A duo of fungi and complex and dynamic bacterial community networks contribute to shape the Ascophyllum nodosum holobiont

A duo of fungi and complex and dynamic bacterial community networks contribute to shape the Ascophyllum nodosum holobiont | 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 A duo of fungi and complex and dynamic bacterial community networks contribute to shape the Ascophyllum nodosum holobiont View ORCID Profile Coralie Rousseau , View ORCID Profile Gwenn Tanguy , View ORCID Profile Erwan Legeay , View ORCID Profile Samuel Blanquart , View ORCID Profile Arnaud Belcour , Sylvie Rousvoal , View ORCID Profile Philippe Potin , View ORCID Profile Catherine Leblanc , View ORCID Profile Simon M. Dittami doi: https://doi.org/10.1101/2025.03.20.643298 Coralie Rousseau 1 Integrative Biology of Marine Models (LBI2M, UMR 8227), Station Biologique de Roscoff, Sorbonne University , CNRS, 29680 Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Coralie Rousseau For correspondence: coralie.rousseau{at}sb-roscoff.fr Gwenn Tanguy 2 Genomer Platform, FR2424, Station Biologique de Roscoff, Sorbonne University , CNRS, 29680 Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gwenn Tanguy Erwan Legeay 2 Genomer Platform, FR2424, Station Biologique de Roscoff, Sorbonne University , CNRS, 29680 Roscoff, France 3 Genomer Platform, FR2424, Station Biologique de Roscoff, Sorbonne University , CNRS, 29680 Roscoff, France & Adaptation and Diversity in the Marine Environment (UMR 7144), Station Biologique de Roscoff, Sorbonne University , CNRS, 29680 Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Erwan Legeay Samuel Blanquart 4 Dyliss, Inria (UMR 6074), CNRS, University of Rennes , Rennes, France , Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Samuel Blanquart Arnaud Belcour 5 MICROCOSME , Inria, 38100 Grenoble, France , Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arnaud Belcour Sylvie Rousvoal 1 Integrative Biology of Marine Models (LBI2M, UMR 8227), Station Biologique de Roscoff, Sorbonne University , CNRS, 29680 Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Philippe Potin 1 Integrative Biology of Marine Models (LBI2M, UMR 8227), Station Biologique de Roscoff, Sorbonne University , CNRS, 29680 Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philippe Potin Catherine Leblanc 1 Integrative Biology of Marine Models (LBI2M, UMR 8227), Station Biologique de Roscoff, Sorbonne University , CNRS, 29680 Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catherine Leblanc For correspondence: coralie.rousseau{at}sb-roscoff.fr Simon M. Dittami 1 Integrative Biology of Marine Models (LBI2M, UMR 8227), Station Biologique de Roscoff, Sorbonne University , CNRS, 29680 Roscoff, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Simon M. Dittami For correspondence: coralie.rousseau{at}sb-roscoff.fr Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract The brown alga Ascophyllum nodosum and its microbiota form a dynamic functional entity named holobiont. Some microbial partners may play a role in seaweed health through bioactive compounds crucial for normal morphology, development, and physiological acclimation. However, the full spectrum of the microbial diversity and its variations according to algal life stage, season, and location have not been comprehensively studied. This study uses 208 short-read metabarcoding samples to characterize the bacterial, archaeal, and microeukaryotic communities of A. nodosum across three nearby sites, four thallus parts, and a monthly survey, aiming to explore the dynamics of ecological interactions within the holobiont. Our results revealed that A. nodosum harbors a predominantly bacterial microbiota, varying significantly across all covariables, while archaea were virtually absent. An innovative normalization using the co-amplified host reads provided an estimation of bacterial abundance, revealing a drastic decline in May, potentially linked to epidermal shedding. In contrast, fungal communities were stable, dominated by Mycophycias ascophylli and Moheitospora sp., which remained closely associated with the host year-round. We identified a core microbiome of 22 ASVs, consistently found in all samples, including Granulosicoccus, a genus consistently abundant in other brown algal microbiota. Sequence clustering revealed multiple species which vary according seasons, even in the overall stable Granulosicoccus genus. Co-occurrence network analysis revealed putative interactions between microbial groups in response to ecological niches. Overall, these findings highlight the dynamic of bacterial interactions and stable fungal associations within the A. nodosum holobiont, providing new insights into the ecology of its microbiota. Introduction Brown seaweeds provide habitats for diverse communities of epi- and endosymbiotic microorganisms (i.e., bacteria, eukaryotes, archaea, and viruses) [ 1 , 2 ]. The algal surface is suitable for the development of microorganisms because of organic substances, which play an important nutritional role for the microbiota [ 3 ]. Some microbial partners are, in turn, necessary for seaweed health by producing bioactive compounds essential for normal morphology, development, and physiological responses to environmental factors [ 4 ]. These host-microorganism associations constitute interacting and sometimes co-dependent units, so-called holobionts [ 2 , 5 – 7 ]. Ascophyllum nodosum is abundant and perennial along the North Atlantic coasts, anchoring to rocky substrates in sheltered intertidal zones. It can live up to 15 years, reach lengths of up to 2 meters, and exhibits slow growth through apical elongation, forming air bladders annually [ 8 – 10 ]. This dioecious species has a slow recruitment process due to limited dispersal and low germling survival rates [ 11 , 12 ], with young individuals requiring approximately five years to reach maturity [ 13 ]. Ascophyllum is a valuable resource due to its bioactive components used in agriculture, food ingredients, health, and cosmetics. However, increasing demand has raised concerns about overexploitation. A. nodosum has been described as engaged in a tripartite symbiosis with the endophytic marine fungus Mycophycias (formerly Mycosphaerella ) ascophylli and the epiphytic red alga Vertebrata (formerly Polysiphonia ) lanosa . The ascomycete M. ascophylli is consistently present throughout the host, without apparent pathogenic effects [ 14 , 15 ], and is thought to protect A. nodosum germlings from desiccation [ 16 ]. V. lanosa is an obligate epiphyte, largely restricted to A. nodosum, causing limited damage to the host despite rhizoid penetration into host tissues [ 17 , 18 ]. These species have undergone considerable coevolution resulting in intricate morphological, physiological, and ecological interactions. However, it is unclear whether these interactions also involve other bacteria or fungi, and whether these fungi and bacteria can increase A. nodosum fitness, as described in other brown algae [ 19 ]. A. nodosum -associated bacteria were first described in 1969 by culturing [ 20 ] and later by electron microscopy [ 21 ]. In 1988, experiments suggest that bacteria shield A. nodosum from digestion by M. ascophylli [ 22 ]. Further studies investigated surface-attached bacteria degrading polysaccharides [ 23 ], the role of endobacteria in quorum-sensing [ 24 ], and epibacterial community responses to stress [ 25 ] and decay [ 26 ]. Recently, Brunet et al. [ 27 ] quantified the epibacterial communities by qPCR across a year-long survey. Despite recent increased scientific interest, the full diversity of microbial communities (bacteria, eukaryotes, archaea, and viruses) associated with A. nodosum remains largely unexplored. This study aims to fill the knowledge gap across three key questions: (i) Does the microbiota change across a small spatial scale, algal thallus parts, and throughout a monthly survey? (ii) Is there a core microbiota? (iii) How do microorganisms associate with each other? To comprehensively describe the holobiont, we used a short-read metabarcoding analysis of bacteria, archaea, and eukaryotes and used co-occurrence networks to infer potential ecological interactions between microorganisms. Material and methods 1. Sample collection A monthly survey was conducted from November 2021 to November 2022 at Pleubian (48.849411, −3.078291, Brittany, France, see map [ 28 ]), a location known for the commercial harvesting of seaweeds, including A. nodosum . The microbial community associated with this alga was characterized in three nearby sampling sites. At each site, four algae were selected arbitrarily (see [ 28 ]) and four parts were sampled: apex, receptacle (during reproductive season only), medium, and basal parts. Algal samples (∼50 mm long) were cut with clean scissors rinsed with 70% ethanol, placed in cryotubes, and