Year-round quantification, structure and dynamics of epibacterial communities from diverse macroalgae reveal a persistent core microbiota and strong host specificities

Year-round quantification, structure and dynamics of epibacterial communities from diverse macroalgae reveal a persistent core microbiota and strong host specificities | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Year-round quantification, structure and dynamics of epibacterial communities from diverse macroalgae reveal a persistent core microbiota and strong host specificities View ORCID Profile Maéva Brunet , Nolwen Le Duff , Tristan Barbeyron , View ORCID Profile François Thomas doi: https://doi.org/10.1101/2024.07.22.604553 Maéva Brunet 1 Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR) , 29680, Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maéva Brunet Nolwen Le Duff 1 Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR) , 29680, Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tristan Barbeyron 1 Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR) , 29680, Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site François Thomas 1 Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR) , 29680, Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for François Thomas For correspondence: francois.thomas{at}sb-roscoff.fr Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Macroalgae-bacteria interactions play pivotal ecological roles in coastal ecosystems. Previous characterization of surface microbiota from various macroalgae evidenced fluctuations based on host tissues, physicochemical and environmental parameters. However, the dynamics and degree of similarity of epibacterial communities colonizing phylogenetically distant algae from the same habitat are still elusive. We conducted a year-long monthly epimicrobiota sampling on five algal species inhabiting an English Channel rocky shore: Laminaria digitata , Ascophyllum nodosum , Fucus serratus (brown algae), Palmaria palmata (red alga) and Ulva sp. (green alga). To go beyond relative compositional data and estimate absolute variations in taxa abundance, we combined qPCR measurements of 16S rRNA gene copies with amplicon metabarcoding. A core microbiome composed of 10 genera was consistently found year-round on all algae. Notably, the abundant genus Granulosicoccus stood out for being the only one present in all samples and displayed an important microdiversity. Algal host emerged as the primary driver of epibacterial community composition, before seasonality, and bacterial taxa specifically associated with one or several algae were identified. Moreover, the impact of seasons on the epimicrobiota varied depending on algal tissues. Overall, this study provides an extensive characterization of the microbiota of intertidal macroalgae and enhances our understanding of algal-bacteria holobionts. Introduction Macroalgae play crucial roles in marine ecosystems, contributing significantly to primary production, nutrient cycling, and habitat provision. Their surface harbors a dense, complex and highly dynamic biofilm of bacteria, archaea, fungi and viruses. Bacteria dominate the microbiota in abundance and diversity, representing up to 10 8 cells.cm -2 ( Egan et al ., 2013 ; Martin et al ., 2015 ), and form intricate interactions with macroalgae. These associations were reported to be either beneficial, detrimental or commensal ( Burgunter-Delamare et al ., 2024 ) and are at the basis of various essential processes in coastal oceans, including roles in nutrient cycling ( Brunet et al ., 2022 ) and in shaping host morphology, reproduction and defense against pathogens ( Wichard et al ., 2015 ; Singh and Reddy, 2016 ). The structure and composition of the microbiota of various brown (eg. Bengtsson et al., 2012 ; Florez et al., 2019 ; Burgunter-Delamare et al., 2023 ), red ( Miranda et al ., 2013 ; Zozaya-Valdés et al ., 2017 ) and green ( Burke et al ., 2011 ; van der Loos et al ., 2023 ) macroalgae has been largely investigated in the past decades using culture-dependent and -independent approaches, allowing for the identification of key members of the epiphytic microbiota. Macroalgae host specific bacterial communities that largely differ from those occurring in the surrounding seawater ( Weigel and Pfister, 2019 ; Lu et al ., 2023 ). Among bacteria associated with macroalgae, the bacterial phyla Pseudomonadota (mostly the classes Alphaproteobacteria and Gammaproteobacteria ), Bacteroidota , Verrucomicrobiota , Actinomycetota and Planctomycetota are the most represented ( Wahl et al ., 2012 ; Hollants et al ., 2013 ; Florez et al ., 2017 ). At the genus level, the algal symbiont Granulosicoccus ( Gammaproteobacteria ) is commonly found as the most abundant taxon on diverse algal hosts ( Singh and Reddy, 2014 ), frequently reported to account for more than 25% of the whole community on kelp tissue ( Brunet et al ., 2021 ; Lemay et al ., 2021 ; Ramírez-Puebla et al ., 2022 ; Weigel et al ., 2022 ). It is now well established that bacterial community composition and density is shaped by biotic and abiotic factors and varies depending on biogeography, host tissue composition or seasonality ( Lachnit et al ., 2011 ; Egan et al ., 2013 ; Lemay et al ., 2018 ; Serebryakova et al ., 2018 ; Weigel and Pfister, 2019 ; Paix et al ., 2021 ; Ramírez-Puebla et al ., 2022 ). Algae release a wide range of bioactive compounds with a broad spectrum of activities that participate in the control of bacterial colonization ( Saha and Fink, 2022 ). It was reported that host taxonomy ( Kuba et al ., 2021 ; Chen et al ., 2022 ), anatomy ( Bengtsson et al ., 2012 ; Weigel and Pfister, 2019 ; Lemay et al ., 2021 ), genetics ( Wood et al ., 2022 ), health ( Fernandes et al ., 2012 ; Marzinelli et al ., 2015 ; Burgunter-Delamare et al ., 2023 ) or life cycle ( Bengtsson et al ., 2010 ; Glasl et al ., 2021 ) influences its surface microbiota. Studies investigating seasonal variations in bacterial communities associated with healthy macroalgae have revealed dynamic patterns characterized by shifts in community composition and diversity ( Lachnit et al ., 2011 ; Burgunter-Delamare et al ., 2023 ). These shifts can often be linked to seasonal changes in environmental factors such as temperature, light or nutrient availability ( Mancuso et al ., 2016 ; Florez et al ., 2019 ), which influence the growth and metabolic activity of both macroalgae and associated bacteria. Additionally, seasonal variations in macroalgal physiology, such as growth rates and reproductive cycles, can indirectly impact bacterial communities by altering the tissues and exudate composition ( Pandey et al ., 2022 ). Most of the works that characterized macroalgal-associated microbiota mainly focused on a single algal species. Only a few recent studies compared algae from different phyla and of distinct chemical composition revealing that algal phylum was a strong driver of epimicrobiota composition ( Kuba et al ., 2021 ) and identifying core taxa ( Chen et al ., 2022 ; Lu et al ., 2023 ). Moreover, temporal dynamics of epiphytic bacterial communities depending on the algal host and type of tissue have been largely overlooked. In this study, the microbiota of three brown ( Laminaria digitata (Hudson) J.V.Lamouroux, 1813, Fucus serratus Linnaeus, 1753 and Ascophyllum nodosum (Linnaeus) Le Jolis, 1863), one red ( Palmaria palmata (Linnaeus) F.Weber & D.Mohr, 1805) and one green ( Ulva sp. Linnaeus, 1753) macroalgal species predominant in the English Channel were investigated throughout a year. This selection of species covers a large phylogenetic range, and different intertidal zonation ( A. nodosum is found on the upper-middle intertidal zone while F. serratus , P. palmata and Ulva sp. are in the lower intertidal zone and L. digitata in the subtidal zone ( Evans, 1957 ; Burel et al ., 2022 )). The sole use of metabarcoding amplicon sequencing has emerged as the preferred method to investigate host-associated microbial communities. The resulting datasets are inherently compositional as they only allow analysis based on relative proportions of microbial taxa ( Gloor et al ., 2017 ). Therefore, an increase in abundance of one taxon can partly be caused by an equivalent decrease of another one, limiting the conclusions that can be drawn ( Jian et al ., 2020 ). Despite the statistical approaches