Thanks,‭ ‬fixed. Figure‭ ‬7.‭ ‬What does the charr_WT vs.‭ ‬charr_M signify in the alignment data‭?‬ Reply : ‬Designates the two alleles,‭ ‬the legend now makes this explicit.‭ ‭ ‬Discussion‭ “‬We are interested in how predictable evolution is a the molecular level and if there certain principles influence the rewiring of developmental and regulatory systems during evolution‭” ‬consider changing to‭ “‬We are interested in the predictability of evolution at the molecular level,‭ ‬especially whether there exist principles that influence the rewiring of developmental and regulatory systems‭”‬.‭ ‬ Reply : ‬Thanks,‭ ‬excellent suggestion,‭ ‬included Discussion.‭ “‬Recent rainbow trout data shows most paralogs from the latest whole genome duplication event retain the same expression pattern32‭ ‬indicating that this scenario is probably uncommon‭; ‬hence it is of considerable interest when two paralogs show distinct expression patterns‭”‬.‭ ‬I do not agree that it is of considerable interest when two paralogs show distinct expression patterns‭ – ‬I could list tens of examples for salmonids.‭ ‬ Reply :‭ ‬Good point,‭ ‬we have revisited this interpretation‭ (‬see also point by rev.‭ ‬1‭)‬.‭ ‭ ‬ Conclusions‭ “‬The results suggest genetic and expression changes in multiple systems relate to divergence among populations.‭” ‬Change to‭ “‬… associated with divergence among populations.‭” Reply : ‬Thanks,‭ ‬fixed. Main comments‭ & ‬caveats RNAseq study design.‭ ‬ I sympathize with the fact that the authors are trying to publish Illumina data that was generated in‭ ‬2009,‭ ‬since‭ (‬obviously‭) ‬the technology has moved on greatly in the last‭ ‬6‭ ‬years,‭ ‬while its costs have been reduced dramatically.‭ ‬Adding to this is the fact that the authors are using a particularly complex transcriptome in terms of high content of similar paralogues‭ (‬and expressed transposable elements‭)‬,‭ ‬without a reference sequence for mapping in their species.‭ ‬I accept the author’s argument that it is more sensible to map against a closely related species with the sequence data rather than to try and create a‭ ‬de novo‭ ‬assembly from‭ ‬36bp reads.‭ ‬I also believe it is sensible to pool read counts for putative paralogous contigs in this study,‭ ‬since the short read length ablates any ability to separate paralogous differences in expression‭ (‬yet does not preclude the generation of useful hypotheses about putative gene expression differences among morphs‭)‬.‭ ‬However,‭ ‬I do question whether the use of Atlantic salmon EST contigs is the best approach here.‭ ‬Firstly,‭ ‬reference assemblies for both Atlantic salmon and rainbow trout are now available,‭ ‬which distinguish paralogous variation.‭ ‬More importantly,‭ ‬using these reference genome data would provide certainty that reads are being mapped to exons from single genes,‭ ‬whereas many of the ESTs will provide a fragmented representation of exon sequences,‭ ‬presumably relying on annotation to piece them back into‭ ‘‬genes‭’ ‬post hoc‭ ‬.‭ ‬In addition,‭ ‬paired‭ ‬100bp Ilumina reads are available at high coverage for Arctic charr‭ (‬e.g.‭ ‬Norman‭ ‬et al.‭ ‬2014‭)‬,‭ ‬which could also be used to generate a specific reference transcriptome to map against in this study,‭ ‬although this might be underrepresented in terms of developmental genes as it is a gill study.‭ ‬Overall,‭ ‬I do wonder how much more information might have been gleaned from this dataset with a different mapping strategy‭? With all the above said,‭ ‬I understand that the authors have built up a large study based around the original mapping to the salmon ESTs and that it would not be routine for them to repeat the study using better reference data.‭ ‬Furthermore,‭ ‬the approach used has definitely led to the generation of several valid hypotheses concerning the nature of gene expression and genetic differences among charr morphs,‭ ‬which have been followed up using independent approaches.‭ ‬ Reply :‭ ‬We thank the reviewer for excellent diagnosis and suggestions.‭ ‬The paper describes the‭ (‬in our humble opinion‭) ‬most sensible summary of the data,‭ ‬as the writing of the paper started‭ ‬2‭ ‬years ago.‭ ‬We did map on the‭ ‬O.mykiss‭ ‬cDNA collection also,‭ ‬got similar results,‭ ‬but opted for reporting on the salmon data to avoid further extending an already long manuscript.‭ ‬We are currently analyzing DE and SNPs on a new assembly‭ (‬100‭ ‬bp PE reads‭ ‬-‭ ‬48‭ ‬samples‭ ‬-‭ ‬3‭ ‬morphs‭ ‬-‭ ‬development‭)‬,‭ ‬and may include a remapping of this dataset in that.‭ ‭ ‬ Methods‭ “‬Biological Replication in RNAseq‭” – ‬a general comment:‭ ‬obviously the design of the study is not optimal because biological variation within developmental stages is not considered in the statistics.‭ ‬Thus,‭ ‬the approach lacks power to detect differences when morph variation is restricted to different developmental stages.‭ ‬I wanted to explain my opinion‭ (‬for the record‭) ‬that the study design is nonetheless useful for identifying constitutive differences between morphs.‭ ‬This is especially true because gene expression variability is likely to be relatively low in embryonic stages‭ (‬compared to a similar study design in adults at least‭)‬.‭ ‬Further,‭ ‬the pooling of individuals will have helped to at least recapture some biological variation at different stages.‭ ‬Thus,‭ ‬as mentioned above,‭ ‬I see the author’s use of RNAseq as a hypothesis-generating approach,‭ ‬which has been quite fruitful in identifying putative differences between different morphs. ‬Reply :‭ ‬We appreciate the reviewers careful analyses of the study and approach.‭ ‬We tried to emphasize the‭ “‬hypothesis-generation‭” ‬aspect during the rewrite. Methods‭ “‬QPCR study design‭”‬.‭ ‬The authors adhere to the MIQE guidelines,‭ ‬but do not always follow the best approaches.‭ ‬Most pertinently,‭ ‬the authors use the‭ ‬2−‭∆∆‬Ct method‭ (‬assuming PCR efficiency of‭ ‬2.0‭) ‬despite having gone to the effort of gaining and reporting efficiencies for each assay,‭ ‬which can be as low as‭ ‬1.72‭ ‬for some genes.‭ ‬The effect of failing to incorporate differences in efficiency are highly established and this is likely to have affected the author’s results.‭ ‬The authors should consider incorporating the effect of differences in efficiency into their analyses.‭ ‬This is likely to have some impact on the study conclusions in my opinion. Reply : ‬Great point.‭ ‬The qPCR primer efficiencies more than‭ ‬1.90‭ ‬can be easily assumed as‭ ‬2‭ ‬because of the negligible effects.‭ ‬Since we used LinReg software for efficiencies not the traditional method,‭ ‬it takes into account the efficiencies for each test for a given primer pair and discard those have different and lower efficiencies.‭ ‬However,‭ ‬the Natterin-like paralogues were below the cut-off.‭ The statistical analyses were done on deltaCt values, prior to transformation based on efficiencies used for visualization. We ‬now report the graphs of their expression adjusting for the lower efficiency, and state in the results “Note however, the efficiency of the primers for the nattl genes ranged from 1.72 to 1.77, which suggests this data should be interpreted with caution.”‭ ‬ Methods‭ “‬Polymorphisms in charr transcriptome‭”‬.‭ ‬While this is not exactly my area of expertise,‭ ‬I struggled to understand the methods behind filtering paralogous variants from SNPs in the data.‭ ‬The authors state‭ “‬As the SNP analysis was done on individual contigs,‭ ‬differences among paralogs appear in the data.‭ ‬However,‭ ‬since each sample is a pool of few individuals,‭ ‬it is very unlikely that we have the same frequency of true SNPs in the samples.‭ ‬This property was used to remove variants that are most likely due to expressed paralogs‭”‬.‭ ‬Can the authors please try to re-explain this in even simpler terms to help me get it‭? ‬I don’t see how this description leads to a robust identification of paralogous variation.‭ ‬Is there an underlying assumption of equal expression among paralogues‭? ‬If so,‭ ‬this is likely to be routinely invalidated.‭ ‬ Reply : ‬We acknowledge this part is a hard read.‭ ‬We rewrote this part of the methods. Here is another summary.‭ ‬Reads from regions that are very similar in paralogous genes can map to both of them.‭ ‬Because we consider also reads that map to many contigs,‭ ‬some of the candidate variants will reflect sequence differences between paralogs,‭ ‬not polymorphism in either paralog.‭ ‬Next we deploy the population genetic argument,‭ ‬since we are sequencing RNA from‭ ‬6‭ ‬chromosomes in each‭ ‬sample,‭ ‬then it is very unlikely that a TRUE SNP will be at the same frequency in all of the‭ ‬8‭ ‬samples.‭ ‬But variants‭ ‬-‭ ‬that are due to differences bwn paralogs‭ ‬-‭ ‬are likely to be similar in frequency because they are unaffected by the population sampling.‭ ‬This filter is designed to toss those out. To emphasize‭ ‬the objective is not‭ ‬to find differences between paralogs,‭ ‬but rather to enrich for true SNPs.‭ ‬This method will toss out many sites separating paralogous genes (but not all because some paralogous genes are differentially expressed between morphs or time points).‭ ‭ ‬Methods‭ “‬Verification of candidate SNPs‭”‬.‭ ‬While it is good that the authors have attempted to verify SNPs identified from their RNAseq data,‭ ‬I don’t believe the data is particularly well incorporated in the results section.‭ ‬It needs to be stated up front the extent to which the SNPs predicted from the RNAseq were independently verified.‭ ‬Also,‭ ‬the methods for this section can be improved,‭ ‬especially‭ “‬we conducted genomic comparisons of the Salmon genome,‭ ‬ESTs and short contigs from the preliminary assembly of the Arctic charr transcriptome‭”‬.‭ ‬None of this information is elaborated on‭ – ‬what is the preliminary assembly of the Arctic charr transcriptome‭? ‬Which version of the salmon genome was used and how‭? ‬Moreover,‭ ‬it would be useful to actually explain in the methods that the genotyping was done on a small number of SB,‭ ‬PL and PI morphs,‭ ‬rather than relying on the reader to extract all the required information from Table S2.‭ ‬I guess overall,‭ ‬the way this section is incorporated into the manuscript needs some thought in terms of improving the reader’s experience.‭ ‬I struggled after reading it several times and am still not sure I have all the information I need.‭ Reply :‭ ‬We fixed the methods section to accommodate both reviewers which brought up similar points.‭ ‬We highlight the sampling‭ (‬8‭ ‬individuals of‭ ‬3‭ ‬morphs‭)‬,‭ ‬and extend the description of the genomic comparisons.‭ ‬We also extend the discussion of those results. Results.‭ “‬Analyses of those reads require an Arctic charr genome sequence or transcriptome assembly from longer and paired end reads.‭” ‬As mentioned already,‭ ‬the latter is available to generate an Arctic charr transcriptome assembly to map against.‭ ‬ Reply : ‬Unfortunately the great Norman et al‭. ‬2014‭ ‬data‭ (‬http://www.ncbi.nlm.nih.gov/pubmed/24368751‭) ‬came to our attention after we had done these analyses,‭ ‬and started working on our new data‭ (‬see above‭)‬.‭ ‬Thus we opted for not redoing the whole analyses for this manuscript,‭ ‬but focus on the verification‭ ‬-‭ ‬and of course working on a new assembly using longer reads. Results‭; ‬Figure‭ ‬3‭ ‬and‭ ‬4.‭ ‬The authors found that around half the genes studied were not differentially expressed among morphs by qPCR.‭ ‬Obviously this is quite a large number,‭ ‬but on closer inspection,‭ ‬I noticed that‭ ‬Ndub6,‭ ‬Ubl5‭ ‬and‭ ‬parp6‭ ‬were not even differentially expressed according to RNAseq.‭ ‬Thus,‭ ‬I am confused at the selection of genes from the RNAseq analysis for verification by qPCR.‭ ‬The authors should explain this selection more transparently and provide clearer indices of the correlation between RNAseq and qPCR results and associated discussion. Reply : ‬This reflects the history of the project,‭ ‬and the difference between the preliminary and final analyses.‭ ‬We decided to report on all the data‭ ‬-‭ ‬but explain better in the manuscript the classification of genes tested with qPCR,‭ ‬at‭ ‬1%,‭ ‬5%‭ ‬and‭ ‬10%‭ ‬FDR.‭ ‬In summary,‭ ‬some of the genes tested were above‭ ‬5%‭ ‬and one even just above‭ ‬10%‭ ‬FDR.‭ ‬Some of those were not corroborated by qPCR.‭ ‬The number of genes is insufficient to do a statistical comparison of the verification rate at the different FDR levels.‭ ‬A table‭ (‬new Table‭ ‬3‭) ‬-‭ ‬supported with few sentences in the results,‭ ‬hopefully clarifies this. ‬ Minor comments,‭ ‬typos and suggested changes Abstract:‭ “‬Species and populations with parallel evolution of specific traits can help illuminate how predictable adaptations and divergence are at the molecular and developmental level.‭ ‬Grammatically‭ – ‬his reads better:‭ “‬…..‭ ‬can help illuminate the predictability of adaptations and divergence at the molecular and developmental level‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Examples of such a species complex are the finches of the Galapagos islands,‭ ‬cichlids in the African great lakes are exciting multi-species systems in this respect‭”‬.‭ ‬Grammatically‭ – ‬reads better:‭ “‬Examples of such species complexes are provided by finches of the Galapagos islands,‭ ‬while cichlids of the African great lakes also provide an exciting multi-species system in the same respect‭”‬ Reply :‭ ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Some northern freshwater fish species exhibit frequent parallelism in trophic structures and life history and in several cases are they found as distinct resource morphs‭” ‬change to‭ “‬….‭ ‬are found as distinct resource morphs‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬in the development of ecological differences in tropic morphology‭” ‬change to‭ “… ‬trophic morphology‭”‬.‭ ‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬The family is estimated to be between‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old‭”‬.‭ ‬This information is not correct‭ – ‬it is correct to state that the age of the salmonid crown‭ (‬based on the cited paper‭; ‬different estimates exist in the literature,‭ ‬e.g.‭ ‬Macqueen and Johnston,‭ ‬2014‭; ‬Campbell‭ ‬et al.