Dual-Axis Life-History Framework Explains Metabolite Exchange and Functional Differentiation in Nodule Microbiomes

Dual-Axis Life-History Framework Explains Metabolite Exchange and Functional Differentiation in Nodule Microbiomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dual-Axis Life-History Framework Explains Metabolite Exchange and Functional Differentiation in Nodule Microbiomes Zining Guo, Jianyang Li, Xiaoyu Ji, Zhengfei Yu, Wanpeng Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8281434/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Deep-sea polymetallic nodule fields are among the most oligotrophic and metal-stressed ecosystems on Earth, where microbial communities play critical roles in driving elemental cycling and maintaining ecosystem stability. However, how environmental stress and resource limitation shape microbial life-history strategies and metabolic interactions in these systems remains poorly understood. Results We investigated the community structure, metabolic potential, and life-history strategies of microbial assemblages inhabiting polymetallic nodules and surrounding sediments by reconstructing 314 high-quality, non-redundant metagenome-assembled genomes (MAGs). Compared to sediment communities, nodule-associated microbes exhibited higher taxonomic novelty and niche specificity, with enrichment of archaeal lineages such as Nitrosopumilaceae. Both habitats harbored broad-spectrum metal resistance genes, predominantly maintained through vertical inheritance, while nodule-inhabiting microbes showed significant enrichment of resistance genes targeting arsenic, chromium, and lead. Functional analyses revealed a spatial division of labor characterized by a “specialization–buffering” relationship, with differentiated contributions to carbon, nitrogen, and sulfur cycling. Metabolic exchange network analysis indicated that sediment microbes engaged in more active exchange of energetic metabolites, including acyl-CoA and amino acids. Taxa with multifunctional life-history strategies (SY, AY, and AS) enhanced energy flow and network stability through intensified organic carbon and amino acid transfer, reflecting functional redundancy and ecological resilience. In contrast, nodule-associated communities, dominated by AS and Y strategies, primarily exchanged small organic acids and inorganic ions, consistent with adaptations toward resource efficiency and environmental persistence. Conclusions These results demonstrate that metabolic strategic differentiation in deep-sea nodule ecosystems reflects a dynamic trade-off between resource availability and environmental stress. Our dual-axis life-history framework provides a new ecological perspective on how functional stability is maintained in deep-sea extreme environments. Polymetallic nodules life-history strategies Metabolic exchange networks metagenomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Microbial communities are the primary drivers of biogeochemical cycling and energy flow in deep-sea ecosystems (Orcutt et al., 2011 ). Deep-sea polymetallic nodules, formed under low sedimentation, oxic bottom waters, and weak currents, are rich in Mn, Fe, and trace metals (e.g., Cu, Co, Ni). These nodules are not only potential mineral resources but also unique microbial habitats (Hein et al., 2013 ). Microbes inhabiting nodules mediate metal redox cycling and exhibit remarkable adaptations to oligotrophic, metal-rich conditions (Röthig et al., 2017 ). As key agents of deep-sea biogeochemistry, they offer insights into microbial survival strategies under extreme energy limitation (Sizikov et al., 2020 ). Nodule-associated microbes employ diverse strategies to cope with metal stress and sustain metabolism. These include metal efflux systems, extracellular polymeric substances (EPS) for detoxification and microenvironment stabilization, and metal-oxidizing metabolisms linked to energy conservation (Lian et al., 2022 ; Zhang et al., 2015). In both nodules and sediments, microbes utilize organic and inorganic carbon sources via chemoautotrophic and heterotrophic pathways, with key roles in nitrification, denitrification, and sulfur cycling (Zhang et al., 2023 ; Wu et al., 2021 ). However, community-level survival depends on metabolic complementarity and interspecies metabolite exchange. Metabolic complementarity enables efficient energy and electron flow among taxa with distinct trophic roles. For example, hydrogenotrophs release organic acids (e.g., acetate, formate) that support heterotrophs (Jiang et al., 2020 ), while sulfate-reducing microbes generate sulfide that fuels metal oxide reduction and supports chemoautotrophs (Wu et al., 2021 ). These interactions enhance energy efficiency and stress resistance in oligotrophic settings. Life-history strategies further shape metabolic networks. The Y-A-S framework (Malik et al., 2020 ) describes trade-offs among growth rate (Y), resource acquisition (A), and stress tolerance (S). In nodule fields, fast-growing taxa degrade substrates and release intermediates, while stress-tolerant taxa efficiently utilize these byproducts. Moreover, “metabolic hub” microbes exhibiting mixed strategic traits can both produce key metabolites and efficiently assimilate metabolic byproducts from other taxa, serving as pivotal nodes that mediate energy flow and functional connectivity within the microbial network (Douglas et al., 2020). This study investigates microbial communities inhabiting polymetallic nodules and surrounding sediments from the eastern Pacific Ocean. Using metagenome-assembled genomes (MAGs), we aim to: (1) characterize microbial composition and metal resistance mechanisms; (2) identify metabolic adaptations to high pressure and low nutrient availability; and (3) explore how life-history strategies shape metabolite exchange and resource utilization under extreme conditions.These findings provide new insights into microbial ecological roles and adaptive strategies in deep-sea, energy-limited ecosystems. Materials and methods Sampling and DNA extraction In October 2023, sediment and polymetallic nodule samples were collected from the Clarion–Clipperton Fracture Zone (eastern Pacific; 4,983–5,211 m depth) during cruise DY135-E2 (Figure S1 , Table S1 ). A total of 17 sediment and 3 nodule samples were obtained using a custom deep-sea sampler and stored at − 80°C until processing. Surface layers (3–5 mm) of nodules were scraped and powdered for DNA extraction. Sediment samples were centrifuged to remove excess water. DNA was extracted using commercial kits (see Supplementary Methods for details), with duplicates per sample to ensure reproducibility. Metagenomic sequencing and assembly DNA libraries were constructed and sequenced on an Illumina HiSeq 2500 platform (2 × 150 bp). Raw reads were quality-filtered and assembled