Metagenomic-based network analysis reveals the importance of vitamin cross-feeding in marine microbial assemblages

Metagenomic-based network analysis reveals the importance of vitamin cross-feeding in marine microbial assemblages | 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 Metagenomic-based network analysis reveals the importance of vitamin cross-feeding in marine microbial assemblages View ORCID Profile Nestor Arandia-Gorostidi , View ORCID Profile Lidia Montiel , View ORCID Profile Francisco Latorre , View ORCID Profile Vanessa Balagué , View ORCID Profile Josep M. Gasol , View ORCID Profile Rafel Simó , View ORCID Profile Caterina R. Giner , View ORCID Profile Ramiro Logares doi: https://doi.org/10.1101/2025.08.08.668683 Nestor Arandia-Gorostidi 1 Department of Marine Biology and Oceanography, Institut de Ciències del Mar , CSIC, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nestor Arandia-Gorostidi For correspondence: arandia{at}icm.csic.es Lidia Montiel 1 Department of Marine Biology and Oceanography, Institut de Ciències del Mar , CSIC, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lidia Montiel Francisco Latorre 1 Department of Marine Biology and Oceanography, Institut de Ciències del Mar , CSIC, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Francisco Latorre Vanessa Balagué 1 Department of Marine Biology and Oceanography, Institut de Ciències del Mar , CSIC, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vanessa Balagué Josep M. Gasol 1 Department of Marine Biology and Oceanography, Institut de Ciències del Mar , CSIC, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Josep M. Gasol Rafel Simó 1 Department of Marine Biology and Oceanography, Institut de Ciències del Mar , CSIC, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rafel Simó Caterina R. Giner 1 Department of Marine Biology and Oceanography, Institut de Ciències del Mar , CSIC, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Caterina R. Giner Ramiro Logares 1 Department of Marine Biology and Oceanography, Institut de Ciències del Mar , CSIC, Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ramiro Logares Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Vitamins play a fundamental role in microbial metabolism and interactions, yet their scarcity in marine environments and the limited ability for de novo synthesis of some vitamins often leads to metabolic dependencies and cross-feeding among microbial taxa. Using a decade-long time-series metagenomics dataset from the Blanes Bay Microbial Observatory (BBMO), we investigated the role of vitamin biosynthesis in structuring the seasonal marine microbial interactions among prokaryotes and eukaryotes. Gene co-occurrence analysis revealed that vitamin-related metabolism had the highest number of associations among all metabolic pathways, underscoring the potential role of vitamins in microbial interactions. Metagenome-assembled genome (MAG) co-occurrence analysis further identified the prokaryotic biosynthesis of cobalamin (B 12 ) and thiamine (B 1 ) as key mediators of these associations. Rather than between complete vitamin synthesizers and auxotrophs, the main associations were found between partial synthesizers, suggesting that vitamer cross-feeding may represent an essential microbial interaction in sustaining vitamin biosynthesis throughout the year. While complete cobalamin synthesizers dominate in summer, partial synthesizers and lower-ligand activators are more prevalent in winter, driving cross-feeding interactions. In contrast, thiamine biosynthesis is more widespread, with complete synthesizers peaking in winter. The association strength, however, was maximum between prokaryotic vitamin producers and eukaryotes, highlighting the dependence of eukaryotes on bacterial vitamin production. These results provide novel insights into seasonal metabolic interdependencies, emphasizing the ecological significance of vitamin biosynthesis in marine ecosystems. INTRODUCTION Interactions among marine microorganisms largely shape the structure and dynamics of marine microbiomes. These interactions are essential across key biogeochemical processes, from the nitrogen cycle, where associations between nitrogen fixers, ammonia oxidizers, and nitrate reducers sustain oceanic nitrogen cycling ( Zehr & Kudela, 2011 ; Pachiadaki et al., 2017 ), to carbon cycling, in which the association between diverse microbial taxa control the degradation and remineralization of organic carbon ( Datta et al., 2016 ). However, understanding how microbes interact, either through cooperation or competition, in natural environments remains a significant challenge due to the complexity and diversity of microbial communities, as well as the difficulty of directly observing their interactions in situ . As a result, the mechanisms and ecological implications of microbial cooperation remain poorly understood. Studies utilizing co-cultures have started to uncover the mechanisms driving specific interactions between marine microorganisms ( Hibbing et al., 2010 ). These interactions are driven by the release and subsequent uptake of a myriad of metabolites produced as a result of microbial metabolism, such as vitamins, amino acids, organic acids, or signaling molecules. The release of these secondary metabolites can profoundly influence the growth and behavior of community members, playing a crucial role in shaping the metabolism and assembly of the entire marine microbial community ( Durham et al., 2015 ; Oña & Kost, 2022 ; Moran et al., 2022 ; Durham, 2021 ). Among these compounds, vitamins have garnered significant attention in recent studies due to their essential role across nearly all forms of life ( Croft et al., 2006 ; Giovannoni, 2012 ; Combs & McClung, 2017 ), despite their widespread scarcity in marine environments ( Panzeca