applied to overcome misinterpretations of bacterial community structure ( Gloor et al ., 2017 ; Shelton et al ., 2022 ), one of the most effective ways to accurately compare microbial communities across samples and to identify patterns that may be obscured when relying solely on relative abundance information, is to generate quantitative microbial abundance data by combining amplicon sequencing with the cell or gene copy density measurements. Several studies demonstrated an improved performance of quantitative approaches over compositional data ( Jian et al ., 2020 ; Lloréns-Rico et al ., 2021 ) and this has been recently implemented to examine human ( Vandeputte et al ., 2017 ; Barlow et al ., 2020 ), freshwater ( Props et al ., 2017 ) or soil ( Camacho-Sanchez, 2023 ) microbiomes. Here we coupled 16S rRNA gene amplicon sequencing with quantitative PCR to accurately estimate the abundance of the different taxa in macroalgal epiphytic communities. We aimed to (i) characterize the shared and specific patterns of epiphytic bacterial communities associated with co-occurring macroalgae species and (ii) assess if seasonality equally impacts microbiomes from phylogenetically distant species. Experimental procedures Sampling The collection of epibiota from algal specimens was previously described in ( Brunet et al ., 2023 ). Briefly, the surface microbiota of intertidal algae was sampled monthly between February 2020 and January 2021 (no sampling in April and May 2020 due to the Covid-19 pandemic). Microbiota were collected in triplicates using sterile flocked nylon swabs (Zymobiomics) on healthy specimens of the brown algae Laminaria digitata (Ldig, 0.5-1 m long), Fucus serratus (Fser) and Ascophyllum nodosum (Anod), the red alga Palmaria palmata (Ppal) and a green alga Ulva sp. (Ulva) at the Bloscon site (48°43’29.982’’ N, 03°58’8.27’’ W) in Roscoff (Brittany, France). Swabbed surface was standardized to 50 cm 2 . Three different regions of the kelp L. digitata were sampled: the basal meristem (young tissue, hereafter LdigB), the medium frond (ca. 20 cm away from the meristem, hereafter LdigM) and the old frond (the blade tip, hereafter LdigO). Only two replicates were retrieved for LdigB in February and Ulva in March. Upon collection, swabs were immediately immersed in DNA/RNA Shield reagent (ZymoBiomics) on ice and stored at -20 °C until DNA extraction. A total of 209 samples were collected ( SuppFile S1 ), scattered into 70 different conditions (7 types of algal tissues at 10 different months). Collection of environmental data A set of 15 environmental parameters measured at 1.3 km from the collection site (Estacade sampling station) between February 2020 and January 2021 was used for correlation analyses. It includes surface seawater temperature (T, °C), salinity (S), dissolved oxygen (O, ml*l -1 ), pH, ammonium (NH 4 , μM), nitrate (NO 3 , μM), nitrite (NO 2 , μM), phosphate (PHO 4 , μM), silicate (SiOH 4 , μM), particulate organic carbon (COP, μg*l -1 ) and nitrogen (NOP, μg*l -1 ), suspended matter (MES, mg*l -1 ), 15 Nitrogen (DN15, °/ °° ) and 13 Carbon isotopes (DC13, °/ °° ) and Chlorophyll a (CHLA, μg*l -1 ). Data were downloaded from the SOMLIT database (Service d’Observation en Milieu Littoral; http://www.somlit.fr , Cocquempot et al., 2019 ) on April 7th, 2022 and are listed in SuppFile S1 . DNA extraction and qPCR assays DNA extraction and qPCR assays were performed as in ( Brunet et al ., 2023 ). Briefly, environmental DNA from swabs was extracted using the DNA/RNA Miniprep kit (ZymoBiomics) following the manufacturer’s instructions. DNA was quantified with the QuantiFluor dsDNA System (Promega) kit and samples normalized at 0.5 ng.μl -1 before qPCR and library preparation. DNA concentrations obtained from each sample are listed in SuppFile S1 . The number of total 16S rRNA gene copies was assessed using qPCR (primers 926F: 5’- AAACTCAAAKGAATTGACGG-3’ /1062R: 5’-CTCACRRCACGAGCTGAC-3’, ( Bacchetti De Gregoris et al ., 2011 ) on a LightCycler 480 Instrument II (Roche). The global predicted coverage of this primer pair is 94.1% of all bacterial sequences present in Silva SSU r138.1 (Silva Testprime with only one mismatch allowed, analysis performed in June 2024). All bacterial phylum-level taxa in the Silva database had predicted coverages above 90%, except 10-bav-F6 (33%), Candidatus Bipolaricaulota ( Acetothermia 50%), Bdellovibrionota (85%), Chrysiogenetota (75%), Candidatus Dependentiae (88%), Dictyoglomota (88%), Candidatus Edwardsbacteria (75%), NKB15 (86%) and Patescibacteria (46%). Among these, only Bdellovibrionota , Candidatus Dependentiae and Patescibacteria were detected in our 16S rRNA gene metabarcoding dataset, with average relative abundance of 1.2, 0.0001 and 0.09%, respectively. qPCR results are listed in SuppFile S1, and MIQE information related to qPCR experiments is available in SuppFile S2 . Library preparation, sequencing and sequence processing The V3-V4 region of 16S rRNA gene was amplified using the primers S-D-Bact-0341-b-S-17 (5’ CCTACGGGNGGCWGCAG 3’) and 799F_rc (5’ CMGGGTATCTAATCCKGTT 3’ ( Thomas et al ., 2020 ). Sequencing was conducted on a MiSeq paired-end sequencing run (300 cycles × 2, Illumina, San Diego, CA, USA) as described previously ( Thomas et al ., 2020 ). 16S rRNA gene amplicon sequences are available at NCBI under BioProject PRJNA1135191.The R package DADA2 (version 1.22.0, Callahan et al. 2016) was used to exclude primers sequences, filter and trim low quality sequences (truncLen=c(280,210), trimLeft = c(10,10), minLen = 150, maxN=0, maxEE=c(2,3), truncQ=2), for denoising, paired-read merging, inferring Amplicon Sequence Variants (ASVs, length between and 400 and 452 nucleotides), chimera removal and taxonomic assignment using the SILVA 138.1 ( Quast et al ., 2013 ; Yilmaz et al ., 2014 ) reference database. Default parameters were used unless otherwise specified. All sequences affiliated to 16S rRNA gene from chloroplasts, mitochondria, cyanobacteria, Eukaryota and Archaea , as well as sequences found in negative controls (PCR molecular grade water) were removed using the R package phyloseq (version 1.38.0, McMurdie and Holmes, 2013 ). Multivariate and statistical analyses Relative ASV abundance was multiplied by the total number of 16S rRNA gene copies quantified with qPCR for each sample, to obtain a table of abundance of each ASV that was used in all subsequent analyses. To assess dissimilarities in community structure between algal tissues, Bray–Curtis dissimilarity index ( Bray et al ., 1957 ) was calculated before non-metric multidimensional scaling (NMDS). Permutational analysis of variance (PERMANOVA, 999 permutations) was calculated using the adonis function to discriminate groups of samples according to algal tissues or seasons ( Anderson, 2001 ), followed by multivariate pairwise comparisons using pairwise.perm.manova. Mean dispersions within groups were calculated using betadisper (999 permutations). To identify environmental parameters that may be associated with microbial community structure, we performed distance-based redundancy analysis (dbRDA; Legendre and Anderson, 1999 ) on the Bray–Curtis dissimilarity matrix with dbrda function. To avoid having highly correlated variables, pH, SiOH4, NOP, PO4, NO3, NH4, DN15 were removed prior to the analysis. Only significant environmental factors were selected for dbRDA visualization (ANOVA, P < 0.05). Months were grouped in 3 different seasons (Winter: Feb, Mar, Jan; Summer: Jun, Jul, Aug; Autumn: Sep, Oct, Nov, Dec) prior to PERMANOVA and betadisper analysis. Differential sequence abundance analysis between the 3 seasons (qPCR-corrected counts from months of the same season were averaged) was performed at the ASV level using the GLM test with the ALDEx2 R package ( Fernandes et al ., 2014 ). To detect differential abundance, this test uses ratios among taxa, which are conserved regardless of whether data are relative or absolute. A standardized approach based on occupancy–redundancy distribution was applied to identify core genera on each algal tissue ( Shade and Stopnisek, 2019 ). Using a presence/absence matrix (1 for presence, 0 for absence), an index was calculated for each genus, as the sum of the occupancy term and the redundancy term for each month. The occupancy term is the replicate average of the presence/absence matrix. The redundancy term is