into contigs using MEGAHIT. Contigs ≥ 1,000 bp were retained for downstream analysis. Detailed parameters are provided in Supplementary Methods. Genome binning and annotation Metagenome-assembled genomes (MAGs) were recovered using MetaBAT2 and MaxBin2 via MetaWrap. High-quality MAGs (> 70% completeness, < 10% contamination) were dereplicated at 99% ANI using dRep. Taxonomy was assigned using GTDB-Tk. Protein-coding genes were predicted using Prodigal. Functional annotation included metal transport (TCDB), nitrogen and sulfur cycling (NCycDB/SCycDB), CAZymes (dbCAN), and small-carbon metabolism (KEGG KO). See Supplementary Methods for full annotation pipeline. Relative abundance and network analysis Clean reads were mapped to MAGs using BWA to estimate relative abundance. Co-occurrence networks were constructed based on Spearman correlations and Random Matrix Theory (RMT) using the iNAP platform. Only MAGs present in > 50% of samples within a group were included. Metabolite exchange prediction Metabolic complementarity and potential metabolite exchange between MAGs were predicted using PhyloMint based on genome-scale metabolic models reconstructed by CarveMe. Dual interactions were defined when both co-occurrence and metabolic complementarity were detected. Metabolites were classified into eight major categories (see Supplementary Methods). Life-history strategy classification MAGs were assigned to life-history strategies (Y, A, S, AS, AY, SY, AVER) using an XGBoost-based framework trained on genomic features (e.g., GC content, rRNA copy number, metabolic versatility). Detailed training set and feature list are provided in Supplementary Methods. Result Recovery of MAGs and dominant microbes in polymetallic nodule field A total of 314 high-quality, non-redundant MAGs were recovered from nodules and surrounding sediments. Sediment yielded 154 MAGs spanning 10 phyla, with 73.1% unclassified at the species level (Table S2 ). Nodule contained 160 MAGs across 22 phyla, and 94.3% remained unclassified, indicating substantially higher taxonomic novelty (Figure S2 , Table S3). Sediment-inhabiting communities were dominated by Proteobacteria (73.3%), followed by Nitrospirota and Desulfobacterota _B. These included putative ammonia-oxidizing autotrophs that may contribute primary production in deep-sea sediments (Figure S3). Nodule-inhabiting MAGs displayed a distinct profile, with Proteobacteria (38.8%), Gemmatimonadota , and Acidobacteriota as dominant phyla, and a notable enrichment of Thermoproteota archaea (9.37%), all belonging to Nitrosopumilaceae (Figure S3). High-abundance MAGs (top 50% of relative abundance) further highlighted habitat-specific patterns: sediments were enriched in Proteobacteria , Methylomirabilota , and Nitrospirota , whereas nodules were dominated by Thermoproteota , Proteobacteria , and Acidobacteriota (Figure S4). Metal resistance in polymetallic nodule field Comparative genomic analyses revealed that MAGs from both habitats encode extensive metal transport and redox-related functions, with Hg and Cu metabolism representing the largest fractions of metal-related genes in sediments (48.3% and 14.1%) and nodules (43.3% and 21.8%) (Fig. 1 A, Table S4 and S5). Their taxonomic distributions, however, differed markedly. In sediments, Hg resistance genes ( merA / merB / mer ) were mainly contributed by Gammaproteobacteria , Methylomirabilota , and Nitrospirota , whereas nodules showed additional enrichment in Gemmatimonadota , occurring in 97.5% of nodule-inhabiting MAGs(Table S6 and S7). Cu-resistance functions (CopB/CopD, CueO, PcoA) were present in 155 sediment-inhabiting MAGs but were significantly abundant and widespread in nodules (1,731 genes), primarily from Alphaproteobacteria and Gemmatimonadota . Multicopper oxidases (MCOs: mnxG , mofA , cotA ) (Table S6 and S7), capable of oxidizing Mn(II) and Fe(II), were strongly enriched in nodules (616 vs. 91 genes), mainly in Alphaproteobacteria and Nitrospirota . Nodule-inhabiting MAGs also contained significantly higher numbers of As-, Cr-, and Pb-resistance functions (1,773 genes, p < 0.01), including ACR3/ArsB/ArsP, AioA, ArsC, NemA/ChrR, and PbrBC/ILT, largely belonged to Alphaproteobacteria and Gemmatimonadota . Fe²⁺ transporters (FeoB) and vacuolar iron transporters (VIT) were detected in 163 and 60 MAGs, respectively, together with eight additional functional categories (Figure S5, Table S8). Iron regulatory and uptake systems—PvdS, FecR, TonB, ExbB/D, and FbpB—comprised > 90% of iron-related functions. Regulatory genes accounted for 41.5% of iron functions in nodules and 30.1% in sediments. pvdS was more abundant in nodules (12.95% vs. 6.92%), mainly in Gammaproteobacteria and Gemmatimonadota (Fig. 1 B). Sediment-inhabiting MAGs showed higher abundance of fbpB , indicating stronger reliance on high-affinity Fe²⁺ uptake under reduced sediment conditions. Furthermore, our analysis of horizontal gene transfer (HGT) events within the nodule field revealed the potential genetic origins of the aforementioned metal resistance and iron transport functional genes (Fig. 1 C). Among 303 total HGT events (205 in sediments; 98 in nodules), only 13 involved metal-related genes (Fig. 1 C, Table S9). Sediment-inhabiting transfers (NemA, Nramp, PvdS) primarily occurred from Gammaproteobacteria to Alphaproteobacteria . In nodules, HGT events (MerA, AioA) occurred exclusively within Proteobacteria , with all recipients belonging to Alphaproteobacteria . The scarcity of metal resistance–related HGT suggested that these traits were predominantly maintained through vertical inheritance, reflecting long-term genomic stability in deep-sea nodule environments. Carbon metabolism In both habitats, glycosyltransferases (GTs) represented the most abundant CAZyme family, followed by glycoside hydrolases (GHs), carbohydrate esterases (CEs), and auxiliary activity enzymes (AAs) (Figure S6). GT2, GT4, GT51, and GT9 were highly enriched in both sediments and nodules, indicating shared glycosyl-transfer capabilities. Although GH genes were abundant in both habitats (1,268 in sediments; 3,241 in nodules), only GH23 showed high prevalence across both environments. Sediment-inhabiting MAGs were enriched in GH102 and GH103, associated with chitin and N-acetylglucosamine polysaccharide degradation, whereas nodule-inhabiting MAGs showed higher abundances of GH1 and GH13, reflecting greater β-glucosidase and amylase activity and distinct carbon-source preferences. CE families exhibited similar profiles across habitats, with CE1, CE11, CE14, and CE4 dominating. GH23 and CE4—both linked to chitin degradation—were abundant in both habitats, suggesting that chitin represents an important carbon reservoir in these oligotrophic deep-sea systems. Microbes from both habitats encoded complete Wood–Ljungdahl (m00377) and