‭ ‬2013‭) ‬is estimated at‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old,‭ ‬but the family dates back much further‭ – ‬to the origin of the WGD event in fact,‭ ‬which occurred more like‭ ‬88-103‭ ‬Ma‭ (‬Macqueen and Johnston,‭ ‬2014‭; ‬Berthelot‭ ‬et al.‭ ‬2014‭)‬.‭ ‬Thus,‭ ‬the last common ancestor to extant salmonid species is what the authors are actually referring to in this sentence. Reply : ‬Thanks so for pointing this out.‭ ‬We changed the text to‭ “‬local adaptation has been extensively studied in the salmonid family,‭ ‬to which Arctic charr belongs‭ {‬Fraser2011‭}‬.‭ ‬The family is estimated to be between‭ ‬88-103‭ ‬million years old‭ {‬Macqueen2014,Berthelot2014c‭}‬.‭ ‬A whole genome duplication event occurred before the radiation of the salmonid family‭ {‬Davidson2010,Moghadam2011,Macqueen2014,Berthelot2014c‭} ‬which has provided time for divergence of ohnologous genes‭ (‬paralogous genes originated by whole genome duplication event‭)‬.‭ ” ‬ Introduction:‭ “‬Furthermore,‭ ‬for data with short reads,‭ ‬mapping to a related reference genome/transcriptome is recommended over de novo assembly‭”‬.‭ ‬While this sentence is technically correct in the context of the work cited,‭ ‬I feel it is being used slightly out of context.‭ ‬For a start,‭ ‬what comprises a‭ ‘‬short read‭’ ‬is undefined.‭ ‬36bp is short,‭ ‬but it is possible to get a sold reference transcriptome using‭ ‬2‭*‬100bp,‭ ‬assuming the appropriate diversity of transcripts is represented and suitable depth is attained.‭ ‬ Reply : ‬Great point,‭ ‬we opted for keeping the point‭ (‬at this place in the ms‭) ‬but changing the wording to:‭ ‬In this study we opted to map the reads‭ (‬36‭ ‬bp‭) ‬to a related reference genome/transcriptome‭ {‬Vijay2013a‭}‬,‭ ‬instead of conducting de novo assembly.‭ Introduction:‭ “‬nuclear genes,‭ ‬reveled both subtle‭” ‬change to‭ “‬nuclear genes,‭ ‬revealed both subtle‭” Reply : ‬Thanks fixed.‭ ‬ Minor comment‭ – ‬AC,‭ ‬PL,‭ ‬LB and SB were already defined in introduction.‭ ‬ Reply : ‬Thanks, removed this. Methods:‭ “‬Fishing in Lake Thingvallavatn was with permissions‭” ‬changed to‭ “‬Fishing in Lake Thingvallavatn was done with permissions‭”‬.‭ ‬ Reply : Ammended. Methods:‭ “‬of differently expressed genes,‭ ‬we preformed clustering analyses‭” ‬change to‭ “‬…we performed clustering analyses‭”‬ Reply : ‬Thanks,‭ ‬fixed. Results:‭ “‬The most drastic changes were seen in processes related to glycolysis‭ (‬GO:0006096,‭ ‬FDR‭ = ‬0.0009‭)‬,‭ ‬were the expression of‭ ‬19‭ ‬out of‭ ‬25‭ ‬genes‭” ‬change to‭ “…‬.‭ ‬where the expression‭”‬.‭ ‬ Reply :‭ ‬Thanks,‭ ‬fixed. Figure‭ ‬7.‭ ‬What does the charr_WT vs.‭ ‬charr_M signify in the alignment data‭?‬ Reply : ‬Designates the two alleles,‭ ‬the legend now makes this explicit.‭ ‭ ‬Discussion‭ “‬We are interested in how predictable evolution is a the molecular level and if there certain principles influence the rewiring of developmental and regulatory systems during evolution‭” ‬consider changing to‭ “‬We are interested in the predictability of evolution at the molecular level,‭ ‬especially whether there exist principles that influence the rewiring of developmental and regulatory systems‭”‬.‭ ‬ Reply : ‬Thanks,‭ ‬excellent suggestion,‭ ‬included Discussion.‭ “‬Recent rainbow trout data shows most paralogs from the latest whole genome duplication event retain the same expression pattern32‭ ‬indicating that this scenario is probably uncommon‭; ‬hence it is of considerable interest when two paralogs show distinct expression patterns‭”‬.‭ ‬I do not agree that it is of considerable interest when two paralogs show distinct expression patterns‭ – ‬I could list tens of examples for salmonids.‭ ‬ Reply :‭ ‬Good point,‭ ‬we have revisited this interpretation‭ (‬see also point by rev.‭ ‬1‭)‬.‭ ‭ ‬ Conclusions‭ “‬The results suggest genetic and expression changes in multiple systems relate to divergence among populations.‭” ‬Change to‭ “‬… associated with divergence among populations.‭” Reply : ‬Thanks,‭ ‬fixed. Competing Interests: No competing interests were disclosed.No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 01 Jun 2015 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 3 (revision) 02 Dec 16 read Version 2 (revision) 25 Apr 16 read read Version 1 01 Jun 15 read read Daniel Macqueen , University of Aberdeen, Aberdeen, UK Anne Dalziel , Laval University, Quebec City, Canada Örjan Östman , Swedish University of Agricultural Sciences, Uppsala, Sweden Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2016 Östman Ö. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 20 Dec 2016 | for Version 3 Örjan Östman , Department of Aquatic Resources, Swedish University of Agricultural Sciences, Uppsala, Sweden 0 Views copyright © 2016 Östman Ö. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have addressed all my comments and incorporated them into the manuscript. So my opinion of the study is it will be an important contribution for further work, and thus, I approve this version of the manuscript. Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Östman Ö. Peer Review Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.10982.r18642) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/4-136/v3#referee-response-18642 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2016 Östman Ö. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Aug 2016 | for Version 2 Örjan Östman , Department of Aquatic Resources, Swedish University of Agricultural Sciences, Uppsala, Sweden 0 Views copyright © 2016 Östman Ö. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome of eggs and larvae from a natural population of small benthic charr (SB) and Icelandic aquaculture charr (AC), which is fast growing and have a ‘limnic-like’ morphology. They find a list of potential candidate genes involved in the ecological differentiation of artic charr (and during the embryonic development). In addition they studied the transcriptome from different tissues of adult AC-charr. From the transcriptome of these populations two populations they developed 12 SNP-markers applied to other sympathric (Lake Thingvallavatn) wild morphs to study if these genes differed between other morphs. Finally they also study mtDNA expression between morphs to find they mainly differ between the benthic morphs and a limnic. The search for genes involved in the ecological divergence of species is an important topic that has exploded the last decade with the new generation of sequencing. I find this study to be an important contribution because of the study system with artic charr is an example of relative recent and rapid divergence into many different morphs/ecological, and the extensive and thorough investigation of the differences in the transcriptome between morphs. However, I think the authors try to stretch their conclusions a bit too far. The study is great as a base for further research in the topic, which I guess is in the pipeline. The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion). The cultivated population has been selected for fast body growth and they are not from Lake Thingvallavatn and little can therefore be said about the genetics of the ecological differentiation of sympatric species. Not very surprising genes related to metabolism seemed upregulated in AC and immunogens upregulated in SB. What does that actually tells us about the genetics of ecological differentiation of natural populations?? Although the aims on p. 4 feels valid, they are not contingent with the previous text in the introduction. Thus, I suggest that the much of the earlier part of the introduction is rewritten to actually address the differences in gene expression between a cultivated morph and its extreme opposite small benthic artic charr. As far as I understand it is only egg that are kept in the same environment, but the parents have been raised in different environments and transgenerational plasticity cannot be ruled out. This is not a major criticism (the ideal case would be to have had them in lines in a common environment of course) but needs to be addressed in the text. The ‘Nattl’ paralogs provide an interesting case where the expression of different paralogs has been studied. But again, are the result difficult to interpret from an ecological niche differentiation perspective. Often is the natural small limnic morph (PL) in between SB and AC (Fig. 5A), but for Nattl1 and Nattl2 AC and SB seem most similar? The connection to the original question is weak and the authors do not conclude more than “…it is not divergence in the expression of one paralog that explains the general nattl expression disparity in the transcriptome.” Fair enough, but that is more about the genetic architecture than the genetics of ecological divergence. In the validation of the transcriptome differences with qPCR of 9 genes/paralogs only 5 was still significant. What conclusions should one make out of that, that around half of the 296 paralogs differing between SB and AC are false detection (despite FDR < 5%). I support the use of qPCR but please comment on the implication of this. I think Fig. 6 should be converted into a bar-graph plot instead (this feels more like a table). To conclude, I think this study is a great contribution for suggesting putative differences in the transcriptome of a fish species. However, the importance for understanding ecological differentiation of sympatric species it is, however, so far limited as it that would require more using natural morphs, replicated populations, back-crosses, investigation of plasticity and how reproductive isolation is maintained etc., which is likely to come. But until that, I suggest the this text should mainly focus on the differences between SB and AC arctic charr, and not try to squeeze in everything in one paper. Minor comments: In the equation on p. 6 I guess M & T is ‘Morph’ and ‘Time’? If so spell out or use M & T consistently. Note that on p. 8 the qPCR of 8 paralogs in embryonic heads is metioned but the results do not come until “Expression differences in the developing heads of benthic and limnetic charr morphs”. Use “.” instead of “,” as decimal sign in Fig. 4. Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 02 Dec 2016 Arnar Palsson, Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland The study by Gudbrandsson et al. reports a thorough analysis of differences in the transcriptome between different ‘morphs’ or ‘populations’ of artic charr. More specifically they have studied the transcriptome of eggs and larvae from a natural population of small benthic charr (SB) and Icelandic aquaculture charr (AC), which is fast growing and have a ‘limnic-like’ morphology. They find a list of potential candidate genes involved in the ecological differentiation of artic charr (and during the embryonic development). In addition they studied the transcriptome from different tissues of adult AC-charr. From the transcriptome of these populations two populations they developed 12 SNP-markers applied to other sympathric (Lake Thingvallavatn) wild morphs to study if these genes differed between other morphs. Finally they also study mtDNA expression between morphs to find they mainly differ between the benthic morphs and a limnic. The search for genes involved in the ecological divergence of species is an important topic that has exploded the last decade with the new generation of sequencing. I find this study to be an important contribution because of the study system with artic charr is an example of relative recent and rapid divergence into many different morphs/ecological, and the extensive and thorough investigation of the differences in the transcriptome between morphs. However, I think the authors try to stretch their conclusions a bit too far. The study is great as a base for further research in the topic, which I guess is in the pipeline. The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion). The cultivated population has been selected for fast body growth and they are not from Lake Thingvallavatn and little can therefore be said about the genetics of the ecological differentiation of sympatric species. Not very surprising genes related to metabolism seemed upregulated in AC and immunogens upregulated in SB. What does that actually tells us about the genetics of ecological differentiation of natural populations?? Although the aims on p. 4 feels valid, they are not contingent with the previous text in the introduction. Thus, I suggest that the much of the earlier part of the introduction is rewritten to actually address the differences in gene expression between a cultivated morph and its extreme opposite small benthic arctic charr. Reply: We thank the reviewer for his comments, we have now carefully reviewed the introduction, results, discussion and conclusions to address his concerns. We provide below an excerpt of most of the changes made. “However, I think the authors try to stretch their conclusions a bit too far” Reply: We acknowledge that the discussion in particular, worded the conclusions about ecological effects too strongly and have toned those down, for example: “The charr developmental transcriptome provides a starting point to investigate the molecular systems that associate with divergence among the highly polymorphic and rapidly evolving Arctic charr in Iceland.” “The embryos were reared in a common garden setting, which minimizes the impact of environmental factors, as we are interested in genes showing expression differences between the two morphs. Those genes might implicate pathways involved in the ecological divergence among charr populations and of course adaptation of the AC charr during breeding 50 “ The use of cultivated charr make sense for comparing the most extreme morphs. But to me it does not make sense for making conclusions about genes involved in the ecological niche differentiation in natural populations, which is the motivation of the study in the introduction and brought up in the discussion) … Reply: We agree with the reviewer’s remarks, the flow of the introduction and partly the discussion was not optimal, with the interpretations overreaching in some places. We have now restructured the introduction, added