Genet identity and season drive gene expression in outplanted Acropora palmata at different reef sites.

Genet identity and season drive gene expression in outplanted Acropora palmata at different reef sites. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genet identity and season drive gene expression in outplanted Acropora palmata at different reef sites. Benjamin Young, Dana Williams, Allan Bright, Annie Peterson, Nikki Traylor-Knowles, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4259333/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Coral reefs are experiencing decreases in coral cover due to anthropogenic influences. Coral restoration is addressing this decline by outplanting large volumes of corals onto reef systems. Understanding how outplanted corals react at a transcriptomic level to different outplant locations over time is important, as it will highlight how habitat affects the coral host and influences physiological measures. In this study, the transcriptomic dynamics of four genets of outplanted Acropora palmata were assessed over a year at three reef sites in the Florida Keys. Genet identity was more important than time of sampling or outplant site, with differing levels of baseline immune and protein production the key drivers. Once accounting for genet, enriched growth processes were identified in the winter, and increased survival and immune expression were found in the summer. The effect of the reef site was small, with hypothesized differences in autotrophic versus heterotrophic dependent on outplant depth. We hypothesize that genotype identity is an important consideration for reef restoration, as differing baseline gene expression could play a role in survivorship and growth. Additionally, outplanting during cooler winter months may be beneficial due to higher expression of growth processes, allowing establishment of outplants on the reef system. Biological sciences/Genetics/Gene expression Biological sciences/Ecology/Conservation Biological sciences/Molecular biology/Transcriptomics Figures Figure 1 Figure 2 Figure 3 Introduction Despite their biological and economic importance, tropical coral reefs are in decline, due to the effects of local and global anthropogenic influences 1–3 . At local scales, factors such as pollution and increased nutrient run-off are decreasing coral fitness making them more susceptible to disease and bleaching events 4–7 . At a global scale, anthropogenic climate change is further causing deleterious problems through ocean warming 3,8,9 and ocean acidification 10–12 . Global and local stressors do not act alone, with the synergistic effects of these variables 2,10,13,14 further reducing coral host fitness, and causing phase shifts from coral to algal reef systems 15–17 . One area trying to stem the decline of tropical coral reefs is coral restoration, which restores stony corals to the reef ecosystem 18–20 . Coral restoration has grown substantially since the early 2000s, with large biomasses of corals outplanted to reef systems 18,21 . Historically, asexual propagation has been the primary focus of restoration activities and involves fragging larger colonies into smaller pieces which show increased growth rates compared to larger colonies 22,23 . These fragged pieces of coral are then replanted on the reefs in clusters allowing fusion, resulting in large coral colonies being formed over a markedly shorter time frame 22,24 . Recently, this method has shown success, with fused outplanted corals subsequently spawning 25,26 . The increase in coral biomass for restoration activities has led to the need to research optimal outplant sites for restoration success. Outplant sites can vary in their morphology (a flat reef structure versus a spur and groove system) and the abiotic conditions they exhibit 27–29 . With different coral species exhibiting habitat preferences 30–32 , it is a key consideration for restoration. Additionally, within species variability also needs to be considered. For example, different genets from a coral species can vary in bleaching resistance 33,34 , disease resistance 35,36 , and growth capabilities 37–39 . The exact mechanisms driving differences between genet responses within a species are multifaceted and may involve the coral host genetics and its microbiome (prokaryotes, viruses, Symbiodiniaceae, protists, and fungi). For the coral host factors influencing gene expression, such as epigenetic modification, gene frontloading, and genetic mutations could be significant in shaping differences between genets, leading to varying levels of baseline gene expression. This may lead to specific processes, such as immune responses, being naturally higher in some genets providing increased resistance to factors such as pathogens. Therefore baseline gene expression may play a key role in predicting resistance and susceptibility, as well as performance for outplanted corals at different reef habitats. By understanding the underlying stressors that coral may encounter at an outplant site, preferential genets can be selected to maximize survivability and growth in restoration activities. In the Caribbean, one species heavily focused on for restoration activities is the critically endangered branching coral Acropora palmata 40–42 . Since the 1980’s, this species has seen drastic declines due to disease outbreaks 43,44 , bleaching events 45,46 , and human influences 47 . Due to the fast-growing nature of A. palmata , it is especially conducive to asexual fragmentation 48 allowing large biomass to be accrued and outplanted onto the reefs. Despite the Coral Restoration Foundation (CRF) prioritizing A. palmata as a key coral species for outplanting, there has so far been no research on how different reef sites influence the transcriptomic profiles of different A. palmata genets. Understanding the transcriptomic profiles will allow the identification of potential resistant and susceptible genets, and underlie the importance of fully characterizing reef site conditions to improve survivability of outplanted corals. In this study, we characterized the gene expression profiles over a year of multiple fragments of four genets of A. palmata at three reef sites in the Florida Keys; Carysfort Reef (25.2209, -80.2102), Pickles Reef (24.9845, -80.4164), and North Dry Rocks (25.1304, -80.2940) (Fig. 1 A). We found that the genet identity was the largest driver of the gene expression, swamping any signal of reef site or sampling time (i.e., time of year). We also found differences in baseline immune activity and metabolic activity between the genets. Once accounting for genet identity, we identified significantly correlated co-expression modules linked to the different sampling times, with these modules linked to the cooler winter months, and hotter summer months. Future work should build on our results and sequence more genets of A. palmata . This should include a wider range of reef environments with varying topologies and abiotic conditions, and a higher frequency of sampling timepoints to generate a more robust transcriptional picture of the lives of outplanted corals. Materials and Methods Field Site Selection Three active restoration field sites in the Northern Florida Keys, managed by the Coral Restoration Foundation (CRF), were chosen for field sampling: Carysfort Reef (25.2209, -80.2102), Pickles Reef (24.9845, -80.4164), and North Dry Rocks (25.1304, -80.2940) (Fig. 1 A). Carysfort Reef was the northernmost sampling site (Fig. 1 A), a shallow reef system with low rugosity. Sampled A. palmata outplants were located between 17-22ft. North Dry Rocks was the middle sampling site (Fig. 1 A), a spur and groove reef system. All sampled A. palmata were located on one spur and ranged from 10–15 ft in depth. Pickles Reef was the most southern sampling site (Fig. 1 A) with low rugosity like Carysfort Reef, with sampled corals between 14–19 ft. These three reef sites provided different latitudinal locations, and different outplant depths of A. palmata for correlative analysis. Locations of the three reef sites in the Florida Keys denoted by red dots. Inset map bottom right identifies the location with a square box of the main map image. Field sampling summaries and samples selected for RNA-seq analysis. The table shows each sampling time point (ST; column 1), reef site (column 2), date of sampling (column 3), the total number of days to sample all reef sites for each sampling time point (column 4), and which subset of sampling time points were selected for RNA-seq analysis. Plot of OISST at the three reef sites studied (colored lines). Seasons are indicated on the figure by blue fill (wet season) and red (dry season). Sampling time point range are indicated by black vertical bars on the plot. Coral Sampling Five sampling time points were conducted at each of the three reef sites: sampling time point one (ST1) during November 2018, sampling time point two (ST2) during February/March 2019, sampling time point three (ST3) during June/July 2019, sampling time point four (ST4) during September 2019, and sampling time point five (ST5) during November 2019 (Fig. 1 B). These covered a full year and the summer and winter months. For each sampling time point, all reefs were sampled in a 7-day period (Fig. 1 B) with SCUBA used to access outplanted A. palmata fragments within clusters. Each cluster contained six to seven fragments of the same genet and clusters were identified for each sampling time point using cow tags placed at initial outplanting (Supplementary Fig. 1). For each sampling time point, clusters underwent health surveys and tissue sampling using a hammer and chisel. The same fragment was sampled for each cluster at each sampling time point through unique numeric identifiers and previously taken photographs. A tissue sample was then collected in pre-labeled zip bags with SCUBA, with tools wiped between samples. On the boat, tissue samples were transferred to 2ml cryovial tubes filled with 1.5ml of RNAlater and placed on ice. All tissue samples were processed and placed on ice within 35 minutes of sampling. All tools used in transferring tissue samples were bleached and wiped down with ethanol between each sample. At the end of each day, samples were placed at -80 o C. Sea Surface Temperature for the Reef Sites Sea surface temperature (SST) was obtained from the National Oceanographic and Atmospheric Association (NOAA) Optimum Interpolation Sea Surface Temperature (OISST) data set 49–51 . To cover all sampling timepoints, data was subset for a temporal range from October 2018 to January 2020, and obtained for each reef site. OISST time course plotting was undertaken in R (v4.0.3) and RStudio (v1.4.1106) using tidyverse 52 and GGplot2 53 . Average temperatures were calculated across all reef sites, as well as average temperature recorded at each reef site for each sampling time point. The average temperature was also calculated for each reef site and the time span it took to sample all reefs at each sampling time point. Sample Choice for Sequencing A total of 1,227 samples of 11 genets of A. palmata were collected from the three reef sites over the five sampling timepoints. Of the 11 genets sampled, four were selected for further processing. These genotypes were selected since they were used in an ex-situ disease experiment 54 and are referred to using CRF designations: CN2, CN4, ML2, and HS1. As high replication of genets within each reef was desired, only four of the five sampling timepoints were chosen for sequencing: ST2 (February/March 2019), ST3 (June 2019), ST4 (September 2019), and ST5 (November 2019). This resulted in 384 samples of A. palmata for 3` RNA-sequencing. RNA Extractions, Complementary DNA Synthesis, and Sequencing DNA and RNA were extracted from the same piece of coral tissue using the Zymo MagBead DNA/RNA kit on the Kingfisher Flex. For an in-depth protocol please see 55 . On completion, eluted DNA was placed at -80 o C for amplicon sequencing. For eluted RNA, 8µl aliquots were used for quality control (nanodrop and qubit), with the remainder sealed and placed at -80 o C for downstream processing. Total RNA was converted to complementary DNA (cDNA) using the Quant-seq FWD kit (Lexogen) following the high yield/quality manufacturers protocol. The qPCR add-on kit (Lexogen) was utilized to ascertain the RNA concentrations needed to reduce under and over PCR cycling. Samples were dual indexed and sent to the Hussman Institute for Human Genomics (Miller School of Medicine, University of Miami) for single-end sequencing on a NOVA-seq. Sample read depth was requested at 4–5 million reads. Transcriptomic Bioinformatics Pre-processing of the 3` RNA-seq