et al., 2009 ; Sañudo-Wilhelmy et al., 2012 ). In particular, B 1 (Thiamine) and B 12 (Cobalamin) are the most required vitamins among microbes ( Sañudo-Wilhelmy et al., 2014 ), serving as main cofactors in central carbon metabolism ( Berg et al., 2007 ; Dowling et al., 2012 ). In marine ecosystems, microbial biosynthesis is the primary source of both B 1 and B 12 . Yet, due to the genetic and metabolic costs associated with these biosynthetic pathways, many microorganisms lack complete de novo synthesis capabilities. This limitation not only promotes mutualistic relationships between vitamin synthesizers and auxotrophs ( Croft et al., 2005 ; Suffridge et al., 2018 ) but also leads to more complex interactions where different microbes contribute to the full synthesis of a given vitamin through the synthesis of vitamin precursors or vitamers ( Suffridge et al., 2020 ; Paerl et al., 2023 ). However, the limited number of studies addressing interactions related to vitamin synthesis within natural microbial assemblages constrains our understanding of their importance in mediating microbial interactions and nutrient dependencies in marine environments. Although co-culture studies have provided valuable insights into microbial interactions ( Sultana et al., 2023 ; Wienhausen et al., 2024 ), their scope remains limited due to the challenges of cultivating many marine microorganisms. Computational tools have become increasingly important over the past two decades for generating hypotheses about microbial interactions in natural environments. For instance, taxonomic co-occurrence and co-exclusion analyses over space or time have provided valuable insights into potential interactions among marine microorganisms, including bacteria-bacteria (Lima-Méndez et al., 2015; Deutschmann et al., 2024 ), and bacteria-phytoplankton interactions ( Arandia-Gorostidi et al., 2022 ), suggesting that microbial interactions influence the seasonal community structure. Even though taxonomic association analyses (based on co-occurrences and co-exclusions) help to describe hypothetical ecological interactions, they provide very limited information on the mechanisms underlying these interactions. Gene-level co-occurrence analyses through metagenomics, on the other hand, offer a more granular view of the potential metabolic bases of microbial interactions ( Fondi et al., 2016 ; Fondi & Patti, 2019), pointing out key metabolites as drivers of these metabolic associations. Recent computational models based on metagenomics have proven highly valuable in uncovering microbial interactions across diverse environments, including hydrogen sulfide-related interactions among hydrothermal microbes ( Kuppa Baskaran et al., 2023 ) and associations mediated by amino acid and vitamin prototrophy and auxotrophy in the global ocean ( Giordano et al., 2024 ). In this study, we aimed to investigate the metabolic bases of the most abundant marine microbial interactions by analyzing a 12-year metagenomic time-series dataset from the Mediterranean Sea. By combining the gene databases with co-occurrence analyses, we examine the key metabolic pathways and potential metabolites that underpin microbial interactions within the Bay of Blanes community over a decade. Furthermore, we leverage metagenome-assembled genomes (MAGs) to identify potential interactions between prokaryotic groups and characterize their seasonal vitamin-related interactions. MATERIAL & METHODS Sample collection and DNA extraction Monthly seawater samples for metagenomic analyses were collected at the Blanes Bay Microbial Observatory (BBMO; http://bbmo.icm.csic.es ) in the Northwestern Mediterranean Sea (41°40’N, 2°48’E). Sampling was conducted at the surface (≈ 1 m depth) between January 2009 and December 2020. Approximately 6 L of seawater was prefiltered first through a 200 μm mesh, and subsequently through a 3 μm pore-size polycarbonate filter (Poretics), and a 0.2 μm Sterivex Millipore filter using a peristaltic pump. For the metagenomics analysis, only the fraction between 3 μm and 0.2 μm was used. The Sterivex units were stored at −80 °C until further analysis. Besides metagenomic analyses, samples were collected to measure total chlorophyll-a concentration ( µ g/l), inorganic nutrients (PO 4 3 − , NH 4 + , NO 2 − , NO 3 − , and SiO 2 ), temperature, and salinity. Water temperature and salinity were sampled in situ using an SAIV A/S SD204 CTD. Chlorophyll a (Chl a ) concentrations were analyzed by fluorometry (10-AU Fluorometer Turned Designs). Inorganic nutrients were measured using an Alliance Evolution II autoanalyzer ( Graßhoff et al., 2009 ). Cell counts to determine bacterial and picoeukaryote total abundance were done by flow cytometry as described in Gasol et al. (2016) . Once in the laboratory, DNA was extracted with a standard phenol–chloroform protocol as described in Massana et al. (1997) and purified using an Amicon unit (Millipore). Sample purification was performed as described in Krabberød et al. (2022) . Shotgun sequencing of the samples was performed in separate batches. Samples between 2009 and 2012 were sequenced using an Illumina Hiseq4000 (2 x 150 bp), while the samples between 2013 and 2020 were sequenced using the Illumina NovaSeq6000 (2 x 150 bp) platform. All samples had a sequencing coverage > 30 gigabases (Gb). Trimming, assembly, and gene prediction Adapters and low-quality reads were removed from the samples using Cutadapt (v3.5, Martin, 2011 ). Each sample was subsequently assembled individually with Megahit (v1.1.3) to generate contigs ( Li et al., 2015 ) using the meta-large option. Protein-coding regions in the contigs were predicted for each sample using Prodigal (v2.6.3, Hyatt et al., 2010 ) and MetaGeneMark (v3.38, Gemayel et al., 2022 ), considering partial Open Reading Frames (ORFs) and a minimum