a separate section on Aquaculture charr, and improved the description of the results. For each result section we tried to make clear where the data support conclusions about the difference between SB and AC only or more general about benthic - limnetic differences (like where the follow up qPCR or SNP validation involved also samples of Lake Thingvallavatn morphs). Also, throughout the manuscript we also brought the contrast of AC and SB charr into sharper focus, and the fact that many patterns can reflect the AC charr domestication, for example: “The aim of this study was to find expression and genetic differences separating the small benthic morph in Lake Thingvallavatn and aquaculture charr, with the long term objective being to reveal the genetic and molecular systems that associate with benthic morphology in charr. The transcriptome reflects the biology of these two morphs, their different histories and ecology. AC-charr will also be shaped by domestication, which may explain for instance the higher expression of metabolic genes in AC-charr.” “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens or in families of Aquaculture charr breed for pathogen resistance.” “The results suggest divergence (adaptive or neutral) in mitochondrial function due to the domestication of aquaculture charr and/or adaptation of the small benthic charr to its habitat. Increase in mitochondrial function in AC charr embryos could reflect higher basal metabolic rate in this aquaculture stock. Alternatively, lower metabolic rate in the SB charr would also be curious in the context of their ecology. Clearly further work is needed to map out the functional differences of mitochondrial related genes in AC charr, more SB populations and hopefully anadromous charr morphs (representing the ancestral state).” As far as I understand it is only egg that are kept in the same environment, but the parents have been raised in different environments and transgenerational plasticity cannot be ruled out. This is not a major criticism (the ideal case would be to have had them in lines in a common environment of course) but needs to be addressed in the text. Reply: We add a sentence about transgenerational plasticity in the discussion. “We raised the embryos in a common garden, but their parents were wild so parental environments and transgenerational plasticity may also have contributed.” The ‘Nattl’ paralogs provide an interesting case where the expression of different paralogs has been studied. But again, are the result difficult to interpret from an ecological niche differentiation perspective. Often is the natural small limnic morph (PL) in between SB and AC (Fig. 5A), but for Nattl1 and Nattl2 AC and SB seem most similar? The connection to the original question is weak and the authors do not conclude more than “…it is not divergence in the expression of one paralog that explains the general nattl expression disparity in the transcriptome.” Fair enough, but that is more about the genetic architecture than the genetics of ecological divergence. Reply: This issue is of interest to us. We wanted to work more on the Nattl genes and confess that the paragraph did not summarize the data properly. We paraphrased it, toning down the interpretation and added a more forward looking statement – about how to build on this dataset “It would be interesting to study further the expression of these and other immunological genes implicated in this transcriptome, in natural charr populations and juveniles challenged with pathogens.” In the validation of the transcriptome differences with qPCR of 9 genes/paralogs only 5 was still significant. What conclusions should one make out of that, that around half of the 296 paralogs differing between SB and AC are false detection (despite FDR < 5%). I support the use of qPCR but please comment on the implication of this. Reply: The transcriptome was done on 4 timepoints, but only 2 of those were used for the qPCR verification. Thus we expect incomplete correlation, because of the contribution of the earliest or latest (in particular) timepoints. We explain this clarification to the results on qPCR verification (Table 3) and added a caveat, “Thus this transcriptome should not be taken at face value, because substantial fraction of signals were false positives. ” I think Fig. 6 should be converted into a bar-graph plot instead (this feels more like a table). Reply: We opted for this heatmap-table representation, as we feel it emphasizes the benthic – limnetic separation most clearly. The other option we explored was indeed a bar-graph (Supplemental figure 2), with extra lines and stars indicating the significance of the post-hoc tests, but felt the heatmap-table captured best the pattern. To conclude, I think this study is a great contribution for suggesting putative differences in the transcriptome of a fish species. However, the importance for understanding ecological differentiation of sympatric species it is, however, so far limited as it that would require more using natural morphs, replicated populations, back-crosses, investigation of plasticity and how reproductive isolation is maintained etc., which is likely to come. But until that, I suggest the this text should mainly focus on the differences between SB and AC arctic charr, and not try to squeeze in everything in one paper. Reply: We acknowledge that with respect to our long term research goals, this can be viewed as a pilot study as the contrast is between the SB and AC charr. In this version we tried to focus more on describing the differences between SB and AC charr, and highlight also results that may reflect the AC-charr biology (see some sentences listed above, and more in the manuscript). By validating differential gene expression and some of the SNPs also on samples from more wild populations, the study also revealed interesting candidates for follow up studies addressing the long term objectives of the group. Minor comments: In the equation on p. 6 I guess M & T is ‘Morph’ and ‘Time’? If so spell out or use M & T consistently. Reply: We opted for spelling out Morph and Time – its more transparent. Note that on p. 8 the qPCR of 8 paralogs in embryonic heads is mentioned but the results do not come until “Expression differences in the developing heads of benthic and limnetic charr morphs”. Reply: Good point, now the next section is referenced “… and 8 in embryonic heads (see next section)...” Use “.” instead of “,” as decimal sign in Fig. 4. Reply: Fixed. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Östman Ö. Peer Review Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.9044.r15587) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/4-136/v2#referee-response-15587 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2016 Macqueen D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 May 2016 | for Version 2 Daniel Macqueen , Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK 0 Views copyright © 2016 Macqueen D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Second review of Gudbrandsson et al . “ The developmental transcriptome of contrasting Arctic charr (Salvelinus alpinus) morphs”. Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range of associated changes into version 2 of their paper. Readers will find these changes, along with several clarifications provided in the published response to reviewers section, to facilitate transparent interpretation of this large and diverse study, including its strengths and caveats. My overall opinion of the study remains unchanged – it is interesting and reports findings of merit that will be followed up on in future work. I am thus happy to approve version 2 of the paper . I did spot a few typos or grammatical issues that the authors might address and had some final comments that might be addressed – all of a minor nature and easy to address. Abstract – “ energy metabolism and blood coagulation genes ” remove “ genes ” Abstract - “ Comparison of single nucleotide polymorphism (SNP) frequencies reveals ” change “ reveals ” to “ revealed ” (for accurate use of tense) Introduction – “ Examples of such species complexes are provided finches of the Galapagos island” should be “ Examples of such species complexes are provided by finches of the Galapagos island ” Introduction: “ Thus we were quite keen to apply RNA-sequencing to analyze ecomorphs in our study system, Arctic charr ”. The authors should add the Latin name for charr here, rather than in the next paragraph. Introduction: “ The family is estimated to be between 88–103 million years old 21,22 . A whole genome duplication event occurred before the radiation of the salmonid family 21–24 which has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event ” It would be simpler to just state that the common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago. The actual age of the salmonid family depends on whether one considers the (extinct) direct ancestors to salmonids that didn’t experience genome duplication to be salmonids. Introduction: “ Furthermore, recent estimates from the rainbow trout (Oncorhynchus mykiss) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that on the order of 4500 were retained in rainbow trout 22 ” This information is inaccurate. Firstly, based on the paper cited (Berthalot et al . 2014), this information should state that around 4,500 pairs of ohnologous genes were retained from Ss4R (i.e. around 9,000 separate genes). More importantly, without going into detail, the stated data represents a non-comprehensive fraction of the genome. I suggest the authors update this part of the text with accurate estimates, since the number of retained Ss4R ohnologue pairs in much larger than what is stated. The authors might also draw in more comprehensive data from the recent publication of the Atlantic salmon genome (Lien et al. Nature, 533, 200–205) 1 . The simplest way to present the information is to state that around half of the original Ss4R ohnologue pairs are still functionally retained (both stated papers are in agreement about that). Figure 1: It would be easier for the reader to link the text and images if the authors updated with ‘a’, ‘b’, ‘c’ and ‘d’ panels for each of the different charr morphs. Introduction: “ In this study, we compare SB-charr from ” should be “ In this study, we compared SB-charr from ” (again, it is correct here to use past tense – the authors should check the rest of the manuscript for similar tense issues). Figure 2: Minor comments – the text “ Map on salmon genes ” is vague and open to several interpretations. Better: “ Map on Atlantic salmon expressed sequence tags ”? References 1. Lien S, Koop BF, Sandve SR, Miller JR, et al.: The Atlantic salmon genome provides insights into rediploidization. Nature . 2016; 533 (7602): 200-5 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 02 Dec 2016 Arnar Palsson, Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland Overview: The authors have addressed the comments made by myself and Anne Dalziel. They have incorporated a range of associated changes into version 2 of their paper. Readers will find these changes, along with several clarifications provided in the published response to reviewers section, to facilitate transparent interpretation of this large and diverse study, including its strengths and caveats. My overall opinion of the study remains unchanged – it is interesting and reports findings of merit that will be followed up on in future work. I am thus happy to approve version 2 of the paper . I did spot a few typos or grammatical issues that the authors might address and had some final comments that might be addressed – all of a minor nature and easy to address. Abstract – “ energy metabolism and blood coagulation genes ” remove “ genes ” Abstract - “ Comparison of single nucleotide polymorphism (SNP) frequencies reveals ” change “ reveals ” to “ revealed ” (for accurate use of tense) Introduction – “ Examples of such species complexes are provided finches of the Galapagos island” should be “ Examples of such species complexes are provided by finches of the Galapagos island ” Introduction: “ Thus we were quite keen to apply RNA-sequencing to analyze ecomorphs in our study system, Arctic charr ”. The authors should add the Latin name for charr here, rather than in the next paragraph. Reply: They have all been fixed. Introduction: “ The family is estimated to be between 88–103 million years old 21,22 . A whole genome duplication event occurred before the radiation of the salmonid family 21–24 which has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event ” It would be simpler to just state that the common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago. The actual age of the salmonid family depends on whether one considers the (extinct) direct ancestors to salmonids that didn’t experience genome duplication to be salmonids. Reply: Good suggestion, we now use this wording. Introduction: “ Furthermore, recent estimates from the rainbow trout (Oncorhynchus mykiss) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that on the order of 4500 were retained in rainbow trout 22 ” This information is inaccurate. Firstly, based on the paper cited (Berthalot et al . 