libraries followed the same pipeline as ( 62 ). Demultiplexing was done by the sequencing facility, and raw reads had adapters and low-quality reads trimmed using BBDuk.sh 56 with recommended parameters from Lexogen ( https://www.lexogen.com/quantseq-data-analysis ). Alignment and quantification utilized Salmon 57 and the annotated A. palmata transcriptome 58,59 . Quantified samples were read into R (v4.0.3) and RStudio (v1.4.1106) using tximport 60 and quantified to the gene level. Samples with less than 1,000,000 total read counts, and genes with less than four counts in greater than 15 samples, were removed. Prior to analysis, hierarchical clustering using hclust 61 was employed to identify any sample groupings that were not explained by available metadata and thus deemed as surrogate variables. Downstream analysis either removed the surrogate variable or incorporated it in statistical models. Principal component analysis (PCA) was run using a variance stabilized transformation (VST) of the raw counts, on all coral samples, using a modified PlotPCA function from DeSeq2 62 . Correlations of principal components (PCs) to metadata variables were done using pcatools::eigencorplot 63 , with significance identified using a Pearson correlation. Visualization of PCs of interest was done using GGPlot2 53 . To identify finer scale patterns, surrogate/batch variables were removed using limma::removeBatchEffect 64 on the VST of raw counts. The batch corrected VST data was then used as input for additional PC analysis. Differential gene expression (DGE) analysis was run using DeSeq2 62 . To identify differences in baseline gene expression between the four genets (ML2, CN2, CN4, and HS1) the Likelihood Ratio Test (LRT) methodology was used. The full model ( ~ Surrogate Variable + Sampling Timepoint + Genet ) and reduced ( ~ Surrogate Variable + Sampling Timepoint ), were used, with alpha set to 0.001. All significant genes were used in gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. To identify finer scale expression profiles between the genets, the VST counts of significant genes from the LRT were used in DEGreport::degpatterns 65 . Visualization of identified gene clusters was done in GGplot2 53 , and GO and KEGG enrichment analysis was undertaken for all identified clusters. Weighted gene co-expression network analysis (WGCNA) was run using the r package WGCNA 66 . To identify co-expression due to sampling time point and reef, input for WGCNA analysis was VST transformed counts with genet and surrogate variable variance removed. Outlier samples were identified using the Ward.D2 methodology, and a single signed network was constructed manually with the following key parameters: softPower = 18, minModuleSize = 30, deepSplit = 2, mergedCutHeight = 0.40, minimumVerbose = 3, and cutHeight = 0.997. The eigengene values of each module were correlated to sampling timepoints (ST2, ST3, ST4, and ST5), OISST SST, reef sites (CF, NDR, and PI), and outplant depth. The highest connected gene within each module was identified (wgcna::chooseTopHubInEachModule). GO and KEGG enrichment analysis was done for each module to ascertain putative biological functions and roles. Gene lists identified from analysis were used as input for enrichment analysis in Cytoscape v.3.8.2 67 and the application BiNGO 68 . Enrichment analysis was run using the hypergeometric test and p-value correction using a Benjamini & Hochberg false discovery rate (FDR). Alpha was set at 0.01 for all GO enrichment analyses. The background universe of genes to test enrichment was the set of genes remaining after initial low count filtering. Visualization of GO enrichment was done in Cytoscape v3.8.2 67 , and further visualization of gene expression to GO terms was run in the R package Complex Heatmap 69 using the VST counts with genet and batch variable removed. Gene lists of interest were also annotated with their KEGG gene identifiers present in the A. palmata annotation file 58,59 . Clusterprofiler v3.18.1 70 was used to identify KEGG pathway enrichment, with enrichment tested against the KEGG Orthology database (organism = “ko”), and alpha set at 0.01. Visualization of enriched pathways was done with clusterprofiler 70 , enrichPlot 1.10.2 71 , GGPlot2 53 and complex heatmap 69 . Results Sea Surface Temperature Between the three reef sites There were minimal differences in temperature profiles at each sampling time point for the three reef sites over the one-year period (Fig. 1C). Seasonally, November 2018 to May 2019 showed average temperatures of ~ 26ºC. May 2019 onwards showed an increase in temperature, with average values around 30ºC and a max in mid-August 2019 of 32ºC. September 2019 onwards saw a decrease back to ~ 26ºC. Due to weather conditions, reefs were sampled at each time point as close as feasibly possible. For ST1 this covered 15 days (14th-26th November 2018), ST2 five days (28th February to 4th March 2019), ST3 seven days (25th June − 2nd July 2019), ST4 two days (25th-26th September 2019), and ST5 two days (18th-19th November 2019) (Fig. 1B). At Carysfort Reef, average OISST for each sampling time point was as follows: ST1 27.01ºC, ST2 26.20ºC, ST3 30.15ºC, ST4 28.55ºC, and ST5 27.44ºC. At North Dry Rocks, average OISST for each sampling time point was as follows: ST1 26.84ºC, ST2 26.41ºC, ST3 30.35ºC, ST4 28.17ºC, and ST 5 26.21ºC. At Pickles Reef, average OISST for each sampling time point was as follows: ST1 27.70ºC, ST2 26.08ºC, ST3 29.38ºC, ST4 28.85ºC, and ST5 27.13ºC. Sequencing Results A total of 374 of the 384 samples were successfully sequenced, with sample read depth ranging from 567,208 to 16,494,255 reads, and an average and median read depth of 1,843,913, and 1,550,645 respectively. A total of 12 samples were removed with < 1 million counts across all genes. This resulted in average and median read depths of 1,877,383 and 1,563,692 respectively, and 362 samples for downstream analysis. Initial hierarchical clustering of the samples identified a strong surrogate variable which was not explained by any metadata variables taken (Supplementary Fig. 2). As such, all downstream differential expression and co-expression analysis included this as a surrogate effect. Filtering of low count genes resulted in 18,937 genes being retained for downstream analysis. These 18,937 genes also constituted the ‘gene universe’ (i.e. background set of genes used to test enrichment for gene lists of interests) used for GO enrichment analysis. Genet identity was the largest driver of gene expression variance. After accounting for the surrogate variable, genet identity was the largest driver of gene expression, with groupings of genet present from PC1 to PC4 (Fig. 2A, B, C). Differential expression analysis, using the LRT, identified 7,096 significant genes between the four genets (Supplementary File 1). The degreport::degpatterns 65 analysis identified six different expression clusters (Supplementary File 2) with differing gene sets and expression profiles (Fig. 2D). Cluster 1 (2,847 genes) showed significant GO and KEGG pathway enrichment of terms related to cell growth and protein production, with KEGG enrichment identifying multiple terms important in immune system signaling pathways and pathogenic infection (Fig. 2E). Genet HS1 showed a strong positive association, and genet CN4 showed a strong negative association with Cluster 1 (Fig. 2D). Cluster 2 (751 genes) identified enrichment of one GO term, Fibroblast growth factor receptor signaling (GO:0008543), and one KEGG term, Lysosome (ko04142). Genet CN4 again showed a strong negative association, and ML2 showed a strong positive association with Cluster 2 (Fig. 2D). Cluster 3 (565 genes) showed GO enrichment of ribosome and translational processes. Enrichment map analysis of the KEGG enrichment results identified one functional module: terms linked to neurodegenerative diseases (Fig. 2E and Supplementary Fig. 3A), with genet CN2 showing a strong positive association and HS1 showing a strong negative association (Fig. 2D). Cluster 4 (1,421 genes) again showed GO enrichment of terms linked to ribosomes and translational processes. Enrichment map analysis of significant KEGG pathways (Supplementary File 4) identified two functional modules; terms linked to pathogenic infection, and neurodegenerative diseases (Fig. 2E and Supplementary Fig. 3B). CN2 and HS1 showed negative associations with Cluster 4, while CN4 and ML2 showed positive associations (Fig. 2D). Cluster 5 (640 genes) showed no significant GO or KEGG enrichment, with genet CN2 showing a strong negative association and genet HS1 showing a strong positive association (Fig. 2D). Cluster 6 (551 genes) showed no GO enrichment, but KEGG enrichment and enrichment map analysis again identified the majority of terms to be grouped in a functional module linked to neurodegenerative diseases (Fig. 2E and Supplementary Fig. 3C). Genet ML2 showed a negative association and CN2 showed a positive association with Cluster 6 (Fig. 2D). Full GO enrichment and KEGG pathway enrichment results are available in Supplementary File 3 and Supplementary File 4 respectively. Co-expression analysis identified modules that significantly correlated to the winter (ST2 and ST5) and summer (ST3 and ST4) seasons once accounting for genet variance. Once accounting for the identified surrogate variable and genet identity, co-expression analysis identified 15 co-expression modules (Supplementary Fig. 4) ranging from 50 to 7,514 genes (Supplementary File 4). Three modules showed significant correlations with all four sampling timepoints (Fig. 3A). The Purple module (278 genes, hub gene = 60S Ribosomal ) showed negative correlations with ST2 and ST3 (-0.14 and − 0.14 respectively) and positive correlations with ST4 and ST5 (0.16 and 0.12 respectively) (Fig. 3A). GO and KEGG enrichment analyses identified terms to be linked to ribosomal and metabolic processes (Supplementary File 5 and Supplementary File 6 respectively). The putative function of the purple module was deemed to be “protein synthesis and homeostatic processes”. The Black module (737 genes, hub gene = HIG1 domain family member 1C ) showed negative correlations with ST2 and ST5 (-0.27 and − 0.41 respectively), and positive correlations to ST3, ST4 (0.24 and 0.4 respectively), and SST (R 2 = 0.5) (Fig. 3A). GO enrichment identified only one significantly enriched term, Endoplasmic reticulum and chaperone complex (GO:0034663) (Supplementary File 5). KEGG enrichment identified several signaling pathways important in general cell proliferation, management, and cell growth. Specifically, this included signaling pathways ( MAPK signaling pathway ( KO:04010), PI3K-Akt signaling pathway (KO:04151), GnRH signaling pathway (KO:04912), Ras signaling pathway (KO:04014), and cAMP signaling pathway ( KO:04024 )) and other terms ( Focal adhesion (KO:04510), Proteoglycans in cancer (KO:05205), and Protein processing in endoplasmic reticulum (KO:04141)) (Supplementary File 6). The putative function of the Black module was assigned as “Organismal cellular maintenance and growth”. The Light Cyan module (59 genes, hub gene = Cell Surface hyaluronidase) showed positive correlations with ST2 and ST5 (0.27 and 0.21 respectively) and negative correlations with ST3, ST4 (-0.44 and − 0.16 respectively), and SST (-0.41) (Fig. 3A). There was only significant KEGG enrichment for one term: Rheumatoid arthritis (KO:05323) (Supplementary File 6). Due to low enrichment, no putative function was assigned to the Light Cyan module. Four modules showed significant correlations to three of the four sampling timepoints: Red, Light Yellow, Midnight Blue, and Turquoise (Fig. 3A). The Red module (1,944 genes, hub gene = Protein CREG1 ) showed negative correlations with ST2 and ST3 (-0.13 and − 0.1 respectively) and a positive correlation with ST4 (0.14) (Fig. 3A). GO enrichment identified three terms all linked to the mitochondrion (Supplementary File 5), while KEGG enrichment identified two main sets of processes: fatty acid metabolic processes and viral associated infections and responses (Supplementary File 6). The putative function of the Red Module was “ Mitochondrial Maintenance and Breakdown ”. The Light-Yellow module (38 genes, hub gene = Cryptochrome-1) showed positive correlations with ST2 and ST3 (0.32 and 0.13 respectively) and a negative correlation with ST5 (-0.35) (Fig. 3A). There was no KEGG enrichment, but GO enrichment identified terms linked to multiple parts of the mitochondrion, as well processes important in fatty acid metabolism, vitamin metabolism, and respiration (Supplementary File 5). Putative function of the Light-Yellow module was