phosphate acetyltransferase–acetate kinase (m00579) pathways, although the K00196 component was absent in nodules (Fig. 2 A). Core C₁-transfer genes ( fhs, folD, metF ) were abundant in both sediments and nodules (395 vs. 291 genes), mainly within Gamma -, Alphaproteobacteria , and Nitrospirae . Sediment-inhabiting communities exhibited stronger acetyl-CoA–to–acetate conversion potential, carrying 104 copies of ackA/pta compared to 36 in nodules. Differences also emerged in CO₂-to-formate reduction: sediment fdhA genes were primarily contributed by Nitrospinia and Gemmatimonadota , whereas nodule-inhabiting communities additionally contributed by Methylomirabilia and Dehalococcoidia . Both habitats encoded reductive TCA cycle (rTCA) genes (m00173), particularly sdhA/sdhB and sucC/sucD (Fig. 2 B). Sediments contained more sdh genes than nodules (441 vs. 262), predominantly from Gammaproteobacteria and Nitrospinia . Finally, acetyl-CoA synthase (ACS) was significantly more abundant in sediments (264 vs. 139), indicating a greater capacity for acetate utilization in sediment communities. Nitrogen cycling and Sulfur metabolism Nitrogen cycling genes were detected in both habitats, but their distributions differed markedly, indicating distinct ecological roles in nitrogen transformations. Nitrogen fixation genes ( nifKH ) occurred only in two nodule-inhabiting MAGs and were absent from sediments. Nitrification potential was limited: sediments contained 6 MAGs with amo/pmo and four MAGs with hao (mainly Gammaproteobacteria and Acidobacteriae ), whereas nodules contained only one hao -bearing MAG. No nitrite oxidation genes were detected, suggesting that complete nitrification is rare. Organic nitrogen metabolism ( nmo ) occurred in 6 sediment- and 12 nodule-inhabiting MAGs, primarily Gammaproteobacteria and Alphaproteobacteria . Dissimilatory nitrate reduction genes ( narGHI/napAB; nirBD/nrfACH ) were detected in 48.7% of sediment-inhabiting MAGs and 18.7% of nodule-inhabiting MAGs, while assimilatory nitrate reduction ( narB/nasAB/NR; nirA ) occurred in 33.9% and 26.8%, respectively, indicating that both communities can utilize nitrate as an electron acceptor. Genes for NO₂⁻ reduction to NO ( nirK/nirS ) were significantly enriched in sediments (30.1% vs. 18.1% in nodules; Fig. 3 A). Sediments also showed stronger potential for NO reduction to N₂O, with 32 MAGs carrying norBC , mainly Gammaproteobacteria , Nitrospinia , and Gemmatimonadota , compared to 4 MAGs in nodules. Complete denitrification was limited, with nosZ detected in only 2 sediment- and 9 nodule-inhabiting MAGs. Overall, sediment-inhabiting communities exhibited greater potential for denitrification and ammonia oxidation and may contribute NO to the overlying seawater. The scarcity of nitrification and nitrite oxidation genes suggests that nitrate in this system is largely supplied by overlying water rather than produced in situ. Genes associated with both dissimilatory sulfite reduction (dsr) and the reverse pathway (rdsr)—including sat, aprAB, dsrB , and qmoABC —were detected in sediments and nodules (Fig. 3 B), indicating the potential for both sulfate/sulfite reduction and sulfur oxidation. Sulfate-reduction genes were abundant in both habitats (933 in sediments; 684 in nodules). sat occurred in 103 sediment- and 91 nodule-inhabiting MAGs, mainly belonged to Gammaproteobacteria , Alphaproteobacteria , and Methylomirabilia . aprAB was also enriched, with significantly higher abundance in sediments (86 vs. 25). Assimilatory sulfate reduction genes ( cys ) were widespread, occurring in 96.7% of sediment-inhabiting MAGs and 89.3% of nodule MAGs (780 vs. 568 genes). Only 6 sediment-inhabiting MAGs carried dsrB , and dsrD was absent from all samples, suggesting that sulfur oxidation may proceed through the reverse Dsr pathway, supported by the presence of fccAB in 10 MAGs. For thiosulfate metabolism, the SOX system was more prevalent and diverse in sediments—especially soxB and the full sox gene cluster—whereas nodules showed enrichment of tetrathionate-pathway genes ( tsdA and tetrathionate reductase), indicating a habitat-specific preference for S₂O₃²⁻ metabolism via tetrathionate intermediates. Genomic clues to microbial metabolic synergies in polymetallic nodule field Microbial functional potential in the nodule field was largely shaped by metabolite exchange and metabolic complementarity. Across all MAGs, eight major metabolite categories were identified (Fig. 4 A). C-, A-, and I-type metabolites showed the highest transfer frequencies in both habitats, underscoring their central roles in community-level metabolic integration. OC-type metabolites were also widely exchanged, consistent with the transformation and turnover of complex organic matter. In contrast, L- and N-type metabolites were infrequently transferred, while OA- and S-type metabolites showed localized distributions, likely linked to taxa with specialized metabolic roles. Nodule-inhabiting communities exhibited higher overall transfer activity—particularly for A, C, I, and OC metabolites—indicating more intensive interactions related to energy metabolism and organic matter processing. Moreover, nodules showed relatively higher percent transfer of L and N metabolites, suggesting enhanced basal metabolism and nucleic acid turnover. Analysis of high-abundance metabolite subclasses further highlighted habitat-specific metabolic preferences. C-type metabolites, key intermediates in β-oxidation and TCA-cycle–related pathways, differed strongly between habitats (Fig. 4 B). Sediments were dominated by long-chain acyl-CoA and aromatic intermediates (e.g., stearoyl-CoA), reflecting active lipid catabolism and biosynthesis. Nodules were enriched in short-chain CoA derivatives and more oxidized intermediates (e.g., octanoyl-CoA), indicating distinct lipid metabolic strategies. Within A-type metabolites, sediments primarily exchanged amino acid and pyrimidine biosynthesis intermediates (e.g., L-homoserine, L-aspartate), consistent with metabolite-assisted nitrogen metabolism. Nodules accumulated specific amino acid derivatives, suggesting selective amino acid cycling or rapid intracellular regulation. I-type metabolites showed similar subclass compositions in both habitats and mainly comprised Mg²⁺, Ca²⁺, Zn²⁺, and Co²⁺ ions. Life history strategies influence metabolite delivery associations Using an XGBoost-based framework, all MAGs were classified into life-history strategy groups (Fig. 5 A). In sediments, MAGs were dominated by SY (25.1%), AY (22.6%), and AS (18.8%), whereas the S group was least abundant (4.4%). In contrast, nodule MAGs were enriched in AS (26.2%), AY (20%), and Y (18.1%), followed by smaller fractions of SY and AVER (6.8%) (Fig. 5 B, Table S11 and S12). Life-history strategies exhibited strong, habitat-specific