fragment length of 250 bp. The resulting dataset, containing 500 million genes, was clustered at 95% identity and 80% coverage of the shorter sequence using Linclust v10 ( Steinegger & Söding, 2018 ), resulting in a final catalog of 231 million genes (ORFs). Predicted genes were functionally annotated with the Kyoto Encyclopedia of Genes and Genomes (KEGG, Kanehisa & Goto, 2000), using Diamond blastp ( Buchfink et al., 2015 ). Genes were taxonomically annotated against the Genome Taxonomy Database (GTDB) release 95 ( Parks et al., 2018 ) using MMSeqs2 (v9-d36de, Steinegger et al., 2017) with a sensitivity value of 5.7, specifying ranks at which the Lowest Common Ancestor (LCA) should be determined. Gene abundances per sample were calculated by mapping metagenome reads back to the catalogue using BWA (v0.7.17, Li et al., 2009) and obtaining the number of counts per gene using HTSeq (v0.10.0, Anders et al., 2015 ). Gene counts were normalized within and among samples by gene length and the abundance of 10 single-copy genes as done by others ( Sunagawa et al., 2013 ; Milanese et al., 2019 ). The generated normalized gene abundance tables include the abundance of each gene (ORF) in each sample. From these, we calculated the corresponding functional abundance tables by adding all the normalized abundances of all genes annotated to a specific KEGG function. Metagenome-assembled genomes (MAG) To reconstruct metagenome-assembled genomes (MAGs), a total of 84 metagenomes from the Blanes Bay Microbial Observatory (BBMO) were used. We used Simka (v1.5, Benoit et al., 2016 ) to estimate Bray-Curtis dissimilarities among metagenomes using 21-mer profiles. These dissimilarities were then clustered using hierarchical clustering (hclust) in R, allowing us to define four distinct metagenomic groups (G1 to G4), which also corresponded to seasonal groupings: G1 (Winter), G2 (Spring), G3 (Early Summer), and G4 (Late Summer). Two metagenomes were excluded during this step as they did not cluster with any group, resulting in 82 metagenomes in total. Each group of metagenomes was co-assembled separately using MEGAHIT (v1.2.8., Li et al., 2015 ). Reads from the original metagenomes were then mapped back to their respective co-assemblies using BWA (v0.7.17-r1188, Li et al., 2009) and SAMtools (v1.8 for G2, G3, G4 and v1.12 for G1, Danecek et al., 2021 ). MAGs were first generated using MetaBAT 2 (v2.12.1, Kang et al., 2019 ) with default parameters and a minimum contig length of 2.5 kb. The depth profiles generated during the MetaBAT run, along with the BAM files, were subsequently used to perform binning with two additional tools: CONCOCT (v0.4.2, Alneberg et al., 2014 ) and MaxBin2 (v2.2.5., Wu et al., 2016 ). All bins from the three binners were refined and consolidated using MetaWRAP (v1.3-4bf5f8a, Uritskiy et al., 2018 ) in default mode. To remove redundant genomes, the resulting MAGs were dereplicated at 99% Average Nucleotide Identity (ANI) using dRep (v2.3.2, Olm et al., 2017 ), yielding a final non-redundant set of 1,505 MAGs. For downstream analyses, only high-quality MAGs with >90% completeness and <10% contamination were selected, resulting in 461 high-quality MAGs used in this study. Dataset preparation and co-occurrence analyses Before determining gene co-occurrence to determine potential metabolic interactions, individual genes were classified according to their metabolic pathways using the KEGG classification ( https://www.genome.jp/kegg/pathway.html ). After metabolism annotation, co-occurrence analysis was performed for the prokaryotic and eukaryotic annotated genes using the Maximal Information-based Nonparametric Exploration (MINE v2, Reshef et al., 2011 ) statistics as well as the maximal information coefficient (MIC) to characterize the degree of dependence between the different genes. MINE parameters were set to “equitability” for the best equitability of MICe, while the alpha, c, and cv values were set to default. The resulting data was processed as follows: first, all negative associations (or edges) and those with a MIC below 0.5 were discarded. Negative associations were discarded due to their more complex and ambiguous interpretation. Unlike positive associations, which suggest shared niches or other ecological interactions, negative associations (typically reflecting a lack of co-occurrence) cannot be reliably interpreted as evidence of antagonism or competition without further ecological or experimental validation. After dataset curation, all KOs were clustered according to their KEGG metabolism, and the number of detected associations between different metabolisms was calculated using R (v4.3.1). As each metabolism included a different number of KOs, the number of edges was normalized by the number of KOs in both connected metabolisms in order to compare the density of associations between the different metabolisms. After normalization, we constructed a network to study the KO co-occurrence across the different metabolisms using Cytoscape (v3.10.1), and the network statistics were analyzed. In these networks, nodes represent KOs, while edges indicate significant associations between them. To determine the edge density between prokaryotic vitamin biosynthesis and eukaryotic metabolisms, eukaryotic and prokaryotic KOs were differentiated before the co-occurrence analysis. After that, only the associations between eukaryotic and prokaryotic metabolisms with Cobalamin de novo biosynthesis (including aerobic and anaerobic pathways) and Thiamine de novo biosynthesis pathways (including all possible pathways) were studied. To enable comparison of association density across different metabolisms, the number of edges per metabolic pathway was normalized by the number of KOs within each pathway, following the same approach used in the previous analysis. MAG’s