equal to 1 if the genus is present in all three replicates and to 0 otherwise. The index was scaled to an upper limit of 1 and calculated separately for each algal tissue. Phylogenetic analysis Phylogenetic analysis was conducted using phylogeny.fr ( Dereeper et al ., 2008 ) for the most abundant Granulosicoccus -affiliated ASVs in the dataset (representing at least 5% of the Granulosicoccus community in at least 5 samples) together with 16S rRNA gene sequences from cultured Granulosicoccus strains and uncultured clones retrieved from Genbank. Sequences were aligned using Muscle (full mode) and the resulting alignment was curated using Gblocks. The 405 conserved positions were used for neighbor-joining tree reconstruction with K2P substitution model and 1,000 bootstraps. The tree was visualized using iTOL ( Letunic and Bork, 2024 ) and rooted at midpoint. Results The structure and dynamic of the bacterial community associated with five phylogenetically distant macroalgae from a temperate rocky shore has been investigated over one year. The ASV relative abundance was multiplied by the total number of 16S rRNA gene copies to consider absolute taxa count and obtain a more precise understanding of the algal epimicrobiome structure. Algal host as a major driver of epiphytic bacterial community composition The quantified number of bacterial 16S rRNA gene copies ( SuppFile S1 ) varied significantly along the year (2-way ANOVA, ‘sampling month’ effect F 9,158 = 5.0, p < 0.001) but not across macroalgal species (F 4,158 = 0.8, p = 0.531) and with no interactions between the two factors (F 36,158 = 1.1, p = 0.3). This proxy for total bacterial abundance was relatively homogeneous for all algae, with year-round average ranging between 7 x 10 6 to 1 x 10 7 16S rRNA gene copies.cm -2 , depending on the algal species. Yet, we could detect a large range of variations spanning more than two orders of magnitude, from a minimum of 1.04 x 10 5 (Ldig-base in February 2020) to a maximum of 5.02 x 10 7 16S rRNA gene copies.cm -2 (Ldig-old in November 2020). The structure of the epiphytic bacterial communities was strongly impacted by the algal host ( Figure 1A ; PERMANOVA, F 4,203 = 22.7, P < 0.001), showing that communities from different algal species sharing the same habitat are distinct from one another. This observation is supported by the fact that 80% of the total number of ASVs (8 316 out of 10 243) are specific to an algal species ( SuppFigure S1 ) and only 1% (103) are present on all algae. However, 83% of these specific ASVs were only found in one sample. Despite their closer phylogenetic distance, brown algae (Ldig, Anod and Fser, all belonging to the class Phaeophyceae) did not share more ASVs or genera than with green (Ulva) and red (Ppal) algae. Besides host taxonomy, the age of algal tissue also influenced the surface density and composition of the microbiota. Indeed, the younger meristematic tissues of L. digitata (Ldig-base) consistently harbored less 16S rRNA gene copies than the medium frond (Ldig-medium, paired t-test, P = 0.002) and the older apical tissues (Ldig-old, paired t-test, P < 0.001). Multivariate analysis also separated Ldig-base epibacterial communities from Ldig-medium and Ldig-old ( Figure 1B ; PERMANOVA, F 2,86 = 12.4, P < 0.001). Download figure Open in new tab Figure 1: NMDS ordination plots of abundance data of all samples ( A ) or L. digitata samples ( B ), based on Bray-Curtis dissimilarity. 95% confidence ellipses for a multivariate t-distribution are depicted for the different type of algal tissues (either algal species or L. digitata blade part). PERMANOVA F-statistic and p-value are displayed. Gammaproteobacteria , Alphaproteobacteria , Bacteroidia, Acidimicrobiia and Planctomycetes were the most abundant bacterial classes on all algae, representing on average 93-98 % of the communities, depending on the algal host ( SuppFigure S2 ). After pooling the ASVs by genus, we observed that 28% of all genera (93 out of 331) were shared between all algae, suggesting they might be ubiquitous algal epibionts ( Figure 2 and SuppFile S3 ). These generalists were mainly Bacteroidia (including Flavobacteriales ), Gammaproteobacteria and Alphaproteobacteria and represented on average 50-75 % of all sequences depending on algae. An occupancy-redundancy index was calculated following Shade and Stopnisek, 2019 method to identify core genera that were consistently found associated with the five studied algal species all year round ( Figure 2B ). Genera that had an index > 0.65 on all algae were defined as core genera: Granulosicoccus ( Gammaproteobacteria ) , Litorimonas , Hellea , Fretibacter (Alphaproteobacteria) , Portibacter , Algitalea, Rubidimonas, Lewinella (Bacteroidota) , Blastopirellula ( Planctomycetota ) and Sva0996 marine group ( Actinomycetota ). These represented only 3% of all genera, yet accounted for 46% of the bacterial abundance on average. The genus Granulosicoccus was the only one to have an index equal to 1 on all algal tissues, meaning it was present in all 209 samples (see below). On the contrary some genera were specifically associated with particular algae. Especially, the genus Arenicella ( Gammaproteobacteria ) was strongly associated with brown algae all year round (index > 0.78, 2.8 x 10 5 copies.cm -2 on average) while scarce on Ppal (index = 0.27, 3.4 x 10 4 copies.cm -2 ) and Ulva (index = 0.2, 4.1 x 10 3 copies.cm -2 ). The opposite was observed for Truepera ( Deinococcota ; index 0.9 on the red and green algae). Some genera were only found strongly associated (index > 0.8) with one algal species, namely Tateyamaria ( Alphaproteobacteria on Anod, Pibocella ( Bacteroidota ) on Fser, Sulfitobacter (Alphaproteobacteria) , Polaribacter (Bacteroidota) and Jannaschia ( Alphaproteobacteria ) on Ulva. Moreover, important differences were observed between the different parts of the Ldig frond ( Figure 2 ). In particular, members of the Sva0996 marine group and of the family Microtrichaceae ( Actinomycetota ) accounted on average for only 3 x 10 4 16S rRNA gene copies.cm -2 (1%) on the meristem compared to 2 x 10 6 (15%) and 4 x 10 6 (21%) on the medium and old frond respectively. These taxa were also overrepresented on Fser (2 x 10 6 , 21%) and Ppal (2 x 10 6 , 20%). Download figure Open in new tab Figure 2: (A) Estimated abundance of the main bacterial genera. Only genera representing more than 5% (triplicate average) of the communities for at least one condition (ie. at one time point for one algae) are shown. (B) Approach to identify core macroalgal genera. Indices are calculated based on abundance-occupancy distributions (see Methods for index calculation). Only genera that have an index > 0.5 on at least one type of tissue are represented. For the Ldig column, the index is an average of the indices calculated on the base, medium and old Ldig tissues. Seasonal succession of epiphytic bacterial communities The number of 16S rRNA gene copies.cm -2 varied differently through seasons depending on the algal host, and no common pattern to all algae could be highlighted ( Figure 3 ). The number of total bacteria tended to reach a peak in late summer / autumn on Ldig, Fser, Ppal and Ulva. Ldig-old, Anod, Fser, Ppal and Ulva samples from March 2020 showed higher bacterial abundances than neighboring months (February and June). The absence of data in April and May (COVID lockdown) prevents us from assessing if this abundance peak was an isolated event caused by specific environmental conditions in March or if it was characteristic of through the entire spring season. Download figure Open in new tab Figure 3: db-RDA plots with vectors representing significant contextual predictors. 