influences on metabolite exchange (Fig. 5 C). In sediments, AY exerted the greatest effects on carbohydrate (gain = 56.6), CoA-derivative (39.5), and inorganic metabolite exchange (34.7). AS strongly influenced inorganic compounds and CoA derivatives (296.1 and 153.4, respectively), but contributed minimally to other categories (gain < 5). In nodules, AY impacted organic compounds, CoA derivatives, carbohydrates, and organic acids, while AS primarily affected organic compounds, carbohydrates, and nucleoside-related metabolites (43.4, 36.7, 34.4). Habitat-specific dominant groups also showed contrasting roles. In sediments, SY strongly affected amino acid (101) and inorganic metabolite exchange (90), whereas in nodules SY was more involved in nucleic acid exchange. Conversely, the Y group in nodules mainly influenced amino acid and inorganic-ion exchange, while in sediments it contributed most to nucleoside and organic acid exchange. Although AVER was low in abundance across both habitats, it consistently influenced CoA derivatives, organic acids, and organic compounds, suggesting a generalist rather than specialized metabolic role. Discussion Microbes from the tubercle zone exhibiting high taxonomic novelty and niche specificity. Microbial communities in deep-sea polymetallic nodule fields exhibited pronounced taxonomic novelty and niche specificity. Genome-resolved analyses recovered numerous previously uncharacterized lineages, underscoring nodule ecosystems as underexplored hotspots of microbial diversity (Zhou et al., 2022 ). The clear compositional separation between sediments and nodules (Figure S2 ) indicates functional divergence shaped by contrasting microenvironments. As solid substrates, nodules create micro-oxic to anoxic interfaces and steep chemical gradients, supporting higher phylogenetic richness and broader ecological niches (Molari et al., 2020 ). Sediments were dominated by Proteobacteria (73.3%) alongside taxa central to anaerobic nitrogen and sulfur cycling, including Nitrospirota (12.3%) and Desulfobacterota _B (10.3%), consistent with active ammonia oxidation and sulfate reduction. In contrast, nodule microbiomes showed reduced Proteobacteria abundance and greater diversity due to enrichment of Gemmatimonadetes and Acidobacteriota , as well as archaeal Nitrosopumilaceae —key ammonia-oxidizing autotrophs that support carbon fixation and nitrogen cycling on oligotrophic nodule surfaces (Wright et al., 2023). The presence of Acidobacteriota further suggests adaptation to acidic or metal-rich microhabitats, potentially mediating Fe–Mn redox transformations (Gonçalves et al., 2024 ). Sediment communities relied largely on heterotrophic carbon degradation and anaerobic methane oxidation (e.g., Methylomirabilota ), whereas nodule habitats were characterized by stronger inorganic chemolithoautotrophic activity, driven by AOA and putative Fe–Mn–metabolizing Acidobacteriota (Mujakić et al., 2022; Wang et al., 2021). Together, these taxonomic and functional distinctions reflect divergent energy acquisition strategies and highlight the complementary metabolic roles that may contribute to nodule formation and deep-sea ecosystem stability. Broad-spectrum, integrative, and vertically transferred anti-metal strategy Metal toxicity is a major selective force shaping microbial genome evolution in polymetallic nodule fields. Consistent with the coexistence of multiple metals, genes conferring resistance to Hg, Cu, As, Fe, and Cr were abundant across both habitats, indicating that microbes employ broad-spectrum and integrated strategies to withstand complex metal stressors (Li et al., 2017 ; Sánchez-Corona et al., 2025 ). These strategies operate at two levels. First, functional interconnections promote co-enrichment of resistance systems; for example, multicopper oxidases (MCOs), central to Cu homeostasis, can also oxidize Fe(II) and Mn(II), driving parallel enrichment of Fe- and Mn-related genes (Pooalai et al., 2022 ). Second, detoxification pathways share conserved oxidative stress responses, enabling enzymes such as MerA, ArsC, and ChrR to contribute to cross-resistance (Yang et al., 2024 ). Metal resistance functions were further integrated through community-level metabolic networks (Sharma et al., 2025 ). Core lineages—including Proteobacteria , Methylomirabilota , and Gemmatimonadota —recurrently contributed functions such as MerAB, CopBD, PcoA, and ArsC, while nodule-associated Nitrosopumilaceae showed notable enrichment of Cu- and Hg-related genes, suggesting key roles in metal transport and redox metabolism. Iron acquisition strategies were similarly diverse. The widespread presence of the TonB–ExbB/D system highlights the dominance of TonB-dependent Fe³⁺–siderophore uptake in deep-sea Gram-negative microbes (Klebba et al., 2021 ). Co-occurrence of FecR (Fe³⁺ uptake regulation) and FbpB (high-affinity Fe²⁺ transport) indicates flexible adaptation to fluctuating redox conditions. The higher abundance of FbpB in sediments suggests enhanced suitability for low oxygen, strongly reducing environments (Payne et al., 2016 ; Garber et al., 2021 ). Despite the extensive repertoire of metal resistance genes, their dissemination appears strongly dominated by vertical inheritance. Only limited HGT events were detected, consistent with the deep sea’s stable, chronic metal exposure regime, which favors long-term genomic conservation rather than frequent lateral gene acquisition (Gillard et al., 2019 ; Aminov et al., 2011; Yang et al., 2023 ). The slow-growth and low-metabolism lifestyles typical of deep-sea microbes, together with defense systems such as CRISPR and restriction–modification pathways (Zhu et al., 2020 ), likely further constrain foreign DNA uptake. Collectively, these features support a conservative evolutionary strategy in which vertical inheritance maintains functional stability under persistent metal stress. Deep-sea microorganisms have developed an ecological–evolutionary stable strategy (ESS) shaped by evolutionary pressures over million-year timescales. The adaptive mechanisms within deep-sea nodule ecosystems follow a logic closer to “genome consolidation driven by slow variables” rather than a “rapid-response mobile gene pool.” This insight is essential for understanding the long-term evolutionary trajectories of deep-sea microorganisms and for assessing the environmental impacts of deep-sea mining. Adaptation strategies drive the acquisition of different carbon substrates. The CAZyme composition and small-carbon metabolic potential of deep-sea nodule field microbiomes reflect clear adaptive differentiation between habitats. GTs were the most abundant CAZy class, with GT2 particularly enriched in nodule-inhabiting microbes, consistent with enhanced EPS and polysaccharide biosynthesis (Kaur et al., 2009 ). On nodule