2014), this information should state that around 4,500 pairs of ohnologous genes were retained from Ss4R (i.e. around 9,000 separate genes). More importantly, without going into detail, the stated data represents a non-comprehensive fraction of the genome. I suggest the authors update this part of the text with accurate estimates, since the number of retained Ss4R ohnologue pairs in much larger than what is stated. The authors might also draw in more comprehensive data from the recent publication of the Atlantic salmon genome (Lien et al. Nature, 533, 200–205) 1 . The simplest way to present the information is to state that around half of the original Ss4R ohnologue pairs are still functionally retained (both stated papers are in agreement about that). Reply: We thank the reviewer for a good point and clarification. We adopt the wording and rewrote part of this paragraph, it now reads: “The common ancestor to salmonids experienced a whole genome duplication 88–103 million years ago, the fourth vertebrate whole- genome duplication (Ss4R) 21 – 24 . This has provided time for divergence of ohnologous genes (paralogous genes originated by whole genome duplication event) in salmonid lineages. Estimates from the rainbow trout ( Oncorhynchus mykiss ) genome suggest that ohnologous genes are lost at a rate of about 170 genes per million years, and that around half of the original Ss4R ohnologue pairs are still functionally retained in rainbow trout 22 .” Figure 1: It would be easier for the reader to link the text and images if the authors updated with ‘a’, ‘b’, ‘c’ and ‘d’ panels for each of the different charr morphs. Reply: This has been fixed. Introduction: “ In this study, we compare SB-charr from ” should be “ In this study, we compared SB-charr from ” (again, it is correct here to use past tense – the authors should check the rest of the manuscript for similar tense issues). Reply: Fixed, we went through the manuscript and corrected a few more errors of this type. Figure 2: Minor comments – the text “ Map on salmon genes ” is vague and open to several interpretations. Better: “ Map on Atlantic salmon expressed sequence tags ”? Reply: Fixed, put “ Map on Atlantic salmon ESTs” in the figure and “ESTs = expressed sequence tags ” into legend. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Macqueen D. Peer Review Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.9044.r13702) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/4-136/v2#referee-response-13702 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2015 Dalziel A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Jul 2015 | for Version 1 Anne Dalziel , Institute for Systems and Integrative Biology (IBIS), Department of Biology, Laval University, Quebec City, QC, Canada 0 Views copyright © 2015 Dalziel A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions In this paper “The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs” Gudbrandsson et al . have tested for differential gene expression at multiple developmental time-points among a number of Artic charr morpho-types from Lake Thingvallavatn (3 wild morphs, 1 studied with RNA-seq and qPCR, the others with qPCR only) and Holar aquaculture (1 domesticated morph, RNA-seq and qPCR). They have also studied multiple tissues/body regions for a subset of the differentially expressed genes found with RNA-seq. The goal of the paper was to find candidate genes that may underlie variation in morphology, with a focus on craniofacial morphology related to benthic vs. limnetic feeding. In general, I think this goal was met and this paper contributes to our understanding of the mechanisms contributing to morphological evolution in a non-genetic model organism. The authors provide an extensive, multi-time point comparison of two morphologically divergent groups of charr reared in a common environment (reducing the influence of phenotypic plasticity) and have collected a tremendous amount of data. This information will help them to hone in on the genetic loci contributing to phenotypic evolution in this very interesting system, and on the effects of domestication. However, there are a number of major issues that do need to be more clearly addressed in the manuscript prior to final publication. I have outlined these comments below. Major Comments Introduction : Requires some reorganization, clarification of what phenotypes have evolved in parallel among morphs, and how the authors separate the effects of domestication (SB vs. AC) from benthic/limnetic evolution (SB/LB vs. PL/AC). a) At present, the introduction focuses upon the utility of instances of parallel evolution to help us determine how repeatable evolutionary change may be. This is definitely true, and the repeated evolution of the dwarf, benthic morph (SB; the focus of the introduction/abstract/discussion) in many lakes strongly argues that this phenotype has evolved via natural selection. However, it is not clear to me if true ‘parallelism’ seen among the SB (small benthic) and LB (large benthivorous) vs. AC (Holar aquaculture) and PL (small planktivorous) morphs because not enough information is provided for me to assess this. To support the argument for parallelism the specific traits that have evolved in parallel among morphs must be displayed and the evolutionary history of these morphs should be clarified (e.g. in paragraph 6 and Figure 1). As well, any related non-parallelism in traits should also be discussed (i.e. how are the domesticated AC and wild PL different?). At present Figure 1 only shows the AC and SB morphs, and does not point out the specific traits they are interested in. This is critical background information for readers who are not familiar with this system. b) The comparison of AC (domestic, limnetic-like head) vs. LB (wild, benthic like head) looks at two confounded variables: domestication and the benthic/limnetic morphology. This should be clearly stated in the introduction, and the use of the additional morphs (PL, LB) in detangling domestication vs. benthic/limnetic evolution should be noted. c) The use of the AC morph is still a bit unclear to me. The argument for point ‘ii) of the availability of abundant AC material’ could be expanded by providing more information on the ‘limnetic’ like features of this morph and why it is an appropriate comparison to a benthic morph, the genetic divergence from the lake Thingvallavatn fish, and also the selection regime it has experienced (selection for limnetic features? What other traits vary with domestication?). d) Paragraph 2 – Much of this paragraph, including discussing the ability to measure gene expression and relate to phenotype in fishes, is unnecessary as fish are no different from other vertebrates in this respect. Instead, the final sentence “One approach to identify pathways related to function or morphological differences is to study gene expression during development” should become the ‘topic sentence’ and expanded upon to explain why gene expression studies are especially relevant ways to link genotype to phenotype in evo-devo studies. e) Better highlight the strengths – The authors have done a wonderful job of assessing multiple developmental time points and rearing fish in a common garden environment. However, they do not highlight these strengths. Some small notes on the importance of controlling for phenotypic plasticity in these traits (which are known to be quite plastic) to better study genetic differentiation would be a nice addition. Methods: a) Page 4 paragraph 1 - Clarify the number of fish used to make the crosses (this will help us determine the likelihood of selecting a full or half-sib for sequencing/qPCR). b) I should note that I am not an expert in the analysis of RNA-seq data, but luckily the first reviewer has done an excellent job of commenting upon these aspects of the project. I fully agree with their comments and suggestions. I would also like to see more information on the methods used to pool samples and how RNA-seq data was normalized among samples, developmental times and morphs. I will also note that the authors often use S.salar for comparisions, not O.mykiss , which is a closer relative to S.alpinus . The reasons for this approach should be discussed. c) I am also not trained as a population geneticist. However, from my experience studying paralogous genes in salmonids, and with respect to the author’s own findings for the Nattl paralogs (Fig 4), I do not think it is prudent to “assume that the expression of paralogous genes is stable… ” in the methods (page 12). In fact, Berthelot et al . (2014) find the opposite (see my comments for the discussion). d) The authors should use their genetic information to test if the fish chosen are siblings with each other (full or half-sibs). This may have important implications for the population genetic analyses. e) Page 5 - It is not appropriate to change the meaning of the word ‘gene’. I think it is much clearer to use the term ‘paralog group’ or ‘gene family’ when referring to the fact that the authors do not study single genes, but instead groups of paralogs. f) Selection of genes for qPCR – the methods by which genes for the qPCR studies (Fig 3) were selected should be clearly noted. From my reading, it seems that most of these genes do not significantly vary among SB and AC at the 1% FDR level (Tables 1 and 2; only Natterin?). Thus, I am assuming these genes are only significant at the 5% FDR level (S1 file) – why focus upon these and not those significant at 1%? As well, it would be good to include information on why different genes were selected for Figure 3 (qPCR validation of whole fish) and Figure 4 (candidate genes-qPCR validation in just the head). Finally, the abbreviations used for qPCR validation should also be listed in Table 1 for easy comparisons among figures/tables. Results & Figures: a) Include an experimental design figure - At present, it is difficult to keep track of all of the morphotypes, tissues, and developmental time points used without referring to the methods. Thus, an experimental design figure summarizing the samples used (morphotype, population, sample size, developmental time point), how they were pooled and which techniques were used to measure gene expression on each sample (RNA-seq and/or qPCR) is needed. b) Include the LB and PL morphs in Figure 1 and clarify traits of interest – The legend states that “differences in size, coloration and head morphology are apparent”, but it would be better to specifically point out the differences they are referring to. F1000 is for a general audience, and this would help non-ichthylogists better understand what ecologically-important traits the authors are interested in (e.g. those related to benthic/limnetic feeding). In addition, the two other morphs used in the qPCR studies should also be displayed (large benthivorous and small planktivorous) to facilitate phenotypic comparisons and assess parallelism in benthic/limnetic feeding and/or the effects of domestication on AC. c) Figure 5- this is actually a table not a figure (?) and is a bit confusing. I think it is much easier to interpret Figure S2 (displaying the data as in Fig 3 and 4), and that Fig 5 and S2 should be switched. It would be great to show significant differences in mRNA content in this, and all other figures, by including symbols. Also, full gene names should be listed in all figure legends. Discussion: The discussion focuses on the SB morph (page 17 – “The objective of this study were to get a handle on genetic and molecular systems that associate with benthic morphology in charr by mainly focusing on the small benthic morph in Lake Thingvallavatn, Iceland”), while the introduction discusses parallel evolution (indicating that the comparisons should be among many morphs). These are two different topics i) mRNA content differences among benthic vs. limnetic morphs changing in parallel or ii) linking mRNA content to phenotype in SB (benthic, wild) vs. AC (limnetic head, domesticated) morphs. In particular, the role of domestication vs. wild fish divergence needs to be addressed. At present these two topics/questions are mixed in the introduction/discussion and should be addressed separately. a) Paragraph on Immune Defences - Is immunity also expected to evolve in parallel in all benthic morphs? Is this predicted to be unique to SB vs. AC? Whatever the case, the parallelism (or not) in these genes should also be discussed, and whether this relates more to domestication in AC or differences between limnetic vs. benthic fish. Much of the functional discussion can also be cut. b) Page 18 – The information about genes found to be differentially expressed among morphs in your prior work should also be in the introduction, as it is background work that explains why you took this transcriptomic approach. This can also be used to explain why you focused in on particular qPCR genes. c) A discussion of domestication related differences vs. benthic/limnetic differences should be included. I think the data from head gene expression is very interesting (Figs 5, S2) and really speaks to this question. d) In general, the role of stochastic evolutionary processes, and not just selection (artificial and natural) should be noted. For example, if the AC charr were simply taken from a stock with a different mtDNA haplotype then these differences in the mtDNA genome might not be adaptive, just random. If the AC fish has much higher mtDNA expression might this be simply a domestication issue and not indicative of selection in SB as stated? Finally, you find that not all mitochondrial transcripts (which are transcribed as a polycistronic transcript) are found at similar levels (Table 1) – what does this tell you about differential degradation/post-transcriptional processes? e) There is no discussion about the “Analyses of polymorphism in Arctic charr transcriptome” (Table 3, 4, 5), except for the mtDNA. Minor Comments Introduction: a) Paragraph 3 – “Furthermore, recent estimates from the rainbow trout….by utilizing multiple data sources the genome assembly problem of this family can be solved”. I am not sure how this statement is relevant to this particular study. This and the following statement seem more appropriate for the methods/discussion to me. b) The morphs being discussed should be clarified throughout the paper. For example, the authors often state “among morphs/among charr populations” but it is not clear which of the many morphs they are referring to (e.g. Paragraph 5, first sentence on allozymes and mtDNA and later sentence on MCHIIa – do you mean all 4 morphs of specific 2-way comparisons? Are some morphs more differentiated than others?) Methods: a) The authors should note why they did not use the PI (large piscivorous) morph in any qPCR studies (in the methods or discussion) as this would be a nice morph to use in their tests for parallelism. b) Page 5 (last paragraph) – the methods used to remove particular variants needs to be clarified. In particular, why the assumptions used to remove variants are valid by referencing past studies. Figures & Results: a) Figure 2. The key for Figure 2 should include a specific heading for morph and time-point with the abbreviations restated [e.g. Timepoint: 141 dpf, Morph: Small Benthic (SB)]. b) Figure 6 – would be helpful to label the protein coding genes in this figure as well as the 12s and 16s RNAs. c) Figure 7 – It is not clear to me which variant is present in which morph. Adding the nucleotide to the x-axis (i.e. frequency of m1829G for B) would make this figure easier to quickly interpret. The “A.charr_WT” and “A.charr_M” should also be defined in the legend and it would be more appropriate to use scientific names for all species. Discussion: a) Discussion of reference 32 – The discussion of reference 32 is not put into the proper context. Figure 6 of this paper (Berthelot et al . 