assigned as “ Metabolic Processes ”. The Midnight-Blue module (62 genes, hub gene = Bromodomain containing protein 2 ) showed positive correlations with ST3, ST4 and SST (0.17, 0.18, and 0.3 respectively) and a negative correlation with ST5 (-0.26) (Fig. 3A). There was no significant GO or KEGG enrichment for this module, thus no putative function was assigned. The Turquoise module (7,415 genes, hub gene Myosin-10) showed positive correlations with ST2 and ST5 (0.14 and 0.22 respectively) and negative correlations with ST4 and SST (-0.289 and − 0.22 respectively) (Fig. 3A). At alpha 0.01, there were 204 significant GO terms (Supplementary File 5) and 192 significant KEGG terms (Supplementary File 6). Both GO and KEGG pathway showed enrichment of terms linked to metabolic processes, immune signaling pathways, and protein synthesis. Due to high enrichment, the turquoise module was designated as “ general organism homeostasis ”. There were no co-expression modules that were significantly correlated to all three reef sites. Co-expression analysis identified two modules that showed correlation to at least two of the three reef sites; Light Yellow module (38 genes, hub gene = Cryptochrome 1) which showed a negative correlation to North Dry Rocks (r2 = -0.45) and a positive correlation to Pickles Reef (r2 = 0.45), and the Cyan module (784 genes, hub gene = uncharacterized protein LOC111326987) which showed a negative correlation to Carysfort (r2 = -0.11) and a positive correlation to North Dry Rocks (r2 = 0.15) (Fig. 3A). The Light Yellow module was assigned the putative function of “metabolic process” from GO and KEGG enrichment analysis. For the Cyan module, GO enrichments identified terms linked to the extracellular space as well as terms important in general organism growth and nervous system cell development (Supplementary File 5). KEGG enrichment only identified two terms; Maturity onset diabetes of the young (KO = map04950) and Notch signaling pathway (KO = map04330) (Supplementary File 6). The putative function of the Cyan module was “growth processes”. Discussion Baseline immune gene expression and putative protein production drove differences in gene expression between the four genets. In this study, we identified that genet identity is the largest driver of gene expression in outplanted A. palmata (Fig. 2 A-C), and this masks the signals of sampling time point and reef location. From our GO and KEGG enrichment analysis, we identified that baseline immune activity seems to be an important difference between genets of A. palmata . Baseline gene expression of an organism can be described as the normal expression levels of an organism not exposed to stimuli. Within a species different levels of baseline expression can therefore exist for different processes. For example, differing levels of immunity between genets could drive factors such as disease susceptibility and resistance. For corals, it is well documented that there are differences in the production of immune-related molecules 72,73 as well as differing levels of disease susceptibility 74–76 . This also holds true within coral species, with different genets showing differing levels of susceptibility to disease 35,36 , and heat stress 33,34 . At present, the exact molecular mechanism(s) of differences in baseline immune activity has not been fully elucidated within a coral species. Epigenetic modification could play a role, with histone modification 77 and DNA methylation 78 being shown to influence immune gene expression in other organisms. In corals, epigenetic modifications can occur due to environmental perturbations 79 as well as specific stress such as nutrients 80 . Similarly, previous work has shown that corals can front-load genes, allowing them to resist stressful conditions 81,82 . We therefore hypothesize that different genets of A. palmata could be using these processes to cause differences in baseline immune gene expression. We previously showed this same difference in immune-related gene expression A. palamta genets in an ex-situ disease experiment 54 . In this study, using the same four genets of A. palmata , we identified the same signal for baseline immune gene expression. This indicates that this signal holds true over time and space, and is therefore important in patterns of resistance and susceptibility in A. palmata genets. The clusters identified from the genet LRT analysis also identified consistent enrichment of GO terms linked to ribosome and translational processes (clusters 3 and 4). These results could indicate that among A. palmata genets different baseline rates of protein synthesis exist, which could affect physiological variables such as growth rates, as well as responses to biotic and abiotic perturbations. High baseline ribosome and translational processes could be linked to faster-growing genets. Specifically, we think genet CN2 may have faster growth rates in reefs as it showed higher abundances of both ribosome and translational and growth processes (cluster 1) but additional work needs to be conducted to provide evidence for this hypothesis. Clusters 4 and 6 both had designations for neurodegenerative diseases, with enrichment map analysis showing high connectivity for these terms (Supplementary Fig. 3A-C). Interestingly, the intersection of the genes within the highly connected neurodegenerative terms for each cluster had minimal overlap among genets (Supplementary Fig. 3D) indicating that different cellular processes are being expressed among the genets. Neurodegenerative diseases are characterized by processes such as misfolded proteins 83,84 , oxidative stress 85,86 , disruption of calcium homeostasis 87,88 , and mitochondrial dysfunction 89,90 . While these terms are specifically linked to human neurodegenerative diseases, the core processes and pathways that are involved in these diseases are present throughout the metazoan tree of life. For example, oxidative phosphorylation is a core organismal process that generates adenosine-triphosphate, the source of energy at a cellular level. Stony coral tissue loss disease research has also identified enrichment of neurodegenerative diseases term ( 93 ), which indicates that the core processes present in well-studied human diseases may show different baseline expression in different coral genets, and thus provide benefits and tradeoffs. Future work should look to fully characterize the potential pathways and processes which are encompassed in these human neural diseases in corals, and subsequently identify how these processes affect the physiological processes of corals, and if this confers any benefits or tradeoffs that can be incorporated into coral restoration projects. The results from this study, paired with past research, underline the importance of not only characterizing the immune repertoire of coral species but also identifying how differences in baseline gene expression among coral genets may influence susceptibility and resistance to biotic and abiotic stressors. From a coral restoration perspective by characterizing the baseline immune performance, outplanting of, for example, more genets that can mount a higher immune response may improve survivorship and restoration of reefscapes. Co-expression analysis identifies higher potential growth genes in A. palmata in the cooler winter months, and shifts to survival and defensive processes in the summer months. Once accounting for genet identity, WGCNA 66 analysis identified co-expression modules significantly correlated to sampling time point with three significant modules (Purple, Black Light Cyan) for the four sampling timepoints (Fig. 3 A). The Black and the Light Cyan module correlations mirrored the summer versus winter months, with correlations also to sea surface temperature (Fig. 3 A). The light cyan module, which was negatively correlated with temperature, may encompass important genes involved in growth processes in Acropora palmata. For instance, the cyan module included genes involved in structural integrity of the extracellular space (collagen genes), and tissue rigidity (tricholycin and reticulobulin genes). The hub gene of this module ( cell surface hyaluronidase ) has also been shown to be important in regulation of cell adhesion, as well as the degradation of hyaluronan which regulates development and structural integrity in the extracellular matrix 92,93 . A hyaluronan-like substance has been identified previously in the coral species Mycetophyllia resi with the putative function important in tissue and skeletal matrix structure 94 . This could indicate that during the cooler winter months, growth processes are favored by outplanted A. palmata on the reef system, which aligns with previous findings that A. palmata grows quicker at cooler temperatures 95 . The Black module showed the inverse compared to the Light Cyan module, with a strong positive correlation (r 2 = 0.5) with sea surface temperatures and sampling time points 3 and 4 (i.e., summer months) (Fig. 3 A) and an inferred function of immune, survival, and metabolic processes (Fig. 3 B). The survival and metabolic inferred function was identified due to the significant enrichment of the MAPK, PI3/AKT, and RAS signaling pathways. MAPK signaling is a highly conserved signaling pathway 96 with importance in gene expression, mitosis, metabolism, immune responses, and survivability 97 . The PI3/AKT signaling pathway is similar, being highly conserved 98 and also playing key roles in the immune system, growth, and survival 99 . The RAS signaling pathway plays key roles in regulation and proper function of both the MAPK and PI3/AKT signaling pathways 100 . The enrichment of these pathways during the warmer summer months suggests that outplanted A. palmata may shift to a more survival and maintenance mode compared to growth processes that occur in the winter. This is plausible and indicates that although A. palmata were not exhibiting bleaching and looked relatively healthy even moderate increases in temperature may induce stress 101,102 . Increased water temperatures can increase abundance and pathogen virulence 103–105 which may explain the increased immune system activation targeted to prevent infection. The Black module also contained immune signaling pathways and processes adding support to this observation. The TNF signaling pathway is an important immune cytokine that can activate additional innate immune responses 106 , is important in cell proliferation and cell death 107 , and consistently shows increased expression in coral disease 91,108,109 and heat 110,111 transcriptomic studies. Reef location did not have a strong effect on gene expression for outplanted genets of Acropora palmata . Principal component analysis and co-expression analysis did not identify a strong effect of the outplant reef site on gene expression between the four genets of A. palmata . There were two co-expression modules that did show significant correlations to at least two of the three reef sites as well as cluster outplant depth (Fig. 3 A). There was only significant GO enrichment for the Light Yellow module, with terms involved in mitochondrial processes and fatty acid metabolism (Supplementary File 5). Coral species are mixotrophic, utilizing both autotrophic processes, where energy is derived from photosynthates produced by the obligate symbiont Symbiodiniceae , and heterotrophic processes, involving passive and active means of acquiring energy. Light availability is therefore the key variable that dictates the level of these factors. Coral species at deeper depths (i.e. species that inhabit shallow to upper mesophotic reef depths ~ 60m) switch to a more heterotrophic lifestyle mode of energy acquisition due to the lower levels of light available causing decreased output from the photosynthetic Symbiodiniceae 112 . With the significant correlation to depth, it is plausible that outplanted A. palmata at Pickles Reef rely more heavily on heterotrophic energy acquisition processes. This hypothesis is further backed up by the enrichment of fatty acid metabolic processes. Corals living at deeper depths in a more heterotrophic lifestyle have been shown to have higher energy reserves than corals at shallow depths in a more autotrophic lifestyle 112,113 . The outplanted A. palmata at Pickles may therefore be utilizing fatty acid stores that they have acquired through higher rates of heterotrophic feeding. Utilization of methodologies such as whole tissue stable isotope analysis of carbon and nitrogen, as well as tissue lipid content analysis would allow a more definitive conclusion of whether reef depth causes shifts in levels of heterotrophic or autotrophic energy acquisition