95% confidence ellipses for a multivariate t-distribution are depicted for the different seasons. PERMANOVA F-statistic and p-value are displayed. T: Temperature; O: Dissolved oxygen; S: Salinity; ChlA: Chlorophyll A; MES: Suspended matter; COP: particulate organic carbon. Globally, the number of observed ASVs did not significantly differ through the year ( SuppFigure S3 ). A 3-fold decrease of the ASV number was only observed in June compared to October (Ldig-medium), December (Ldig-old) or September (Ulva). Distance-based ReDundancy Analysis (dbRDA) and PERMANOVA ( Figure 4 ) showed clear dissimilarities between the three identified seasons (winter, summer and autumn) for all algae (except between winter and autumn with Anod and Ulva) and suggested these seasonal variations might be partly explained by environmental parameters, especially seawater temperature, salinity and chlorophyll A concentration. Download figure Open in new tab Figure 4: (A) Phylogenetic tree of the main 16S rRNA ASVs affiliated to Granulosicoccus and reference sequences (Genbank accession in brackets). Pie charts represent year-round average abundance of each ASV on different algal species. HA: host-associated. (B) Heatmap of the abundance of the main ASVs assigned to the genus Granulosicoccus . Only Granulosicoccus ASVs representing at least 5% of the Granulosicoccus community in at least 5 conditions (out of 70) are displayed. Abundances of triplicates were averaged and square root transformation was applied. Asterisks on the right panel of each alga denote differentially abundant ASVs through seasons. F: February; M: March; Jn: June; Jl: July; A: August; S: September; O: October; N: November; D: December; Ja: January. No single ASV was found to vary consistently through seasons on all algae ( SuppFile S4 ). This might be partly explained by the low proportion of ASVs present on all algae, as mentioned above. Anod-associated communities were the least impacted by seasons with only 9 ASVs differentially abundant ( SuppFile S4, Figure 2A ). Bacterial communities’ seasonal dynamics varied depending on the part of the Ldig blade. The basal part exhibited less variations than the older ones (15 differentially abundant ASVs compared to 55 and 92 on medium and old fronds, respectively). On the basal tissues, 2 ASVs from the genus Peredibacter ( Bdellovibrionota ) were more abundant during summer months (3 x 10 4 16S rRNA gene copies.cm -2 , against 2 x 10 2 in winter and 6 x 10 3 in autumn, respectively) and 3 ASVs from the family Rhodobacteraceae ( Alphaproteobacteria ) less abundant in winter. The most outstanding abundance variations on the older tissues were observed for ASVs from the classes Acidimicrobiia (Sva0996 marine group and the family Microtrichaceae ) and Bacteroidia (orders Chitinophagales and Flavobacteriales ), that peaked in autumn on the medium frond and in autumn and winter at the tip part. The community succession on Fser was characterized by a significantly higher abundance of Micavibrionales ( Alphaproteobacteria ) in summer (2 x 10 4 16S rRNA gene copies.cm -2 , against 0 in winter and 2 x 10 3 in autumn). Also, the abundance of 28 Chitinophagales ( Bacteroidota ) ASVs significantly varied between seasons but exhibited distinct abundance patterns. Only few variations were observed with the red alga Ppal (24 differentially abundant ASVs). The most notable succession was a peak of the Aquimarina ( Flavobacteriales ) population in winter (1 x 10 5 16S rRNA gene copies.cm -2 compared to none in summer and 4 x 10 3 autumn). Summer communities displayed high specificity at the surface of Ulva. Thirty-one ASVs were differentially abundant, in particular, members of the genus Truepera ( Deinococcales ), of the families Micotrichaceae ( Actinomycetota ) and Saprospiraceae ( Bacteroidota ) exhibited a sharp decrease in summer while alphaproteobacteria from orders Rhodobacterales and Caulobacterales ( Litorimonas and Fretibacter ) observed an opposite trend. Granulosicoccus are ubiquitous members of macroalgal epibiota The genus Granulosicoccus ( Gammaproteobacteria ) was the only one present in all 208 samples, covering the 5 different algal species and the 10 sampling months. It was also the most abundant genus, representing 15.4% of the communities on average (up to 42% on Ldig-base), and accounting for 1.2 x 10 6 copies.cm -2 on average (up to 7.7 x 10 6 copies.cm -2 on Ppal). It comprised 464 different ASVs, the highest number of ASVs for all genera in our dataset, suggesting a large micro-diversity. Half of these ASVs (236) were only found in one condition (ie. on one type of tissue at one specific time point). Phylogenetic analysis of the most abundant Granulosicoccus ASVs (representing at least 5% of the Granulosicoccus community in at least 5 conditions) confirmed this large diversity and separated three main clades ( Figure 4 ), likely representing new species compared to the 4 validly described species. Clades 2 and 3 clustered with reference sequences from cultured Granulosicoccus strains and uncultured clones. This notably included other marine host-associated strains, retrieved from the brown alga Undaria , the red alga Gracilaria , the seagrass Zostera marina , sponge and plankton ( Kurilenko et al ., 2010 ; Park et al ., 2014 ; Heins and Harder, 2023 ). Clade 1 comprised 5 ASVs that clustered away from other sequences. In addition, all ASVs were not equally distributed on the different algal species. Only two closely related Granulosicoccus ASVs were consistently detected on all algal species (ASV11 and to a lesser extent ASV62). Most of the ASVs were specific or strongly linked to a unique algal species, e.g. ASV1, ASV7, ASV61, ASV208 and ASV49 to Ldig, ASV34, ASV70, ASV123 ASV91 and ASV57 to Anod, ASV73 to Fser and ASV31 to Ppal ( Figure 4A ). While clade 2 encompassed ASVs detected on multiple algae, clade 1 and clade 3 comprised ASVs more abundant on red and brown algae, respectively. Granulosicoccus ASVs associated with medium and old Ldig tissues exhibited stronger significant seasonal variations than Granulosicoccus ASVs associated with the other algal species ( Figure 4B ). Moreover, even ASVs found on the same tissue displayed distinct variations patterns, e.g. for Ldig-medium where ASV1, 7 and 61 peaked in summer and ASV66, 208 and 139 in autumn. Discussion Next-generation sequencing methods have become indispensable tools in microbial ecology, opening new avenues for studying microbial communities. However, these approaches are limited, as they yield relative abundance profiles of microbial taxa, which may not accurately reflect true variations in the actual taxon abundances in the environment ( Gloor et al ., 2017 ; Lloréns-Rico et al ., 2021 ). In this study, we estimated actual ASV abundance by multiplying the ASV relative abundance matrix by the corresponding number of 16S rRNA gene copies estimated through qPCR ( Barlow et al ., 2020 ; Jian et al ., 2020 ). This approach has been previously applied to soil microbiota ( Lou et al ., 2018 ; Azarbad et al ., 2022 ), revealing that quantification provides a more comprehensive understanding of bacterial community dynamics than compositional data, which are more prone to false-positive changes. Absolute abundance of bacterial taxa associated with macroalgae has been previously characterized but focusing only on a few targeted taxa using specific qPCR primers or FISH probes ( Ramírez-Puebla et al ., 2022 ; Brunet et al ., 2023 ). Here we implemented this method to quantitatively characterize the whole macroalgal epimicrobiota composition for the first time, aiming to assess true taxa variations across different algal hosts and seasons. Although providing valuable improvements over sole amplicon sequencing to estimate absolute abundance of different taxa, coupling with qPCR still has some limitations. At least three limitations are common with the metabarcoding- only approach: (1) potential saturation of the swabs during sampling that could lead to underestimate bacterial abundance on densely colonized algal surfaces; (2) variation in extraction efficiencies depending on algal tissue composition (e.g. polysaccharide content); (3) unequal lysis of different bacterial taxa. Here, we used flocked nylon swabs, previously shown to outperform other swab types in terms of collection efficiency and DNA recovery from microbiomes ( Bruijns et al ., 2018 ; Wise et al ., 2021 ). The large variation in DNA concentrations retrieved from swabs (minimum 0.11 ng.µl -1 , median 7.57 ng.µl -1 , maximum 41.46 ng.µl -1 , 92% of samples below 24 ng.µl -1 , SuppFile S1 ) further suggests that if saturation occurred, it only affected a limited number of samples. Moreover, DNA recovery from swabs did not differ depending on algal species (on average Anod: 9.3 ng.µl -1 ; Fser 8.9 ng.µl -1 ; Ldig 10.3 ng.µl -1 ; Ppal 10.6 ng.µl -1 ; Ulva 6.9 ng.