2014) shows that there are many genes that have no correlation among expression patterns and/or differences in expression levels (1573, 1248, and 1895=4716 paralog pairs), and that together these represent more than the 1,407 correlated/similar expression level paralogs. This section of the discussion needs to be modified. b) The Norman et al . (2014) paper should be mentioned earlier – if this is available why was it not used for their analyses? As well, the last sentence in this paragraph can be cut as it is evident. c) Page 18 – “Our new data also demonstrate differences in craniofacial elements between AC- and SB-charr, along a limnetic vs. benthic axis 79 ”. Are you referring to ref 79 or data from this study? If you are referring to 79, clarify and note what you found. This occurs a few times in the discussion General grammatical errors There are a number of grammatical errors throughout this paper (e.g. “31 genes were higher expressed in SB and 40 genes higher in AC-charr”; “that may help sculpture benthic vs. limnetic heads” pg 19). Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 25 Apr 2016 Arnar Palsson, Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland Major Comments Introduction:‭ ‬Requires some reorganization,‭ ‬clarification of what phenotypes have evolved in parallel among morphs,‭ ‬and how the authors separate the effects of domestication‭ (‬SB vs.‭ ‬AC‭) ‬from benthic/limnetic evolution‭ (‬SB/LB vs.‭ ‬PL/AC‭)‬.‭ ‬a‭) ‬At present,‭ ‬the introduction focuses upon the utility of instances of parallel evolution to help us determine how repeatable evolutionary change may be.‭ ‬This is definitely true,‭ ‬and the repeated evolution of the dwarf,‭ ‬benthic morph‭ (‬SB‭; ‬the focus of the introduction/abstract/discussion‭) ‬in many lakes strongly argues that this phenotype has evolved via natural selection.‭ ‬However,‭ ‬it is not clear to me if true‭ ‘‬parallelism‭’ ‬seen among the SB‭ (‬small benthic‭) ‬and LB‭ (‬large benthivorous‭) ‬vs.‭ ‬AC‭ (‬Holar aquaculture‭) ‬and PL‭ (‬small planktivorous‭) ‬morphs because not enough information is provided for me to assess this.‭ ‬To support the argument for parallelism the specific traits that have evolved in parallel among morphs must be displayed and the evolutionary history of these morphs should be clarified‭ (‬e.g.‭ ‬in paragraph‭ ‬6‭ ‬and Figure‭ ‬1‭)‬.‭ ‬As well,‭ ‬any related non-parallelism in traits should also be discussed‭ (‬i.e.‭ ‬how are the domesticated AC and wild PL different‭?)‬.‭ ‬At present Figure‭ ‬1‭ ‬only shows the AC and SB morphs,‭ ‬and does not point out the specific traits they are interested in.‭ ‬This is critical background information for readers who are not familiar with this system. Reply: ‭ ‬These are excellent suggestions.‭ ‬At the end of the intro we stress the difference between the aims of our research program‭ (‬study the genetics of parallel evolution‭) ‬and the aims of this study‭ (‬get a handle on differences between sympatric morphs,‭ ‬with the AC as possible outgroup‭)‬.‭ ‬The morphs studied here do not represent parallel evolution of benthic phenotypes‭ (‬SB and LB are both from the same lake and appear to be closely related‭ ‬-‭ ‬Kapralova et al‭ ‬2011‭)‬.‭ ‬Analyses of that question requires further studies.‭ ‬This data can implicate genes that separate PL/AC and SB/LB and may be studied in such follow up analyses of more populations.‭ ‬We have updated figure‭ ‬1‭ ‬as advised‭ ‬-‭ ‬including the‭ ‬4‭ ‬morphs studied,‭ ‬expanded on the legend and also provide an overview of research approach‭ (‬part B‭)‬.‭ ‬ b‭) ‬The comparison of AC‭ (‬domestic,‭ ‬limnetic-like head‭) ‬vs.‭ ‬LB‭ (‬wild,‭ ‬benthic like head‭) ‬looks at two confounded variables:‭ ‬domestication and the benthic/limnetic morphology.‭ ‬This should be clearly stated in the introduction,‭ ‬and the use of the additional morphs‭ (‬PL,‭ ‬LB‭) ‬in detangling domestication vs.‭ ‬benthic/limnetic evolution should be noted.‭ ‬c‭) ‬The use of the AC morph is still a bit unclear to me.‭ ‬The argument for point‭ ‘‬ii‭) ‬of the availability of abundant AC material‭’ ‬could be expanded by providing more information on the‭ ‘‬limnetic‭’ ‬like features of this morph and why it is an appropriate comparison to a benthic morph,‭ ‬the genetic divergence from the lake Thingvallavatn fish,‭ ‬and also the selection regime it has experienced‭ (‬selection for limnetic features‭? ‬What other traits vary with domestication‭?)‬.‭ Reply (‬b and c‭)‬:‭ ‬The reviewer is correct,‭ ‬AC and SB are separated by multiple traits,‭ ‬and the data probably reveal signals associating with most of them.‭ ‬Unfortunately the AC charr is not well characterized phenotypically,‭ ‬thus we can not address the question of other traits.‭ ‬We focus mainly on the head and jaw morphology,‭ ‬as these attributes distinguish benthic and limnetic morphs.‭ T‬he revised intro elaborates on the choice of AC,‭ ‬and how the follow up work on the morphs from Lake Thingvallavatn can help us sort this out.‭ ‬This point is also picked up in the discussion. d‭) ‬Paragraph‭ ‬2‭ – ‬Much of this paragraph,‭ ‬including discussing the ability to measure gene expression and relate to phenotype in fishes,‭ ‬is unnecessary as fish are no different from other vertebrates in this respect.‭ ‬Instead,‭ ‬the final sentence‭ “‬One approach to identify pathways related to function or morphological differences is to study gene expression during development‭” ‬should become the‭ ‘‬topic sentence‭’ ‬and expanded upon to explain why gene expression studies are especially relevant ways to link genotype to phenotype in evo-devo studies.‭ Reply : ‬We restructured and shortened this paragraph around this topic sentence‭ ‬-‭ ‬and gave more room for the previous RNAseq study on Arctic charr. ‬ e‭) ‬Better highlight the strengths‭ – ‬The authors have done a wonderful job of assessing multiple developmental time points and rearing fish in a common garden environment.‭ ‬However,‭ ‬they do not highlight these strengths.‭ ‬Some small notes on the importance of controlling for phenotypic plasticity in these traits‭ (‬which are known to be quite plastic‭) ‬to better study genetic differentiation would be a nice addition. Reply : ‬Great advice,‭ ‬we tried to integrate this into the last paragraph of the intro.‭ ‬ Methods:‭ ‬a‭) ‬Page‭ ‬4‭ ‬paragraph‭ ‬1‭ ‬-‭ ‬Clarify the number of fish used to make the crosses‭ (‬this will help us determine the likelihood of selecting a full or half-sib for sequencing/qPCR‭)‬. Reply : ‬We did bulk crosses,‭ ‬joining eggs from‭ ‬5-10‭ ‬females in a can and sperm from‭ ‬3-5‭ ‬males‭ (‬SB,‭ ‬PL,‭ ‬LB‭) ‬and single parent cross for AC.‭ ‬Each sample included RNA pooled from‭ ‬3‭ ‬embryos,‭ ‬so there is a chance that full sibs were sequenced,‭ ‬but unlikely.‭ ‬The embryos/samples for qPCR are from similar pools. Now described better in methods.‭ ‬ b‭) ‬I should note that I am not an expert in the analysis of RNA-seq data,‭ ‬but luckily the first reviewer has done an excellent job of commenting upon these aspects of the project.‭ ‬I fully agree with their comments and suggestions.‭ ‬I would also like to see more information on the methods used to pool samples and how RNA-seq data was normalized among samples,‭ ‬developmental times and morphs.‭ ‬I will also note that the authors often use‭ ‬S.salar for comparisions,‭ ‬not‭ ‬O.mykiss,‭ ‬which is a closer relative to S.alpinus.‭ ‬The reasons for this approach should be discussed.‭ Reply : ‬The RNA was isolated from individual embryos,‭ ‬quantified and then united‭ (‬in equal concentrations‭) ‬prior to cDNA synthesis.‭ The read counts per gene are normalized per million reads in sample. Not normalized with other variables. ‬ c‭) ‬ I am also not trained as a population geneticist.‭ ‬However,‭ ‬from my experience studying paralogous genes in salmonids,‭ ‬and with respect to the author’s own findings for the Nattl paralogs‭ (‬Fig‭ ‬4‭)‬,‭ ‬I do not think it is prudent to‭ “‬assume that the expression of paralogous genes is stable‭… ” ‬in the methods‭ (‬page‭ ‬12‭)‬. ‭ ‬In fact,‭ ‬Berthelot‭ ‬et al.‭ (‬2014‭) ‬find the opposite‭ (‬see my comments for the discussion‭)‬.‭ ‬ Reply :‭ ‬Excellent suggestion.‭ ‬We corrected our misunderstanding,‭ ‬added this fact into the intro and discussion,‭ ‬and reinterpreted our data in this light. d‭) ‬The authors should use their genetic information to test if the fish chosen are siblings with each other‭ (‬full or half-sibs‭)‬.‭ ‬This may have important implications for the population genetic analyses.‭ Reply :‬The fish chosen for pop-gen work are random sample from spawning grounds‭ ‬-‭ ‬assumed to be not sibling groups.‭ ‬Our earlier study‭ (K‬apralova‭ ‬2011‭) ‬showed no family structure in charr collected this way from the lake. ‬e‭) ‬Page‭ ‬5‭ ‬-‭ ‬It is not appropriate to change the meaning of the word‭ ‘‬gene‭’‬.‭ ‬I think it is much clearer to use the term‭ ‘‬paralog group‭’ ‬or‭ ‘‬gene family‭’ ‬when referring to the fact that the authors do not study single genes,‭ ‬but instead groups of paralogs. ‬ Reply : ‬Excellent suggestion.‭ ‬We amended this., and use paralog group throughout. f‭) ‬Selection of genes for qPCR‭ – ‬the methods by which genes for the qPCR studies‭ (‬Fig‭ ‬3‭) ‬were selected should be clearly noted.‭ ‬From my reading,‭ ‬it seems that most of these genes do not significantly vary among SB and AC at the‭ ‬1%‭ ‬FDR level‭ (‬Tables‭ ‬1‭ ‬and‭ ‬2‭; ‬only Natterin‭?)‬.‭ ‬Thus,‭ ‬I am assuming these genes are only significant at the‭ ‬5%‭ ‬FDR level‭ (‬S1‭ ‬file‭) – ‬why focus upon these and not those significant at‭ ‬1%‭?‬ ‭ ‬As well,‭ ‬it would be good to include information on why different genes were selected for Figure‭ ‬3‭ (‬qPCR validation of whole fish‭) ‬and Figure‭ ‬4‭ (‬candidate genes-qPCR validation in just the head‭)‬.‭ ‬Finally,‭ ‬the abbreviations used for qPCR validation should also be listed in Table‭ ‬1‭ ‬for easy comparisons among figures/tables.‭ Reply : ‬Very important point.‭ ‬We deliberately studied some genes with less statistical support‭ (‬FDR between‭ ‬5%‭ ‬and‭ ‬10%‭)‬,‭ ‬to gauge the differences in the genes with less support and in particular to have a bigger pool of candidates that may relate to the specific developmental process‭ (‬like head and jaw formation‭)‬.‭ ‬Of course we can not assert that all the genes with strongest DE signal in the transcriptome are true positives,‭ ‬but the data can be used for hypothesis generation.‭ ‬We also amended table‭ ‬1‭ ‬and the figure legends accordingly. Results‭ & ‬Figures:‭ ‬ a‭) ‬Include an experimental design figure‭ ‬-‭ ‬At present,‭ ‬it is difficult to keep track of all of the morphotypes,‭ ‬tissues,‭ ‬and developmental time points used without referring to the methods.‭ ‬Thus,‭ ‬an experimental design figure summarizing the samples used‭ (‬morphotype,‭ ‬population,‭ ‬sample size,‭ ‬developmental time point‭)‬,‭ ‬how they were pooled and which techniques were used to measure gene expression on each sample‭ (‬RNA-seq and/or qPCR‭)‬ ‭ ‬is needed. b‭) ‬Include the LB and PL morphs in Figure‭ ‬1‭ ‬and clarify traits of interest‭ – ‬The legend states that‭ “‬differences in size,‭ ‬coloration and head morphology are apparent‭”‬,‭ ‬but it would be better to specifically point out the differences they are referring to.‭ ‬F1000‭ ‬is for a general audience,‭ ‬and this would help non-ichthylogists better understand what ecologically-important traits the authors are interested in‭ (‬e.g.‭ ‬those related to benthic/limnetic feeding‭)‬. ‭ ‬In addition,‭ ‬the two other morphs used in the qPCR studies should also be displayed‭ (‬large benthivorous and small planktivorous‭) ‬to facilitate phenotypic comparisons and assess parallelism in benthic/limnetic feeding and/or the effects of domestication on AC. Reply : (‬a and b‭) ‬Excellent suggestions.‭ ‬Now picture‭ ‬1‭ ‬has all‭ ‬4‭ ‬morphs,‭ ‬and a schematic describing the work flow and samples.‭ ‬c‭) ‬Figure‭ ‬5-‭ ‬this is actually a table not a figure‭ (?) ‬and is a bit confusing.‭ ‬I think it is much easier to interpret Figure S2‭ (‬displaying the data as in Fig‭ ‬3‭ ‬and‭ ‬4‭)‬,‭ ‬and that Fig‭ ‬5‭ ‬and S2‭ ‬should be switched.‭ ‬It would be great to show significant differences in mRNA content in this,‭ ‬and all other figures,‭ ‬by including symbols.‭ ‬Also,‭ ‬full gene names should be listed in all figure legends.‭ Reply : ‬We acknowledge that this graph is not the simplest,‭ ‬but would like to keep it over Figure S2.‭ ‬Our reasoning is that this graph illustrates the sharp differences between the limnetic‭ (‬AC-PL‭) ‬and benthic‭ (‬SB-LB‭)‬,‭ ‬which are the main result in this section.‭ ‬But we will of course switch them,‭ ‬or possibly join both in a single figure ‭?? ‬if the reviewer insists or the editors recommend it. Discussion:‭ ‬The discussion focuses on the SB morph‭ (‬page‭ ‬17‭ – “‬The objective of this study were to get a handle on genetic and molecular systems that associate with benthic morphology in charr by mainly focusing on the small benthic morph in Lake Thingvallavatn,‭ ‬Iceland‭”)‬,‭ ‬while the introduction discusses parallel evolution‭ (‬indicating that the comparisons should be among many morphs‭)‬.‭ ‬These are two different topics i‭) ‬mRNA content differences among benthic vs.‭ ‬limnetic morphs changing in parallel or ii‭) ‬linking mRNA content to phenotype in SB‭ (‬benthic,‭ ‬wild‭) ‬vs.‭ ‬AC‭ (‬limnetic head,‭ ‬domesticated‭) ‬morphs.‭ ‬In particular,‭ ‬the role of domestication vs.‭ ‬wild fish divergence needs to be addressed.