metabolic capabilities and co-occurence Only MAGs with completeness above 90% and contamination below 10% were considered (totaling 461 MAGs). The potential ability to synthesize de novo cobalamin and thiamine was explored in these MAGs by comparing the completeness of the pathways for synthesizing both vitamins, looking at the presence of the different KOs according to the KEGG database. We classified a MAG as a complete cobalamin synthesizer if it contained more than 75% of the orthologs required for each step of cobalamin biosynthesis: corrinoid ring biosynthesis, nucleotide loop assembly, and lower ligand activation (via CobC and CobT enzymes). A MAG was categorized as a partial cobalamin synthesizer if it possessed more than 75% of the orthologs for corrinoid ring biosynthesis and nucleotide loop assembly but lacked the necessary genes for lower ligand activation. Finally, a MAG was designated as a lower ligand activator if it contained less than 75% of the orthologs for corrinoid ring biosynthesis and/or nucleotide loop assembly but retained the ability to activate the lower ligand. Thiamine biosynthesis completeness was assessed based on the ability to synthesize its two main vitamers: 4-amino-5-hydroxymethyl-2-methylpyrimidine (HMP) and 4-methyl-5-(2-hydroxyethyl)thiazole (THZ). These compounds are produced through distinct pathways and subsequently joined by thiE to form thiamine. We classified pyrimidine auxotrophs as those MAGs that contain thiE and the thiG enzyme required for THZ synthesis but lack thiC, which is responsible for HMP synthesis. Thiazole auxotrophs were defined as MAGs possessing thiE and thiC but missing thiG, while dual auxotrophs were those containing thiE but lacking both thiC and thiG. After determining the vitamin pathway completeness in each MAG, the co-occurrence analysis was performed with MINE using the same parameters as described earlier as well as the MAG’s abundances based on Reads Per Kilobase of Genome per Gigabase of Metagenome (RPKG). The network visualization and statistical analyses were performed in Cytoscape as described above. In the network, nodes represent individual MAGs, while edges indicate significant associations between them. To determine the main core MAGs in the network, we used the approach described in Krabberød et al. (2022) . For each MAG, we calculated 1) Degree: number of edges per node, 2) Betweenness centrality, which measures the frequency with which a MAG appears on the shortest paths between other MAGs in the network (meaning the most direct routes connecting two nodes in a network, using the fewest number of steps or edges); and 3) Closeness centrality, which indicates the proximity of a node to all other nodes in the network. We identify as “core” MAGs the ones that are within the first quartile for the three statistics. Among these, we defined the “main core MAGs” as those that ranked within the top 5% for each of the three centrality measures, indicating their central structural role in the network. RESULTS Environmental analysis Between January 2009 and December 2020, the analysis of the time series ( Figure 1 ) revealed seasonal variations in temperature, with maximum values recorded in August and September, peaking at 26.7 °C in August 2020, and minimum values observed between February and March, reaching a lowest temperature of 11.9 °C in March 2010. Chlorophyll a concentration also exhibited seasonal fluctuations, ranging from 0.1 to 2.9 µg l⁻¹, with peak values occurring in late winter and/or autumn. Inorganic nutrient concentrations showed distinct trends, with nitrate levels consistently peaking in winter, whereas ammonium and phosphate exhibited less predictable patterns. Download figure Open in new tab Figure 1. Temporal variation of environmental properties at the Blanes Bay Microbial Observatory (Catalonia) between 2009 and 2021. The figure shows monthly measurements of temperature, nitrate (NO₃⁻), ammonium (NH₄⁺), phosphate (PO₄³⁻), chlorophyll a concentration, and prokaryote abundance. Potential interactions among functions Gene annotation identified a total of 8,370 prokaryotic KOs and 7,552 eukaryotic KOs. However, to filter the total number of KOs used for analysis, only the top 50% of the most abundant KOs from each group were used as input for the MINE analysis. The resulting network consisted of 2,225 nodes (representing KOs) and 2,377,290 edges (or associations). For further analysis, only positive associations with a maximal information coefficient (MIC) of 0.5 or higher were considered. Nodes were clustered according to the pathway and metabolic classifications assigned to each KO in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (ww.genome.jp/kegg/pathway.html), and their temporal associations were analyzed ( Figure 2 ). The analysis revealed that pathways related to cofactor and vitamin metabolism exhibited the highest number of associations, with an average of 7.6 edges per node, followed closely by central carbohydrate metabolism (7.4 edges per node). Although many edges connected KOs within the same metabolic group, most vitamin metabolism-related KOs were associated with central carbohydrate metabolism, showing an average of 16.5 edges per node. Similarly, the metabolism of purine and amino acids (including lysine and histidine) played a central role in the network, with an average of 5.9 edges per node. Download figure Open in new tab Figure 2. Heatmap comparing the number of edges (log scale) between KEGG metabolisms normalized by the number of KOs for each metabolism group. The Cofactor and Vitamin Metabolism group is highlighted. When comparing the number of edges per node for prokaryotic vitamin biosynthesis-related KOs with other prokaryotic and eukaryotic metabolic pathways ( Figure 3 ), we found that prokaryotic vitamin biosynthesis KOs exhibited