in outplanted A. palmata , which leads to changes in gene expression. Conclusions and future directions for genomic analysis of outplanted corals We identified that genet identity is an important factor in outplanted A. palmata , and a leading driver of differences is baseline immune gene expression. Since we identified an intra-population variation in response to phenotypically healthy corals, more A. palmata genets would help identify whether this pattern holds at a population level. This research can then be used to identify how baseline profiles influence outplant performance. Additional ex-situ experiments of multiple genets of A. palmata to different biotic and abiotic stressors would also help identify the “winner” and “loser” genets. These results could be incorporated into restoration practices potentially increasing outplant survivability by matching fitter genets to preferred reef systems. Future work looking at the effects of outplant sites on coral gene expression should include a wider range of reef habitats. Despite the reef habitats used in this study exhibiting different topologies and rugosity, they are all still offshore reef sites within close proximity. Inshore and offshore reef systems have been shown to have different abiotic conditions influencing the performance of resident coral populations. By monitoring outplanted coral at different reef systems, and abiotic conditions it would allow characterization of gene pathways that are affected due to these environmental conditions. Declarations Acknowledgments We would like to thank Caroline Dennison, Allyson DeMerlis, Samara Neufield, and Ana Palacio-Castro for helping collect samples of Acropora palmata in the field. We would also like to thank all the staff at the Huntsman Institute for Human Genomics (University of Miami, Miami), for sample quality control and sequencing. We also thank Dr. Sheila Kitchen and Dr. Iliana Baums for continued use of the Acropora palmata genome. Finally, we thank the OAR NOAA omics initiative for providing funding support for BYD, SMR, and support for molecular lab work and sequencing. Data Accessibility and Benefit-Sharing All analysis scripts and pipelines can be found at https://github.com/benyoung93/acropora_palmata_field_transcriptomics, with all RNA-seq raw reads available on the NCBI under accession under PRJNA1081901. Author Contributions Initial study conception was SMR, with RNA-seq sample choice by SMR, BDY, and NTK. 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Supplementary Files Supplementaryfigures.docx Supplementaryfile1.xlsx Supplementaryfile2.xlsx Supplementaryfile3.xlsx Supplementaryfile4.xlsx Supplementaryfile5.xlsx Supplementaryfile6.xlsx Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Aug, 2024 Reviews received at journal 20 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviews received at journal 20 May, 2024 Reviewers agreed at journal 01 May, 2024 Reviews received at journal 01 May, 2024 Reviewers agreed at journal 01 May, 2024 Reviewers invited by journal 30 Apr, 2024 Editor assigned by journal 25 Apr, 2024 Editor invited by journal 18 Apr, 2024 Submission checks completed at journal 18 Apr, 2024 First submitted to journal 12 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4259333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":301389941,"identity":"c43395f9-00a2-49d0-bf38-9d714eeca442","order_by":0,"name":"Benjamin Young","email":"data:image/png;base64,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","orcid":"","institution":"University of Miami","correspondingAuthor":true,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Young","suffix":""},{"id":301389942,"identity":"a10bdf6d-9f51-4f5e-a016-f78a1153156c","order_by":1,"name":"Dana Williams","email":"","orcid":"","institution":"Southeast Fisheries Science Center","correspondingAuthor":false,"prefix":"","firstName":"Dana","middleName":"","lastName":"Williams","suffix":""},{"id":301389943,"identity":"6d87f2e7-d8f0-432d-a673-d1bd3ab0b058","order_by":2,"name":"Allan Bright","email":"","orcid":"","institution":"Southeast Fisheries Science Center","correspondingAuthor":false,"prefix":"","firstName":"Allan","middleName":"","lastName":"Bright","suffix":""},{"id":301389944,"identity":"adacaee6-f4ae-4251-a1cb-24755e4b4f9a","order_by":3,"name":"Annie Peterson","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Annie","middleName":"","lastName":"Peterson","suffix":""},{"id":301389945,"identity":"a2577566-6b0e-4424-aa54-573371399473","order_by":4,"name":"Nikki Traylor-Knowles","email":"","orcid":"","institution":"University of Miami","correspondingAuthor":false,"prefix":"","firstName":"Nikki","middleName":"","lastName":"Traylor-Knowles","suffix":""},{"id":301389946,"identity":"8b8a7aee-6074-4e55-9f3b-c2df2c7e59f4","order_by":5,"name":"Stephanie Rosales","email":"","orcid":"","institution":"Cooperative Institute of Marine and Atmospheric Science","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Rosales","suffix":""}],"badges":[],"createdAt":"2024-04-12 18:29:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4259333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4259333/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-80479-y","type":"published","date":"2024-11-27T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56362138,"identity":"45590788-d54f-4bf3-bcf5-1344bfb79c80","added_by":"auto","created_at":"2024-05-13 07:53:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReef location, outplant sampling summaries, and sea surface temperatures (SST) for the three reef sites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Locations of the three reef sites in the Florida Keys denoted by red dots. Inset map bottom right identifies the location with a square box of the main map image.\u003c/p\u003e\n\u003cp\u003eB) Field sampling summaries and samples selected for RNA-seq analysis. The table shows each sampling time point (ST; column 1), reef site (column 2), date of sampling (column 3), the total number of days to sample all reef sites for each sampling time point (column 4), and which subset of sampling time points were selected for RNA-seq analysis.\u003c/p\u003e\n\u003cp\u003eC) Plot of OISST at the three reef sites studied (colored lines). Seasons are indicated on the figure by blue fill (wet season) and red (dry season). Sampling time point range are indicated by black vertical bars on the plot.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/5cba91b02ae1dc94ad67246a.jpg"},{"id":56362139,"identity":"88e99b80-e0fa-403e-b2e1-e2e8a2f33d49","added_by":"auto","created_at":"2024-05-13 07:53:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenet identity was the largest driver of gene expression variance, with expression profile clusters identifying different baseline expressions among genets\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eA) Principal component (PC) 1 (16% variance) and PC2 (14% variance) of the four genets with 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eB) PC 2 (14% variance) and PC3 (12% variance) of the four genets with 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eC) PC 3 (12% variance) and PC4 (10% variance) of the four genets with 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eD) The six identified clusters from degreport::degpattern of the significant LRT genes identified between the four genets. Each box identifies a cluster, with the x-axis showing the genet, and the y-axis the computed expression z-score for each genet. A value closer to 1 indicates higher expression, a value closer to 0 indicates neutral expression, and a value closer to -1 indicates lower expression.\u003c/p\u003e\n\u003cp\u003eE) Inferred function identified from GO and KEGG enrichment analysis for each cluster. Cells with “—” indicate no inferred function due to low or no significantly enriched GO or KEGG terms.\u003c/p\u003e\n\u003cp\u003eFor A) - D); tan = genet CN2, blue = genet CN4, green = genet HS1, and gray = genet ML2.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/b26bcf571ae59c31a43b6968.jpg"},{"id":56362137,"identity":"32a954ce-5b84-4b5e-b808-b06e596a306b","added_by":"auto","created_at":"2024-05-13 07:53:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThere were significantly correlated co-expression modules to sampling time point and reef location when accounting for genet variance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Heatmap of co-expression modules and their respective correlation and significance to metadata variables of interest. columns are split into a) sampling time point 2-5 and average OISST, b) reef locations, and c) cluster outplant depth. Heatmap rows are split into sets of co-expression modules. The heatmap fill; red = positive correlation, blue = negative correlation. Text in each cell identifies the Pearson correlation (upper value) and p-value (lower value). White cells denote a significance of \u0026gt;0.05 and were removed. Bar chart to the right of the heatmap indicates the number of genes in each respective co-expression module.\u003c/p\u003e\n\u003cp\u003eB) Identified hub gene and putative function of each module as ascertained from significant GO and KEGG enrichment pathway analyses. Full GO and KEGG enrichment results used to infer function are available in Supplementary File 5 and 6 respectively.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/8484d3fc58789be9d1eb911a.jpg"},{"id":70389871,"identity":"4dfc8868-57df-44dc-b050-acf1de6442eb","added_by":"auto","created_at":"2024-12-02 17:29:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1236482,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/0af3f25a-696e-4c97-a897-99a2c9bef0dc.pdf"},{"id":56362146,"identity":"d87f2bb1-8182-4b42-9ede-c9d73a3769e6","added_by":"auto","created_at":"2024-05-13 07:53:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6310739,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/cdf6f38dfb8fcae1bae40a5d.docx"},{"id":56362140,"identity":"addcb980-3de4-4790-99da-23f8dd5b8584","added_by":"auto","created_at":"2024-05-13 07:53:34","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2107329,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/013d0451d52c9ec0dd1347b3.xlsx"},{"id":56362144,"identity":"f936ccef-99db-4d81-ac93-43fb62f7ae74","added_by":"auto","created_at":"2024-05-13 07:53:35","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":7951763,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/f028cb0e6d4a727711a38fbe.xlsx"},{"id":56362142,"identity":"1f77ba10-1f98-490c-a4a6-5388f2e59108","added_by":"auto","created_at":"2024-05-13 07:53:34","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1040294,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/668216607c41c8feccad3dd3.xlsx"},{"id":56362143,"identity":"10a184f7-a353-461d-93c3-aa3910d7f458","added_by":"auto","created_at":"2024-05-13 07:53:34","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":3075489,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/252bd173c5dc4556780f7430.xlsx"},{"id":56362145,"identity":"24a6f1e5-86fa-4246-903e-fbb042914997","added_by":"auto","created_at":"2024-05-13 07:53:38","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":6035885,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/d91f80e4831c594a943584ac.xlsx"},{"id":56362141,"identity":"962435ae-ef3a-4af7-a683-df8d0c77b8c4","added_by":"auto","created_at":"2024-05-13 07:53:34","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":2020507,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4259333/v1/53fd3f76f44575df7b0d5815.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genet identity and season drive gene expression in outplanted Acropora palmata at different reef sites.