‭ ‬At present these two topics/questions are mixed in the introduction/discussion and should be addressed separately.‭ Reply : ‬We tried to separate these two aims more clearly in the revised discussion.‭ ‬The strategy was to use the AC vs SB contrast for hypothesis generation,‭ ‬as the first aim is central to our program.‭ ‬We have now added sentences on the domestication in two parts of the discussion. ‬a‭) ‬Paragraph on Immune Defenses‭ ‬-‭ ‬Is immunity also expected to evolve in parallel in all benthic morphs‭? ‬Is this predicted to be unique to SB vs.‭ ‬AC‭? ‬Whatever the case,‭ ‬the parallelism‭ (‬or not‭) ‬in these genes should also be discussed,‭ ‬and whether this relates more to domestication in AC or differences between limnetic vs.‭ ‬benthic fish. ‭ ‬Much of the functional discussion can also be cut. Reply : ‬Good question,‭ ‬we assume it to be so,‭ ‬but that may be wrong.‭ ‬We moved the discussion towards this question and away from functional description.‭ ‬b‭) ‬Page‭ ‬18‭ – ‬The information about genes found to be differentially expressed among morphs in your prior work should also be in the introduction,‭ ‬as it is background work that explains why you took this transcriptomic approach.‭ ‬This can also be used to explain why you focused in on particular qPCR genes. Reply: ‬We added a sentence in the intro about the published papers,‭ ‬that this transcriptome made available.‭ ‬In those papers we focused on genes with putative craniofacial effects,‭ ‬though the focus in this study was broader.‭ ‬ c‭) ‬A discussion of domestication related differences vs.‭ ‬benthic/limnetic differences should be included.‭ ‬I think the data from head gene expression is very interesting‭ (‬Figs‭ ‬5,‭ ‬S2‭) ‬and really speaks to this question.‭ ‬d‭) ‬In general,‭ ‬the role of stochastic evolutionary processes,‭ ‬and not just selection‭ (‬artificial and natural‭) ‬should be noted.‭ ‬For example,‭ ‬if the AC charr were simply taken from a stock with a different mtDNA haplotype then these differences in the mtDNA genome might not be adaptive,‭ ‬just random. ‭ ‬If the AC fish has much higher mtDNA expression might this be simply a domestication issue and not indicative of selection in SB as stated‭?‬ ‭ ‬Finally,‭ ‬you find that not all mitochondrial transcripts‭ (‬which are transcribed as a polycistronic transcript‭) ‬are found at similar levels‭ (‬Table‭ ‬1‭) – ‬what does this tell you about differential degradation/post-transcriptional processes‭?‬ e‭) ‬There is no discussion about the‭ “‬Analyses of polymorphism in Arctic charr transcriptome‭” (‬Table‭ ‬3,‭ ‬4,‭ ‬5‭)‬,‭ ‬except for the mtDNA. Reply: (‬c,d,e‭) Excellent suggestions. ‬We added in the final discussion section few sentences on domesticated charr vs Benthic/limnetic.‭ ‬Unfortunately we do not have quantitative data on the phenotypes‭ (‬head shape,‭ ‬and jaw‭) ‬of the AC charr and acknowledge that we categorize it as limnetic based on general features.‭ We gladly added a sentence citing neutral forces,‭ ‬and are acutely aware that much of the divergence is likely due to history,‭ ‬drift etc.‭ ‬The domestication can certainly be the driver for the higher expression in AC‭ ‬-‭ ‬but we need transcriptomes from more populations/morphs to address that point.‭ ‬And yes,‭ ‬the variance in RNA levels from different parts of the mtDNA do indeed suggest differential half life of the various RNA species.‭ ‬Some are certainly degraded and others most probably actively utilized‭ ‬/‭ ‬protected.‭ ‬We decided not to follow that thought further though,‭ ‬as the MS already consists of quite a few threads already. We also added sentences on the genetic polymorphism,‭ ‬before focusing more on the mtDNA.‭ ‬The main reason we dont want to elaborate to much on the SNPs is that we feel these data are mainly for generating hypotheses,‭ ‬and that more work is needed to substantiate SNPs and study their distribution in other populations. ‭ ‬Minor Comments‭ ‬Introduction:‭ ‬ a‭) ‬Paragraph‭ ‬3‭ – “‬Furthermore,‭ ‬recent estimates from the rainbow trout‭…‬.by utilizing multiple data sources the genome assembly problem of this family can be solved‭”‬.‭ ‬I am not sure how this statement is relevant to this particular study.‭ ‬This and the following statement seem more appropriate for the methods/discussion to me. ‬ Reply: ‬We deleted this sentence and simplified the paragraph. ‬ b‭) ‬The morphs being discussed should be clarified throughout the paper.‭ ‬For example,‭ ‬the authors often state‭ “‬among morphs/among charr populations‭” ‬but it is not clear which of the many morphs they are referring to‭ (‬e.g.‭ ‬Paragraph‭ ‬5,‭ ‬first sentence on allozymes and mtDNA and later sentence on MCHIIa‭ – ‬do you mean all‭ ‬4‭ ‬morphs of specific‭ ‬2-way comparisons‭? ‬Are some morphs more differentiated than others‭?) Reply: ‬We tried to clarify this in various places in the manuscript,‭ ‬but in some cases we refer to morphs in general.‭ ‬Genetic separation can be estimated with Fst values either between pairs or over a larger set of groups‭ (‬populations,‭ ‬morphs‭)‬.‭ ‬In the intro we cite the work done to date in Iceland,‭ ‬which highlights the need for more pop.‭ ‬genetic analyses.‭ ‬ ‭ ‬ Methods:‭ ‬ a‭) ‬The authors should note why they did not use the PI‭ (‬large piscivorous‭) ‬morph in any qPCR studies‭ (‬in the methods or discussion‭) ‬as this would be a nice morph to use in their tests for parallelism.‭ Reply: The PI charr is very rare in the lake and hard to catch.‭ ‬We later captured few sexually mature individuals,‭ ‬and generated couple of families,‭ ‬that were used for one study‭ (‬Ahi et al‭ ‬Evodevo 2015‭)‬. ‬b‭) ‬Page‭ ‬5‭ (‬last paragraph‭) – ‬the methods used to remove particular variants needs to be clarified.‭ ‬In particular,‭ ‬why the assumptions used to remove variants are valid by referencing past studies. ‬ Reply: ‬Many of the principles are common to most pipelines for removing spurious variants.‭ ‬In addition we applied filters necessitated by the properties of our dataset‭ (‬pool of individuals‭)‬,‭ ‬the mapping to an outgroup and paralogs due to salmonid genome complexity. ‬ Figures‭ & ‬Results:‭ ‬ a‭) ‬Figure‭ ‬2.‭ ‬The key for Figure‭ ‬2‭ ‬should include a specific heading for morph and time-point with the abbreviations restated‭ [‬e.g.‭ ‬Timepoint:‭ ‬141‭ ‬dpf,‭ ‬Morph:‭ ‬Small Benthic‭ (‬SB‭)]‬. Reply: ‬Now fixed.‭ ‬ ‭ ‬ b‭) ‬Figure‭ ‬6‭ – ‬would be helpful to label the protein coding genes in this figure as well as the‭ ‬12s and‭ ‬16s RNAs.‭ ‬ Reply: ‬Now fixed.‭ ‬ ‭ ‬ c‭) ‬Figure‭ ‬7‭ – ‬It is not clear to me which variant is present in which morph.‭ ‬Adding the nucleotide to the x-axis‭ (‬i.e.‭ ‬frequency of m1829G for B‭) ‬would make this figure easier to quickly interpret.‭ ‬The‭ “‬A.charr_WT‭” ‬and‭ “‬A.charr_M‭” ‬should also be defined in the legend and it would be more appropriate to use scientific names for all species.‭ ‬ Reply: ‬Now fixed‭ ‬ Discussion:‭ ‬ a‭) ‬Discussion of reference‭ ‬32‭ – ‬The discussion of reference‭ ‬32‭ ‬is not put into the proper context.‭ ‬Figure‭ ‬6‭ ‬of this paper‭ (‬Berthelot‭ ‬et al.‭ ‬2014‭) ‬shows that there are many genes that have no correlation among expression patterns and/or differences in expression levels‭ (‬1573,‭ ‬1248,‭ ‬and‭ ‬1895‭=‬4716‭ ‬paralog pairs‭)‬,‭ ‬and that together these represent more than the‭ ‬1,407‭ ‬correlated/similar expression level paralogs.‭ ‬This section of the discussion needs to be modified.‭ Reply: ‬Really valuable point,‭ ‬that we are especially grateful for.‭ ‬That we have added this fact to the intro and altered our interpretations in the discussion. ‬ b‭) ‬The Norman‭ ‬et al.‭ (‬2014‭) ‬paper should be mentioned earlier‭ – ‬if this is available why was it not used for their analyses‭? ‬As well,‭ ‬the last sentence in this paragraph can be cut as it is evident. Reply: ‬The Norman papers are now presented more clearly in the intro.‭ ‬There are historical reasons for not including their data in our analyses,‭ ‬we had completed the analyses for this manuscript when they became available and have since then focused our data analyses efforts on another transcriptome generated in the lab‭ (‬with longer reads‭)‬.‭ c‭) ‬Page‭ ‬18‭ – “‬Our new data also demonstrate differences in craniofacial elements between AC-‭ ‬and SB-charr,‭ ‬along a limnetic vs.‭ ‬benthic axis79‭”‬.‭ ‬Are you referring to ref‭ ‬79‭ ‬or data from this study‭? ‬If you are referring to‭ ‬79,‭ ‬clarify and note what you found.‭ ‬This occurs a few times in the discussion‭ Reply: ‬Ref‭ ‬79‭ ‬is a related study that built in part on the data presented here.‭ ‬We have now rephrased this in the manuscript,‭ ‬hopefully to the better. View more View less Competing Interests No competing interests were disclosed.No competing interests were disclosed. reply Respond Report a concern Dalziel A. Peer Review Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.6869.r9419) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/4-136/v1#referee-response-9419 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2015 Macqueen D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Jul 2015 | for Version 1 Daniel Macqueen , Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK 0 Views copyright © 2015 Macqueen D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Review of Gudbrandsson et al . “The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs”. The work is founded on the solid premise that rapidly evolving phenotypes in nature can be underpinned by changes at the transcriptome level. The model system here is Arctic charr populations that have evolved (since the last ice age) major differences in phenotypes along the ‘benthic’ - ‘limnetic’ axis, with strong differences in head morphology linked to feeding specializations. The work provides an extensive analysis of transcriptome and genetic differences between different morphs and populations. It is interesting, generally well-written and has merit on many levels. It is also rather hard going, since so much ground is covered on diverse areas. The study also comes with a large number of caveats, of which the authors are undoubtedly aware. Overall though, I am supportive of this work, as it represents one of the most detailed analyses of molecular mechanisms linked to rapid phenotypic evolution in Arctic charr. I see it as a great start point for future work and a source of several new findings and hypotheses. I suggest that the paper be indexed in F1000 Research as long as its caveats are transparent and the authors address my comments. I list below a number of suggestions that may help the authors improve the work, or that at least highlight study limitations for the benefit of interested readers. I also provide a number of minor comments and suggestions, which should help improve the manuscript more incrementally. Main comments & caveats RNAseq study design. I sympathize with the fact that the authors are trying to publish Illumina data that was generated in 2009, since (obviously) the technology has moved on greatly in the last 6 years, while its costs have been reduced dramatically. Adding to this is the fact that the authors are using a particularly complex transcriptome in terms of high content of similar paralogues (and expressed transposable elements), without a reference sequence for mapping in their species. I accept the author’s argument that it is more sensible to map against a closely related species with the sequence data rather than to try and create a de novo assembly from 36bp reads. I also believe it is sensible to pool read counts for putative paralogous contigs in this study, since the short read length ablates any ability to separate paralogous differences in expression (yet does not preclude the generation of useful hypotheses about putative gene expression differences among morphs). However, I do question whether the use of Atlantic salmon EST contigs is the best approach here. Firstly, reference assemblies for both Atlantic salmon and rainbow trout are now available, which distinguish paralogous variation. More importantly, using these reference genome data would provide certainty that reads are being mapped to exons from single genes, whereas many of the ESTs will provide a fragmented representation of exon sequences, presumably relying on annotation to piece them back into ‘genes’ post hoc . In addition, paired 100bp Ilumina reads are available at high coverage for Arctic charr (e.g. Norman et al . 