stored on ice. In the laboratory, samples were stored at −80°C. 2. DNA extraction and sequencing The algal samples were free-dried and finely ground to access the epi- and endophytic microbial communities. The DNA extraction was based on CTAB extraction buffer, chloroform/isoamyl separation, and the extracted DNA was purified twice with a NucleoSpin Plant II kit (MachereyNagel, Germany) and with AMPure XP Beads (Beckman Coulter, Brea, CA, USA) [ 28 ]. To reduce the number of samples and sequencing costs, and facilitate the bioinformatics analysis, three amplicon datasets were built based on the following research questions by pooling at equal concentration the DNA: - “Site”: Does microbiota vary between the three sampling sites? The strategy involved pooling extracted DNA from the three thallus parts of one individual to remove the potential thallus part effect (apex, medium and base – no receptacle as it was not always present). Four pooled DNA samples from each site were examined for two arbitrarily chosen sampling dates: November 2021 and March 2022 (3 sites * 4 individuals * 2 months = 24 samples) - “Algal parts”: Does the microbiota vary depending on the part of the alga? Samples from four individuals across four thallus parts were analyzed from site 1 for two arbitrarily choses sampling dates: January and April 2022 (4 parts * 4 individuals * 2 months = 32 samples). - “Season”: Does the microbiota change with the season? The strategy involved pooling extracted DNA from three thallus parts from one individual alga collected over 12 months at site 1 (12 months * 4 individuals = 48 samples). We furthermore included three replicates each of a bacterial and fungal mock community containing 10 bacterial and 10 fungal DNAs [ 28 ], as well as 8 negative controls. PCR reactions and library preparation were carried out as described in [ 28 ], using the universal primers 515-F/926-R [ 29 ] to target bacteria, archaea, and eukaryotes, and ITS2 nested PCR (ITS-1/ITS4 and 5,8S-FUN/ITS4-FUN, [ 30 , 31 ]) to increase taxonomic resolution for fungi. Libraries were sequenced on a NovaSeq 6000 SP at the Genomics Core Facility GenoA platform (Nantes, France) using 500 cycles (kit ref. 20028402). 3. Bioinformatic analysis 2.1 Cleaning, taxonomy assignment, and decontamination steps Bioinformatics analyses were carried out as described in [ 28 ]. Briefly, the 16S and 18S (i.e., SSU) and ITS sequences were quality-controlled, and primers were removed with Cutadapt [ 32 ]. For the SSU sequences, the analysis was carried out by following the online pipeline ( https://astrobiomike.github.io/amplicon/16S_and_18S_mixed ) to separate 16S and 18S, with magicblast tool [ 33 ]. Each set of 16S, 18S, and ITS files was filtered independently where the forward and reverse reads of the 16S and ITS datasets were merged, and those of the 18S dataset were concatenated. They were denoised into Amplicon Sequence Variants (ASVs) with the DADA2 v1.28.0 [ 34 ] in R v.4.2.3. Taxonomy was assigned using SILVA NR99 v138.1, PR2 v.5.0.0, and UNITE v.9, respectively. Short unassigned ITS sequences were compared with full-length ITS consensus sequences from our previous study [ 28 ] to aid their assignment. SSU sequences shorter than <350 bp and plastid sequences were deleted. The remaining sequences were decontaminated using microDecon package [ 35 ]. All sequences containing homopolymers longer than 5 bases were also excluded. Finally, remaining chimera were removed using vsearch v.2.22.1 [ 36 ] with the --uchime_ref option and the SILVA non-redundant small subunit database v.138.1 as reference. 2.2 Dataset normalizations As the universal primer amplified also the 18S of the Ascophyllum nodosum host, we were able to use these host reads as an internal reference to detect variations in the proportion of microbial reads: microbial read abundances were divided by the abundance of A. nodosum reads ( Supplementary Figure 1 ) in the same sample and multiplied by the median of all A. nodosum reads across all samples. As the Illumina sequencing is insufficient to discriminate sequences among the Fucaceae family, all ASVs identified as “ Fucus ”, “ Silvetia ” and “Phaeophyceae” were summarized as Ascophyllum for this purpose. SSU ASVs accounting for less than 0.005% of the total reads in all samples were removed from datasets after normalization. As the ITS primers did not amplify the host, the fungal dataset was normalized by the median and the rare ASVs with under 0.01% relative abundance were removed for the three datasets. This threshold was increased compared to the SSU dataset considering that initial number of sequenced reads for the ITS was lower. 2.3 Beta and alpha diversities The beta diversity was estimated from raw datasets using the Bray-Curtis dissimilarity index with non-metric multidimensional scaling (nMDS) ordinations and statistical analyses were based on a PERMANOVA test with 999 permutations. The alpha diversity was further calculated with the Shannon index on normalized data using the microbiome package v1.22.1 and compared using ANOVA tests (stats package v.4.3.3) and the Tukey post-hoc tests (rstatix package v.0.7.2). We removed the sample L1B4_APRIL as it was an outlier in the NMDS plot and because it contained 4 times fewer reads than the average sample, maybe because of a low initial DNA concentration (2.96 ng/μL). Then, the “Sites”, “Algal parts” and “Season” datasets were processed separately. L1I1_MARCH from the “Sites” and “Season” datasets and L1R3_APRIL from “Algae parts” showed inconsistent behavior among multiple analyses such as alpha and beta diversities and barplots. To confirm if these two samples are outliers, a Grubbs test on read abundances in Past4 v.4.15 [ 37 ] was performed and confirmed the hypothesis with a p-value < 0.05. They were removed for the rest of the analyses. 2.4. Core microbiota Intermediate core microbiota was first identified separately for the “Algal parts” and the “Sites and Season” datasets with Venn diagrams (MicEco package v.0.9.19) and pie charts (ggplot2, package v.3.5.0). The intersection of both intermediate core microbiota then formed the global core community of A. nodosum . Only ASVs present in 100% of the samples were considered part of the core. 2.5 Heatmap To distinguish different fungal ecotypes, especially concerning Moheitospora sp. or Mycophycias ascophylli , all three ITS datasets were merged and the sequences were clustered at 99% identity (e.g., Operational Taxonomic Unit, OTU) with vsearch v.2.22.1 [ 36 ]. OTUs were filtered to remove rare OTUs with under 0.01% relative abundance over the entire dataset. Clustered sequences were aligned with Mafft v.7 using the iterative refinement method G-INS-i. The alignment was curated with Gblocks allowing smaller final blocks, gap positions within the final blocks, and less strict flanking positions and a phylogenetic tree was calculated on phylogeny.fr [ 38 ] using PhyML, the GTR+G+I substitution model, and an approximate Likelihood-Ratio test. A heatmap was generated with ggplot2 package v.3.5.0. 2.6 Co-occurrence network For the co-occurrence network, both bacterial and fungal sequences were clustered at 99% identity as above. They were separately normalized by the median and then merged. The merged dataset was filtered with a minimum occurrence larger than 45 reads in at least 3 samples [ 39 ]. Putative associations between covarying OTUs were inferred with SpiecEasi v.1.1.3 [ 40 ] using the “glasso” probabilistic inference method and a lambda.min.ratio of 0.01. Significant covariances were converted to correlations with the function cov2cor [ 39 ]. Only positive edges were considered because most module detection algorithms allow only positive edges. The Leiden module algorithm detection was chosen after comparing eight algorithms (Fast Greedy, Infomap, Label Propagation, Leading Eigenvector, Leiden, Louvain, Spinglass, and Walktrap) using the igraph R package v.2.0.3 [ 41 ] as reported in [ 42 ]. To visualize the network, the Gephi software v.0.10.1 [ 43 ] with the Force Atlas2 algorithm was used. Metabolic capabilities from the taxonomic affiliations of each module were inferred using EsMeCaTa v.0.5.4 [ 44 ]. The metabolic enrichments, implemented in EsMeCaTa, were performed with Geasapy [ 45 ] and the resulting heatmap with Orsum [ 46 ]. Results 1. Shifts in seasonal bacterial diversity and global community patterns across datasets The alpha diversity of the Ascophyllum -associated microbiome in the SSU dataset varied significantly over the year ( Figure 1A ) but not between “sites” and “algal parts” ( Supplementary Figure 2 ). The diversity was the lowest in May 2022 and differed significantly from the samples in November 2021, March, June, September, and October 2022 ( Figure 1A ). Concerning the ITS diversity, no significant differences were detected in any dataset ( Supplementary Figure 3 ). Download figure Open in new tab Figure 1 | Variation of SSU diversity and communities across the three datasets. A. Alpha diversity (Shannon index) of season dataset. Significant differences according to an ANOVA with a Tukey post-hoc test are indicated by different letters next to the bars. B. The NMDS plot concerns the beta diversity of the “Sites” dataset and shows significant differences for months (p-value=0.015) but not for sites. C. The NMDS concerns the “Algal parts” dataset and shows significant differences for the algal parts (p-value=0.001) and month (p-value=0.001). D. The NMDS concerns the “Season” dataset and shows significant differences for season (p-value=0.001) and month (p-value=0.001). Statistical analyses were based on a PERMANOVA test. The SSU communities were stable at a small geographic scale in November 2021 and March 2022 ( Figure 1B ). Along the algal thallus, different groups emerged, separated by months and thallus parts ( Figure 1C ). In January, the base and medium parts were similar, except for one base from April, and distinct from the receptacle and apex. Seasonal variations were observed with distinct cluster between fall/winter and spring/summer ( Figure 1D ). ITS profiling revealed only one significant difference between January and April in the “algal parts” dataset ( Supplementary Figure 4 ). 2. Dynamic and highly diverse bacterial communities: dominance of seven classes The A. nodosum bacterial communities are mainly composed of 7 classes ( Figure 2 ), with Alphaproteobacteria and Gammaproteobacteria consistently dominating. The most abundant genera include an unassigned Alphaproteobacterial genus, Granulosicoccus (Gammaproteobacteria), Pleurocapsa PCC-7319 (Cyanobacteria), Blastopirellula (Planctomycetes), and Algisphaera (Phycisphaerae). Download figure Open in new tab Figure 2 | Variation of the 16S diversities of Ascophyllum nodosum at Pleubian sites (Brittany, France). The boxplot figures at the top show the number of total bacterial reads after normalization with A. nodosum nuclear 18S rDNA reads (see methods section 2.2 ). Here significant differences are estimated with an ANOVA and a Tukey post-hoc test and indicated by different letters next to the bars. No letter means no significant differences. The barplots show the variation in relative abundance of bacteria at the genus levels for the “Site” (A. St1=Site 1, St2=Site 2, St3=Site 3), “Algal parts” (B. Rp=Receptacle, Ap=Apex, Md=Medium, Bs=Base) and “Season” (C). The different shades of blue represent the Alphaproteobacteria, orange the Gammaproteobacteria, dark pink Bacteroidia, green Cyanobacteriia, violet Planctomycetes, yellow Verrucomicrobiae, pink Phycisphaerae, and grey other bacteria in small abundance, not included in the 7 major classes. “NA” corresponds to unassigned taxonomy. Site dataset Bacterial abundances relative to the host remained relatively stable across sites and months ( Figure 2A , boxplot) - despite site-dependent variations in terms of data dispersion - except at site 2 in November, where the unassigned Alphaproteobacterial genus was less abundant than in other samples. Algal parts dataset No significant differences were detected in bacterial read abundance relative to the host, in the different algal parts. Regarding the community structure, Granulosicoccus was the dominant genus in every sample except for the medium and basal parts in January where the unassigned Alphaproteobacterial genus were found in high relative abundance ( Figure 2B ). We also separately analyzed the abundance of host plastid reads relative to nuclear reads across algal parts, revealing a significantly lower proportion of plastid reads in the receptacles ( Supplementary figure 5B ). Seasonal dataset Normalized bacterial read abundance was relatively stable from November 2021 to March 2022 ( Figure 2C , boxplot) before it drastically dropped in April and May and then increased from July to October. These observations are supported by an ANOVA and a Tukey post-hoc test, which yielded significant results in November 2021, December, March, May, July, and September, with all significantly differing from May. From November 2021 to March 2022, the bacterial communities were dominated by one ASV affiliated to an unassigned Alphaproteobacteria, but this group was less abundant from April to November 2022. From August, the genus Pleurocapsa PCC-7319 increased in abundance to reach the highest levels in September, October, and November 2022. The ratio of plastid to nuclear host reads was also highly variable across samples (up to 5-fold difference), although no significant seasonal pattern was detected ( Supplementary figure 5A ). 4. Stable eukaryotic communities dominated by two fungi Fungi belonging to Ascomycota class dominated the 18S sequences (>70% of total reads), in particular representatives of the classes Dothideomycetes and Sordariomycetes ( Figure 3.1 ). Similarly, for the ITS sequences two dominant fungi identified as Mycophycias ascophylli (Dothideomycetes) and Moheitospora sp. (Sordariomycetes, recently renamed Juncigena sp.) were observed in almost every sample ( Figure 3.2 ). Download figure Open in new tab Figure 3 | 18S (1.) and ITS (2.) based-primers diversities of the Ascophyllum nodosum microbiome at Pleubian sites (Brittany, France). The boxplot figures at the top show the number of eukaryotic reads after normalization with A. nodosum nuclear rDNA reads (see methods section 2.2 ). The barplots (1.A, 1.B, 1.C) show the variation of eukaryotes in relative abundance at the genus levels for the “Site” (A. St1=Site 1, St2=Site 2, St3=Site 3), “Algal parts” (B. Rp=Receptacle, Ap=Apex, Md=Medium, Bs=Base) and “Season” (C). The different shades of green represent the Ascomycota class, blue the Phaeophyceae, orange the Arthropoda, dark pink non-assigned genera at the phylum level, and grey other eukaryotes found in small abundance, not included in 4 major families. “NA” corresponds to unassigned phylogeny. The heatmap shows the variation of fungal strains across the three datasets (2.A, 2.B, 2.C). ASV were clustered at 99% (OTUs) of identity to distinct strains. OTUs not included in the three abundant species ( Mycophycias ascophylli , Moheitospora sp. and Lulworthiaceae sp.) were summed in the “Other fungi” category. The stronger the red, the more abundant is the OTUs. The phylogenetic tree on the left was based on a sequence alignment of 22 sequences with the MAFFT (G-INS-I iterative strategy), curation with Gblocks (329 positions remained), and was calculated using PhML (SH-LIKE, GTR+G+I substitution model). Support values under 0.70 have been removed and there is no support value for the ‘other’ category because it represents several fungi. The three most abundant fungal genera were highlighted in the tree: red for Moheitospora sp. , blue for Mycophycias ascophylli, and yellow for Lulworthiaceae sp . The host-normalized eukaryotic read abundance remained stable across datasets ( Figure 3.1.A, 3.1.B, 3.1.C , boxplots), following similar patterns as the bacterial communities, except that seasonal variations were less pronounced ( Figure 3.1.C ). In the algal parts dataset ( Figure 3.1.B ), however, Phaeophyceae_XX;NA were abundant in January in the receptacles and then decreased from apex to basal parts. In April, this group was observed in low abundance, and two of three receptacles were dominated by the Diathrodes genus in the Arthropoda phylum. 5. The A. nodosum core microbiome The four-thallus parts shared 28 ASVs and a total of 60 ASVs were consistently present only on specific algal parts: 16 ASVs were consistently observed only in receptacles, 4 in apex, 10 in medium, and 30 in basal parts ( Figure 4A.1 ). The core community specific to basal parts was the most taxonomically diverse with 7 different classes, followed by that of the receptacles, the medium and the apical parts, the latter comprising only 3 classes in the core community ( Figure 4A.3 ). Download figure Open in new tab Figure 4 | Core microbiome (Bacteria and Eukaryota, SSU) of Ascophyllum nodosum specifically associated with the four algal parts (A.1) and over the four seasons (A.2) (i.e., “intermediate core microbiota”). Numbers in the Venn diagrams represent the number of ASVs. The bubble plot (A.3) represents genera consistently associated specifically with the four algal parts and the four seasons. The global core microbiome is formed by the intersection of both intermediate core microbiota (B.1). The pie chart shows the genera these 22 ASVs belong to and their relative read numbers in the global core microbiome. The color shows the bacterial class. The phylogenetic tree (B.2) was based on a sequence alignment of 22 core sequences with the MAFFT (G-INS-I iterative strategy), curation with Gblocks (373 positions remained), and was calculated using PhML (SH-LIKE, GTR+G+I substitution model). Support values (SH-like as approximate Likelihood-Ratio test) under 0.75 have been removed. The ASV sharing more than 99% of identity, performed with vsearch tool, were highlighted in blue, yellow and orange. In the seasonal dataset, we identified 46 ASVs present on every individual the entire year, and another 45 ASVs were found consistently only in one season (20 ASVs observed only in fall, 10 in winter, 10 in spring, and 5 in summer; Figure 4A.2 ). The fall microbiome also exhibited the most diversified core community with 6 classes, followed by winter, spring and summer ( Figure 4A.3 ). The intersection of the core of the algal parts and season, comprised 22 ASVs with a large dominance of Granulosicoccus , followed by an assigned Alphaproteobacteria ASV ( Figure 4B.1 ). Among the 22 ASVs, two pairs of Granulosicoccus and Roseibacillus shared more than 99% of identity ( Figure 4B.2 ). 