surfaces—where mineral-bound organic matter is limited—EPS likely supports biofilm formation, mitigates metal toxicity, and adsorbs scarce organic substrates, forming a localized carbon reservoir (Wang et al., 2022 ; Kayoumu et al., 2025 ). Similar EPS-driven adaptations are documented in other oligotrophic deep-sea systems such as vent chimneys (Chen et al., 2024 ). In contrast, sediment-inhabiting microbiomes were enriched in degradative GH families, especially GH102 and GH103, indicating specialization toward chitin and GlcNAc-based polysaccharide degradation (Jiang et al., 2022 ). Together with high GH23 and CE4 abundances, these patterns show that chitin represents a major bioavailable carbon pool derived from particulate organic matter and cellular debris. Thus, nodule-inhabiting communities favor carbon acquisition via EPS production and complex polysaccharide synthesis, whereas sediments rely on organic matter breakdown. Both habitats encoded complete Wood–Ljungdahl and acetogenesis pathways, demonstrating capacity for C₁-based carbon–energy coupling. However, sediments showed significantly higher abundances of ackA/pta and ACS, indicating stronger potential for acetate assimilation and fermentative metabolism, likely reflecting anaerobic or micro-oxic niches (Schütze et al., 2020 ). The broad taxonomic distribution of fdhA —notably in Methylomirabilia, Dehalococcoidia , and Nitrospiria —further suggests that formate oxidation contributes to energy generation, potentially linked to metal reduction (Al-Bassam et al., 2018 ). Nodule-inhabiting communities showed notable activity in the reductive TCA (rTCA) cycle, supported by widespread sdhA/B distribution. This strategy enables CO₂ assimilation and reductive metabolism under metal-rich, redox-fluctuating, and low-organic conditions (Leng et al., 2023 ). Such metabolic flexibility—using endogenous small-carbon intermediates or inorganic carbon fixation—provides a competitive advantage in oligotrophic nodule microhabitats (Chen et al., 2023). Adaptive Diversity and Ecological Strategy Differentiation in Nitrogen and Sulfur Cycling Sediment-inhabiting microbiomes exhibited greater nitrogen-cycle gene diversity and more complete pathways, whereas nodule-inhabiting communities reflected the distinct metabolic constraints of mineral–microbe interfaces (Cerqueira et al., 2018 ; Wasmund et al., 2017 ). The limited nitrogen-cycling capacity in nodules suggests that mineral phases provide alternative electron-transfer routes, shaping microbial metabolic strategies. Both habitats lacked nitrogen fixation and complete nitrification pathways, indicating that microorganisms rely primarily on seawater-derived nitrate as an electron acceptor. The absence of ammonia-oxidation genes further supports the external origin of nitrate. Thus, sediment-inhabiting microbes function largely as “nitrate processors”, regulating nitrogen transformation and release rather than replenishing endogenous nitrogen pools (Zhang et al., 2023 ). Sediments habitats possessed both dissimilatory ( narGHI/napAB ) and assimilatory ( narB/nasAB ) nitrate reduction genes, reflecting a dual nitrate-utilization strategy that enables flexible metabolic switching under carbon-limited, redox-variable deep-sea conditions (Jiang et al., 2023 ). In contrast, nitrate-reduction genes were substantially less abundant in nodules, consistent with a greater reliance on metal redox processes for energy generation. This divergence highlights clear ecological niche separation between the two habitats. Denitrification potential also differed markedly. Sediments showed frequent nirK/nirS , indicating enhanced NO production capacity, whereas this step was strongly attenuated in the more oxidizing nodule microenvironment. Genes encoding norBC and nosZ were extremely rare in nodules. Rather than representing functional loss, this genomic “incompleteness” likely reflects an energy-conserving strategy adapted to metal-rich substrates (Oba et al., 2024 ). Because NO and N₂O reduction yield substantially less energy than oxygen or metal-oxide reduction, nodule-associated microbes appear to adopt partial denitrification, truncating energetically costly steps. This streamlined strategy facilitates rapid adjustment to fluctuating electron acceptors at mineral interfaces and represents a mineral-regulated survival mode in polymetallic nodule-inhabiting ecosystems (Ma et al., 2025 ). In contrast to the incomplete nitrogen cycle, sulfur pathways displayed greater ecological complexity and adaptability. In sediments, the widespread presence of dsrB and asrA , together with the absence of dsrD , indicates the operation of the reverse Dsr (rDSR) pathway, a key strategy for oxidizing reduced sulfur substrates (e.g., H₂S, S²⁻) under oxidizing conditions (Nagar et al., 2022 ). In the dark, metal oxide–rich deep sea, the rDSR pathway channels electrons toward sulfate formation, supporting efficient and stable energy generation and enabling microbes to respond flexibly to redox fluctuations (Neukirchen et al., 2023 ; Whaley-Martin et al., 2023 ). The co-occurrence of fccAB and soxB further suggests a multistep oxidation cascade from H₂S → S⁰ → S₂O₃²⁻. Nodule-inhabiting communities, however, primarily utilized soxB and sudAB -mediated pathways, targeting the oxidation of intermediate sulfur compounds. This pattern is consistent with habitat-level resource differences: reduced sulfur donors in sediments derive largely from organic matter degradation and sulfate reduction, whereas nodule surfaces—low in reduced sulfur—provide mainly thiosulfate and other intermediates as available substrates (Zhou et al., 2025 ). As a result, sediments support a more metabolically active and flexible sulfur-cycling network, while nodule-inhabiting microbiomes operate a more streamlined configuration constrained by the solid mineral matrix and limited electron acceptor availability. This contrast highlights a broader ecological trade-off: microbial communities in polymetallic nodule fields do not evolve toward maximal functional breadth, but toward resource-adapted energetic optimization (Malik et al., 2020 ), balancing substrate availability, redox structure, and metabolic efficiency. Sediments and nodules highly differentiated in terms of electron acceptor utilization, substrate conversion efficiency, and energy budgeting strategies. Sediments fulfill the system-level role of “functional completeness and redundancy,” whereas nodules represent “high-efficiency, low-energy extreme adaptation units.” This complementary structure suggested that the functional stability of deep-sea nodule regions arises from the coordinated division of labor among distinct ecological units during energy–matter transformation, rather than from the metabolic capacity of any single environment. Life strategies