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite their biological and economic importance, tropical coral reefs are in decline, due to the effects of local and global anthropogenic influences \u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. At local scales, factors such as pollution and increased nutrient run-off are decreasing coral fitness making them more susceptible to disease and bleaching events \u003csup\u003e4\u0026ndash;7\u003c/sup\u003e. At a global scale, anthropogenic climate change is further causing deleterious problems through ocean warming \u003csup\u003e3,8,9\u003c/sup\u003e and ocean acidification \u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. Global and local stressors do not act alone, with the synergistic effects of these variables \u003csup\u003e2,10,13,14\u003c/sup\u003e further reducing coral host fitness, and causing phase shifts from coral to algal reef systems \u003csup\u003e15\u0026ndash;17\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne area trying to stem the decline of tropical coral reefs is coral restoration, which restores stony corals to the reef ecosystem \u003csup\u003e18\u0026ndash;20\u003c/sup\u003e. Coral restoration has grown substantially since the early 2000s, with large biomasses of corals outplanted to reef systems \u003csup\u003e18,21\u003c/sup\u003e. Historically, asexual propagation has been the primary focus of restoration activities and involves fragging larger colonies into smaller pieces which show increased growth rates compared to larger colonies \u003csup\u003e22,23\u003c/sup\u003e. These fragged pieces of coral are then replanted on the reefs in clusters allowing fusion, resulting in large coral colonies being formed over a markedly shorter time frame \u003csup\u003e22,24\u003c/sup\u003e. Recently, this method has shown success, with fused outplanted corals subsequently spawning \u003csup\u003e25,26\u003c/sup\u003e. The increase in coral biomass for restoration activities has led to the need to research optimal outplant sites for restoration success. Outplant sites can vary in their morphology (a flat reef structure versus a spur and groove system) and the abiotic conditions they exhibit \u003csup\u003e27\u0026ndash;29\u003c/sup\u003e. With different coral species exhibiting habitat preferences \u003csup\u003e30\u0026ndash;32\u003c/sup\u003e, it is a key consideration for restoration. Additionally, within species variability also needs to be considered. For example, different genets from a coral species can vary in bleaching resistance \u003csup\u003e33,34\u003c/sup\u003e, disease resistance \u003csup\u003e35,36\u003c/sup\u003e, and growth capabilities \u003csup\u003e37\u0026ndash;39\u003c/sup\u003e. The exact mechanisms driving differences between genet responses within a species are multifaceted and may involve the coral host genetics and its microbiome (prokaryotes, viruses, Symbiodiniaceae, protists, and fungi). For the coral host factors influencing gene expression, such as epigenetic modification, gene frontloading, and genetic mutations could be significant in shaping differences between genets, leading to varying levels of baseline gene expression. This may lead to specific processes, such as immune responses, being naturally higher in some genets providing increased resistance to factors such as pathogens. Therefore baseline gene expression may play a key role in predicting resistance and susceptibility, as well as performance for outplanted corals at different reef habitats. By understanding the underlying stressors that coral may encounter at an outplant site, preferential genets can be selected to maximize survivability and growth in restoration activities.\u003c/p\u003e \u003cp\u003eIn the Caribbean, one species heavily focused on for restoration activities is the critically endangered branching coral \u003cem\u003eAcropora palmata\u003c/em\u003e \u003csup\u003e40\u0026ndash;42\u003c/sup\u003e. Since the 1980\u0026rsquo;s, this species has seen drastic declines due to disease outbreaks \u003csup\u003e43,44\u003c/sup\u003e, bleaching events \u003csup\u003e45,46\u003c/sup\u003e, and human influences \u003csup\u003e47\u003c/sup\u003e. Due to the fast-growing nature of \u003cem\u003eA. palmata\u003c/em\u003e, it is especially conducive to asexual fragmentation \u003csup\u003e48\u003c/sup\u003e allowing large biomass to be accrued and outplanted onto the reefs. Despite the Coral Restoration Foundation (CRF) prioritizing \u003cem\u003eA. palmata\u003c/em\u003e as a key coral species for outplanting, there has so far been no research on how different reef sites influence the transcriptomic profiles of different \u003cem\u003eA. palmata\u003c/em\u003e genets. Understanding the transcriptomic profiles will allow the identification of potential resistant and susceptible genets, and underlie the importance of fully characterizing reef site conditions to improve survivability of outplanted corals.\u003c/p\u003e \u003cp\u003eIn this study, we characterized the gene expression profiles over a year of multiple fragments of four genets of \u003cem\u003eA. palmata\u003c/em\u003e at three reef sites in the Florida Keys; Carysfort Reef (25.2209, -80.2102), Pickles Reef (24.9845, -80.4164), and North Dry Rocks (25.1304, -80.2940) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We found that the genet identity was the largest driver of the gene expression, swamping any signal of reef site or sampling time (i.e., time of year). We also found differences in baseline immune activity and metabolic activity between the genets. Once accounting for genet identity, we identified significantly correlated co-expression modules linked to the different sampling times, with these modules linked to the cooler winter months, and hotter summer months. Future work should build on our results and sequence more genets of \u003cem\u003eA. palmata\u003c/em\u003e. This should include a wider range of reef environments with varying topologies and abiotic conditions, and a higher frequency of sampling timepoints to generate a more robust transcriptional picture of the lives of outplanted corals.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eField Site Selection\u003c/p\u003e \u003cp\u003eThree active restoration field sites in the Northern Florida Keys, managed by the Coral Restoration Foundation (CRF), were chosen for field sampling: Carysfort Reef (25.2209, -80.2102), Pickles Reef (24.9845, -80.4164), and North Dry Rocks (25.1304, -80.2940) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Carysfort Reef was the northernmost sampling site (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), a shallow reef system with low rugosity. Sampled \u003cem\u003eA. palmata\u003c/em\u003e outplants were located between 17-22ft. North Dry Rocks was the middle sampling site (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), a spur and groove reef system. All sampled \u003cem\u003eA. palmata\u003c/em\u003e were located on one spur and ranged from 10\u0026ndash;15 ft in depth. Pickles Reef was the most southern sampling site (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) with low rugosity like Carysfort Reef, with sampled corals between 14\u0026ndash;19 ft. These three reef sites provided different latitudinal locations, and different outplant depths of \u003cem\u003eA. palmata\u003c/em\u003e for correlative analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLocations of the three reef sites in the Florida Keys denoted by red dots. Inset map bottom right identifies the location with a square box of the main map image.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eField sampling summaries and samples selected for RNA-seq analysis. The table shows each sampling time point (ST; column 1), reef site (column 2), date of sampling (column 3), the total number of days to sample all reef sites for each sampling time point (column 4), and which subset of sampling time points were selected for RNA-seq analysis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePlot of OISST at the three reef sites studied (colored lines). Seasons are indicated on the figure by blue fill (wet season) and red (dry season). Sampling time point range are indicated by black vertical bars on the plot.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eCoral Sampling\u003c/p\u003e \u003cp\u003eFive sampling time points were conducted at each of the three reef sites: sampling time point one (ST1) during November 2018, sampling time point two (ST2) during February/March 2019, sampling time point three (ST3) during June/July 2019, sampling time point four (ST4) during September 2019, and sampling time point five (ST5) during November 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These covered a full year and the summer and winter months. For each sampling time point, all reefs were sampled in a 7-day period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) with SCUBA used to access outplanted \u003cem\u003eA. palmata\u003c/em\u003e fragments within clusters. Each cluster contained six to seven fragments of the same genet and clusters were identified for each sampling time point using cow tags placed at initial outplanting (Supplementary Fig.\u0026nbsp;1). For each sampling time point, clusters underwent health surveys and tissue sampling using a hammer and chisel. The same fragment was sampled for each cluster at each sampling time point through unique numeric identifiers and previously taken photographs. A tissue sample was then collected in pre-labeled zip bags with SCUBA, with tools wiped between samples. On the boat, tissue samples were transferred to 2ml cryovial tubes filled with 1.5ml of RNAlater and placed on ice. All tissue samples were processed and placed on ice within 35 minutes of sampling. All tools used in transferring tissue samples were bleached and wiped down with ethanol between each sample. At the end of each day, samples were placed at -80\u003csup\u003eo\u003c/sup\u003eC.\u003c/p\u003e \u003cp\u003eSea Surface Temperature for the Reef Sites\u003c/p\u003e \u003cp\u003eSea surface temperature (SST) was obtained from the National Oceanographic and Atmospheric Association (NOAA) Optimum Interpolation Sea Surface Temperature (OISST) data set \u003csup\u003e49\u0026ndash;51\u003c/sup\u003e. To cover all sampling timepoints, data was subset for a temporal range from October 2018 to January 2020, and obtained for each reef site. OISST time course plotting was undertaken in R (v4.0.3) and RStudio (v1.4.1106) using tidyverse \u003csup\u003e52\u003c/sup\u003e and GGplot2 \u003csup\u003e53\u003c/sup\u003e. Average temperatures were calculated across all reef sites, as well as average temperature recorded at each reef site for each sampling time point. The average temperature was also calculated for each reef site and the time span it took to sample all reefs at each sampling time point.\u003c/p\u003e \u003cp\u003eSample Choice for Sequencing\u003c/p\u003e \u003cp\u003eA total of 1,227 samples of 11 genets of \u003cem\u003eA. palmata\u003c/em\u003e were collected from the three reef sites over the five sampling timepoints. Of the 11 genets sampled, four were selected for further processing. These genotypes were selected since they were used in an \u003cem\u003eex-situ\u003c/em\u003e disease experiment \u003csup\u003e54\u003c/sup\u003e and are referred to using CRF designations: CN2, CN4, ML2, and HS1. As high replication of genets within each reef was desired, only four of the five sampling timepoints were chosen for sequencing: ST2 (February/March 2019), ST3 (June 2019), ST4 (September 2019), and ST5 (November 2019). This resulted in 384 samples of \u003cem\u003eA. palmata\u003c/em\u003e for 3` RNA-sequencing.\u003c/p\u003e \u003cp\u003eRNA Extractions, Complementary DNA Synthesis, and Sequencing\u003c/p\u003e \u003cp\u003eDNA and RNA were extracted from the same piece of coral tissue using the Zymo MagBead DNA/RNA kit on the Kingfisher Flex. For an in-depth protocol please see \u003csup\u003e55\u003c/sup\u003e. On completion, eluted DNA was placed at -80\u003csup\u003eo\u003c/sup\u003eC for amplicon sequencing. For eluted RNA, 8\u0026micro;l aliquots were used for quality control (nanodrop and qubit), with the remainder sealed and placed at -80\u003csup\u003eo\u003c/sup\u003eC for downstream processing.\u003c/p\u003e \u003cp\u003eTotal RNA was converted to complementary DNA (cDNA) using the Quant-seq FWD kit (Lexogen) following the high yield/quality manufacturers protocol. The qPCR add-on kit (Lexogen) was utilized to ascertain the RNA concentrations needed to reduce under and over PCR cycling. Samples were dual indexed and sent to the Hussman Institute for Human Genomics (Miller School of Medicine, University of Miami) for single-end sequencing on a NOVA-seq.\u0026nbsp;Sample read depth was requested at 4\u0026ndash;5\u0026nbsp;million reads.\u003c/p\u003e \u003cp\u003eTranscriptomic Bioinformatics\u003c/p\u003e \u003cp\u003ePre-processing of the 3` RNA-seq libraries followed the same pipeline as (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Demultiplexing was done by the sequencing facility, and raw reads had adapters and low-quality reads trimmed using BBDuk.sh \u003csup\u003e56\u003c/sup\u003e with recommended parameters from Lexogen (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.lexogen.com/quantseq-data-analysis\u003c/span\u003e\u003cspan address=\"https://www.lexogen.com/quantseq-data-analysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Alignment and quantification utilized Salmon \u003csup\u003e57\u003c/sup\u003e and the annotated \u003cem\u003eA. palmata\u003c/em\u003e transcriptome \u003csup\u003e58,59\u003c/sup\u003e. Quantified samples were read into R (v4.0.3) and RStudio (v1.4.1106) using tximport \u003csup\u003e60\u003c/sup\u003e and quantified to the gene level. Samples with less than 1,000,000 total read counts, and genes with less than four counts in greater than 15 samples, were removed.