2014), which could also be used to generate a specific reference transcriptome to map against in this study, although this might be underrepresented in terms of developmental genes as it is a gill study. Overall, I do wonder how much more information might have been gleaned from this dataset with a different mapping strategy? With all the above said, I understand that the authors have built up a large study based around the original mapping to the salmon ESTs and that it would not be routine for them to repeat the study using better reference data. Furthermore, the approach used has definitely led to the generation of several valid hypotheses concerning the nature of gene expression and genetic differences among charr morphs, which have been followed up using independent approaches. Methods “Biological Replication in RNAseq” – a general comment: obviously the design of the study is not optimal because biological variation within developmental stages is not considered in the statistics. Thus, the approach lacks power to detect differences when morph variation is restricted to different developmental stages. I wanted to explain my opinion (for the record) that the study design is nonetheless useful for identifying constitutive differences between morphs. This is especially true because gene expression variability is likely to be relatively low in embryonic stages (compared to a similar study design in adults at least). Further, the pooling of individuals will have helped to at least recapture some biological variation at different stages. Thus, as mentioned above, I see the author’s use of RNAseq as a hypothesis-generating approach, which has been quite fruitful in identifying putative differences between different morphs. Methods “QPCR study design”. The authors adhere to the MIQE guidelines, but do not always follow the best approaches. Most pertinently, the authors use the 2 −∆∆Ct method (assuming PCR efficiency of 2.0) despite having gone to the effort of gaining and reporting efficiencies for each assay, which can be as low as 1.72 for some genes. The effect of failing to incorporate differences in efficiency are highly established and this is likely to have affected the author’s results. The authors should consider incorporating the effect of differences in efficiency into their analyses. This is likely to have some impact on the study conclusions in my opinion. Methods “ Polymorphisms in charr transcriptome”. While this is not exactly my area of expertise, I struggled to understand the methods behind filtering paralogous variants from SNPs in the data. The authors state “ As the SNP analysis was done on individual contigs, differences among paralogs appear in the data. However, since each sample is a pool of few individuals, it is very unlikely that we have the same frequency of true SNPs in the samples. This property was used to remove variants that are most likely due to expressed paralogs ”. Can the authors please try to re-explain this in even simpler terms to help me get it? I don’t see how this description leads to a robust identification of paralogous variation. Is there an underlying assumption of equal expression among paralogues? If so, this is likely to be routinely invalidated. Methods “ Verification of candidate SNPs”. While it is good that the authors have attempted to verify SNPs identified from their RNAseq data, I don’t believe the data is particularly well incorporated in the results section. It needs to be stated up front the extent to which the SNPs predicted from the RNAseq were independently verified. Also, the methods for this section can be improved, especially “ we conducted genomic comparisons of the Salmon genome, ESTs and short contigs from the preliminary assembly of the Arctic charr transcriptome ”. None of this information is elaborated on – what is the preliminary assembly of the Arctic charr transcriptome? Which version of the salmon genome was used and how? Moreover, it would be useful to actually explain in the methods that the genotyping was done on a small number of SB, PL and PI morphs, rather than relying on the reader to extract all the required information from Table S2. I guess overall, the way this section is incorporated into the manuscript needs some thought in terms of improving the reader’s experience. I struggled after reading it several times and am still not sure I have all the information I need. Results . “ Analyses of those reads require an Arctic charr genome sequence or transcriptome assembly from longer and paired end reads .” As mentioned already, the latter is available to generate an Arctic charr transcriptome assembly to map against. Results ; Figure 3 and 4. The authors found that around half the genes studied were not differentially expressed among morphs by qPCR. Obviously this is quite a large number, but on closer inspection, I noticed that Ndub6 , Ubl5 and parp6 were not even differentially expressed according to RNAseq. Thus, I am confused at the selection of genes from the RNAseq analysis for verification by qPCR. The authors should explain this selection more transparently and provide clearer indices of the correlation between RNAseq and qPCR results and associated discussion. Minor comments, typos and suggested changes Abstract: “ Species and populations with parallel evolution of specific traits can help illuminate how predictable adaptations and divergence are at the molecular and developmental level . Grammatically – his reads better: “ ….. can help illuminate the predictability of adaptations and divergence at the molecular and developmental level” Introduction: “ Examples of such a species complex are the finches of the Galapagos islands, cichlids in the African great lakes are exciting multi-species systems in this respect” . Grammatically – reads better: “ Examples of such species complexes are provided by finches of the Galapagos islands, while cichlids of the African great lakes also provide an exciting multi-species system in the same respect” Introduction: “ Some northern freshwater fish species exhibit frequent parallelism in trophic structures and life history and in several cases are they found as distinct resource morphs ” change to “ …. are found as distinct resource morphs ” Introduction: “ in the development of ecological differences in tropic morphology ” change to “… trophic morphology ”. Introduction: “ The family is estimated to be between 63.2 and 58.1 million years old ”. This information is not correct – it is correct to state that the age of the salmonid crown (based on the cited paper; different estimates exist in the literature, e.g. Macqueen and Johnston, 2014; Campbell et al . 2013 ) is estimated at 63.2 and 58.1 million years old, but the family dates back much further – to the origin of the WGD event in fact, which occurred more like 88-103 Ma (Macqueen and Johnston, 2014; Berthelot et al . 2014). Thus, the last common ancestor to extant salmonid species is what the authors are actually referring to in this sentence. Introduction: “ Furthermore, for data with short reads, mapping to a related reference genome/transcriptome is recommended over de novo assembly ”. While this sentence is technically correct in the context of the work cited, I feel it is being used slightly out of context. For a start, what comprises a ‘short read’ is undefined. 36bp is short, but it is possible to get a sold reference transcriptome using 2*100bp, assuming the appropriate diversity of transcripts is represented and suitable depth is attained. Introduction: “ nuclear genes, reveled both subtle ” change to “ nuclear genes, revealed both subtle ” Minor comment – AC, PL, LB and SB were already defined in introduction. Methods: “ Fishing in Lake Thingvallavatn was with permissions ” changed to “ Fishing in Lake Thingvallavatn was done with permissions ”. Methods: “ of differently expressed genes, we preformed clustering analyses ” change to “ …we performed clustering analyses ” Results: “ The most drastic changes were seen in processes related to glycolysis (GO:0006096, FDR = 0.0009), were the expression of 19 out of 25 genes ” change to “…. where the expression ”. Figure 7. What does the charr_WT vs. charr_M signify in the alignment data? Discussion “ We are interested in how predictable evolution is a the molecular level and if there certain principles influence the rewiring of developmental and regulatory systems during evolution ” consider changing to “ We are interested in the predictability of evolution at the molecular level, especially whether there exist principles that influence the rewiring of developmental and regulatory systems” . Discussion. “ Recent rainbow trout data shows most paralogs from the latest whole genome duplication event retain the same expression pattern 32 indicating that this scenario is probably uncommon; hence it is of considerable interest when two paralogs show distinct expression patterns”. I do not agree that it is of considerable interest when two paralogs show distinct expression patterns – I could list tens of examples for salmonids. Conclusions “ The results suggest genetic and expression changes in multiple systems relate to divergence among populations .” Change to “ … associated with divergence among populations .” Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 25 Apr 2016 Arnar Palsson, Institute of Life and Environmental Sciences, University of Iceland, Reykjavik, 101, Iceland Main comments‭ & ‬caveats RNAseq study design.‭ ‬ I sympathize with the fact that the authors are trying to publish Illumina data that was generated in‭ ‬2009,‭ ‬since‭ (‬obviously‭) ‬the technology has moved on greatly in the last‭ ‬6‭ ‬years,‭ ‬while its costs have been reduced dramatically.‭ ‬Adding to this is the fact that the authors are using a particularly complex transcriptome in terms of high content of similar paralogues‭ (‬and expressed transposable elements‭)‬,‭ ‬without a reference sequence for mapping in their species.‭ ‬I accept the author’s argument that it is more sensible to map against a closely related species with the sequence data rather than to try and create a‭ ‬de novo‭ ‬assembly from‭ ‬36bp reads.‭ ‬I also believe it is sensible to pool read counts for putative paralogous contigs in this study,‭ ‬since the short read length ablates any ability to separate paralogous differences in expression‭ (‬yet does not preclude the generation of useful hypotheses about putative gene expression differences among morphs‭)‬.‭ ‬However,‭ ‬I do question whether the use of Atlantic salmon EST contigs is the best approach here.‭ ‬Firstly,‭ ‬reference assemblies for both Atlantic salmon and rainbow trout are now available,‭ ‬which distinguish paralogous variation.‭ ‬More importantly,‭ ‬using these reference genome data would provide certainty that reads are being mapped to exons from single genes,‭ ‬whereas many of the ESTs will provide a fragmented representation of exon sequences,‭ ‬presumably relying on annotation to piece them back into‭ ‘‬genes‭’ ‬post hoc‭ ‬.‭ ‬In addition,‭ ‬paired‭ ‬100bp Ilumina reads are available at high coverage for Arctic charr‭ (‬e.g.‭ ‬Norman‭ ‬et al.‭ ‬2014‭)‬,‭ ‬which could also be used to generate a specific reference transcriptome to map against in this study,‭ ‬although this might be underrepresented in terms of developmental genes as it is a gill study.‭ ‬Overall,‭ ‬I do wonder how much more information might have been gleaned from this dataset with a different mapping strategy‭? With all the above said,‭ ‬I understand that the authors have built up a large study based around the original mapping to the salmon ESTs and that it would not be routine for them to repeat the study using better reference data.‭ ‬Furthermore,‭ ‬the approach used has definitely led to the generation of several valid hypotheses concerning the nature of gene expression and genetic differences among charr morphs,‭ ‬which have been followed up using independent approaches.‭ ‬ Reply :‭ ‬We thank the reviewer for excellent diagnosis and suggestions.‭ ‬The paper describes the‭ (‬in our humble opinion‭) ‬most sensible summary of the data,‭ ‬as the writing of the paper started‭ ‬2‭ ‬years ago.‭ ‬We did map on the‭ ‬O.mykiss‭ ‬cDNA collection also,‭ ‬got similar results,‭ ‬but opted for reporting on the salmon data to avoid further extending an already long manuscript.‭ ‬We are currently analyzing DE and SNPs on a new assembly‭ (‬100‭ ‬bp PE reads‭ ‬-‭ ‬48‭ ‬samples‭ ‬-‭ ‬3‭ ‬morphs‭ ‬-‭ ‬development‭)‬,‭ ‬and may include a remapping of this dataset in that.‭ ‭ ‬ Methods‭ “‬Biological Replication in RNAseq‭” – ‬a general comment:‭ ‬obviously the design of the study is not optimal because biological variation within developmental stages is not considered in the statistics.‭ ‬Thus,‭ ‬the approach lacks power to detect differences when morph variation is restricted to different developmental stages.‭ ‬I wanted to explain my opinion‭ (‬for the record‭) ‬that the study design is nonetheless useful for identifying constitutive differences between morphs.‭ ‬This is especially true because gene expression variability is likely to be relatively low in embryonic stages‭ (‬compared to a similar study design in adults at least‭)‬.‭ ‬Further,‭ ‬the pooling of individuals will have helped to at least recapture some biological variation at different stages.‭ ‬Thus,‭ ‬as mentioned above,‭ ‬I see the author’s use of RNAseq as a hypothesis-generating approach,‭ ‬which has been quite fruitful in identifying putative differences between different morphs. ‬Reply :‭ ‬We appreciate the reviewers careful analyses of the study and approach.‭ ‬We tried to emphasize the‭ “‬hypothesis-generation‭” ‬aspect during the rewrite. Methods‭ “‬QPCR study design‭”‬.‭ ‬The authors adhere to the MIQE guidelines,‭ ‬but do not always follow the best approaches.‭ ‬Most pertinently,‭ ‬the authors use the‭ ‬2−‭∆∆‬Ct method‭ (‬assuming PCR efficiency of‭ ‬2.0‭) ‬despite having gone to the effort of gaining and reporting efficiencies for each assay,‭ ‬which can be as low as‭ ‬1.72‭ ‬for some genes.‭ ‬The effect of failing to incorporate differences in efficiency are highly established and this is likely to have affected the author’s results.‭ ‬The authors should consider incorporating the effect of differences in efficiency into their analyses.‭ ‬This is likely to have some impact on the study conclusions in my opinion. Reply : ‬Great point.‭ ‬The qPCR primer efficiencies more than‭ ‬1.90‭ ‬can be easily assumed as‭ ‬2‭ ‬because of the negligible effects.‭ ‬Since we used LinReg software for efficiencies not the traditional method,‭ ‬it takes into account the efficiencies for each test for a given primer pair and discard those have different and lower efficiencies.‭ ‬However,‭ ‬the Natterin-like paralogues were below the cut-off.‭ The statistical analyses were done on deltaCt values, prior to transformation based on efficiencies used for visualization. We ‬now report the graphs of their expression adjusting for the lower efficiency, and state in the results “Note however, the efficiency of the primers for the nattl genes ranged from 1.72 to 1.77, which suggests this data should be interpreted with caution.”‭ ‬ Methods‭ “‬Polymorphisms in charr transcriptome‭”‬.‭ ‬While this is not exactly my area of expertise,‭ ‬I struggled to understand the methods behind filtering paralogous variants from SNPs in the data.‭ ‬The authors state‭ “‬As the SNP analysis was done on individual contigs,‭ ‬differences among paralogs appear in the data.‭ ‬However,‭ ‬since each sample is a pool of few individuals,‭ ‬it is very unlikely that we have the same frequency of true SNPs in the samples.‭ ‬This property was used to remove variants that are most likely due to expressed paralogs‭”‬.‭ ‬Can the authors please try to re-explain this in even simpler terms to help me get it‭? ‬I don’t see how this description leads to a robust identification of paralogous variation.