6. Putative microbial interactions: the co-occurrence network analysis We investigated groups of co-varying OTUs that could share the same ecological niches via a co-occurrence network analysis. After merging SSU OTUs with ITS OTUs, the final matrix contained 12,090 OTUs. After filtering out low-abundance OTUs, 531 OTUs remained for co-occurrence analyses, representing 99.3% of the total reads in the dataset. A total of 520 OTUs were correlated with 4130 positive (72.6%) and 1552 negative (27.3%) edges. Only 11 OTUs were isolated. For the visualization, we only used the positive correlations. In the inferred network, 8 modules comprising OTUs with similar distribution patterns were identified ( Figure 5A, B ). Module 4 was dominant with 88.1% of reads (including Ascophyllum , Mycophycias , Moheitospora and Granulosicoccus ), followed by seven modules representing between 6.5% and less than 1% of the reads. Among the 19 OTUs found in the core microbiome, 14 OTUs fell into module 4, followed by module 8 with 3 OTUs, and module 1 and 6 (1 OTUs each) ( Figure 5A ). Some of the core genera were represented in more than one module, such as Granulosicoccus in modules 4 and 6. Download figure Open in new tab Figure 5 | Co-occurrence analysis of the Ascophyllum nodosum microbiota (SSU and ITS). A. Each node represented an OTU (ASVs clustered at 99% identity) while each edge represents a positive correlation obtained from the covariance matrix calculated with SPIEC-EASI. The nodes are colored according to the module they were assigned to, and node size illustrates the betweenness centrality. The network was represented using Gephi and the Force Atlas layout algorithm. Yellow and black stars represent OTUs found in the core microbiome. Node with indicated names seem important as high betweenness centrality or directly in association with Ascophyllum. B. Specific interactions with Ascophyllum nodosum , Mycophycias ascophylli, and Moheitospora sp. are illustrated by selecting edges with weight < 0.2. C. Presentation of the 8 modules detected with the Leiden algorithm, ordered by the trend observed in algal part abundance, with the number of OTUs (n) and the relative abundance of each module. The taxonomic composition of the module at class level is illustrated by the pie charts, and the relative microbial abundance across seasons and algal parts is shown in the plots to the right. The Y axis represent the average ASVs abundance found in each algal part or across each month (X axis), where each sample was normalized by the total sum of the ASV in all samples, so each ASV has the same weight. The functional predictions based on the taxonomy were estimated with EsMeCaTa and the percentage of enriched functions in each module is given in the last column. By adding the measure of the betweenness centrality, we identified key nodes that have an impact on the network structure. The larger the node, the more important the OTUs is likely to be within the interactions. Few larger nodes were observed in the modules 2 and 3, whereas the largest were found in the module 5, associated with the Maribacter and Roseobacter genera. Mycophycias ascophylli and other members of the core microbiome were amongst the largest nodes in module 4 ( Figure 6A and Supplementary table 1). The strongest associations of the host Ascophyllum were with the OTU_3 of Granulosicoccus , Mycophycias , Rubritalea , Yersinia , Algisphaera , and one unclassified bacterium ( Figure 5B ). Download figure Open in new tab Figure 6 | The heatmap shows the variation of genera found in the core microbiome of Ascophyllum nodosum across the monthly survey. The OTUs come from the co-occurrence network ordered by a Pearson correlation. The stronger the red, the more abundant is the OTUs (log10). OTU names in red colors indicate genera with different OTUs that were characteristic for warmer and colder months. We also investigated the correlation between module distribution and metadata such as seasons and algal tissues ( Figure 5C ). Microbes associated with modules 1, 2, and 3 were linked to the basal part of the alga whereas module 5, observed at the opposite in the network, was linked to the receptacles from November 2021 to March 2022. Modules 4, 6, and 7 were present all the year. We further investigated the metabolic enrichments based on the taxonomic composition of each module to understand why these microorganisms co-varied together. Among 23,585 predicted functional annotations (GO terms or EC numbers), few were enriched in specific modules (maximum 0.13% for module 1), for example module 5, which was enriched in the siderophore metabolic process ( Supplementary Figure 6 and Supplementary Table 2). 7. Seasonal dynamics of OTUs within the stable genera of the core microbiome Prompted by the observation of OTUs corresponding to the same core genera in different modules, we also performed detailed investigation of seasonal variations among the OTUs belonging to genera of the core microbiome. Three groups of OTUs emerged ( Figure 6 ): one exhibiting a stable pattern over the year, one most abundant in colder months between November 2021 and March 2022, and another one mostly found between April and November 2022. Our analyses confirm that some of core genera had OTUs in the different groups. Notably, the Granulosicoccus OTU_3 was highly stable, while OTUs 42 and 44 were most abundant in winter, and OTU_51 was most abundant in summer. Similarly, for Rubripirellula we observed OTU_86 with a pattern of highest abundance in winter, OTU_57 was dominant in summer, and OTU_145 was relatively stable. This variability suggests the succession of different ecotypes within the core genera. Discussion 1. Bacterial vs fungal communities: complex and dynamic vs simple and stable In this study, we explored the Ascophyllum microbiome from multiple angles, examining its composition under natural conditions across nearby sampling sites, different parts of the algal thallus, and season over a one-year in situ survey. Although the eukaryotic component was observed in A. nodosum , the bacteria dominated (98% of the symbiont SSU reads, but extraction biases and SSU copy number differences are likely to impact this figure). Archaeal sequences (<0.001%) were rare and mostly chimeras, suggesting that archaea are absent in A. nodosum, unlike in the brown alga Laminaria rodriguezii [ 47 ]. This supports the idea that bacteria are key microbial symbionts [ 2 ]. A. nodosum harbors a complex and dynamic bacterial community, in contrast to the stable and simple fungal communities. The bacterial communities comprised 7 different classes with multiple genera, which showed diverse dynamics between months. Microbial composition varied with season and tissues types, aligning with previous studies on macroalgal-associated bacteria [ 27 , 48 – 51 ]. Microbial dynamics across tissue types could be related to the physiology and the age of the alga. Given Ascophyllum ’s apical growth [ 10 ], the apex likely host an establishing microbiome, while the basal region likely harbors a more mature microbiome. In contrast, eukaryotic analysis revealed the predominance of only two fungi: Mycophycias ascophylli, and Moheitospora sp. (recently renamed Juncigena sp.), belonging to Dothideomycetes and Sordariomycetes, respectively. Our study confirms that Moheitospora sp. is a central component of the fungal community in A. nodosum as shown previously by Vallet et al. [ 19 ] through culture. Both of these two fungi could contribute to the so-called “lichenization” of A. nodosum [ 14 ]. Unlike A. nodosum , other brown and red macroalgae usually exhibit higher fungal diversity [ 24 , 52 , 53 ]. This suggests a regulation of fungal colonization originating either from the host, the bacteria, or the fungi. Putative antifungal molecules could be synthetized by fungi, as reported in the marine fungi Aspergillus sp., and Penicillium sp. [ 54 – 56 ]. Bacteria may also control fungal presence, since antibiotic treatment of A. nodosum result in M. ascophylli hyphae penetrating algal cells and degrading the host [ 22 ]. Genome sequencing of these symbionts could provide further indication regarding these hypotheses. The role of Moheitospora sp. could also be tested experimentally using its available isolate [ 19 ] as shown for M. ascophylli [ 16 ]. Another perspective is to look at the viral partners as they remain largely understudied, with only a few reports detailing the viral communities associated with seaweeds [ 57 – 61 ]. This lack of knowledge of viruses may limit our capacity to understand the full spectrum of processes occurring within the A. nodosum holobiont. 