regulated along dual axes shape the transmission of distinct metabolites Metabolite exchange between nodule- and sediment-inhabiting communities differed markedly, emphasizing the influence of mineral–microbe interfaces on metabolic cooperation (Zhang et al., 2025 ). Nodule-associated microbes formed a more dynamic network centered on energy-related intermediates, with both habitats showing frequent exchange of acyl-CoA derivatives, indicating syntrophic interactions via fatty-acid metabolism. Sediments exchanged predominantly long-chain acyl-CoA and aromatic intermediates (e.g., stearoyl-CoA), consistent with energy storage and complex organic matter degradation (Garay et al., 2014 ). In contrast, nodules were enriched in short-chain acyl-CoA (e.g., octanoyl-CoA), reflecting rapid, efficient lipid catabolism adapted to oligotrophic, episodically supplied conditions (Ma et al., 2021 ). Sediment communities, supported by relatively higher organic matter inputs, exhibited complex metabolic division of labor. The extensive exchange of succinate not only underscored its role as a TCA-cycle intermediate but also suggested potential interspecies signaling functions that reinforce metabolic synergy. Carbohydrate transport patterns further differentiated the two habitats: sediments showed frequent exchange of phosphorylated sugars, indicating an active carbohydrate co-metabolic network involving coordinated polysaccharide degradation, monosaccharide conversion, and phosphorylation (Liu et al., 2024 ). In contrast, nodule microbes exchanged far fewer carbohydrate metabolites, likely due to the physicochemical inaccessibility of organic matter bound to mineral surfaces, which limits cooperative degradation (Kleber et al., 2021 ). Microbial life-history strategies govern the allocation of resources to growth, acquisition, and stress tolerance, thereby shaping ecological roles and the structure of metabolite-exchange networks (Malik et al., 2020 ). Distinct strategies exhibit characteristic resource-use preferences and metabolic specializations, which in turn determine the direction and intensity of metabolite exchange. Applying the Y–A–S framework revealed clear functional differentiation among strategic groups. In resource-rich sediments, communities were dominated by SY, AY, and AS strategists, collectively forming a multifunctional assemblage adapted to fluctuating niches (Peng et al., 2024 ). AY and AS groups primarily mediated carbohydrate and inorganic compound exchange, respectively, while SY lineages played key roles in amino-acid and ion exchange, supporting nitrogen cycling and ionic homeostasis. These complementary functions help maintain ecosystem stability through rapid responses to resource pulses and enhanced resilience (Pascual-García et al., 2020 ). In nutrient-limited nodule habitats, a different equilibrium emerged. AS strategists dominated the exchange of organic compounds and nucleosides, AY mediated carbohydrate and organic-acid turnover, and Y specialized in amino-acid and inorganic-ion exchange. These patterns indicate streamlined, efficiency-oriented metabolism under intensified stress and limited carbon supply. Strategic groups also displayed pronounced functional plasticity across environments: SY microbes shifted from amino-acid/ion exchange in sediments to nucleic-acid exchange in nodules, while Y-type lineages reversed their roles across habitats. AVER populations, although low in abundance, consistently exchanged CoA derivatives, organic acids, and complex organic compounds, functioning as cross-environment metabolic hubs (Loos et al., 2024 ). Integrating life-history traits with metabolite-exchange networks revealed a dual-axis regulatory framework driven by resource availability and environmental stress intensity. Sediments, with higher resource inputs and niche heterogeneity, favored multifunctional SY–AY–AS assemblages, promoting functional redundancy, metabolic plasticity, and ecological stability. Nodules, characterized by nutrient scarcity and metal-induced stress, selected for streamlined AS- or AY–Y-type combinations that maximize acquisition efficiency and survival under energy-limited regimes. These contrasting but complementary strategies maintain the balance between community stability and biogeochemical efficiency. This study proposes a perspective on microbial metabolic organization in deep-sea ecology: microbial functions are not simply additive but are structured by an “ecological niche–energy model” determined by life-history strategies. Accordingly, it offers a basis for understanding how deep-sea ecosystems construct sustainable metabolic networks. Conclusion The deep-sea polymetallic nodule field, as an extreme low-energy and metal-enriched habitat, hosts microbial communities with remarkable diversity and functional specialization. Our findings show that nodule- and sediment-associated microbiomes form a spatially stratified and functionally complementary system. Nodule communities are enriched in taxa with strong metal resistance and low-energy metabolic capacities, relying on metabolic complementarity and substrate exchange to sustain activity under chronic stress. In contrast, sediment communities exhibit higher taxonomic diversity, functional redundancy, and broader metabolic potential, acting as a functional reservoir that buffers environmental fluctuations and supplements nodule-surface processes. This spatial-functional partitioning illustrates how microorganisms maintain ecosystem stability through synergistic interactions between extreme (nodule) and moderate (sediment) microenvironments. The sustained cycling of key elements (C, N, S) under severe energy limitation emerges from this differentiated yet interconnected structure. We propose that such division of labor underpins the long-term persistence of polymetallic nodule ecosystems and serves as a representative model for energy-deprived deep-sea biomes. More broadly, this layered organization highlights how microbial life exploits spatial heterogeneity and cross-community cooperation to maintain functional stability under extreme conditions, providing a conceptual framework for adaptive strategies in other resource-limited ecosystems. Declarations Acknowledgements We thank the professors of the Third Institute of Oceanography, Ministry of Natural Resources, for sharing data and offering insightful advice during the study. We further acknowledge the officers, technicians, and crew members of the DY79 deep-sea expedition for their professional support in sampling operations and logistical coordination. Funding This research was supported by the National Key Research and Development Program of China (Grant No. 2023YFC2811402 awarded to X.Y. Guan). Data Availability Statement The metagenomic sequencing data generated in this study have been deposited in the NCBI BioProject database under the accession number PRJNA1403823. The data are publicly available and accessible for peer review. No metabolomic data were generated or analysed in this study. Ethics approval and consent to participate: Not applicable.Consent for publication: Not applicable.Ethics declaration: Not applicable. References Al-Bassam, M. M., Kim, J. N., Zaramela, L. S., Kellman, B. P., Zuniga, C., Wozniak, J. M., ... & Zengler, K. (2018). Optimization of carbon and energy utilization through differential translational efficiency. Nature communications , 9 (1), 4474. Aminov, R. I. (2011). Horizontal gene exchange in environmental microbiota. Front Microbiol 2: 158 . Chen, S., Xie, Z. X., Yan, K. Q., Chen, J. W., Li, D. X., Wu, P. F., ... & Wang, D. Z. (2024). Functional vertical connectivity of microbial communities in the ocean. 