µl -1 , SuppFile S1 ), suggesting tissue composition did not largely affect extraction efficiency. An additional bias lies in the use of different sets of primers for qPCR and amplicon sequencing. Indeed, qPCR requires short amplicon size for optimal efficiency and specificity (< 200 bp, Debode et al., 2017 ), whereas optimal primers for 16S metabarcoding cover a region of at least approximately 450 bp after merging, to encompass both conserved and hypervariable regions. Yet, this bias is minimal since the qPCR primers covered most of the taxa found in the 16S metabarcoding dataset (see Methods). Common core of the epiphytic microbiome The large number of samples collected in this study, encompassing 7 types of algal tissues among 5 algal species at 10 sampling months, allow for the identification of a core epiphytic microbiome that would be consistently found on algae all year round. The identification of these core microbes is crucial as they likely have more substantial impact on the host’s biology than other members of the microbiome ( Neu et al ., 2021 ). Here the core microbiota was identified based on occupancy and redundancy criteria as recently used in soil microbial ecology studies (eg. Hodgson et al., 2024 ). The identified core genera, especially Granulosicoccus , Litorimonas and Hellea , concur with previous works that revealed the preponderance of these taxa at the surface of macroalgae ( Weigel and Pfister, 2019 ; Lemay et al ., 2021 ; Wood et al ., 2022 ; Burgunter-Delamare et al ., 2023 ; Lu et al ., 2023 ). Granulosicoccus was the dominant genus on all algae, both in terms of presence/absence and abundance. Predominance of Granulosicoccus on the surface of other brown algae was previously suggested using relative compositional data (eg. Lemay et al., 2021 ; Weigel et al., 2022 ; Burgunter-Delamare et al., 2023 ) and quantitative cell counts using FISH probes ( Ramírez-Puebla et al ., 2022 ). Here we quantify this ubiquity on a larger temporal scale and on other algal species, extending this observation to red and green algae. Using an estimate of 3 rRNA operons per cell (as seen in the complete reference genome of Granulosicoccus antarcticus ( Kang et al ., 2018 )), the maximum observed density of 7.7 x 10 6 16S rRNA gene copies.cm -2 on the red algae P. palmata would correspond to ca. 2 millions Granulosicoccus cell.cm -2 . Using FISH counts on the kelp Nereocystis luetkeana in summer, Ramírez-Puebla et al., 2022 reported 5.5 x 10 4 and 6.5 x 10 6 Granulosicoccus cells.cm -2 on basal and old tissues, respectively. Comparable values can be inferred from our dataset on the basal and old tissues of the kelp L. digitata in summer (5 x 10 5 and 2.3 x 10 6 Granulosicoccus cells.cm -2 , respectively), confirming qPCR correction of metabarcoding compositional data is a useful approach to estimate absolute abundances of specific taxa. Granulosicoccus sequences were scattered into multiple ASVs that fall within distinct clades whose abundance patterns across algal hosts and seasons differ. These clades exhibit distinct tissue specificity and could be characterized either as generalist (clade 2) for containing taxa colonizing all types of tissue or specialist for clades 1 and 3 that encompass taxa that are more specific to particular hosts. These data indicate that ASVs from a same genus colonize distinct ecological niches and underscore important genetic and functional diversity within core bacterial genera associated with macroalgae. Similar observations were made in recent studies where different genomes of core epiphytic genera, especially Granulosicoccus , were differentially abundant at the surface of the bull kelp N. luetkeana depending on its location ( Weigel et al ., 2022 ) or on Fucus sp. depending on season and geographical location ( Park et al ., 2022 ). The analysis of Granulosicoccus genomes revealed strong abilities for an epiphytic lifestyle, that would allow fast colonization and stable associations with the host. In particular, genes involved in motility and chemotaxis, B12 vitamin biosynthesis, DMSP metabolism, transport and utilization of sugars, were found abundant in Granulosicoccus metagenomes ( Kang et al ., 2018 ; Weigel et al ., 2022 ). Host algal tissue is a major driver of epiphytic community composition Our study revealed that the type of algal tissue, whether it is different algal species or different blade parts, was a stronger driver of epiphytic community composition than seasonality. This observation is consistent with previous reports stating that host characteristics may have a greater impact on shaping the structure of macroalgae-associated microbial communities than environmental variables ( Marzinelli et al ., 2015 ; Burgunter-Delamare et al ., 2023 ). Chemical composition of algal tissues varies depending on algal taxonomy ( Mišurcová, 2011 ) or tissue age ( Küpper et al ., 1998 ). Significant differences of concentration of nutritional factors such as proteins, minerals, lipids or sugars were largely shown. Moreover, polyphenol content was reported to be 10-20 times higher in A. nodosum and F. serratus compared to the other algae collected in this study ( Pandey et al ., 2022 ). Species-specific differences were also reported for iodine, as its concentration was shown to be approximately 5 and 20 times higher in L. digitata compared to A. nodosum or F. serratus and P. palmata or Ulva sp., respectively ( Nitschke et al ., 2018 ). Both iodine and polyphenols are known to be algal defense compounds involved in the regulation of microbial colonization (eg. Cosse et al., 2009 ; Besednova et al., 2020 ). Such chemical variations were also observed across seasons but were much less pronounced than between algal species ( Nitschke et al ., 2018 ; Pandey et al ., 2022 ). Therefore, if these compounds are drivers of community composition, their limited annual variations within the tissues could partly explain why seasons had a smaller impact on community composition than algal host. Interestingly, microbiota composition similarities were not higher between brown algae ( L. digitata , A. nodosum and F. serratus ) than with the red or green algae. Similarly, no common pattern was found among species inhabiting the same intertidal zone. Then, despite the important discrepancies observed in the microbiota of distinct algal species, neither higher taxonomic level (algal phylum) nor algae location on the foreshore appeared to be strong community drivers, corroborating observations made by Chen et al., 2022 and Selvarajan et al., 2019 . Nevertheless, the abundance of certain taxa displayed important differences between algal phyla, Arenicella was consistently associated only with brown algae, whereas Truepera was consistently found only with red and green algae. Truepera phylum specificity was also reported in ( Lu et al ., 2023 ) with different algal species. This work shows that different parts of the L. digitata blade support distinct bacterial communities and that total bacteria abundance and diversity are higher on older tissues, in accordance with studies carried out on the same species ( Ihua et al ., 2020 ) and on other kelp species ( Bengtsson et al ., 2012 ; Weigel and Pfister, 2019 ; Lemay et al ., 2021 ; Ramírez-Puebla et al ., 2022 ; Burgunter-Delamare et al ., 2023 ). Moreover, the specific colonization of older L. digitata tissues by members of the Sva0996 marine group, in comparison to the meristem, concur with the one observed in ( Brunet et al ., 2021 ) where the abundance of this taxon was linked with tissue decomposition and degradation. Then, the presence of such taxa on old damaged tissues might be related to their capacity to use complex algal derived organic matter such as polysaccharides ( Brunet et al ., 2022 ). Seasonal variations of the surface microbiota have received unequal attention depending on the algal host. To our knowledge, to date, there is no report of these fluctuations on Ascophyllum sp. or Palmaria sp., while a few studies were conducted on Fucus sp. ( Lachnit et al ., 2011 ; Park et al ., 2022 ), Ulva sp. ( Tujula et al ., 2010 ; Lachnit et al ., 2011 ) and Laminaria sp. ( Corre and Prieur, 1990 ; Bengtsson et al ., 2010 ; Brunet et al ., 2021 ). Seasonal microbial variations are known to be influenced by a combination of biotic and abiotic factors. Here we showed that seasons affect epiphytic community composition for all algae but in different ways. Community richness decreased in summer for L. digitata and Ulva sp. Temperature and salinity were environmental