2. Host normalization highlights epidermal shedding as a potential driver of bacterial dynamics over the year In general, metabarcoding analysis only provides relative quantification of the microbiome, although qPCR has been used to mitigate this restriction [ 27 , 62 ]. Here, we circumvent this limitation without further experimentation, by leveraging our access to the A. nodosum 18S rRNA gene allowing us to calculate the proportion of microbial to host reads. We also evaluated the plastid 16S reads for this normalization, but found them to differ up to 5-fold compared the nuclear marker, demonstrating that the two genes cannot be used interchangeably for normalization. Indeed, at least in Archaeplastida, chloroplast abundance does not only vary with tissue type but may be actively regulated by plants depending on the environment [ 63 ], making the 18S rRNA gene the likely better choice for normalization. Based on the proportion of microbial to nuclear host reads, we conclude that the abundance of A. nodosum -associated bacteria relative to the host varies over the year. This is globally in line with a previous study by Brunet et al. [ 27 ], but our analysis also highlights a drastic drop in bacterial abundance in May, possibly due to epidermal shedding. This process was first observed in 1981 where small pieces of A. nodosum were detached occasionally from the thallus surface [ 64 ] and considered an adaptation for long-lived macroalgae to control epiphytes [ 65 , 66 ]. The phenomenon was observed [ 67 ] and quantified weekly, revealing regular fluctuations of the surface microbiome throughout the warmer months of the year [ 68 ]. However, we observe that the reduction in the ratio of microbial to host reads did not concern all microbes. A large proportion of the unassigned Alphaproteobacterial ASV and other bacteria seem to be impacted by the process, while, e.g., one Granulosicoccus OTUs or the two main fungi remained abundant over the year. In the context of shedding, this would mean that these consistently abundant organisms are either fast colonizers rapidly taking hold on the free surface, or that these microbes also penetrate into deeper cell layers and are less affected by shedding. The latter is likely true at least for the most abundant of the two fungi, M. ascophylli , which has even been found intracellularly [ 8 ] or in the intercellular spaces embedded in the thick cell walls of the medulla and cortex tissues [ 69 ]. Regardless of the reasons driving these different patterns, our observations suggest that even within a genus, distribution patterns and thus the underlying ecological niches and strategies of different OTUs differ. Fine taxonomic distinction will therefore be crucial in future investigation of these taxa. 3. Stability revealed: 22 key ASVs in the core microbiome of A. nodosum Identifying core microbiomes has become widespread to identify potential key actors in host biology. In our study, the metric for quantifying the core microbiome is 100% occupancy, meaning that microbial taxa need to be present in every sample to be considered in the core. While this is a very stringent measure [ 70 ], the overall high stability of the A. nodosum microbiome in combination with the high sequencing coverage make this a viable choice for our dataset. Among the 22 core amplicon sequence variants (ASVs) present in all Ascophyllum individuals, several genera, in particular Granulosicoccus , Litorimonas, Hellea, Blastopirellula, and Roseibacillus are generalists that have been observed on a diverse array of brown, green, and red macroalgae [ 27 , 50 , 51 , 71 – 78 ]. The ubiquity of the genus Granulosicoccus in several brown algae suggests that it is especially well-suited for life as a symbiont. Metagenome-assembled genomes (MAG) of Granulosicoccus were annotated in the bull kelp Nereocystis luetkeana , and harbored genes for synthesizing cobalamin possibly providing this and other vitamins to the algal host [ 79 ]. However, evidence of metabolic exchanges between Granulosicoccus and the algal host has not yet been completed. Our core microbiome analyses also consolidate the tight association of M. ascophylli (Dothideomycetes;NA) with the Ascophyllum , as all samples contained the fungus in high abundance. The symbiotic association between Ascophyllum and this fungus has already been described and reviewed [ 8 , 14 , 53 , 80 – 83 ]. Previous light microscopy analyses have shown M. ascophylli to be present at the highest concentration in receptacles [ 15 ], which is not consistent with our findings, but this likely point to a limitation of normalization method – indeed the male receptacles produce very high numbers of sperm cells each with a complete copy of the host genome thus potentially biasing the number of host reads used as a reference in this case. Regardless of these differences, the constant presence of the fungus is consistent with its supposed role in host development, fitness, and protection against biotic and abiotic stresses [ 16 , 84 ]. 4. Co-occurrence network reveals microbial contributions to energy synthesis in receptacles Our co-occurrence networks analysis revealed that A. nodosum was not the central node in the network, and the identified central nodes are not those in association with the host, except for M. ascophylli . Very few nodes directly in association with the seaweed. This suggests that Ascophyllum may be a surface for few key microbial colonizers (whether endophytic or epiphytic), as Mycophycias and Granulosicoccus, which may be the base for the biofilm formation. These analyses also highlight module 4, which appears to be key module, as it is the most abundant module in terms of read coverage and contains many species present in the core microbiome. Module 4 could be linked to early-colonizing or intracellular microorganisms, such as those not responding to shedding phenomenon, and may include functions that contribute to the holobiont’s overall stability, while e.g. module 5 may be linked to reproduction. Because modules regroup highly connected OTUs that share the same environment and potentially exhibit coherent ecological strategies [ 85 ], we expected to find the modules to be enriched in functions related to niche adaptation. One example of such adaptations concerned the functioning of receptacles. In the reproductive period, energy should be allocated to reproduction and not to vegetative growth [ 13 ]. Iron, essential for ATP production in photosynthesis and respiration, is present in low concentration and poor solubility in seawater [ 86 , 87 ]. To facilitate iron acquisition, many bacteria synthesize siderophores [ 88 ]. Functions present in the module 5 were enriched in siderophore metabolic processes (GO: 0009237, Supplementary figure 6 ), with Roseobacter and Marinomonas, two bacteria likely possessing genes involved in siderophore production [ 89 ] (Supplementary Table 1), occupying structuring positions in the receptacles. As these enriched metabolic functions are merely predictions based on the microbial taxonomy, they need to be confirmed by complementary approaches, such as metagenomic analyses. Overall, our analyses highlight that very few metabolic functions are enriched across modules, underscoring the functional stability of the holobiont. Conclusion and perspectives Our study provides a comprehensive overview of the microbial communities associated with Ascophyllum nodosum, highlighting the complexity of its holobiont and the dynamic interactions between its bacterial and fungal components. Using metabarcoding analysis with universal primers and using the host 18S for normalization, we found that Ascophyllum harbors a predominantly bacterial microbiota, with no detectable archaea. Community composition and structure vary across algal tissues and seasons, including a notable drop in bacterial abundance in May, potentially linked to epidermal shedding. In contrast, the fungal communities in Ascophyllum appear stable and simple, primarily consisting of M. ascophylli and Moheitospora sp., both of which appear to maintain a close association with the host throughout the year. Our