\u003c/p\u003e \u003cp\u003ePrior to analysis, hierarchical clustering using hclust \u003csup\u003e61\u003c/sup\u003e was employed to identify any sample groupings that were not explained by available metadata and thus deemed as surrogate variables. Downstream analysis either removed the surrogate variable or incorporated it in statistical models.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was run using a variance stabilized transformation (VST) of the raw counts, on all coral samples, using a modified PlotPCA function from DeSeq2 \u003csup\u003e62\u003c/sup\u003e. Correlations of principal components (PCs) to metadata variables were done using pcatools::eigencorplot \u003csup\u003e63\u003c/sup\u003e, with significance identified using a Pearson correlation. Visualization of PCs of interest was done using GGPlot2 \u003csup\u003e53\u003c/sup\u003e. To identify finer scale patterns, surrogate/batch variables were removed using limma::removeBatchEffect \u003csup\u003e64\u003c/sup\u003e on the VST of raw counts. The batch corrected VST data was then used as input for additional PC analysis.\u003c/p\u003e \u003cp\u003eDifferential gene expression (DGE) analysis was run using DeSeq2 \u003csup\u003e62\u003c/sup\u003e. To identify differences in baseline gene expression between the four genets (ML2, CN2, CN4, and HS1) the Likelihood Ratio Test (LRT) methodology was used. The full model (\u003cem\u003e~\u0026thinsp;Surrogate Variable\u0026thinsp;+\u0026thinsp;Sampling Timepoint\u0026thinsp;+\u0026thinsp;Genet\u003c/em\u003e) and reduced (\u003cem\u003e~\u0026thinsp;Surrogate Variable\u0026thinsp;+\u0026thinsp;Sampling Timepoint\u003c/em\u003e), were used, with alpha set to 0.001. All significant genes were used in gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. To identify finer scale expression profiles between the genets, the VST counts of significant genes from the LRT were used in DEGreport::degpatterns \u003csup\u003e65\u003c/sup\u003e. Visualization of identified gene clusters was done in GGplot2 \u003csup\u003e53\u003c/sup\u003e, and GO and KEGG enrichment analysis was undertaken for all identified clusters.\u003c/p\u003e \u003cp\u003eWeighted gene co-expression network analysis (WGCNA) was run using the r package WGCNA \u003csup\u003e66\u003c/sup\u003e. To identify co-expression due to sampling time point and reef, input for WGCNA analysis was VST transformed counts with genet and surrogate variable variance removed. Outlier samples were identified using the Ward.D2 methodology, and a single signed network was constructed manually with the following key parameters: softPower\u0026thinsp;=\u0026thinsp;18, minModuleSize\u0026thinsp;=\u0026thinsp;30, deepSplit\u0026thinsp;=\u0026thinsp;2, mergedCutHeight\u0026thinsp;=\u0026thinsp;0.40, minimumVerbose\u0026thinsp;=\u0026thinsp;3, and cutHeight\u0026thinsp;=\u0026thinsp;0.997. The eigengene values of each module were correlated to sampling timepoints (ST2, ST3, ST4, and ST5), OISST SST, reef sites (CF, NDR, and PI), and outplant depth. The highest connected gene within each module was identified (wgcna::chooseTopHubInEachModule). GO and KEGG enrichment analysis was done for each module to ascertain putative biological functions and roles.\u003c/p\u003e \u003cp\u003eGene lists identified from analysis were used as input for enrichment analysis in Cytoscape v.3.8.2 \u003csup\u003e67\u003c/sup\u003e and the application BiNGO \u003csup\u003e68\u003c/sup\u003e. Enrichment analysis was run using the hypergeometric test and p-value correction using a Benjamini \u0026amp; Hochberg false discovery rate (FDR). Alpha was set at 0.01 for all GO enrichment analyses. The background universe of genes to test enrichment was the set of genes remaining after initial low count filtering. Visualization of GO enrichment was done in Cytoscape v3.8.2 \u003csup\u003e67\u003c/sup\u003e, and further visualization of gene expression to GO terms was run in the R package Complex Heatmap \u003csup\u003e69\u003c/sup\u003e using the VST counts with genet and batch variable removed.\u003c/p\u003e \u003cp\u003eGene lists of interest were also annotated with their KEGG gene identifiers present in the \u003cem\u003eA. palmata\u003c/em\u003e annotation file \u003csup\u003e58,59\u003c/sup\u003e. Clusterprofiler v3.18.1 \u003csup\u003e70\u003c/sup\u003e was used to identify KEGG pathway enrichment, with enrichment tested against the KEGG Orthology database (organism = \u0026ldquo;ko\u0026rdquo;), and alpha set at 0.01. Visualization of enriched pathways was done with clusterprofiler \u003csup\u003e70\u003c/sup\u003e, enrichPlot 1.10.2 \u003csup\u003e71\u003c/sup\u003e, GGPlot2 \u003csup\u003e53\u003c/sup\u003e and complex heatmap \u003csup\u003e69\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSea Surface Temperature Between the three reef sites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were minimal differences in temperature profiles at each sampling time point for the three reef sites over the one-year period (Fig. 1C). Seasonally, November 2018 to May 2019 showed average temperatures of ~\u0026thinsp;26\u0026ordm;C. May 2019 onwards showed an increase in temperature, with average values around 30\u0026ordm;C and a max in mid-August 2019 of 32\u0026ordm;C. September 2019 onwards saw a decrease back to ~\u0026thinsp;26\u0026ordm;C. Due to weather conditions, reefs were sampled at each time point as close as feasibly possible. For ST1 this covered 15 days (14th-26th November 2018), ST2 five days (28th February to 4th March 2019), ST3 seven days (25th June \u0026minus;\u0026thinsp;2nd July 2019), ST4 two days (25th-26th September 2019), and ST5 two days (18th-19th November 2019) (Fig. 1B).\u003c/p\u003e\n\u003cp\u003eAt Carysfort Reef, average OISST for each sampling time point was as follows: ST1 27.01\u0026ordm;C, ST2 26.20\u0026ordm;C, ST3 30.15\u0026ordm;C, ST4 28.55\u0026ordm;C, and ST5 27.44\u0026ordm;C. At North Dry Rocks, average OISST for each sampling time point was as follows: ST1 26.84\u0026ordm;C, ST2 26.41\u0026ordm;C, ST3 30.35\u0026ordm;C, ST4 28.17\u0026ordm;C, and ST 5 26.21\u0026ordm;C. At Pickles Reef, average OISST for each sampling time point was as follows: ST1 27.70\u0026ordm;C, ST2 26.08\u0026ordm;C, ST3 29.38\u0026ordm;C, ST4 28.85\u0026ordm;C, and ST5 27.13\u0026ordm;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 374 of the 384 samples were successfully sequenced, with sample read depth ranging from 567,208 to 16,494,255 reads, and an average and median read depth of 1,843,913, and 1,550,645 respectively. A total of 12 samples were removed with \u0026lt;\u0026thinsp;1\u0026nbsp;million counts across all genes. This resulted in average and median read depths of 1,877,383 and 1,563,692 respectively, and 362 samples for downstream analysis. Initial hierarchical clustering of the samples identified a strong surrogate variable which was not explained by any metadata variables taken (Supplementary Fig.\u0026nbsp;2). As such, all downstream differential expression and co-expression analysis included this as a surrogate effect. Filtering of low count genes resulted in 18,937 genes being retained for downstream analysis. These 18,937 genes also constituted the \u0026lsquo;gene universe\u0026rsquo; (i.e. background set of genes used to test enrichment for gene lists of interests) used for GO enrichment analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenet identity was the largest driver of gene expression variance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter accounting for the surrogate variable, genet identity was the largest driver of gene expression, with groupings of genet present from PC1 to PC4 (Fig. 2A, B, C). Differential expression analysis, using the LRT, identified 7,096 significant genes between the four genets (Supplementary File 1). The degreport::degpatterns \u003csup\u003e65\u003c/sup\u003e analysis identified six different expression clusters (Supplementary File 2) with differing gene sets and expression profiles (Fig. 2D). Cluster 1 (2,847 genes) showed significant GO and KEGG pathway enrichment of terms related to cell growth and protein production, with KEGG enrichment identifying multiple terms important in immune system signaling pathways and pathogenic infection (Fig. 2E). Genet HS1 showed a strong positive association, and genet CN4 showed a strong negative association with Cluster 1 (Fig. 2D). Cluster 2 (751 genes) identified enrichment of one GO term, \u003cem\u003eFibroblast growth factor receptor signaling\u003c/em\u003e (GO:0008543), and one KEGG term, \u003cem\u003eLysosome\u003c/em\u003e (ko04142). Genet CN4 again showed a strong negative association, and ML2 showed a strong positive association with Cluster 2 (Fig. 2D). Cluster 3 (565 genes) showed GO enrichment of ribosome and translational processes. Enrichment map analysis of the KEGG enrichment results identified one functional module: terms linked to neurodegenerative diseases (Fig. 2E and Supplementary Fig. 3A), with genet CN2 showing a strong positive association and HS1 showing a strong negative association (Fig. 2D). Cluster 4 (1,421 genes) again showed GO enrichment of terms linked to ribosomes and translational processes. Enrichment map analysis of significant KEGG pathways (Supplementary File 4) identified two functional modules; terms linked to pathogenic infection, and neurodegenerative diseases (Fig. 2E and Supplementary Fig. 3B). CN2 and HS1 showed negative associations with Cluster 4, while CN4 and ML2 showed positive associations (Fig. 2D). Cluster 5 (640 genes) showed no significant GO or KEGG enrichment, with genet CN2 showing a strong negative association and genet HS1 showing a strong positive association (Fig. 2D). Cluster 6 (551 genes) showed no GO enrichment, but KEGG enrichment and enrichment map analysis again identified the majority of terms to be grouped in a functional module linked to neurodegenerative diseases (Fig. 2E and Supplementary Fig. 3C). Genet ML2 showed a negative association and CN2 showed a positive association with Cluster 6 (Fig. 2D). Full GO enrichment and KEGG pathway enrichment results are available in Supplementary File 3 and Supplementary File 4 respectively.\u003c/p\u003e\n\u003cp\u003eCo-expression analysis identified modules that significantly correlated to the winter (ST2 and ST5) and summer (ST3 and ST4) seasons once accounting for genet variance.\u003c/p\u003e\n\u003cp\u003eOnce accounting for the identified surrogate variable and genet identity, co-expression analysis identified 15 co-expression modules (Supplementary Fig. 4) ranging from 50 to 7,514 genes (Supplementary File 4). Three modules showed significant correlations with all four sampling timepoints (Fig. 3A). The Purple module (278 genes, hub gene\u0026thinsp;=\u0026thinsp;\u003cem\u003e60S Ribosomal\u003c/em\u003e) showed negative correlations with ST2 and ST3 (-0.14 and \u0026minus;\u0026thinsp;0.14 respectively) and positive correlations with ST4 and ST5 (0.16 and 0.12 respectively) (Fig. 3A). GO and KEGG enrichment analyses identified terms to be linked to ribosomal and metabolic processes (Supplementary File 5 and Supplementary File 6 respectively). The putative function of the purple module was deemed to be \u0026ldquo;protein synthesis and homeostatic processes\u0026rdquo;. The Black module (737 genes, hub gene\u0026thinsp;=\u0026thinsp;\u003cem\u003eHIG1 domain family member 1C\u003c/em\u003e) showed negative correlations with ST2 and ST5 (-0.27 and \u0026minus;\u0026thinsp;0.41 respectively), and positive correlations to ST3, ST4 (0.24 and 0.4 respectively), and SST (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.5) (Fig. 3A). GO enrichment identified only one significantly enriched term, \u003cem\u003eEndoplasmic reticulum and chaperone complex\u003c/em\u003e (GO:0034663) (Supplementary File 5). KEGG enrichment identified several signaling pathways important in general cell proliferation, management, and cell growth. Specifically, this included signaling pathways (\u003cem\u003eMAPK signaling pathway (\u003c/em\u003eKO:04010), \u003cem\u003ePI3K-Akt signaling pathway\u003c/em\u003e (KO:04151), \u003cem\u003eGnRH signaling pathway\u003c/em\u003e (KO:04912), \u003cem\u003eRas signaling pathway\u003c/em\u003e (KO:04014), and cAMP signaling pathway (\u003cem\u003eKO:04024\u003c/em\u003e)) and other terms (\u003cem\u003eFocal adhesion\u003c/em\u003e (KO:04510), \u003cem\u003eProteoglycans in cancer\u003c/em\u003e (KO:05205), and \u003cem\u003eProtein processing in endoplasmic reticulum\u003c/em\u003e (KO:04141)) (Supplementary File 6). The putative function of the Black module was assigned as \u0026ldquo;Organismal cellular maintenance and growth\u0026rdquo;. The Light Cyan module (59 genes, hub gene\u0026thinsp;=\u0026thinsp;Cell Surface hyaluronidase) showed positive correlations with ST2 and ST5 (0.27 and 0.21 respectively) and negative correlations with ST3, ST4 (-0.44 and \u0026minus;\u0026thinsp;0.16 respectively), and SST (-0.41) (Fig. 3A). There was only significant KEGG enrichment for one term: \u003cem\u003eRheumatoid arthritis\u003c/em\u003e (KO:05323) (Supplementary File 6). Due to low enrichment, no putative function was assigned to the Light Cyan module.