parameters that could explain the observed seasonal discrepancies. Seawater temperature shapes microbial community variations, in particular, it was already shown that increasing temperatures lead to a decrease in community richness on kelp ( Paix et al ., 2021 ) and in seawater (Sunagawa et al .). Salinity was also reported as an important structuring variable of Ulva -associated bacterial communities ( van der Loos et al ., 2023 ). Anod-associated microbiota was the most stable across seasons among all five algae. A. nodosum is known to observe monthly epidermal shedding, a way for longer-lived marine macroalgae to rid themselves of epibionts via the removal of the outer cell layer of the thallus ( Halat et al ., 2015 ; Garbary et al ., 2017 ). Therefore, it is likely that the newly exposed thallus, free of epibionts, does not present any alterations or damages and is monthly colonized by the same early bacterial epibionts. This study, through the extensive number of collected samples and the analysis of absolute taxa abundance, revealed true microbiota variations based on hosts and seasons, providing novel insights in the structure and fluctuations of bacterial communities associated with macroalgae. Future studies should focus on deciphering the ecological and metabolic functions of the identified core epiphytic taxa to gain further insights into epiphyte–host interactions. Contributions (following CRediT taxonomy) Conceptualization, Funding acquisition, Project administration: FT; Data curation, Formal analysis, Visualization, Writing-Original draft preparation: MB, FT; Investigation: MB, NLD, FT; Supervision: FT, TB. Writing-Review & Editing: MB, NLD, TB, FT. Download figure Open in new tab Figure S1: (A) Upset plot of the bacterial genera found in the algal microbiota. Set size represents the total amount of genera present on each alga. Taxonomy is displayed at the class level for the genera shared by all algae and the ones specific to one algal species. (B) Upset plot of the 10 243 ASVs found in the algal microbiota. Set size represents the total amount of ASVs present on each alga. Download figure Open in new tab Figure S2: Estimated abundance of the main bacterial classes. Only classes representing more than 3% (triplicate average) of the communities for at least one condition (ie. at one time point for one algae) are shown. Download figure Open in new tab Figure S3: Fluctuation of the number of observed ASVs. When significant ANOVA results were found (P < 0.05), a post hoc Tukey HSD test was calculated. Accordingly, different letters indicate significant differences between sampling months. File S1: Characteristic of the 209 samples of macroalgal epibiota collected during this study. File S2: Details of the qPCR assay on the environmental samples, following the Minimum Information for publication of Quantitative real-time PCR Experiments (MIQE) guidelines. File S3: List of the 331 assigned taxonomic genera. The number of samples in which they were found is indicated for each alga. File S4: List of differentially abundant ASVs through seasons. A red excel cell indicates the abundance of the ASV varies significantly through seasons on the associated algal tissue. The different sheets named after each algal tissue display the season average abundance of the differentially abundant ASVs. The genus (or the deepest known taxonomic level) assigned to each ASV is mentioned. Acknowledgments The authors thank Manon Choulot for her help during sample collection, and Dr. Simon Dittami for useful discussions on the manuscript. This work has benefited from the facilities of the Genomer platform and from the computational resources of the ABiMS bioinformatics platform (FR 2424, CNRS-Sorbonne Université, Roscoff), which are part of the Biogenouest core facility network. This work was funded by the French Government via the National Research Agency programs ALGAVOR (ANR-18-CE02-0001-01) and IDEALG (ANR-10-BTBR-04). References ↵ Anderson , M . ( 2001 ) A new method for non-parametric multivariate analysis of variance . 26 : 32 – 46 . OpenUrl ↵ Azarbad , H. , Tremblay , J. , Bainard , L.D. , and Yergeau , E . ( 2022 ) Relative and Quantitative Rhizosphere Microbiome Profiling Results in Distinct Abundance Patterns . Front Microbiol 12 : 798023 .. OpenUrl ↵ Bacchetti De Gregoris , T. , Aldred , N. , Clare , A.S. , and Burgess , J.G. ( 2011 ) Improvement of phylum- and class-specific primers for real-time PCR quantification of bacterial taxa . J Microbiol Methods 86 : 351 – 356 . OpenUrl CrossRef PubMed Web of Science ↵ Barlow , J.T. , Bogatyrev , S.R. , and Ismagilov , R.F . ( 2020 ) A quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities . Nat Commun 11 : 2590 . OpenUrl CrossRef ↵ Bengtsson , M.M. , Sjøtun , K. , Lanzén , A. , and Øvreås , L . ( 2012 ) Bacterial diversity in relation to secondary production and succession on surfaces of the kelp Laminaria hyperborea . ISME Journal 6 : 2188 – 2198 . OpenUrl ↵ Bengtsson , M.M. , Sjøtun , K. , and øvreås , L. ( 2010 ) Seasonal dynamics of bacterial biofilms on the kelp Laminaria hyperborea . Aquatic Microbial Ecology 60 : 71 – 83 . OpenUrl ↵ Besednova , N.N. , Andryukov , B.G. , Zaporozhets , T.S. , Kryzhanovsky , S.P. , Kuznetsova , T.A. , Fedyanina , L.N. , et al. ( 2020 ) Algae polyphenolic compounds and modern antibacterial strategies: Current achievements and immediate prospects . Biomedicines 8 : 342 . OpenUrl ↵ Bray , J.R. , Curtis , J.T. , and Roger , J . ( 1957 ) An ordination of the upland forest communities of Southern Wisconsin . Ecological Monographs 27 : 325 – 349 . OpenUrl CrossRef ↵ Bruijns , B.B. , Tiggelaar , R.M. , and Gardeniers , H . ( 2018 ) The extraction and recovery efficiency of pure DNA for different types of swabs . J Forensic Sci 63 : 1492 – 1499 . OpenUrl CrossRef ↵ Brunet , M. , de Bettignies , F. , Le Duff , N. , Tanguy , G. , Davoult , D. , Leblanc , C. , et al. ( 2021 ) Accumulation of detached kelp biomass in a subtidal temperate coastal ecosystem induces succession of epiphytic and sediment bacterial communities . Environ Microbiol 23 : 1638 – 1655 . OpenUrl ↵ Brunet , M. , Le Duff , N. , Barbeyron , T. , and Thomas , F. ( 2022 ) Consuming fresh macroalgae induces specific catabolic pathways, stress reactions and Type IX secretion in marine flavobacterial pioneer degraders . ISME Journal 16 : 2027 – 2039 . OpenUrl ↵ Brunet , M. , Le Duff , N. , Rigaut-Jalabert , F. , Romac , S. , Barbeyron , T. , and Thomas , F. ( 2023 ) Seasonal dynamics of a glycan-degrading flavobacterial genus in a tidally mixed coastal temperate habitat . Environ Microbiol 25 : 3192 – 3206 . OpenUrl ↵ Burel , T. , Schaal , G. , Grall , J. , Le Duff , M. , and Gall , E.A. ( 2022 ) Clear-cut wave height thresholds reveal dominance shifts in assemblage patterns on rocky shores . Mar Ecol Prog Ser 683 : 21 – 36 . OpenUrl ↵ Burgunter-Delamare , B. , Rousvoal , S. , Legeay , E. , Tanguy , G. , Fredriksen , S. , Boyen , C. , and Dittami , S.M . ( 2023 ) The Saccharina latissima microbiome: Effects of region, season, and physiology . Front Microbiol 13 : 1050939 . OpenUrl ↵ Burgunter-Delamare , B. , Shetty , P. , Vuong , T. , and Mittag , M . ( 2024 ) Exchange or Eliminate: The secrets of algal-bacterial relationships . Plants 13 : 829 . OpenUrl ↵ Burke , C. , Steinberg , P. , Rusch , D. , Kjelleberg , S. , and Thomas , T . ( 2011 ) Bacterial community assembly based on functional genes rather than species . Proc Natl Acad Sci U S A 108 : 14288 – 14293 . OpenUrl Abstract / FREE Full Text ↵ Camacho-Sanchez , M . ( 2023 ) Quantitative metabarcoding of soil fungi and bacteria . Research Square . doi: 10.21203/rs.3.rs-2885222/v1 OpenUrl CrossRef ↵ Chen , J. , Zang , Y. , Yang , Z. , Qu , T. , Sun , T. , Liang , S. , et al. ( 2022 ) Composition and functional diversity of epiphytic bacterial and fungal communities on marine macrophytes in an intertidal zone . Front Microbiol 13 : 839465 . OpenUrl ↵ Cocquempot , L. , Delacourt , C. , Paillet , J. , Riou , P. , Aucan , J. , Castelle , B. , et al. ( 2019 ) Coastal ocean and nearshore observation: A French case study . Front Mar Sci 6 : 00324 . OpenUrl ↵ Corre , S. and Prieur , D . ( 1990 ) Density and morphology of epiphytic bacteria on the kelp Laminaria digitata . Botanica Marina 33 : 515 – 523 . OpenUrl ↵ Cosse , A. , Potin , P. , and Leblanc , C . ( 2009 ) Patterns of gene