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Q., Cowley, E. S., Trembath-Reichert, E., & Anantharaman, K. (2025). Diversity and ecology of microbial sulfur metabolism. Nature Reviews Microbiology , 23 (2), 122-140. Zhu, F. C., Lian, C. A., & He, L. S. (2020). Genomic characterization of a novel Tenericutes bacterium from deep-sea Holothurian intestine. Microorgan isms 8: 1874 . Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.docx SupportingInformationTableS.xlsx GraphicalAbstract.pdf Metal transporter and redox-related functions encoded by MAGs. The dendrogram represents the composition of MAGs harboring genes associated with metal transport or redox processes, with each block corresponding to a single MAG. Block size reflects the number of related genes carried, and block color denotes phylum-level taxonomy. Circular blocks indicate sediment-derived MAGs, whereas square blocks indicate nodule-derived MAGs. Iron uptake transporters significantly enriched in both nodule and sediment samples. In the split-square tree, the upper half represents sediment samples and the lower half represents nodule samples. Horizontal gene transfer (HGT) events identified in the deep-sea nodule region.Genes involved in iron acquisition were abundant across both habitats. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8281434","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598731178,"identity":"abc1cc38-9d43-4527-855f-32ba3503f2a1","order_by":0,"name":"Zining Guo","email":"","orcid":"","institution":"China University of Geosciences Beijing","correspondingAuthor":false,"prefix":"","firstName":"Zining","middleName":"","lastName":"Guo","suffix":""},{"id":598731188,"identity":"06a15923-0dc8-4953-8a14-5ef46d15a472","order_by":1,"name":"Jianyang Li","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Jianyang","middleName":"","lastName":"Li","suffix":""},{"id":598731190,"identity":"e1e2eff3-1990-4502-84f2-a64e0a033e40","order_by":2,"name":"Xiaoyu Ji","email":"","orcid":"","institution":"China University of Geosciences Beijing","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Ji","suffix":""},{"id":598731193,"identity":"00ada31a-a419-4896-9f17-45a9f8adde3c","order_by":3,"name":"Zhengfei Yu","email":"","orcid":"","institution":"China University of Geosciences Beijing","correspondingAuthor":false,"prefix":"","firstName":"Zhengfei","middleName":"","lastName":"Yu","suffix":""},{"id":598731197,"identity":"e399ce22-2b02-4ca7-88cd-701379060698","order_by":4,"name":"Wanpeng Wang","email":"","orcid":"","institution":"Ministry of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Wanpeng","middleName":"","lastName":"Wang","suffix":""},{"id":598731199,"identity":"d55d94e6-eb85-479f-8687-dae839a0ce08","order_by":5,"name":"Xiangyu Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYPACGyDmAWI24rWkka7lMAla5COSnz3mbTsvb3B+7QGGD2WHGfhnN+DXYngjzdxwZtttww033iUwzjh3mEHizgECWmYkmEl8bLudYHDjjAEzb9thBgOJBEJa0r9JJLadg2j5S4wWeYkckC0HEgzO9xgwMxKjxYDnTZnkjHPJhjNv8CUc7DmXziNxg5At7enbpHnK7OT5zp89+OBHmbUc/wxCthwAEoyg6AC6B8Tmwa8eZEsDiPwDxPwHCCoeBaNgFIyCEQoAoTpFzP/oGNUAAAAASUVORK5CYII=","orcid":"","institution":"China University of Geosciences Beijing","correspondingAuthor":true,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2025-12-04 16:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8281434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8281434/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103742662,"identity":"4df9a068-0c76-4a6e-9d80-736d894ead85","added_by":"auto","created_at":"2026-03-02 11:13:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":573362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial metal utilization and metal resistance mechanisms in deep-sea nodule fields.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e Metal transporter and redox-related functions encoded by MAGs. The dendrogram represents the composition of MAGs harboring genes associated with metal transport or redox processes, with each block corresponding to a single MAG. Block size reflects the number of related genes carried, and block color denotes phylum-level taxonomy. Circular blocks indicate sediment-derived MAGs, whereas square blocks indicate nodule-derived MAGs. \u003cstrong\u003eB.\u003c/strong\u003e Iron uptake transporters significantly enriched in both nodule and sediment samples. In the split-square tree, the upper half represents sediment samples and the lower half represents nodule samples. \u003cstrong\u003eC.\u003c/strong\u003eHorizontal gene transfer (HGT) events identified in the deep-sea nodule region.Genes involved in iron acquisition were abundant across both habitats.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-8281434/v1/c621ab73f02885f396ca7bda.png"},{"id":103742671,"identity":"6756730f-cc70-4645-9af3-9938c69bc328","added_by":"auto","created_at":"2026-03-02 11:13:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":255875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmall-molecule carbon fixation among MAGs. A.\u003c/strong\u003e Composition of MAGs in the nodule field possessing the Wood–Ljungdahl pathway (m00377) and the phosphate acetyltransferase–acetate kinase pathway (m00579). \u003cstrong\u003eB.\u003c/strong\u003eComposition of MAGs with high abundance of other carbon fixation pathways. The horizontal bar charts indicate the proportion of genomes carrying each functional pathway. Numbers adjacent to the charts represent the total number of genomes encoding the corresponding function. In the split-square diagrams, the upper half corresponds to sediment-derived MAGs, and the lower half corresponds to nodule-derived MAGs.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-8281434/v1/c6e0297eae6b019d539ed1d2.png"},{"id":104399994,"identity":"7da58efa-d026-442e-b344-b1a9a7c1dec1","added_by":"auto","created_at":"2026-03-11 12:08:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNitrogen and sulfur metabolic transformations among MAGs.