‭ ‬Is there an underlying assumption of equal expression among paralogues‭? ‬If so,‭ ‬this is likely to be routinely invalidated.‭ ‬ Reply : ‬We acknowledge this part is a hard read.‭ ‬We rewrote this part of the methods. Here is another summary.‭ ‬Reads from regions that are very similar in paralogous genes can map to both of them.‭ ‬Because we consider also reads that map to many contigs,‭ ‬some of the candidate variants will reflect sequence differences between paralogs,‭ ‬not polymorphism in either paralog.‭ ‬Next we deploy the population genetic argument,‭ ‬since we are sequencing RNA from‭ ‬6‭ ‬chromosomes in each‭ ‬sample,‭ ‬then it is very unlikely that a TRUE SNP will be at the same frequency in all of the‭ ‬8‭ ‬samples.‭ ‬But variants‭ ‬-‭ ‬that are due to differences bwn paralogs‭ ‬-‭ ‬are likely to be similar in frequency because they are unaffected by the population sampling.‭ ‬This filter is designed to toss those out. To emphasize‭ ‬the objective is not‭ ‬to find differences between paralogs,‭ ‬but rather to enrich for true SNPs.‭ ‬This method will toss out many sites separating paralogous genes (but not all because some paralogous genes are differentially expressed between morphs or time points).‭ ‭ ‬Methods‭ “‬Verification of candidate SNPs‭”‬.‭ ‬While it is good that the authors have attempted to verify SNPs identified from their RNAseq data,‭ ‬I don’t believe the data is particularly well incorporated in the results section.‭ ‬It needs to be stated up front the extent to which the SNPs predicted from the RNAseq were independently verified.‭ ‬Also,‭ ‬the methods for this section can be improved,‭ ‬especially‭ “‬we conducted genomic comparisons of the Salmon genome,‭ ‬ESTs and short contigs from the preliminary assembly of the Arctic charr transcriptome‭”‬.‭ ‬None of this information is elaborated on‭ – ‬what is the preliminary assembly of the Arctic charr transcriptome‭? ‬Which version of the salmon genome was used and how‭? ‬Moreover,‭ ‬it would be useful to actually explain in the methods that the genotyping was done on a small number of SB,‭ ‬PL and PI morphs,‭ ‬rather than relying on the reader to extract all the required information from Table S2.‭ ‬I guess overall,‭ ‬the way this section is incorporated into the manuscript needs some thought in terms of improving the reader’s experience.‭ ‬I struggled after reading it several times and am still not sure I have all the information I need.‭ Reply :‭ ‬We fixed the methods section to accommodate both reviewers which brought up similar points.‭ ‬We highlight the sampling‭ (‬8‭ ‬individuals of‭ ‬3‭ ‬morphs‭)‬,‭ ‬and extend the description of the genomic comparisons.‭ ‬We also extend the discussion of those results. Results.‭ “‬Analyses of those reads require an Arctic charr genome sequence or transcriptome assembly from longer and paired end reads.‭” ‬As mentioned already,‭ ‬the latter is available to generate an Arctic charr transcriptome assembly to map against.‭ ‬ Reply : ‬Unfortunately the great Norman et al‭. ‬2014‭ ‬data‭ (‬http://www.ncbi.nlm.nih.gov/pubmed/24368751‭) ‬came to our attention after we had done these analyses,‭ ‬and started working on our new data‭ (‬see above‭)‬.‭ ‬Thus we opted for not redoing the whole analyses for this manuscript,‭ ‬but focus on the verification‭ ‬-‭ ‬and of course working on a new assembly using longer reads. Results‭; ‬Figure‭ ‬3‭ ‬and‭ ‬4.‭ ‬The authors found that around half the genes studied were not differentially expressed among morphs by qPCR.‭ ‬Obviously this is quite a large number,‭ ‬but on closer inspection,‭ ‬I noticed that‭ ‬Ndub6,‭ ‬Ubl5‭ ‬and‭ ‬parp6‭ ‬were not even differentially expressed according to RNAseq.‭ ‬Thus,‭ ‬I am confused at the selection of genes from the RNAseq analysis for verification by qPCR.‭ ‬The authors should explain this selection more transparently and provide clearer indices of the correlation between RNAseq and qPCR results and associated discussion. Reply : ‬This reflects the history of the project,‭ ‬and the difference between the preliminary and final analyses.‭ ‬We decided to report on all the data‭ ‬-‭ ‬but explain better in the manuscript the classification of genes tested with qPCR,‭ ‬at‭ ‬1%,‭ ‬5%‭ ‬and‭ ‬10%‭ ‬FDR.‭ ‬In summary,‭ ‬some of the genes tested were above‭ ‬5%‭ ‬and one even just above‭ ‬10%‭ ‬FDR.‭ ‬Some of those were not corroborated by qPCR.‭ ‬The number of genes is insufficient to do a statistical comparison of the verification rate at the different FDR levels.‭ ‬A table‭ (‬new Table‭ ‬3‭) ‬-‭ ‬supported with few sentences in the results,‭ ‬hopefully clarifies this. ‬ Minor comments,‭ ‬typos and suggested changes Abstract:‭ “‬Species and populations with parallel evolution of specific traits can help illuminate how predictable adaptations and divergence are at the molecular and developmental level.‭ ‬Grammatically‭ – ‬his reads better:‭ “‬…..‭ ‬can help illuminate the predictability of adaptations and divergence at the molecular and developmental level‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Examples of such a species complex are the finches of the Galapagos islands,‭ ‬cichlids in the African great lakes are exciting multi-species systems in this respect‭”‬.‭ ‬Grammatically‭ – ‬reads better:‭ “‬Examples of such species complexes are provided by finches of the Galapagos islands,‭ ‬while cichlids of the African great lakes also provide an exciting multi-species system in the same respect‭”‬ Reply :‭ ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬Some northern freshwater fish species exhibit frequent parallelism in trophic structures and life history and in several cases are they found as distinct resource morphs‭” ‬change to‭ “‬….‭ ‬are found as distinct resource morphs‭”‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬in the development of ecological differences in tropic morphology‭” ‬change to‭ “… ‬trophic morphology‭”‬.‭ ‬ Reply : ‬Thanks‭ ‬-‭ ‬fixed. Introduction:‭ “‬The family is estimated to be between‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old‭”‬.‭ ‬This information is not correct‭ – ‬it is correct to state that the age of the salmonid crown‭ (‬based on the cited paper‭; ‬different estimates exist in the literature,‭ ‬e.g.‭ ‬Macqueen and Johnston,‭ ‬2014‭; ‬Campbell‭ ‬et al.‭ ‬2013‭) ‬is estimated at‭ ‬63.2‭ ‬and‭ ‬58.1‭ ‬million years old,‭ ‬but the family dates back much further‭ – ‬to the origin of the WGD event in fact,‭ ‬which occurred more like‭ ‬88-103‭ ‬Ma‭ (‬Macqueen and Johnston,‭ ‬2014‭; ‬Berthelot‭ ‬et al.‭ ‬2014‭)‬.‭ ‬Thus,‭ ‬the last common ancestor to extant salmonid species is what the authors are actually referring to in this sentence. Reply : ‬Thanks so for pointing this out.‭ ‬We changed the text to‭ “‬local adaptation has been extensively studied in the salmonid family,‭ ‬to which Arctic charr belongs‭ {‬Fraser2011‭}‬.‭ ‬The family is estimated to be between‭ ‬88-103‭ ‬million years old‭ {‬Macqueen2014,Berthelot2014c‭}‬.‭ ‬A whole genome duplication event occurred before the radiation of the salmonid family‭ {‬Davidson2010,Moghadam2011,Macqueen2014,Berthelot2014c‭} ‬which has provided time for divergence of ohnologous genes‭ (‬paralogous genes originated by whole genome duplication event‭)‬.‭ ” ‬ Introduction:‭ “‬Furthermore,‭ ‬for data with short reads,‭ ‬mapping to a related reference genome/transcriptome is recommended over de novo assembly‭”‬.‭ ‬While this sentence is technically correct in the context of the work cited,‭ ‬I feel it is being used slightly out of context.‭ ‬For a start,‭ ‬what comprises a‭ ‘‬short read‭’ ‬is undefined.‭ ‬36bp is short,‭ ‬but it is possible to get a sold reference transcriptome using‭ ‬2‭*‬100bp,‭ ‬assuming the appropriate diversity of transcripts is represented and suitable depth is attained.‭ ‬ Reply : ‬Great point,‭ ‬we opted for keeping the point‭ (‬at this place in the ms‭) ‬but changing the wording to:‭ ‬In this study we opted to map the reads‭ (‬36‭ ‬bp‭) ‬to a related reference genome/transcriptome‭ {‬Vijay2013a‭}‬,‭ ‬instead of conducting de novo assembly.‭ Introduction:‭ “‬nuclear genes,‭ ‬reveled both subtle‭” ‬change to‭ “‬nuclear genes,‭ ‬revealed both subtle‭” Reply : ‬Thanks fixed.‭ ‬ Minor comment‭ – ‬AC,‭ ‬PL,‭ ‬LB and SB were already defined in introduction.‭ ‬ Reply : ‬Thanks, removed this. Methods:‭ “‬Fishing in Lake Thingvallavatn was with permissions‭” ‬changed to‭ “‬Fishing in Lake Thingvallavatn was done with permissions‭”‬.‭ ‬ Reply : Ammended. Methods:‭ “‬of differently expressed genes,‭ ‬we preformed clustering analyses‭” ‬change to‭ “‬…we performed clustering analyses‭”‬ Reply : ‬Thanks,‭ ‬fixed. Results:‭ “‬The most drastic changes were seen in processes related to glycolysis‭ (‬GO:0006096,‭ ‬FDR‭ = ‬0.0009‭)‬,‭ ‬were the expression of‭ ‬19‭ ‬out of‭ ‬25‭ ‬genes‭” ‬change to‭ “…‬.‭ ‬where the expression‭”‬.‭ ‬ Reply :‭ ‬Thanks,‭ ‬fixed. Figure‭ ‬7.‭ ‬What does the charr_WT vs.‭ ‬charr_M signify in the alignment data‭?‬ Reply : ‬Designates the two alleles,‭ ‬the legend now makes this explicit.‭ ‭ ‬Discussion‭ “‬We are interested in how predictable evolution is a the molecular level and if there certain principles influence the rewiring of developmental and regulatory systems during evolution‭” ‬consider changing to‭ “‬We are interested in the predictability of evolution at the molecular level,‭ ‬especially whether there exist principles that influence the rewiring of developmental and regulatory systems‭”‬.‭ ‬ Reply : ‬Thanks,‭ ‬excellent suggestion,‭ ‬included Discussion.‭ “‬Recent rainbow trout data shows most paralogs from the latest whole genome duplication event retain the same expression pattern32‭ ‬indicating that this scenario is probably uncommon‭; ‬hence it is of considerable interest when two paralogs show distinct expression patterns‭”‬.‭ ‬I do not agree that it is of considerable interest when two paralogs show distinct expression patterns‭ – ‬I could list tens of examples for salmonids.‭ ‬ Reply :‭ ‬Good point,‭ ‬we have revisited this interpretation‭ (‬see also point by rev.‭ ‬1‭)‬.‭ ‭ ‬ Conclusions‭ “‬The results suggest genetic and expression changes in multiple systems relate to divergence among populations.‭” ‬Change to‭ “‬… associated with divergence among populations.‭” Reply : ‬Thanks,‭ ‬fixed. View more View less Competing Interests No competing interests were disclosed.No competing interests were disclosed. reply Respond Report a concern Macqueen D. Peer Review Report For: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs [version 3; peer review: 2 approved, 1 approved with reservations] . F1000Research 2016, 4 :136 ( https://doi.org/10.5256/f1000research.6869.r8970) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/4-136/v1#referee-response-8970 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Click here to access the data. The problem Spreadsheet data files may not format correctly if your computer is using different default delimiters (symbols used to separate values into separate cells) - a spreadsheet created in one region is sometimes misinterpreted by computers in other regions. You can change the regional settings on your computer so that the spreadsheet can be interpreted correctly. How to fix it Save downloaded CSV file Open spreadsheet program (e.g. Excel) Click the ‘Data’ tab at the top Click the ‘From text’ icon (top left) Browse for downloaded CSV file, click ‘Import’ Ensure ‘Delimited’ radio button is selected, click ‘Next’ Check one of the appropriate delimiter checkboxes (you can visualize the formatting by looking at the data preview below these options) Click ‘Finish’ Downloaded data do not display as expected? Download the data (1.74MB) Dataset citation: Gudbrandsson J, Ahi EP, Franzdottir SR et al. . Dataset 1 in: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs. F1000Research 2016, 4 :136 (https://doi.org/10.5256/f1000research.6402.d48005) Close Click here to access the data. The problem Spreadsheet data files may not format correctly if your computer is using different default delimiters (symbols used to separate values into separate cells) - a spreadsheet created in one region is sometimes misinterpreted by computers in other regions. You can change the regional settings on your computer so that the spreadsheet can be interpreted correctly. How to fix it Save downloaded CSV file Open spreadsheet program (e.g. Excel) Click the ‘Data’ tab at the top Click the ‘From text’ icon (top left) Browse for downloaded CSV file, click ‘Import’ Ensure ‘Delimited’ radio button is selected, click ‘Next’ Check one of the appropriate delimiter checkboxes (you can visualize the formatting by looking at the data preview below these options) Click ‘Finish’ Downloaded data do not display as expected? Download the data (27.44KB) Dataset citation: Gudbrandsson J, Ahi EP, Franzdottir SR et al. . Dataset 2 in: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs. F1000Research 2016, 4 :136 (https://doi.org/10.5256/f1000research.6402.d48006) Close Click here to access the data. The problem Spreadsheet data files may not format correctly if your computer is using different default delimiters (symbols used to separate values into separate cells) - a spreadsheet created in one region is sometimes misinterpreted by computers in other regions. You can change the regional settings on your computer so that the spreadsheet can be interpreted correctly. How to fix it Save downloaded CSV file Open spreadsheet program (e.g. Excel) Click the ‘Data’ tab at the top Click the ‘From text’ icon (top left) Browse for downloaded CSV file, click ‘Import’ Ensure ‘Delimited’ radio button is selected, click ‘Next’ Check one of the appropriate delimiter checkboxes (you can visualize the formatting by looking at the data preview below these options) Click ‘Finish’ Downloaded data do not display as expected? Download the data (17.17KB) Dataset citation: Gudbrandsson J, Ahi EP, Franzdottir SR et al. . Dataset 3 in: The developmental transcriptome of contrasting Arctic charr ( Salvelinus alpinus ) morphs. F1000Research 2016, 4 :136 (https://doi.org/10.5256/f1000research.6402.d48007) Close Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. 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