\u003c/p\u003e\n\u003cp\u003eFour modules showed significant correlations to three of the four sampling timepoints: Red, Light Yellow, Midnight Blue, and Turquoise (Fig. 3A). The Red module (1,944 genes, hub gene\u0026thinsp;=\u0026thinsp;\u003cem\u003eProtein CREG1\u003c/em\u003e) showed negative correlations with ST2 and ST3 (-0.13 and \u0026minus;\u0026thinsp;0.1 respectively) and a positive correlation with ST4 (0.14) (Fig. 3A). GO enrichment identified three terms all linked to the mitochondrion (Supplementary File 5), while KEGG enrichment identified two main sets of processes: fatty acid metabolic processes and viral associated infections and responses (Supplementary File 6). The putative function of the Red Module was \u0026ldquo;\u003cem\u003eMitochondrial Maintenance and Breakdown\u003c/em\u003e\u0026rdquo;. The Light-Yellow module (38 genes, hub gene\u0026thinsp;=\u0026thinsp;\u003cem\u003eCryptochrome-1)\u003c/em\u003e showed positive correlations with ST2 and ST3 (0.32 and 0.13 respectively) and a negative correlation with ST5 (-0.35) (Fig. 3A). There was no KEGG enrichment, but GO enrichment identified terms linked to multiple parts of the mitochondrion, as well processes important in fatty acid metabolism, vitamin metabolism, and respiration (Supplementary File 5). Putative function of the Light-Yellow module was assigned as \u0026ldquo;\u003cem\u003eMetabolic Processes\u003c/em\u003e\u0026rdquo;. The Midnight-Blue module (62 genes, hub gene\u0026thinsp;=\u0026thinsp;\u003cem\u003eBromodomain containing protein 2\u003c/em\u003e) showed positive correlations with ST3, ST4 and SST (0.17, 0.18, and 0.3 respectively) and a negative correlation with ST5 (-0.26) (Fig. 3A). There was no significant GO or KEGG enrichment for this module, thus no putative function was assigned. The Turquoise module (7,415 genes, hub gene Myosin-10) showed positive correlations with ST2 and ST5 (0.14 and 0.22 respectively) and negative correlations with ST4 and SST (-0.289 and \u0026minus;\u0026thinsp;0.22 respectively) (Fig. 3A). At alpha 0.01, there were 204 significant GO terms (Supplementary File 5) and 192 significant KEGG terms (Supplementary File 6). Both GO and KEGG pathway showed enrichment of terms linked to metabolic processes, immune signaling pathways, and protein synthesis. Due to high enrichment, the turquoise module was designated as \u0026ldquo;\u003cem\u003egeneral organism homeostasis\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eThere were no co-expression modules that were significantly correlated to all three reef sites.\u003c/p\u003e\n\u003cp\u003eCo-expression analysis identified two modules that showed correlation to at least two of the three reef sites; Light Yellow module (38 genes, hub gene\u0026thinsp;=\u0026thinsp;Cryptochrome 1) which showed a negative correlation to North Dry Rocks (r2 = -0.45) and a positive correlation to Pickles Reef (r2\u0026thinsp;=\u0026thinsp;0.45), and the Cyan module (784 genes, hub gene\u0026thinsp;=\u0026thinsp;uncharacterized protein LOC111326987) which showed a negative correlation to Carysfort (r2 = -0.11) and a positive correlation to North Dry Rocks (r2\u0026thinsp;=\u0026thinsp;0.15) (Fig. 3A). The Light Yellow module was assigned the putative function of \u0026ldquo;metabolic process\u0026rdquo; from GO and KEGG enrichment analysis. For the Cyan module, GO enrichments identified terms linked to the extracellular space as well as terms important in general organism growth and nervous system cell development (Supplementary File 5). KEGG enrichment only identified two terms; \u003cem\u003eMaturity onset diabetes of the young\u003c/em\u003e (KO\u0026thinsp;=\u0026thinsp;map04950) and \u003cem\u003eNotch signaling pathway\u003c/em\u003e (KO\u0026thinsp;=\u0026thinsp;map04330) (Supplementary File 6). The putative function of the Cyan module was \u0026ldquo;growth processes\u0026rdquo;.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBaseline immune gene expression and putative protein production drove differences in gene expression between the four genets.\u003c/p\u003e \u003cp\u003eIn this study, we identified that genet identity is the largest driver of gene expression in outplanted \u003cem\u003eA. palmata\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C), and this masks the signals of sampling time point and reef location. From our GO and KEGG enrichment analysis, we identified that baseline immune activity seems to be an important difference between genets of \u003cem\u003eA. palmata\u003c/em\u003e. Baseline gene expression of an organism can be described as the normal expression levels of an organism not exposed to stimuli. Within a species different levels of baseline expression can therefore exist for different processes. For example, differing levels of immunity between genets could drive factors such as disease susceptibility and resistance. For corals, it is well documented that there are differences in the production of immune-related molecules \u003csup\u003e72,73\u003c/sup\u003e as well as differing levels of disease susceptibility \u003csup\u003e74–76\u003c/sup\u003e. This also holds true within coral species, with different genets showing differing levels of susceptibility to disease \u003csup\u003e35,36\u003c/sup\u003e, and heat stress \u003csup\u003e33,34\u003c/sup\u003e. At present, the exact molecular mechanism(s) of differences in baseline immune activity has not been fully elucidated within a coral species. Epigenetic modification could play a role, with histone modification \u003csup\u003e77\u003c/sup\u003e and DNA methylation \u003csup\u003e78\u003c/sup\u003e being shown to influence immune gene expression in other organisms. In corals, epigenetic modifications can occur due to environmental perturbations \u003csup\u003e79\u003c/sup\u003e as well as specific stress such as nutrients \u003csup\u003e80\u003c/sup\u003e. Similarly, previous work has shown that corals can front-load genes, allowing them to resist stressful conditions \u003csup\u003e81,82\u003c/sup\u003e. We therefore hypothesize that different genets of \u003cem\u003eA. palmata\u003c/em\u003e could be using these processes to cause differences in baseline immune gene expression. We previously showed this same difference in immune-related gene expression \u003cem\u003eA. palamta\u003c/em\u003e genets in an \u003cem\u003eex-situ\u003c/em\u003e disease experiment \u003csup\u003e54\u003c/sup\u003e. In this study, using the same four genets of \u003cem\u003eA. palmata\u003c/em\u003e, we identified the same signal for baseline immune gene expression. This indicates that this signal holds true over time and space, and is therefore important in patterns of resistance and susceptibility in \u003cem\u003eA. palmata\u003c/em\u003e genets.\u003c/p\u003e \u003cp\u003eThe clusters identified from the genet LRT analysis also identified consistent enrichment of GO terms linked to ribosome and translational processes (clusters 3 and 4). These results could indicate that among \u003cem\u003eA. palmata\u003c/em\u003e genets different baseline rates of protein synthesis exist, which could affect physiological variables such as growth rates, as well as responses to biotic and abiotic perturbations. High baseline ribosome and translational processes could be linked to faster-growing genets. Specifically, we think genet CN2 may have faster growth rates in reefs as it showed higher abundances of both ribosome and translational and growth processes (cluster 1) but additional work needs to be conducted to provide evidence for this hypothesis.\u003c/p\u003e \u003cp\u003eClusters 4 and 6 both had designations for neurodegenerative diseases, with enrichment map analysis showing high connectivity for these terms (Supplementary Fig.\u0026nbsp;3A-C). Interestingly, the intersection of the genes within the highly connected neurodegenerative terms for each cluster had minimal overlap among genets (Supplementary Fig.\u0026nbsp;3D) indicating that different cellular processes are being expressed among the genets. Neurodegenerative diseases are characterized by processes such as misfolded proteins \u003csup\u003e83,84\u003c/sup\u003e, oxidative stress \u003csup\u003e85,86\u003c/sup\u003e, disruption of calcium homeostasis \u003csup\u003e87,88\u003c/sup\u003e, and mitochondrial dysfunction \u003csup\u003e89,90\u003c/sup\u003e. While these terms are specifically linked to human neurodegenerative diseases, the core processes and pathways that are involved in these diseases are present throughout the metazoan tree of life. For example, oxidative phosphorylation is a core organismal process that generates adenosine-triphosphate, the source of energy at a cellular level. Stony coral tissue loss disease research has also identified enrichment of neurodegenerative diseases term (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e), which indicates that the core processes present in well-studied human diseases may show different baseline expression in different coral genets, and thus provide benefits and tradeoffs. Future work should look to fully characterize the potential pathways and processes which are encompassed in these human neural diseases in corals, and subsequently identify how these processes affect the physiological processes of corals, and if this confers any benefits or tradeoffs that can be incorporated into coral restoration projects.\u003c/p\u003e \u003cp\u003eThe results from this study, paired with past research, underline the importance of not only characterizing the immune repertoire of coral species but also identifying how differences in baseline gene expression among coral genets may influence susceptibility and resistance to biotic and abiotic stressors. From a coral restoration perspective by characterizing the baseline immune performance, outplanting of, for example, more genets that can mount a higher immune response may improve survivorship and restoration of reefscapes.\u003c/p\u003e \u003cp\u003eCo-expression analysis identifies higher potential growth genes in \u003cem\u003eA. palmata\u003c/em\u003e in the cooler winter months, and shifts to survival and defensive processes in the summer months.\u003c/p\u003e \u003cp\u003eOnce accounting for genet identity, WGCNA \u003csup\u003e66\u003c/sup\u003e analysis identified co-expression modules significantly correlated to sampling time point with three significant modules (Purple, Black Light Cyan) for the four sampling timepoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The Black and the Light Cyan module correlations mirrored the summer versus winter months, with correlations also to sea surface temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The light cyan module, which was negatively correlated with temperature, may encompass important genes involved in growth processes in \u003cem\u003eAcropora palmata.