the highest number of associations with eukaryotic metabolic pathways. Among these, eukaryotic central metabolic processes, particularly carbon fixation and carbohydrate metabolism, showed the strongest connectivity with prokaryotic vitamin biosynthesis KOs. Furthermore, when comparing the number of associations with the biosynthesis of individual vitamins (cobalamin, Thiamine, Biotin, and Riboflavin, Figure 3 and Supplementary Figure 2), we observed that cobalamin biosynthesis was more related to eukaryotic and prokaryotic metabolism (average 19.0 edges per node), followed by Thiamine (12.3 edges per node), while Biotin and Riboflavin showed 8.8 and 3.2 edges per node respectively. To further compare the association between prokaryotic and eukaryotic metabolisms with the biosynthesis of individual vitamins, we calculated the ratio of edge density between all the KOs involved in the prokaryotic biosynthesis of each vitamin with the KOs of the main central metabolisms for both prokaryotic and eukaryotic groups (Supplementary Figure 3). The results revealed stronger association metrics between prokaryotic vitamin synthesizers and eukaryotes than between prokaryotes themselves. Download figure Open in new tab Figure 3. Co-occurrence networks generated with MINE, based on distinct KOs involved in the biosynthesis of thiamine (top) and cobalamin (bottom). All known biosynthetic pathways for each vitamin were considered for KO selection. Vitamin synthesis KOs are connected to various prokaryotic (grey) and eukaryotic (green) metabolic pathways. The proximity of the nodes to the central thiamine or cobalamin biosynthesis node reflects their number of connections, with closer nodes indicating higher edge density and thus greater connectivity within each metabolic context. MAG analysis and vitamin biosynthesis pathway completeness Completeness of the de novo vitamin biosynthesis pathways was determined in recovered MAGs with genome completeness > 90%. A total of 461 MAGs were used, with an average genome completeness of 94.9%, an average contamination of 2%, and a GC content of 0.48. The average genome size per MAG was 3.12 Mb, and the number of predicted genes was 1,315,247 (average 2,853 genes per MAG). The selected MAGs represented, on average, around 10% of the total metagenome reads per sample (peaking at 36% in January 2011), while accounting for 23% of the abundance of all MAGs ( Figure 4 ). Download figure Open in new tab Figure 4. Metagenomic read recruitment and abundance of metagenome-assembled genomes (MAGs) at the Blanes Bay Microbial Observatory (BBMO) from 2009 to 2020. Top panel shows the percentage of metagenomic reads mapped to MAGs with ≥90% completeness. Bottom panel represents the total abundance of all MAGs (orange bars) and MAGs with ≥90% completeness (purple bars), expressed in RPKG (Reads Per Kilobase of genome per Gigabase of metagenome). The black line indicates the percentage of MAGs with ≥90% completeness relative to the total MAG abundance. Overall, cobalamin de novo biosynthesis pathways exhibited the lowest completeness values, with only 11.5% of the MAGs (53 out of 461) containing a complete cobalamin biosynthesis pathway (Supplementary Figure 1). This includes the ability to synthesize the corrinoid ring through either aerobic or anaerobic pathways, assemble the nucleotide loop, and produce the complete lower ligand, requiring both CobC and CobT enzymes. In contrast, 2.8% of the MAGs were able to partial cobalamin biosynthesis (including aerobic or anaerobic corrinoid ring biosynthesis and nucleotide loop assembly but no ability for activation of the lower-ligand), and 11.1% of the MAGs were lower ligand activators (unable to synthesize the corrinoid ring and the nucleotide loop assembly, but able to activate the lower-ligand). Such a low abundance of cobalamin complete and partial synthesizers contrasts with the ability of almost the entire set of MAGs to synthesize Riboflavin (92.4%), and 40.8% of the MAGs showing at least one complete pathway for the de novo synthesis of Biotin. As with cobalamin synthesizers, we observed the presence of both complete and partial thiamine synthesizers. Complete thiamine synthesizers, those with the thiC , thiG (or thi4 ), and thiE (or thiN ) genes required to synthesize all thiamine vitamers (HMP-P, THZ-P, and their combination), constituted 32.5% of all MAGs. Partial synthesizers, capable of producing only the HMP or THZ precursors, represented 35.8% of the MAGs. Among these partial synthesizers, 36.2% lacked the thiC gene, making them pyrimidine B1 auxotrophs dependent on external pyrimidine. In contrast, 6.2% were thiazole auxotrophs, lacking thiG or thi4 . Dual auxotrophs, lacking both pyrimidine synthase ( thiC ) and thiazole synthase ( thiG or thi4 ) but retaining thiamine monophosphate synthase ( thiE or thiN ), accounted for 46.9% of MAGs. The Rhodobacteraceae order was the most abundant group of cobalamin synthesizers, accounting for 36% of the total cobalamin prototrophic MAGs (Supplementary Figure 4). In contrast, the rest of the vitamin prototrophy exhibited greater diversity, with the Flavobacteriales order being the most abundant group. Specifically, Flavobacteriales represented 11.5% of Thiamine, 17.3% of Biotin, and 24% of Riboflavin prototrophs. Distribution of vitamin synthesizers The co-occurrence analysis of the MAG dataset (including only the MAGs with a completeness > 90%) revealed two distinct subnetworks, each exhibiting clear seasonal differentiation ( Figure 5 ). One subnetwork comprised 108 MAGs, which were almost exclusively present in the summer, while the other included 172 MAGs that appeared only during the winter. The taxonomical analysis of the MAGs revealed that the summer network was dominated by the Rhodobacterales and Puniceispirillales orders (25% of the MAGs