identification of a core microbiome with 22 ASVs suggests the importance of certain microbial taxa in maintaining host biology, such as the genus Granulosicoccus . While the latter genus demonstrates significant stability, our findings also reveal that within this genus different OTUs are subject to seasonal variation, highlighting its intraspecific diversity and adaptability. The co-occurrence network and its module-based analyses add another layer of complexity to our understanding, showing how microbial groups interact in response to ecological niches, such as parts of the thallus or varying seasonal conditions. Further investigation using metagenomic and functional approaches will be key to uncovering the adaptations and the roles of key microorganisms and understanding their contributions to the overall health and stability of the A. nodosum holobiont. Author contributions CR, CL and SD designed and conceptualized the research. CR collected samples, performed laboratory work, analyzed data and wrote the manuscript. GT and EL performed sequencing preparations. SB and AB implemented the EsMeCaTa tool and contributed to the functional analyses. SR helped for sampling and DNA extractions. PP discussed around algal physiology, provided funding and reviewed the manuscript. CL and SD collected samples, supervised the research, reviewed the manuscript and provided funding. All authors have read and approved the manuscript. Funding This work was funded by the SEABIOZ ANR project (AAPG2020, ANR-20-CE43-0013) and has received financial support from the Centre National de la Recherche Scientifique (CNRS) through the MITI interdisciplinary programs (ALGOMETABIONTE PRIME project). It has also partially been supported by the CNRS and Sorbonne University through the UMR8227 annual financial supports. Data availability The raw sequence reads and metadata are available in the European Nucleotide Archive (ENA) database under BioProject PRJEB85975 and sample accession numbers ERS23809686-ERS23809790. ASV table with final abundances, taxonomy, and sequences are available in supplementary tables. Scripts are available on GitHub ( https://github.com/rssco/novaseq_ascophyllum ). Supplementary figures Download figure Open in new tab Supplementary Figure 1 | Distribution of A. nodosum read abundance across the three datasets Download figure Open in new tab Supplementary figure 2 | Alpha diversity for SSU (Shannon index) of the sites (A) and the algal part datasets (B). No significant differences are observed according to an ANOVA test. Download figure Open in new tab Supplementary figure 3 | Alpha diversity for ITS (Shannon index) of the sites (A), the algal part (B) and season (C) datasets. No significant differences are observed according to an ANOVA test. Download figure Open in new tab Supplementary figure 4 | Beta diversity of the fungal communities across the four algal parts, showing significant results between month (p-value=0.010). Statistical analyses were based on a PERMANOVA test. Download figure Open in new tab Supplementary figure 5 | Following the ratio of plastid read to host reads over each month of the year (A) and across algal thallus parts (B). Significant differences according to an ANOVA (non-significant for season, p = 0.0005 for algal parts) test with a Tukey post-hoc test are indicated by different * Download figure Open in new tab Supplementary figure 6 | Metabolic enrichment of GO terms and EC number predictions of 5 modules present in the co-occurrence network. Annotation predictions were based on taxonomic affiliation with EsMeCaTa tool. Module 3, 6 and 7 are not enriched and not represented here. Metabolites in Q1 corresponds to high enrichment. Supplementary tables Supplementary table 1 | Betweenness centrality of each ASV present in the co-occurrence network and their attributed module. The abundance of each ASV is also indicated. Supplementary table 2 | Amplicon sequences variants module 5 which are estimate to have gene in siderophore metabolic process (GO:0009237). Supplementary table 3 | ASV table with abundance, taxonomy and references for SSU dataset Supplementary table 4 | OTU table with abundance, taxonomy and references for ITS dataset. Acknowledgments We would like to thank Cécile le Guillard (Plant Nutrition R&D Department, Centre Mondial de l’Innovation of Groupe Roullier, 35400 Saint-Malo, France), Soizic Prado and Gautier Demoulinger (Molecules of Communication and Adaptation of Microorganisms, UMR 7245, MNHN, CNRS, 75000 Paris, France and Plant Nutrition R&D Department, Centre Mondial de l’Innovation of Groupe Roullier, 35400 Saint-Malo, France) for discussions and help during sampling. We would like to thank Nicolas Henry (ABiMS, FR2424, Station Biologique de Roscoff, Sorbonne Université, CNRS, 29680 Roscoff, France) for discussions on universal primers and Loïs Maignien (Biology and Ecology of Deep-Sea Ecosystems, IUEM, 29280 Plouzané, France) for his help with co-occurrence network. We thank Gaëtan Burgaud (LUBEM, Université de Bretagne Occidentale, 29280 Plouzané, France) for his help for ITS primer choices and taxonomic assignations and for his advices during manuscript reviewing. We would like to thank Pauline Hamon-Guiraud (Dyliss, Inria, UMR 6074, CNRS, University of Rennes, Rennes, France) for help in EsMeCaTa tool. We thank François Thomas (Integrative Biology of Marine Models, UMR 8227, Station Biologique de Roscoff, Sorbonne Université, CNRS, 29680 Roscoff, France) for discussions around microorganisms and for reviewing this manuscript. We also thank for their support, the Genomer platform and the Roscoff Bioinformatics platform, ABIMS ( http://abims.sb-roscoff.fr ), respectively part of EMBRC-France (financially supported by the Investments of the Future program, ANR-10-INSB-02) and of the Institut Français de Bioinformatique (ANR-11-INBS-0013), and of the BioGenouest network. We are most grateful to the Genomics Core Facility GenoA, member of Biogenouest and France Genomique and to the Bioinformatics Core Facility BiRD, member of Biogenouest and Institut Français de Bioinformatique (IFB) (ANR-11-INBS-0013) for the use of their resources and their technical support. Footnotes coralie.rousseau{at}sb-roscoff.fr , gtanguy{at}sb-roscoff.fr , erwan.legeay{at}sb-roscoff.fr , samuel.blanquart{at}inria.fr , arnaud.belcour{at}inria.fr , sylvie.rousvoal{at}sb-roscoff.fr , philippe.potin{at}sb-roscoff.fr , catherine.leblanc{at}sb-roscoff.fr , simon.dittami{at}sb-roscoff.fr , Reference 1. ↵ Cotas J et al. Ecosystem services provided by seaweeds . Hydrobiology 2023 ; 2 : 75 – 96 . doi: 10.3390/hydrobiology2010006 OpenUrl CrossRef 2. ↵ Egan S et al. The seaweed holobiont: understanding seaweed–bacteria interactions . FEMS Microbiol Rev 2013 ; 37 : 462 – 476 . doi: 10.1111/1574-6976.12011 OpenUrl CrossRef PubMed Web of Science 3. ↵ Wahl M et al. The second skin: Ecological tole of epibiotic biofilms on marine organisms . Front Microbiol 2012 ; 3 . doi: 10.3389/fmicb.2012.00292 OpenUrl CrossRef PubMed 4. ↵ Singh RP , Reddy CRK . Seaweed-microbial interactions: key functions of seaweed-associated bacteria . FEMS Microbiol Ecol 2014 ; 88 : 213 – 230 . doi: 10.1111/1574-6941.12297 OpenUrl CrossRef PubMed 5. ↵ Dittami SM et al. A community perspective on the concept of marine holobionts: current status, challenges, and future directions . PeerJ 2021 ; 9 : e10911 . doi: 10.7717/peerj.10911 OpenUrl CrossRef PubMed 6. van der Loos LM , Eriksson BK , Falcão Salles J . The macroalgal holobiont in a changing sea . Trends Microbiol 2019 ; 27 : 635 – 650 . doi: 10.1016/j.tim.2019.03.002 OpenUrl CrossRef PubMed 7. ↵ Saha M et al. Progress and future directions for seaweed holobiont research . New Phytol 2024 ; 244 : 364 – 376 . doi: 10.1111/nph.20018 OpenUrl CrossRef PubMed 8. ↵ Garbary DJ et al. Ascophyllum and its symbiontsA complex symbiotic community on North Atlantic Shores. Algal and Cyanobacteria Symbioses . WORLD SCIENTIFIC (EUROPE ), 2015 , 547 – 572 . 9. Marbà N et al. Climate change stimulates the growth of the intertidal macroalgae Ascophyllum nodosum near the northern distribution limit . Ambio 2017 ; 46 : 119 – 131 . doi: 10.1007/s13280-016-0873-7 OpenUrl CrossRef 10. ↵ Pereira L et al. A concise review of the brown macroalga Ascophyllum nodosum (Linnaeus) Le Jolis . J Appl Phycol 2020 ; 32 : 3561 – 3584 . doi: 10.1007/s10811-020-02246-6 OpenUrl CrossRef 11. ↵ Dudgeon S , Petraitis PS . Scale-Dependent Recruitment and Divergence of Intertidal Communities . Ecology 2001 ; 82 : 991 – 1006 . doi: 10.1890/0012-9658(2001)082 [0991:SDRADO]2.0.CO;2 OpenUrl CrossRef Web of Science 12. ↵ Dudgeon S , Petraitis PS . First year demography of the foundation species, Ascophyllum nodosum, and its community implications . Oikos 2005 ; 109 : 405 – 415 . doi: 10.1111/j.0030-1299.2005.13782.x OpenUrl CrossRef Web of Science 13. ↵ Liu X et al. Seaweed reproductive biology: environmental and genetic controls . Bot Mar 2017 ; 60 : 89 – 108 . doi: 10.1515/bot-2016-0091 OpenUrl CrossRef 14. ↵ Kohlmeyer J , Kohlmeyer E . Is Ascophyllum nodosum Lichenized ? 