expression induced by oligoguluronates reveal conserved and environment-specific molecular defense responses in the brown alga Laminaria digitata . New Phytologist 182 : 239 – 250 . OpenUrl CrossRef PubMed Web of Science ↵ Debode , F. , Marien , A. , Janssen , É. , Bragard , C. , and Berben , G . ( 2017 ) The influence of amplicon length on real-time PCR results . Biotechnol. Agron. Soc. Environ 21 : 3 – 11 . OpenUrl ↵ Dereeper , A. , Guignon , V. , Blanc , G. , Audic , S. , Buffet , S. , Chevenet , F ., et al. ( 2008 ) Phylogeny.fr: robust phylogenetic analysis for the non-specialist . Nucleic Acids Res 36 : W465 – 9 . OpenUrl CrossRef PubMed Web of Science ↵ Egan , S. , Harder , T. , Burke , C. , Steinberg , P. , Kjelleberg , S. , and Thomas , T . ( 2013 ) The seaweed holobiont: Understanding seaweed-bacteria interactions . FEMS Microbiol Rev 37 : 462 – 476 . OpenUrl CrossRef PubMed Web of Science ↵ Evans , R.G . ( 1957 ) The intertidal ecology of some localities on the Atlantic coast of France . Journal of Ecology 45 : 245 – 271 . OpenUrl ↵ Fernandes , A.D. , Reid , J.N.S. , Macklaim , J.M. , McMurrough , T.A. , Edgell , D.R. , and Gloor , G.B . ( 2014 ) Unifying the analysis of high-throughput sequencing datasets: Characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis . Microbiome 2 : 15 . OpenUrl CrossRef PubMed ↵ Fernandes , N. , Steinberg , P. , Rusch , D. , Kjelleberg , S. , and Thomas , T . ( 2012 ) Community structure and functional gene profile of bacteria on healthy and diseased thalli of the red seaweed Delisea pulchra . PLoS One 7 : e50854 . OpenUrl CrossRef PubMed ↵ Florez , J.Z. , Camus , C. , Hengst , M.B. , and Buschmann , A.H . ( 2017 ) A functional perspective analysis of macroalgae and epiphytic bacterial community interaction . Front Microbiol 8 : 02561 . OpenUrl ↵ Florez , J.Z. , Camus , C. , Hengst , M.B. , Marchant , F. , and Buschmann , A.H . ( 2019 ) Structure of the epiphytic bacterial communities of Macrocystis pyrifera in localities with contrasting nitrogen concentrations and temperature . Algal Res 44 : 101706 . OpenUrl ↵ Garbary , D.J. , Brown , N.E. , Macdonell , H.J. , and Toxopeus , J. ( 2017 ) Ascophyllum and its symbionts-A complex symbiotic community on North Atlantic Shores . Algal and Cyanobacteria Symbioses 547 – 572 . ↵ Glasl , B. , Haskell , J.B. , Aires , T. , Serrão , E.A. , Bourne , D.G. , Webster , N.S. , and Frade , P.R . ( 2021 ) Microbial surface biofilm responds to the growth-reproduction-senescence cycle of the dominant coral reef macroalgae Sargassum spp . Life 11 : 1199 . OpenUrl ↵ Gloor , G.B. , Macklaim , J.M. , Pawlowsky-Glahn , V. , and Egozcue , J.J . ( 2017 ) Microbiome datasets are compositional: And this is not optional . Front Microbiol 8 : 0224 . OpenUrl ↵ Halat , L. , Galway , M.E. , Gitto , S. , and Garbary , D.J . ( 2015 ) Epidermal shedding in Ascophyllum nodosum (Phaeophyceae): Seasonality, productivity and relationship to harvesting . Phycologia 54 : 599 – 608 . OpenUrl ↵ Heins , A. and Harder , J . ( 2023 ) Particle-associated bacteria in seawater dominate the colony-forming microbiome on ZoBell marine agar . FEMS Microbiol Ecol 99 : fiac151 . OpenUrl ↵ Hodgson , R.J. , Liddicoat , C. , Cando-Dumancela , C. , Fickling , N.W. , Peddle , S.D. , Ramesh , S. , and Breed , M.F . ( 2024 ) Increasing aridity strengthens the core bacterial rhizosphere associations in the pan-palaeotropical C4 grass, Themeda triandra . Applied Soil Ecology 201 : 105514 . OpenUrl ↵ Hollants , J. , Leliaert , F. , De Clerck , O. , and Willems , A. ( 2013 ) What we can learn from sushi: A review on seaweed-bacterial associations . FEMS Microbiol Ecol 83 : 1 – 16 . OpenUrl CrossRef PubMed Web of Science ↵ Ihua , M.W. , FitzGerald , J.A. , Guiheneuf , F. , Jackson , S.A. , Claesson , M.J. , Stengel , D.B. , and Dobson , A.D.W . ( 2020 ) Diversity of bacteria populations associated with different thallus regions of the brown alga Laminaria digitata . PLoS One 15 : e0242675 . OpenUrl CrossRef ↵ Jian , C. , Luukkonen , P. , Yki-Järvinen , H. , Salonen , A. , and Korpela , K . ( 2020 ) Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling . PLoS One 15 : e0227285 . OpenUrl CrossRef ↵ Kang , I. , Lim , Y. , and Cho , J.C . ( 2018 ) Complete genome sequence of Granulosicoccus antarcticus type strain IMCC3135 T , a marine gammaproteobacterium with a putative dimethylsulfoniopropionate demethylase gene . Mar Genomics 37 : 176 – 181 . OpenUrl CrossRef ↵ Kuba , G.M. , Spalding , H.L. , Hill-Spanik , K.M. , and Fullerton , H . ( 2021 ) Microbiota-Macroalgal relationships at a Hawaiian intertidal bench are influenced by macroalgal phyla and associated thallus complexity . mSphere 6 : 1128 . OpenUrl ↵ Küpper , F.C. , Schweigert , N. , Ar Gall , E. , Legendre , J.M. , Vilter , H. , and Kloareg , B . ( 1998 ) Iodine uptake in Laminariales involves extracellular, haloperoxidase-mediated oxidation of iodide . Planta 207 : 163 – 171 . OpenUrl CrossRef Web of Science ↵ Kurilenko , V. V. , Christen , R. , Zhukova , N. V. , Kalinovskaya , N.I. , Mikhailov , V. V. , Crawford , R.J. , and Ivanova , E.P . ( 2010 ) Granulosicoccus coccoides sp. nov., isolated from leaves of seagrass ( Zostera marina ) . Int J Syst Evol Microbiol 60 : 972 – 976 . OpenUrl CrossRef PubMed ↵ Lachnit , T. , Meske , D. , Wahl , M. , Harder , T. , and Schmitz , R . ( 2011 ) Epibacterial community patterns on marine macroalgae are host-specific but temporally variable . Environ Microbiol 13 : 655 – 665 . OpenUrl CrossRef PubMed Web of Science ↵ Legendre , P. and Anderson , M.J . ( 1999 ) Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments . Ecological Monographs 69 : 1 – 24 . OpenUrl CrossRef Web of Science ↵ Lemay , M.A. , Davis , K.M. , Martone , P.T. , and Parfrey , L.W . ( 2021 ) Kelp-associated microbiota are structured by host taxonomy . J Phycol 57 : 1119 – 1130 . OpenUrl CrossRef ↵ Lemay , M.A. , Martone , P.T. , Hind , K.R. , Lindstrom , S.C. , and Wegener Parfrey , L . ( 2018 ) Alternate life history phases of a common seaweed have distinct microbial surface communities . Mol Ecol 27 : 3555 – 3568 . OpenUrl CrossRef ↵ Letunic , I. and Bork , P . ( 2024 ) Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool . Nucleic Acids Res 52 : W78 – W82 . OpenUrl ↵ Lloréns-Rico , V. , Vieira-Silva , S. , Gonçalves , P.J. , Falony , G. , and Raes , J . ( 2021 ) Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases . Nat Commun 12 : 3562 . OpenUrl ↵ van der Loos , L.M. , D’hondt , S. , Engelen , A.H. , Pavia , H. , Toth , G.B. , Willems , A ., et al. ( 2023 ) Salinity and host drive Ulva -associated bacterial communities across the Atlantic–Baltic Sea gradient . In Molecular Ecology . John Wiley and Sons Inc , pp. 6260 – 6277 . ↵ Lou , J. , Yang , L. , Wang , H. , Wu , L. , and Xu , J . ( 2018 ) Assessing soil bacterial community and dynamics by integrated high-throughput absolute abundance quantification . PeerJ 6 : e4514 . OpenUrl CrossRef ↵ Lu , D.C. , Wang , F.Q. , Amann , R.I. , Teeling , H. , and Du , Z.J . ( 2023 ) Epiphytic common core bacteria in the microbiomes of co-located green ( Ulva ), brown ( Saccharina ) and red ( Grateloupia , Gelidium ) macroalgae . Microbiome 11 : 126 . OpenUrl ↵ Mancuso , F.P. , D’Hondt , S. , Willems , A. , Airoldi , L. , and De Clerck , O. ( 2016 ) Diversity and temporal dynamics of the epiphytic bacterial communities associated with the canopy-forming seaweed Cystoseira compressa (Esper) Gerloff and Nizamuddin . Front Microbiol 7 : 00476 . OpenUrl ↵ Martin , M. , Barbeyron , T. , Martin , R. , Portetelle , D. , Michel , G. , and Vandenbol , M . ( 2015 ) The cultivable surface microbiota of the brown alga Ascophyllum nodosum is enriched in macroalgal-polysaccharide-degrading bacteria . Front Microbiol 6 : 01487 . OpenUrl ↵ Marzinelli , E.M. , Campbell , A.H. , Zozaya Valdes , E. , Vergés , A. , Nielsen , S. , Wernberg , T. , et al. ( 2015 ) Continental-scale variation in seaweed host-associated bacterial communities is a function of host condition, not geography . Environ Microbiol 17 : 4078 – 4088 . OpenUrl CrossRef ↵ McMurdie , P.J. and Holmes , S . ( 2013 ) Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data . PLoS One 8 : e61217 . OpenUrl CrossRef PubMed ↵ Miranda , L.N. , Hutchison , K. , Grossman , A.R. , and Brawley , S.H . ( 2013 ) Diversity and abundance of the bacterial community of the red macroalga Porphyra