each). The MAG with the highest abundance (RPKG) was G2.161 (Puniceispirillales order). On the other hand, we found a higher diversity in the winter network, with most MAGs belonging to the Rhodospirillales and Acidimicrobiales orders (11% each), although other relevant orders such as Synechococcales and Nitrososphaerales were also found (the one with the highest abundance (RPKG) was the archaeal MAG G3.122, of order Nitrososphaerales). Download figure Open in new tab Figure 5. (Top) Association network of MAGs (>90% completeness, and <10% contamination), categorized by cobalamin biosynthesis completeness: complete (green), partial (purple), and lower ligand activators (orange). The summer and winter subnetworks are indicated by grey circles. (Bottom) Relative abundance of MAGs grouped at the class level based on their cobalamin biosynthesis classification: Complete Synthesizers, Partial Synthesizers, and Lower Ligan Activators. The distribution of cobalamin and thiamine synthesizers (including both partial and complete synthesizers) was also analyzed to track vitamin-related interactions. Complete cobalamin synthesizers were present primarily during the summer, representing 20.5% of the summer subnetwork MAGs, and decreased to 6.7% in winter. This group was vastly dominated by Alphaproteobacteria, with Puniceispirillales and Rhodobacterales as the main families. On the other hand, partial cobalamin synthesizers and lower ligand activators were mainly found in the winter subnetwork (representing 3.0% and 8.5% of winter subnetwork MAGs, respectively). Nitrososphaeria dominated the partial synthesizer group, while the lower ligand activators were more diverse ( Figure 5 ). Regarding thiamine, complete synthesizers showed a higher prevalence in winter (40%) compared to summer (22.5%), while partial synthesizers were more equally distributed over seasons (28.4% in winter and 28.8% in summer, Supplementary Figure 5). However, pyrimidine auxotrophs and thiazole auxotrophs showed distinct seasonal distributions, with the former dominating in summer and the latter more prevalent in winter, largely due to the high abundance of the thiazole auxotrophic Nitrososphaeria group. Core MAGs in the co-occurrence network To identify the key vitamin synthesizers connecting with other microbial groups, we analyzed network metrics, including degree (number of edges per node), betweenness centrality, and closeness centrality. We considered core nodes to be those within the top 25th percentile in all three statistics in both the summer and winter seasons ( Table 1 ), as proposed by Krabberød et al. (2022) . Additionally, we included more conservative percentiles (15th and 5th), with the most central nodes in each percentile determined by their betweenness centrality and closeness centrality. In winter, the most central node (within the 5th percentile) belonged to the Nitrososphaerales order (MAG.G3.122), while in summer, it was part of the Alphaproteobacteria class (Thalassobaculales order, MAG.G4.44). Interestingly, the two most central nodes in winter were both cobalamin synthesizers, either partial or complete. In summer, while complete cobalamin synthesizers comprised 20.5% of the MAGs, they accounted for 40% of the central nodes. In contrast, thiamine complete synthesizers were scarcely represented among the most central nodes in summer, with only a single MAG detected. However, their presence was markedly higher in winter, where they featured prominently on the central node list. View this table: View inline View popup Download powerpoint Table 1. Statistics of the most central nodes (MAGs) found in the Summer and Winter subnetworks calculated according to the Degree, Betweenness Centrality, and Closeness Centrality of each node (following the terminology in the Methods section and in Krabberød et al., 2022 ). Degree indicates the number of edges per node. Betweenness centrality, represents the most direct path connecting two nodes, using the fewest number of edges in between. Closeness centrality indicates the proximity of a node to all the other nodes in the network. For each MAG, the taxonomic affiliation and the ability to synthesize Cobalamin and Thiamine through different pathways are indicated. DISCUSSION The seasonal taxonomic co-occurrence among marine microbes has been extensively explored in several time series study sites like the San Pedro Ocean Time series station (SPOT, Steel et al., 2011; Chow et al., 2014 ; Cram et al., 2015 ) or the BBMO ( Giner et al., 2019 ; Krabberød et al., 2022 ; Auladell et al., 2022 ; Deutschmann et al., 2023 ), elucidating the main associations among taxa (including prokaryotes and eukaryotes). While these studies have contributed significantly to our understanding of microbial interactions, relying solely on taxonomic co-occurrence may limit insights into the functional bases of specific microbial interactions. By contrast, our approach leverages gene and MAG co-occurrence, providing a more nuanced and functionally relevant perspective on metabolic microbial interactions. Recent research employing ecosystem modeling and genome-resolved metagenomics has underscored the critical role of microbial auxotrophies, particularly amino acid and vitamin B auxotrophies, in shaping microbial dynamics in the global ocean ( Giordano et al., 2024 ). Our observations support the central role of vitamin-related metabolism in marine microbial interactions, as evidenced by the higher number of associations involving vitamin metabolism-related genes compared to other metabolic pathways; in agreement with previous studies ( Croft et al., 2005 ; Kazamia et al., 2012 ; Sultana et al., 2023 ). Furthermore, our findings emphasize that the seasonal variation in the genomic capacity for vitamin biosynthesis (reflected in the distinct temporal distribution of