1972 ; 15 : 109 – 112 . doi: 10.1515/botm.1972.15.2.109 OpenUrl CrossRef 15. ↵ Garbary DJ , Gautam A . The Ascophyllum , Polysiphonia , Mycosphaerella symbiosis. I . Population Ecology of Mycosphaerella from Nova Scotia . 1989 ; 32 : 181 – 186 . doi: 10.1515/botm.1989.32.2.181 OpenUrl CrossRef 16. ↵ Garbary DJ , London JF . The Ascophyllum , Polysiphonia , Mycosphaerella symbiosis V . Fungal infection protects A. nodosum from desiccation . 1995 ; 38 : 529 – 534 . doi: 10.1515/botm.1995.38.1-6.529 OpenUrl CrossRef 17. ↵ Rawlence DJ , Taylor ARA . The rhizoids of Polysiphonia lanosa . Can J Bot 1970 ; 48 : 607 – 611 . doi: 10.1139/b70-083 OpenUrl CrossRef 18. ↵ Lining T , Garbary DJ . The Ascophyllum / Polysiphonia / Mycosphaerella Symbiosis. III . Experimental Studies on the Interactions between P. lanosa and A. nodosum . 1992 ; 35 : 341 – 350 . doi: 10.1515/botm.1992.35.4.341 OpenUrl CrossRef 19. ↵ Vallet M et al. Chemically-mediated interactions between macroalgae, their fungal endophytes, and protistan pathogens . Front Microbiol 2018 ; 9 : 3161 . doi: 10.3389/fmicb.2018.03161 OpenUrl CrossRef 20. ↵ Chan ECS , McManus EA . Distribution, characterization, and nutrition of marine microorganisms from the algae Polysiphonia lanosa and Ascophyllum nodosum . Can J Microbiol 1969 ; 15 : 409 – 420 . doi: 10.1139/m69-073 OpenUrl CrossRef PubMed Web of Science 21. ↵ Cundell AM , Sleeter TD , Mitchell R . Microbial populations associated with the surface of the brown alga Ascophyllum nodosum . Microb Ecol 1977 ; 4 : 81 – 91 . OpenUrl CrossRef PubMed Web of Science 22. ↵ Fries L . Ascophyllum nodosum (phaeophyta) in axenic culture and its response to the endophytic fungus Mycosphaerella ascophylli and epiphytic bacteria . J Phycol 1988 ; 24 : 333 – 337 . doi: 10.1111/j.1529-8817.1988.tb04475.x OpenUrl CrossRef 23. ↵ Martin M et al. The cultivable surface microbiota of the brown alga Ascophyllum nodosum is enriched in macroalgal-polysaccharide-degrading bacteria . Front Microbiol 2015 ; 6 : 1487 . doi: 10.3389/fmicb.2015.01487 OpenUrl CrossRef 24. ↵ Tourneroche A et al. Bacterial–fungal interactions in the kelp endomicrobiota drive autoinducer-2 quorum sensing . Front Microbiol 2019 ; 10 : 1693 . doi: 10.3389/fmicb.2019.01693 OpenUrl CrossRef PubMed 25. ↵ Zhu X et al. Impact of high pressure treatment on shelf life and microbial profile of wild harvested Ascophyllum nodosum and aquacultured Alaria esculenta during storage . LWT 2022 ; 170 : 114022 . doi: 10.1016/j.lwt.2022.114022 OpenUrl CrossRef 26. ↵ Ihua M et al. Microbial population changes in decaying Ascophyllum nodosum result in macroalgal-polysaccharide-degrading bacteria with potential applicability in enzyme-assisted extraction technologies . Mar Drugs 2019 ; 17 : 200 . doi: 10.3390/md17040200 OpenUrl CrossRef PubMed 27. ↵ Brunet M et al. Year-round quantification, structure and dynamics of epibacterial communities from diverse macroalgae reveal a persistent core microbiota and strong host specificities. 2024 . bioRxiv , 2024 ., 2024.07.22.604553 28. ↵ Rousseau C , et al. Comparison of short and long-read metabarcoding sequencing: challenges and solutions for plastid read removal and microbial community exploration of seaweed samples . 2024 . 2024 . 29. ↵ Parada AE , Needham DM , Fuhrman JA . Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples . Environ Microbiol 2016 ; 18 : 1403 – 1414 . doi: 10.1111/1462-2920.13023 OpenUrl CrossRef PubMed 30. ↵ Rämä T et al. Fungi sailing the arctic ocean: Speciose communities in North Atlantic driftwood as revealed by high-throughput amplicon sequencing . Microb Ecol 2016 ; 72 : 295 – 304 . doi: 10.1007/s00248-016-0778-9 OpenUrl CrossRef PubMed 31. ↵ Taylor DL et al. Accurate estimation of fungal diversity and abundance through improved lineage-specific primers optimized for Illumina amplicon sequencing . Appl Environ Microbiol 2016 ; 82 : 7217 – 7226 . doi: 10.1128/AEM.02576-16 OpenUrl Abstract / FREE Full Text 32. ↵ Martin M . Cutadapt removes adapter sequences from high-throughput sequencing reads . EMBnet.journal 2011 ; 17 : 10 – 12 . doi: 10.14806/ej.17.1.200 OpenUrl CrossRef PubMed 33. ↵ Boratyn GM et al. Magic-BLAST, an accurate RNA-seq aligner for long and short reads . BMC Bioinformatics 2019 ; 20 : 405 . doi: 10.1186/s12859-019-2996-x OpenUrl CrossRef PubMed 34. ↵ Callahan BJ et al. DADA2: High-resolution sample inference from Illumina amplicon data . Nat Methods 2016 ; 13 : 581 – 583 . doi: 10.1038/nmeth.3869 OpenUrl CrossRef PubMed 35. ↵ McKnight DT et al. microDecon: a highly accurate read-subtraction tool for the post-sequencing removal of contamination in metabarcoding studies . Environ DNA 2019 ; 1 : 14 – 25 . doi: 10.1002/edn3.11 OpenUrl CrossRef 36. ↵ Rognes T et al. VSEARCH: a versatile open source tool for metagenomics . PeerJ 2016 ; 4 : e2584 . doi: 10.7717/peerj.2584 OpenUrl CrossRef PubMed 37. ↵ Hammer O , Harper DAT , Ryan PD . PAST: Paleontological Statistics Software Package for education and data analysis . Palaeontologia Electronica 2001 ; 4 : 9 . 38. ↵ Dereeper A et al. Phylogeny.fr: robust phylogenetic analysis for the non-specialist . Nucleic Acids Res 2008 ; 36 : W465 - 469 . doi: 10.1093/nar/gkn180 OpenUrl CrossRef PubMed Web of Science 39. ↵ Lemonnier C et al. Linking spatial and temporal dynamic of bacterioplankton communities with ecological strategies across a coastal frontal area . Front Mar Sci 2020 ; 7 . doi: 10.3389/fmars.2020.00376 OpenUrl CrossRef 40. ↵ Kurtz ZD et al. Sparse and compositionally robust inference of microbial ecological networks . PLOS Comput Biol 2015 ; 11 : e1004226 . doi: 10.1371/journal.pcbi.1004226 OpenUrl CrossRef PubMed 41. ↵ igraph – Network analysis software . https://igraph.org/ . 42. ↵ Kaneko H et al. Predicting global distributions of eukaryotic plankton communities from satellite data . ISME Commun 2023 ; 3 : 101 . doi: 10.1038/s43705-023-00308-7 OpenUrl CrossRef PubMed 43. ↵ Bastian M , Heymann S , Jacomy M . Gephi: An open source software for exploring and manipulating networks . Proc Int AAAI Conf Web Soc Media 2009 ; 3 : 361 – 362 . doi: 10.1609/icwsm.v3i1.13937 OpenUrl CrossRef 44. ↵ Belcour A , et al. EsMeCaTa: Estimating metabolic capabilities from taxonomic affiliations. 2022 . bioRxiv , 2022 ., 2022.03.16.484574 45. ↵ Fang Z , Liu X , Peltz G . GSEApy: a comprehensive package for performing gene set enrichment analysis in Python . Bioinformatics 2023 ; 39 : btac757 . doi: 10.1093/bioinformatics/btac757 OpenUrl CrossRef PubMed 46. ↵ Ozisik O , Térézol M , Baudot A. orsum: a Python package for filtering and comparing enrichment analyses using a simple principle . BMC Bioinformatics 2022 ; 23 : 293 . doi: 10.1186/s12859-022-04828-2 OpenUrl CrossRef PubMed 47. ↵ Trias R et al. Abundance and composition of epiphytic bacterial and archaeal ammonia oxidizers of marine red and brown macroalgae . Appl Environ Microbiol 2012 ; 78 : 318 – 325 . doi: 10.1128/AEM.05904-11 OpenUrl Abstract / FREE Full Text 48. ↵ Staufenberger T et al. Phylogenetic analysis of bacteria associated with Laminaria saccharina . FEMS Microbiol Ecol 2008 ; 64 : 65 – 77 . doi: 10.1111/j.1574-6941.2008.00445.x OpenUrl CrossRef PubMed Web of Science 49. Paix B et al. A multi-omics analysis suggests links between the differentiated surface metabolome and epiphytic microbiota along the thallus of a mediterranean seaweed holobiont . Front Microbiol 2020 ; 11 : 494 . doi: 10.3389/fmicb.2020.00494 OpenUrl CrossRef PubMed 50. ↵ Quigley CTC et al. Bacterial communities show algal host ( Fucus spp. ) zone differentiation across the stress gradient of the intertidal zone . Front Microbiol 2020 ; 11 : 563118 . doi: 10.3389/fmicb.2020.563118 OpenUrl CrossRef 51. ↵ Lemay MA et al. Kelp-associated microbiota are structured by host anatomy . J Phycol 2021 ; 57 : 1119 – 1130 . doi: 10.1111/jpy.13169 OpenUrl CrossRef PubMed 52. ↵ Bonthond G et al. Fungal endophytes vary by species, tissue type, and life cycle stage in intertidal macroalgae . J Phycol 2022 ; 58 : 330 – 342 . doi: 10.1111/jpy.13237 OpenUrl CrossRef PubMed 53. ↵ Rousseau C et al. A review on the chemical ecology of the Fucaceae holobionts: from fundamental knowledge to applications . Comptes Rendus Chim 2024 ; 26 : 1 – 25 . doi: 10.5802/crchim.271 OpenUrl CrossRef 54. ↵ Wang J et al. Antifungal new oxepine-containing alkaloids and xanthones from the deep-sea-derived fungus Aspergillus versicolor SCSIO 05879 . J Agric Food Chem 2016 ; 64 : 2910 – 2916 . doi: 10.1021/acs.jafc.6b00527 OpenUrl CrossRef 55. Dasanayaka S a. HK , et al. New dibenzodioxocinone and pyran-3,5-dione derivatives from the deep-sea-derived fungus Penicillium canescens SCSIO z053 . J Asian Nat Prod Res 2020 ; 22 : 338 – 345 . doi: 10.1080/10286020.2019.1575819 OpenUrl CrossRef PubMed 56. ↵ Stal LJ , Cretoiu MS Burgaud G , et al. Marine Fungi . In: Stal LJ , Cretoiu MS (eds.), The Marine Microbiome . Cham : Springer International Publishing , 2022 , 243 – 295 . 