\u003c/em\u003e For instance, the cyan module included genes involved in structural integrity of the extracellular space (collagen genes), and tissue rigidity (tricholycin and reticulobulin genes). The hub gene of this module (\u003cem\u003ecell surface hyaluronidase\u003c/em\u003e) has also been shown to be important in regulation of cell adhesion, as well as the degradation of hyaluronan which regulates development and structural integrity in the extracellular matrix \u003csup\u003e92,93\u003c/sup\u003e. A hyaluronan-like substance has been identified previously in the coral species \u003cem\u003eMycetophyllia resi\u003c/em\u003e with the putative function important in tissue and skeletal matrix structure \u003csup\u003e94\u003c/sup\u003e. This could indicate that during the cooler winter months, growth processes are favored by outplanted \u003cem\u003eA. palmata\u003c/em\u003e on the reef system, which aligns with previous findings that \u003cem\u003eA. palmata\u003c/em\u003e grows quicker at cooler temperatures \u003csup\u003e95\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Black module showed the inverse compared to the Light Cyan module, with a strong positive correlation (r\u003csup\u003e2\u003c/sup\u003e = 0.5) with sea surface temperatures and sampling time points 3 and 4 (i.e., summer months) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and an inferred function of immune, survival, and metabolic processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The survival and metabolic inferred function was identified due to the significant enrichment of the MAPK, PI3/AKT, and RAS signaling pathways. MAPK signaling is a highly conserved signaling pathway \u003csup\u003e96\u003c/sup\u003e with importance in gene expression, mitosis, metabolism, immune responses, and survivability \u003csup\u003e97\u003c/sup\u003e. The PI3/AKT signaling pathway is similar, being highly conserved \u003csup\u003e98\u003c/sup\u003e and also playing key roles in the immune system, growth, and survival \u003csup\u003e99\u003c/sup\u003e. The RAS signaling pathway plays key roles in regulation and proper function of both the MAPK and PI3/AKT signaling pathways \u003csup\u003e100\u003c/sup\u003e. The enrichment of these pathways during the warmer summer months suggests that outplanted \u003cem\u003eA. palmata\u003c/em\u003e may shift to a more survival and maintenance mode compared to growth processes that occur in the winter. This is plausible and indicates that although \u003cem\u003eA. palmata\u003c/em\u003e were not exhibiting bleaching and looked relatively healthy even moderate increases in temperature may induce stress \u003csup\u003e101,102\u003c/sup\u003e. Increased water temperatures can increase abundance and pathogen virulence \u003csup\u003e103–105\u003c/sup\u003e which may explain the increased immune system activation targeted to prevent infection. The Black module also contained immune signaling pathways and processes adding support to this observation. The TNF signaling pathway is an important immune cytokine that can activate additional innate immune responses \u003csup\u003e106\u003c/sup\u003e, is important in cell proliferation and cell death \u003csup\u003e107\u003c/sup\u003e, and consistently shows increased expression in coral disease \u003csup\u003e91,108,109\u003c/sup\u003e and heat \u003csup\u003e110,111\u003c/sup\u003e transcriptomic studies.\u003c/p\u003e \u003cp\u003eReef location did not have a strong effect on gene expression for outplanted genets of \u003cem\u003eAcropora palmata\u003c/em\u003e.\u003c/p\u003e \u003cp\u003ePrincipal component analysis and co-expression analysis did not identify a strong effect of the outplant reef site on gene expression between the four genets of \u003cem\u003eA. palmata\u003c/em\u003e. There were two co-expression modules that did show significant correlations to at least two of the three reef sites as well as cluster outplant depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). There was only significant GO enrichment for the Light Yellow module, with terms involved in mitochondrial processes and fatty acid metabolism (Supplementary File 5). Coral species are mixotrophic, utilizing both autotrophic processes, where energy is derived from photosynthates produced by the obligate symbiont \u003cem\u003eSymbiodiniceae\u003c/em\u003e, and heterotrophic processes, involving passive and active means of acquiring energy. Light availability is therefore the key variable that dictates the level of these factors. Coral species at deeper depths (i.e. species that inhabit shallow to upper mesophotic reef depths ~ 60m) switch to a more heterotrophic lifestyle mode of energy acquisition due to the lower levels of light available causing decreased output from the photosynthetic \u003cem\u003eSymbiodiniceae\u003c/em\u003e \u003csup\u003e112\u003c/sup\u003e. With the significant correlation to depth, it is plausible that outplanted \u003cem\u003eA. palmata\u003c/em\u003e at Pickles Reef rely more heavily on heterotrophic energy acquisition processes. This hypothesis is further backed up by the enrichment of fatty acid metabolic processes. Corals living at deeper depths in a more heterotrophic lifestyle have been shown to have higher energy reserves than corals at shallow depths in a more autotrophic lifestyle \u003csup\u003e112,113\u003c/sup\u003e. The outplanted \u003cem\u003eA. palmata\u003c/em\u003e at Pickles may therefore be utilizing fatty acid stores that they have acquired through higher rates of heterotrophic feeding. Utilization of methodologies such as whole tissue stable isotope analysis of carbon and nitrogen, as well as tissue lipid content analysis would allow a more definitive conclusion of whether reef depth causes shifts in levels of heterotrophic or autotrophic energy acquisition in outplanted \u003cem\u003eA. palmata\u003c/em\u003e, which leads to changes in gene expression.\u003c/p\u003e "},{"header":"Conclusions and future directions for genomic analysis of outplanted corals","content":"\u003cp\u003eWe identified that genet identity is an important factor in outplanted \u003cem\u003eA. palmata\u003c/em\u003e, and a leading driver of differences is baseline immune gene expression. Since we identified an intra-population variation in response to phenotypically healthy corals, more \u003cem\u003eA. palmata\u003c/em\u003e genets would help identify whether this pattern holds at a population level. This research can then be used to identify how baseline profiles influence outplant performance. Additional \u003cem\u003eex-situ\u003c/em\u003e experiments of multiple genets of \u003cem\u003eA. palmata\u003c/em\u003e to different biotic and abiotic stressors would also help identify the “winner” and “loser” genets. These results could be incorporated into restoration practices potentially increasing outplant survivability by matching fitter genets to preferred reef systems.\u003c/p\u003e\u003cp\u003eFuture work looking at the effects of outplant sites on coral gene expression should include a wider range of reef habitats. Despite the reef habitats used in this study exhibiting different topologies and rugosity, they are all still offshore reef sites within close proximity. Inshore and offshore reef systems have been shown to have different abiotic conditions influencing the performance of resident coral populations. By monitoring outplanted coral at different reef systems, and abiotic conditions it would allow characterization of gene pathways that are affected due to these environmental conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Caroline Dennison, Allyson DeMerlis, Samara Neufield, and Ana Palacio-Castro for helping collect samples of \u003cem\u003eAcropora palmata\u0026nbsp;\u003c/em\u003ein the field. We would also like to thank all the staff at the Huntsman Institute for Human Genomics (University of Miami, Miami), for sample quality control and sequencing. We also thank Dr. Sheila Kitchen and Dr. Iliana Baums for continued use of the \u003cem\u003eAcropora palmata\u0026nbsp;\u003c/em\u003egenome. Finally, we thank the OAR NOAA omics initiative for providing funding support for BYD, SMR, and support for molecular lab work and sequencing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Accessibility and Benefit-Sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analysis scripts and pipelines can be found at https://github.com/benyoung93/acropora_palmata_field_transcriptomics, with all RNA-seq raw reads available on the NCBI under accession under PRJNA1081901.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitial study conception was SMR, with RNA-seq sample choice by SMR, BDY, and NTK. DW, AB, AP, SMR, BDY all performed fieldwork and field coral processing. BDY performed all lab work and bioinformatic analyses. BDY wrote the first draft of the manuscript, with all authors then providing feedback and edits before submission for peer review and publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHoegh-Guldberg, O. Coral reef ecosystems and anthropogenic climate change. \u003cem\u003eReg. Environ. Change \u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 215\u0026ndash;227 (2011).\u003c/li\u003e\n\u003cli\u003eHoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. \u0026amp; Dove, S. Coral Reef Ecosystems under Climate Change and Ocean Acidification. \u003cem\u003eFront. Mar. Sci. \u003c/em\u003e\u003cstrong\u003e4\u003c/strong\u003e, (2017).\u003c/li\u003e\n\u003cli\u003eHughes, T. P. \u003cem\u003eet al.\u003c/em\u003e Global warming and recurrent mass bleaching of corals. \u003cem\u003eNature \u003c/em\u003e\u003cstrong\u003e543\u003c/strong\u003e, 373\u0026ndash;377 (2017).\u003c/li\u003e\n\u003cli\u003eTracy, A. 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Biol. \u003c/em\u003e\u003cstrong\u003e218\u003c/strong\u003e, 1223\u0026ndash;1234 (2015).\u003c/li\u003e\n\u003cli\u003eCarmignani, A. \u003cem\u003eet al.\u003c/em\u003e Levels of autotrophy and heterotrophy in mesophotic corals near the end photic zone. \u003cem\u003eFront. Mar. 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Coral restoration is addressing this decline by outplanting large volumes of corals onto reef systems. Understanding how outplanted corals react at a transcriptomic level to different outplant locations over time is important, as it will highlight how habitat affects the coral host and influences physiological measures. In this study, the transcriptomic dynamics of four genets of outplanted \u003cem\u003eAcropora palmata\u003c/em\u003ewere assessed over a year at three reef sites in the Florida Keys. Genet identity was more important than time of sampling or outplant site, with differing levels of baseline immune and protein production the key drivers. Once accounting for genet, enriched growth processes were identified in the winter, and increased survival and immune expression were found in the summer. The effect of the reef site was small, with hypothesized differences in autotrophic versus heterotrophic dependent on outplant depth. We hypothesize that genotype identity is an important consideration for reef restoration, as differing baseline gene expression could play a role in survivorship and growth. Additionally, outplanting during cooler winter months may be beneficial due to higher expression of growth processes, allowing establishment of outplants on the reef system.\u003c/p\u003e","manuscriptTitle":"Genet identity and season drive gene expression in outplanted Acropora palmata at different reef sites.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-13 07:53:28","doi":"10.21203/rs.3.rs-4259333/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-21T04:48:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-20T20:56:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186212896610903317316311759940623565313","date":"2024-08-16T17:47:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25751736188855666443409034310046777400","date":"2024-08-16T14:50:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-20T18:39:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35e0da68-9bac-434b-9a1f-d7a48076465b","date":"2024-05-01T16:49:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-01T05:06:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4f8aeabc-4d0b-4d18-8ed1-8e3f27a7c488","date":"2024-05-01T04:34:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-30T13:42:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-25T16:56:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-18T17:40:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-18T17:39:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-12T18:17:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11f8b14f-2d8d-44c6-b716-6a5099b49612","owner":[],"postedDate":"May 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31801904,"name":"Biological sciences/Genetics/Gene expression"},{"id":31801905,"name":"Biological sciences/Ecology/Conservation"},{"id":31801906,"name":"Biological sciences/Molecular biology/Transcriptomics"}],"tags":[],"updatedAt":"2024-12-02T17:25:11+00:00","versionOfRecord":{"articleIdentity":"rs-4259333","link":"https://doi.org/10.1038/s41598-024-80479-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-27 15:57:20","publishedOnDateReadable":"November 27th, 2024"},"versionCreatedAt":"2024-05-13 07:53:28","video":"","vorDoi":"10.1038/s41598-024-80479-y","vorDoiUrl":"https://doi.org/10.1038/s41598-024-80479-y","workflowStages":[]},"version":"v1","identity":"rs-4259333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4259333","identity":"rs-4259333","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}