complete and partial vitamin synthesizers) has the potential to influence the temporal interaction dynamics of prokaryotic and eukaryotic assemblages. Vitamins are typically present at extremely low concentrations in marine systems, often in the order of picomolar (pM). Vitamin concentrations in the Mediterranean Sea remain at low pM level ( Bonnet et al., 2013 ; Suffridge et al., 2018 ), suggesting a potential limitation for microbial activity. Earlier studies suggested that trace-level vitamin concentrations are sufficient to sustain microbial growth, since vitamins, as cofactors, are needed in low amounts and can be recycled (Droop et al., 2007; Paerl et al., 2018 ). Yet recent growth studies have shown that vitamins are often present at growth-limiting concentrations for auxotrophic microorganisms in the global oceans ( Gregor et al., 2023 ). Therefore, de novo vitamin B synthesis is necessary to meet the vitamin requirements of many eukaryotes and prokaryotes in oligotrophic regions. Yet, despite the widespread presence of several vitamin biosynthesis genes in the metagenomes, microbes with complete vitamin biosynthesis pathways are scarce, most likely due to the high complexity of vitamin de novo synthesis (particularly for cobalamin, for which more than 30 genes are required for its complete synthesis). Our results align with previous observations ( Sañudo-Wilhelmy et al., 2014 ) showing that approximately 41% of the microbial population possess a complete (or nearly complete, >75%) pathway for Biotin biosynthesis, while 32.5% of the species exhibited the capability to synthesize Thiamine de novo (Supplementary Figure 1). In contrast, the overall low abundance of MAGs with a complete pathway for the de novo synthesis of cobalamin (a vitamin required by most prokaryotic and half of the eukaryotic marine microorganisms; Sañudo-Wilhelmy et al., 2014 ; Zoccarato et al., 2022 ) highlights the imbalance between B 12 producers and auxotrophs. This scarcity likely drives the higher number of associations (edges) per node observed between central metabolic pathways and B 12 synthesis genes compared to those for B 1 or B 7 biosynthesis. It is important to acknowledge that our reliance on complete MAGs to resolve the complex steps of vitamin biosynthesis limits our analysis to only a subset of the microbial community. Nonetheless, the strong seasonal trends of MAGs observed here closely align with those reported in a recent study on vitamin biosynthesis genes in the Mediterranean Sea ( Beauvais et al., 2023 ). Moreover, the proportion of vitamin synthesizers identified in our dataset is consistent with previous observations ( Sañudo-Wilhelmy et al., 2012 ), suggesting that our findings provide a representative picture of the vitamin-related interactions occurring at the community level. The recently described phenomenon of cobalamin lower ligand cross-feeding likely contributes to the high number of B 12 -related correlations observed in our network analyses ( Wienhausen et al., 2024 ). Cobalamin biosynthesis requires the assembly of three key components: the corrin ring, the nucleotide loop, and the activated form of the lower ligand (alpha-ribazole). The lower ligand, typically 5,6-dimethylbenzimidazole (DMB) and catalyzed by the bluB enzyme, is essential for cobalamin activation and is incorporated through the action of CobC and CobT enzymes, which convert it into its activated form, α-ribazole. Wienhausen et al. (2024) demonstrated that the lower ligand can be exchanged between microorganisms through cross-feeding, compensating for the limited number of microbes with a complete cobalamin biosynthesis pathway. The strong associations between partial synthesizers and lower-ligand activators, which exhibit the highest edge density between both groups (see Supplementary Table 1), further support the idea that ligand cross-feeding likely represents an important microbial interaction throughout the year. However, the pronounced seasonal variation between complete and partial synthesizers suggests that cross-feeding interactions are more prevalent in winter. Notably, the number of associations between partial synthesizers and lower-ligand activators was significantly higher in winter (44 edges) compared to summer (2 edges), strongly indicating a seasonal shift in cobalamin biosynthesis strategies. However, the bluB enzyme, responsible for the production of DMB ( Gray & Escalante-Semerena, 2007 ; Wienhausen et al., 2024 ), was found in only a small fraction of MAGs: 7% of MAGs containing CobC and CobT enzymes and just 12 MAGs with complete cobalamin biosynthesis pathways. Remarkably, nearly half of the MAGs encoding bluB are incapable of synthesizing any cobalamin-related pathways (complete, partial, or lower-ligand activation), suggesting that these microbes might primarily contribute to DMB production for cross-feeding. Seasonal trends further highlight the role of bluB , with 46% of MAGs containing bluB exclusive to the winter subnetwork compared to only 13% in summer. These findings support our previous findings that cross-feeding interactions become essential for achieving complete cobalamin synthesis during winter when fully independent synthesizers are less prevalent. An example of a potential ligand cross-feeding interaction in winter is observed between the Nitrosopumilus (MAG bin.G3.122), the main central core of the winter subnetwork ( Table 1 ), and different Lower-Ligand Activator MAGs. Previous studies have suggested that members of Nitrosopumilus can serve as an important source of B 12 ( Doxey et al., 2015 ; Qin et al., 2017 ; Santoro et al., 2015). However, our study reveals that Nitrosopumilus , despite being the dominant partial B 12 producer in winter (accounting for up to 10% of total metagenome reads in winter), lacks the ability to activate