57. ↵ Lachnit T , Thomas T , Steinberg P . Expanding our understanding of the seaweed holobiont: RNA viruses of the red alga Delisea pulchra . Front Microbiol 2016 ; 6 . doi: 10.3389/fmicb.2015.01489 OpenUrl CrossRef PubMed 58. McKeown DA et al. Phaeoviral infections are present in Macrocystis , Ecklonia and Undaria (Laminariales) and are influenced by wave exposure in Ectocarpales . Viruses 2018 ; 10 : 410 . doi: 10.3390/v10080410 OpenUrl CrossRef PubMed 59. Beattie DT et al. Novel ssDNA viruses detected in the virome of bleached, habitat-forming kelp Ecklonia radiata . Front Mar Sci 2018 ; 4 : 441 . doi: 10.3389/fmars.2017.00441 OpenUrl CrossRef 60. Chiba Y et al. Viral RNA genomes identified from marine macroalgae and a diatom . Microbes Environ 2020 ; 35 : ME20016 . doi: 10.1264/jsme2.ME20016 OpenUrl CrossRef PubMed 61. ↵ van der Loos LM et al. Highly divergent CRESS DNA and picorna-like viruses associated with bleached thalli of the green seaweed Ulva . Microbiol Spectr 2023 ; 11 : e00255 – 23 . doi: 10.1128/spectrum.00255-23 OpenUrl CrossRef 62. ↵ Brunet M et al. Seasonal dynamics of a glycan-degrading flavobacterial genus in a tidally mixed coastal temperate habitat . Environ Microbiol 2023 ; 25 : 3192 – 3206 . doi: 10.1111/1462-2920.16505 OpenUrl CrossRef 63. ↵ Belbin FE et al. Integration of light and circadian signals that regulate chloroplast transcription by a nuclear-encoded sigma factor . New Phytol 2017 ; 213 : 727 – 738 . doi: 10.1111/nph.14176 OpenUrl CrossRef PubMed 64. ↵ Filion-Myklebust C , Norton TA . Epidermis shedding in the brown seaweed Ascophyllum nodosum (L.) Le Jolis, and its ecological significance . 1981 ; 2 : 45 – 51 . OpenUrl 65. ↵ Moss BL . The control of epiphytes by Halidrys siliquosa (L.) Lyngb. (Phaeophyta, Cystoseiraceae) . Phycologia 1982 ; 21 : 185 – 188 . doi: 10.2216/i0031-8884-21-2-185.1 OpenUrl CrossRef Web of Science 66. ↵ Russell G , Veltkamp CJ . Epiphyte survival on skin-shedding macrophytes . Mar Ecol Prog Ser 1984 ; 18 : 149 – 153 . OpenUrl CrossRef 67. ↵ Garbary D et al. Cell Division in the absence of mitosis: The unusual case of the Fucoid Ascophyllum nodosum (L .) Le Jolis (Phaeophyceae). Algae 2009 ; 24 : 239 – 248 . doi: 10.4490/ALGAE.2009.24.4.239 OpenUrl CrossRef 68. ↵ Halat L et al. Epidermal shedding in Ascophyllum nodosum (Phaeophyceae): Seasonality, productivity and relationship to harvesting . Phycologia 2015 ; 54 : 599 – 608 . doi: 10.2216/15-32.1 OpenUrl CrossRef 69. ↵ Xu H , Deckert RJ , Garbary DJ . Ascophyllum and its symbionts. X. Ultrastructure of the interaction between A. nodosum (Phaeophyceae) and Mycophycias ascophylli (Ascomycetes) . Botany 2008 ; 86 : 185 – 193 . doi: 10.1139/B07-122 OpenUrl CrossRef 70. ↵ Custer GF et al. Comparative analysis of core microbiome assignments: Implications for ecological synthesis . mSystems 2023 ; 8 : e01066 – 22 . doi: 10.1128/msystems.01066-22 OpenUrl CrossRef PubMed 71. ↵ Dogs M et al. Rhodobacteraceae on the marine brown alga Fucus spiralis are abundant and show physiological adaptation to an epiphytic lifestyle . Syst Appl Microbiol 2017 ; 40 : 370 – 382 . doi: 10.1016/j.syapm.2017.05.006 OpenUrl CrossRef 72. James AK et al. Giant kelp microbiome altered in the presence of epiphytes . Limnol Oceanogr Lett 2020 ; 5 : 354 – 362 . doi: 10.1002/lol2.10157 OpenUrl CrossRef 73. Weigel BL , Pfister CA . Oxygen metabolism shapes microbial settlement on photosynthetic kelp blades compared to artificial kelp substrates . Environ Microbiol Rep 2021 ; 13 : 176 – 184 . doi: 10.1111/1758-2229.12923 OpenUrl CrossRef PubMed 74. Capistrant-Fossa KA et al. The microbiome of the habitat-forming brown alga Fucus vesiculosus (Phaeophyceae) has similar cross-Atlantic structure that reflects past and present drivers1 . J Phycol 2021 ; 57 : 1681 – 1698 . doi: 10.1111/jpy.13194 OpenUrl CrossRef PubMed 75. Park J et al. Alternative approaches to identify core bacteria in Fucus distichus microbiome and assess their distribution and host-specificity . Environ Microbiome 2022 ; 17 : 55 . doi: 10.1186/s40793-022-00451-z OpenUrl CrossRef PubMed 76. Burgunter-Delamare B et al. The Saccharina latissima microbiome: Effects of region, season, and physiology . Front Microbiol 2023 ; 13 : 1050939 . doi: 10.3389/fmicb.2022.1050939 OpenUrl CrossRef PubMed 77. Lu D-C et al. Epiphytic common core bacteria in the microbiomes of co-located green ( Ulva ), brown ( Saccharina ) and red ( Grateloupia , Gelidium) macroalgae. Microbiome 2023 ; 11 : 126 . doi: 10.1186/s40168-023-01559-1 OpenUrl CrossRef PubMed 78. ↵ King NG et al. Consistency and variation in the kelp microbiota: Patterns of bacterial community structure across spatial scales . Microb Ecol 2023 ; 85 : 1265 – 1275 . doi: 10.1007/s00248-022-02038-0 OpenUrl CrossRef 79. ↵ Weigel BL et al. Functional insights into the kelp microbiome from metagenome-assembled genomes . mSystems 2022 ; 7 : e01422 – 21 . doi: 10.1128/msystems.01422-21 OpenUrl CrossRef PubMed 80. ↵ Cotton AD . Notes on marine pyrenomycetes . Trans Br Mycol Soc 1907 ; 3 : 92 – 99 . doi: 10.1016/S0007-1536(07)80023-4 OpenUrl CrossRef 81. Kohlmeyer J , Volkmann-Kohlmeyer B. Mycophycias , a new genus for the mycobionts of Apophlaea , Ascophyllum and Pelvetia . Systema Ascomycetum 16 ( 1-2 ): 1 – 7 . - Biota of NZ . https://biotanz.landcareresearch.co.nz/references/1cb0f0bb-36b9-11d5-9548-00d0592d548c . ( 1998 , date last accessed). 82. Seckbach J Garbary DJ , Deckert RJ . Three part harmony — Ascophyllum and its symbionts . In: Seckbach J (ed.), Symbiosis: Mechanisms and Model Systems . Dordrecht : Springer Netherlands , 2002 , 309 – 321 . 83. ↵ Webber FC . Observations on the structure, life history and biology of Mycosphaerella ascophylli . Trans Br Mycol Soc 1967 ; 50 : 583 – 601 . doi: 10.1016/S0007-1536(67)80090-1 OpenUrl CrossRef 84. ↵ Garbary DJ , MacDonald KA . The Ascophyllum / Polysiphonia / Mycosphaerella symbiosis . IV. Mutualism in the Ascophyllum / Mycosphaerella interaction . 1995 ; 38 : 221 – 226 . doi: 10.1515/botm.1995.38.1-6.221 OpenUrl CrossRef 85. ↵ Eiler A , Heinrich F , Bertilsson S . Coherent dynamics and association networks among lake bacterioplankton taxa . ISME J 2012 ; 6 : 330 – 342 . doi: 10.1038/ismej.2011.113 OpenUrl CrossRef PubMed 86. ↵ Shaked Y , Lis H . Disassembling iron availability to phytoplankton . Front Microbiol 2012 ; 3 . doi: 10.3389/fmicb.2012.00123 OpenUrl CrossRef PubMed 87. ↵ Tortell PD et al. Marine bacteria and biogeochemical cycling of iron in the oceans . FEMS Microbiol Ecol 1999 ; 29 : 1 – 11 . doi: 10.1111/j.1574-6941.1999.tb00593.x OpenUrl CrossRef Web of Science 88. ↵ Kramer J , Özkaya Ö , Kümmerli R . Bacterial siderophores in community and host interactions . Nat Rev Microbiol 2020 ; 18 : 152 – 163 . doi: 10.1038/s41579-019-0284-4 OpenUrl CrossRef PubMed 89. ↵ Bertrand C et al. Genomic insights into indole-3-acetic acid catabolism in the marine algae-associated bacterium, Marinomonas sp. NFXS50 . Access Microbiol 2024 . doi: 10.1099/acmi.0.000856.v1 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted March 20, 2025. 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 A duo of fungi and complex and dynamic bacterial community networks contribute to shape the Ascophyllum nodosum holobiont 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 A duo of fungi and complex and dynamic bacterial community networks contribute to shape the Ascophyllum nodosum holobiont Coralie Rousseau , Gwenn Tanguy , Erwan Legeay , Samuel Blanquart , Arnaud Belcour , Sylvie Rousvoal , Philippe Potin , Catherine Leblanc , Simon M. Dittami bioRxiv 2025.03.20.643298; doi: https://doi.org/10.1101/2025.03.20.643298 Share This Article: Copy Citation Tools A duo of fungi and complex and dynamic bacterial community networks contribute to shape the Ascophyllum nodosum holobiont Coralie Rousseau , Gwenn Tanguy , Erwan Legeay , Samuel Blanquart , Arnaud Belcour , Sylvie Rousvoal , Philippe Potin , Catherine Leblanc , Simon M. Dittami bioRxiv 2025.03.20.643298; doi: https://doi.org/10.1101/2025.03.20.643298 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 Ecology Subject Areas All Articles Animal Behavior and Cognition (7636) Biochemistry (17697) Bioengineering (13896) Bioinformatics (41957) Biophysics (21457) Cancer Biology (18595) Cell Biology (25522) Clinical Trials (138) Developmental Biology (13381) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24323) Genetics (15612) Genomics (22511) Immunology (17738) Microbiology (40401) Molecular Biology (17184) Neuroscience (88624) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7644) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)