umbilicalis : Did bacterial farmers produce macroalgae? PLoS One 8 : e58269 . OpenUrl CrossRef PubMed ↵ Mišurcová , L . ( 2011 ) Chemical composition of seaweeds . In Handbook of Marine Macroalgae: Biotechnology and Applied Phycology . John Wiley and Sons , pp. 171 – 192 . ↵ Neu , A.T. , Allen , E.E. , and Roy , K. ( 2021 ) Defining and quantifying the core microbiome: Challenges and prospects . PNAS 118 : e2104429118 . OpenUrl Abstract / FREE Full Text ↵ Nitschke , U. , Walsh , P. , McDaid , J. , and Stengel , D.B . ( 2018 ) Variability in iodine in temperate seaweeds and iodine accumulation kinetics of Fucus vesiculosus and Laminaria digitata (Phaeophyceae, Ochrophyta) . J Phycol 54 : 114 – 125 . OpenUrl CrossRef ↵ Paix , B. , Potin , P. , Schires , G. , Le Poupon , C. , Misson , B. , Leblanc , C. , et al. ( 2021 ) Synergistic effects of temperature and light affect the relationship between Taonia atomaria and its epibacterial community: a controlled conditions study . Environ Microbiol 23 : 6777 – 6797 . OpenUrl ↵ Pandey , D. , Hansen , H.H. , Dhakal , R. , Aryal , N. , Rai , S.P. , Sapkota , R. , et al. ( 2022 ) Interspecies and seasonal variations in macroalgae from the Nordic region: Chemical composition and impacts on rumen fermentation and microbiome assembly . J Clean Prod 363 : 132456 . OpenUrl ↵ Park , J. , Davis , K. , Lajoie , G. , and Parfrey , L.W . ( 2022 ) Alternative approaches to identify core bacteria in Fucus distichus microbiome and assess their distribution and host-specificity . Environmental Microbiomes 17 : 55 . OpenUrl ↵ Park , S. , Jung , Y.T. , Won , S.M. , Park , J.M. , and Yoon , J.H . ( 2014 ) Granulosicoccus undariae sp. nov., a member of the family Granulosicoccaceae isolated from a brown algae reservoir and emended description of the genus Granulosicoccus . Antonie van Leeuwenhoek, International Journal of General and Molecular Microbiology 106 : 845 – 852 . OpenUrl CrossRef PubMed ↵ Props , R. , Kerckhof , F.M. , Rubbens , P. , Vrieze , J. De , Sanabria , E.H. , Waegeman , W. , et al. ( 2017 ) Absolute quantification of microbial taxon abundances . ISME Journal 11 : 584 – 587 . OpenUrl ↵ Quast , C. , Pruesse , E. , Yilmaz , P. , Gerken , J. , Schweer , T. , Yarza , P. , et al. ( 2013 ) The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools . Nucleic Acids Res 41 : D590 – 596 . OpenUrl CrossRef PubMed Web of Science ↵ Ramírez-Puebla , S.T. , Weigel , B.L. , Jack , L. , Schlundt , C. , Pfister , C.A. , and Mark Welch , J.L . ( 2022 ) Spatial organization of the kelp microbiome at micron scales . Microbiome 10 : 52 . OpenUrl CrossRef ↵ Saha , M. and Fink , P . ( 2022 ) Algal volatiles – the overlooked chemical language of aquatic primary producers . Biological Reviews 97 : 2162 – 2173 . OpenUrl CrossRef ↵ Selvarajan , R. , Sibanda , T. , Venkatachalam , S. , Ogola , H.J.O. , Christopher Obieze , C. , and Msagati , T.A . ( 2019 ) Distribution, interaction and functional profiles of epiphytic bacterial communities from the rocky intertidal seaweeds, South Africa . Sci Rep 9 : 19835 . OpenUrl ↵ Serebryakova , A. , Aires , T. , Viard , F. , Serrão , E.A. , and Engelen , A.H . ( 2018 ) Summer shifts of bacterial communities associated with the invasive brown seaweed Sargassum muticum are location and tissue dependent . PLoS One 13 : e0206734 . OpenUrl ↵ Shade , A. and Stopnisek , N . ( 2019 ) Abundance-occupancy distributions to prioritize plant core microbiome membership . Curr Opin Microbiol 49 : 50 – 58 . OpenUrl ↵ Shelton , A. , Gold , Z. , Jensen , A. , D’Agnese , E. , Alan , E. , Van Cise , A. , et al. ( 2022 ) Toward quantitative metabarcoding . Ecology 104 : e3906 . OpenUrl ↵ Singh , R.P. and Reddy , C.R.K . ( 2014 ) Seaweed-microbial interactions: Key functions of seaweed-associated bacteria . FEMS Microbiol Ecol 88 : 213 – 230 . OpenUrl CrossRef PubMed ↵ Singh , R.P. and Reddy , C.R.K . ( 2016 ) Unraveling the functions of the macroalgal microbiome . Front Microbiol 6 : 01488 . OpenUrl Sunagawa , S. , Coelho , L. P. , Chaffron , S. , Kultima , J.R. , Labadie , K. , et al. Structure and function of the global ocean microbiome . Science 348 : 1 – 9 . ↵ Thomas , F. , Dittami , S.M. , Brunet , M. , Le Duff , N. , Tanguy , G. , Leblanc , C. , and Gobet , A. ( 2020 ) Evaluation of a new primer combination to minimize plastid contamination in 16S rDNA metabarcoding analyses of alga-associated bacterial communities . Environ Microbiol Rep 12 : 30 – 37 . OpenUrl ↵ Tujula , N.A. , Crocetti , G.R. , Burke , C. , Thomas , T. , Holmström , C. , and Kjelleberg , S . ( 2010 ) Variability and abundance of the epiphytic bacterial community associated with a green marine Ulvacean alga . ISME Journal 4 : 301 – 311 . OpenUrl ↵ Vandeputte , D. , Kathagen , G. , D’Hoe , K. , Vieira-Silva , S. , Valles-Colomer , M. , Sabino , J. , et al. ( 2017 ) Quantitative microbiome profiling links gut community variation to microbial load . Nature 551 : 507 – 511 . OpenUrl CrossRef PubMed ↵ Wahl , M. , Goecke , F. , Labes , A. , Dobretsov , S. , and Weinberger , F . ( 2012 ) The second skin: Ecological role of epibiotic biofilms on marine organisms . Front Microbiol 3 : 00292 . OpenUrl ↵ Weigel , B.L. , Miranda , K.K. , Fogarty , E.C. , Watson , A.R. , and Pfister , C.A. ( 2022 ) Functional insights into the kelp microbiome from metagenome-assembled genomes . mSystems 7 : e01422 – 21 . OpenUrl ↵ Weigel , B.L. and Pfister , C.A . ( 2019 ) Successional dynamics and seascape-level patterns of microbial communities on the canopy-forming kelps Nereocystis luetkeana and Macrocystis pyrifera . Front Microbiol 10 : 00346 . OpenUrl ↵ Wichard , T. , Charrier , B. , Mineur , F. , Bothwell , J.H. , De Clerck , O. , and Coates , J.C. ( 2015 ) The green seaweed Ulva : A model system to study morphogenesis . Front Plant Sci 6 : 00072 . OpenUrl ↵ Wise , N.M. , Wagner , S.J. , Worst , T.J. , Sprague , J.E. , and Oechsle , C.M . ( 2021 ) Comparison of swab types for collection and analysis of microorganisms . Microbiologyopen 10 : e1244 . OpenUrl ↵ Wood , G. , Steinberg , P.D. , Campbell , A.H. , Vergés , A. , Coleman , M.A. , and Marzinelli , E.M . ( 2022 ) Host genetics, phenotype and geography structure the microbiome of a foundational seaweed . Mol Ecol 31 : 2189 – 2206 . OpenUrl ↵ Yilmaz , P. , Parfrey , L.W. , Yarza , P. , Gerken , J. , Pruesse , E. , Quast , C. , et al. ( 2014 ) The SILVA and “all-species Living Tree Project (LTP)” taxonomic frameworks . Nucleic Acids Res 42 : D643 – 648 . OpenUrl CrossRef PubMed Web of Science ↵ Zozaya-Valdés , E. , Roth-Schulze , A.J. , Egan , S. , and Thomas , T . ( 2017 ) Microbial community function in the bleaching disease of the marine macroalgae Delisea pulchra . Environ Microbiol 19 : 3012 – 3024 . OpenUrl View the discussion thread. Back to top Previous Next Posted July 22, 2024. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Year-round quantification, structure and dynamics of epibacterial communities from diverse macroalgae reveal a persistent core microbiota and strong host specificities Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Year-round quantification, structure and dynamics of epibacterial communities from diverse macroalgae reveal a persistent core microbiota and strong host specificities Maéva Brunet , Nolwen Le Duff , Tristan Barbeyron , François Thomas bioRxiv 2024.07.22.604553; doi: https://doi.org/10.1101/2024.07.22.604553 Share This Article: Copy Citation Tools Year-round quantification, structure and dynamics of epibacterial communities from diverse macroalgae reveal a persistent core microbiota and strong host specificities Maéva Brunet , Nolwen Le Duff , Tristan Barbeyron , François Thomas bioRxiv 2024.07.22.604553; doi: https://doi.org/10.1101/2024.07.22.604553 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7644) Biochemistry (17728) Bioengineering (13916) Bioinformatics (42037) Biophysics (21489) Cancer Biology (18637) Cell Biology (25553) Clinical Trials (138) Developmental Biology (13401) Ecology (19941) Epidemiology (2067) Evolutionary Biology (24367) Genetics (15622) Genomics (22547) Immunology (17764) Microbiology (40475) Molecular Biology (17208) Neuroscience (88747) Paleontology (667) Pathology (2842) Pharmacology and Toxicology (4834) Physiology (7659) Plant Biology (15175) Scientific Communication and Education (2047) Synthetic Biology (4304) Systems Biology (9835) Zoology (2272)