the lower ligand. Interestingly, it retains the capacity to synthesize DMB through the presence of the bluB enzyme. This observation is further supported by available metagenomic data confirming the absence of CobC and CobT in Nitrosopumilus (Thaumarchaeota) genomes (Santoro et al., 2015), indicating the inability of this group to activate the lower ligand independently. Consequently, Nitrosopumilus could provide DMB to the environment but requires interactions with Lower-Ligand Activators to complete cobalamin biosynthesis. Our findings suggest that Verrucomicrobia (order Opitutales, bin.G4.517), one of the main groups in winter ( Krabberød et al., 2022 ), and Puniceispirillales (bin.G1.296) are likely key contributors in this cross-feeding process, as they show a very close association with the Nitrosopumilus MAG (MIC 0.64 and 0.82). However, the relative abundance of Lower-Ligand Activators exhibited high interannual variability, likely driven by their greater diversity compared to partial synthesizers. This variability resulted in shifts in the dominant Lower-Ligand Activator groups over the years, with different members of Alphaproteobacteria , Gammaproteobacteria , Bacteroidetes , and even Actinomycetia taking on the role. This suggests that functional redundancy within the microbial community allows different taxa to compensate for shifts in group dominance ( Ramond et al., 2025 ), maintaining the essential cross-feeding interactions needed for cobalamin biosynthesis in winter. In contrast to cobalamin biosynthesis-related associations, de novo thiamine biosynthesis exhibited a significantly lower number of associations, likely reflecting the reduced need for exchanges to meet thiamine requirements due to the higher number of microorganisms capable of synthesizing thiamine (with 2.6 times more complete thiamine synthesizers compared to cobalamin). Despite the overall lower number of associations, thiamine synthesizers seem relevant in the winter network, with 4 of the 15 most central nodes in the winter subnetwork identified as Complete Thiamine Synthesizers. Such relevance in winter could be related to thiamine’s role as a cofactor in the TCA cycle, essential for metabolizing organic compounds such as carbohydrates and proteins ( Rapala-Kozik, 2011 ). The BBMO site is characterized by a seasonal chlorophyll pattern, peaking in late winter ( Gasol et al., 2016 ) due to blooms of diatoms and autotrophic picoplankton ( Nunes et al., 2018 ; Vallina et al., 2023 ). This abundance of autotrophs likely increases the availability of organic molecules, stimulating microbial activity and respiration (García-Martín et al., 2015) and thus raising the demand for thiamine, which could explain the observed peak in thiamine-related interactions during winter. For thiamine, maximum edge density (indicating a preferential association) was observed between complete thiamine synthesizers and auxotrophs (Supplementary Figure 1), highlighting the potential dependence of certain taxa on vitamin producers. Interestingly, thiazole auxotrophs also showed the highest edge density with pyrimidine auxotrophs, suggesting that, similar to cobalamin, cross-feeding interactions involving thiamine precursors may become particularly relevant when complete synthesizers are scarce. The overall higher prevalence of pyrimidine auxotrophy aligns with observations from the Baltic Sea ( Paerl et al., 2018 ), while its increased occurrence in summer further suggests elevated environmental levels of the HMP during this period, consistent with observations from other marine environments ( Bittner et al., 2024 ). Conversely, thiazole auxotrophs, mostly Nitrosopumilus, may utilize HET released by some cells of the highly abundant complete thiamine synthesizers during winter, reducing its dependency on other synthesizers to meet their B1 requirements. Taken together, our findings provide new insights into the seasonal dynamics of vitamin biosynthesis and its role in structuring microbial interactions in marine ecosystems, particularly in relation to cobalamin and thiamine. By leveraging gene co-occurrence rather than solely relying on taxonomic data, our results suggest that complete and partial vitamin synthesis are among the main microbial interactions throughout the year. Based on experimental approaches of previous studies, our results highlight the critical importance of cross-feeding interactions, particularly for cobalamin, where lower ligand activation and seasonal succession in the microbial assemblage composition play a key role in maintaining vitamin availability. The distinct seasonal strategies on the biosynthesis of vitamins, with partial and complete synthesizers showing a different temporal distribution, highlight the dynamic nature of the vitamin-related microbial interactions. Yet, the consistent seasonal pattern of vitamin biosynthesis genes contrasts with the interannual taxonomic shifts among vitamin producers, suggesting that functional redundancy (particularly in lower ligand activation) helps maintain key metabolic functions across the 12-year time series. Altogether, our results highlight vitamins as one of the main drivers of ecological interactions between eukaryotic and prokaryotic microorganisms in the Mediterranean Sea. Acknowledgement We thank all members of the Blanes Bay Microbial Observatory (BBMO) team for their collaboration in the sample collection. We are also grateful to the multiple projects that fund the BBMO initiative ( https://bbmo.icm.csic.es ). The analyses of the project have been performed at the Marbits bioinformatics core at ICM-CSIC ( https://marbits.icm.csic.es ) and at the Supercomputing Center of Galicia (CESGA). This work has been partially funded by the MAORI project (PID2022-136281NB-I00). 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