\u003c/strong\u003e MAGs from sediment and nodule metagenomes with the capacity to process nitrogen- and sulfur-containing compounds (shown in panels A and B, respectively). Horizontal bar charts indicate the proportion of genomes encoding each function. Numbers beside each chart represent the total number of genomes carrying the corresponding metabolic capability. In the split-square diagrams, the upper half represents sediment-derived MAGs and the lower half represents nodule-derived MAGs.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-8281434/v1/ae377eae45f632bd99a14fe2.png"},{"id":104399999,"identity":"817399e1-52ad-4af2-b5bd-de5e74939742","added_by":"auto","created_at":"2026-03-11 12:08:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolite transfer among MAGs in the deep-sea nodule field.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003ePie charts and bar plots showing the total number and composition of major metabolite categories transferred among MAGs. \u003cstrong\u003eB.\u003c/strong\u003e Heatmap illustrating the top five metabolite subclasses transferred within each of the dominant major categories (A: amino acids, C: CoA derivatives, I: inorganic ions, and S:carbohydrates). Heatmap colors indicate the number of metabolites received by each MAG (as the recipient), normalized using log10 transformation.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-8281434/v1/6830660a721c11c0232e1101.png"},{"id":104400026,"identity":"b9b8e6bc-a3c7-42db-a173-64837239967e","added_by":"auto","created_at":"2026-03-11 12:08:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":515773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfluence of different life-history strategies on metabolite exchange in the deep-sea nodule field.A.\u003c/strong\u003e To investigate the effects of life-history strategies on metabolite exchange, a classification and regression model was constructed based on the Y–A–S framework using an XGBoost decision-tree algorithm. we screened genes involved in carbon metabolism, nitrogen cycling, and sulfur metabolism from all sediment- and nodule-derived MAGs to represent resource acquisition capacity (A). Genes related to metal resistance were selected as indicators of environmental stress tolerance (S), and tRNA gene counts were used as proxies for intrinsic growth capacity (Y)(Figure S7, Table S10). The relative abundance proportions of these gene categories were then calculated for each MAG. We defined the dominant life-history strategy as follows: (1) If one capacity category exceeded 50%, or exceeded 40% while the other two categories were each below 30%, the MAG was assigned to the strategy dominated by that single capacity. (2) If two capacity categories were both above 30% and the third was below 30%, the MAG was assigned to a mixed strategy dominated by those two capacities. (3) If all three capacities were below 40%, the MAG was classified as AVERAGE, representing a balanced strategy without clear specialization toward resource acquisition, intrinsic growth, or stress tolerance. Based on these criteria, seven life-history strategy groups were obtained (Detailed information was provided in SI). We subsequently performed regression analyses between these life-history categories and the classes of exchanged metabolites to quantify the influence of each life-history strategy on different metabolite categories. \u003cstrong\u003eB.\u003c/strong\u003e Ternary diagram showing the distribution of MAGs assigned to different life-history strategy categories according to the classification scheme described in panel A. \u003cstrong\u003eC.\u003c/strong\u003eImportance of each life-history strategy for major metabolite categories. Each block in the treemap represents a metabolite category, and block size reflects the gain value predicted by the XGBoost model, indicating its contribution to metabolite exchange.\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-8281434/v1/c36aa98071d85b99e9eecf61.png"},{"id":108877668,"identity":"80f36832-040e-4d3d-9df5-2721641b1fb4","added_by":"auto","created_at":"2026-05-09 15:25:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1979349,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8281434/v1/ea3f0431-716a-4c97-a54c-524c4bad233e.pdf"},{"id":103742665,"identity":"dbd6a72e-acb2-4c40-9337-0abbd4e08096","added_by":"auto","created_at":"2026-03-02 11:13:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2972361,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8281434/v1/a69227c06e0d469fbad4d05d.docx"},{"id":103742668,"identity":"2b9f1e4a-6597-49af-b342-be91bfdf86cc","added_by":"auto","created_at":"2026-03-02 11:13:51","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":601590,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformationTableS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8281434/v1/d948a41fc4494057ec1bd7ee.xlsx"},{"id":103742663,"identity":"6ff142db-394e-45a5-b2b6-60acc030ee3d","added_by":"auto","created_at":"2026-03-02 11:13:51","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1517091,"visible":true,"origin":"","legend":" Metal transporter and redox-related functions encoded by MAGs. The dendrogram represents the composition of MAGs harboring genes associated with metal transport or redox processes, with each block corresponding to a single MAG. Block size reflects the number of related genes carried, and block color denotes phylum-level taxonomy. Circular blocks indicate sediment-derived MAGs, whereas square blocks indicate nodule-derived MAGs. Iron uptake transporters significantly enriched in both nodule and sediment samples. In the split-square tree, the upper half represents sediment samples and the lower half represents nodule samples. Horizontal gene transfer (HGT) events identified in the deep-sea nodule region.Genes involved in iron acquisition were abundant across both habitats.","description":"","filename":"GraphicalAbstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8281434/v1/7bb09d5e5c6ea55d1d07c29d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual-Axis Life-History Framework Explains Metabolite Exchange and Functional Differentiation in Nodule Microbiomes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicrobial communities are the primary drivers of biogeochemical cycling and energy flow in deep-sea ecosystems (Orcutt et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Deep-sea polymetallic nodules, formed under low sedimentation, oxic bottom waters, and weak currents, are rich in Mn, Fe, and trace metals (e.g., Cu, Co, Ni). These nodules are not only potential mineral resources but also unique microbial habitats (Hein et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Microbes inhabiting nodules mediate metal redox cycling and exhibit remarkable adaptations to oligotrophic, metal-rich conditions (R\u0026ouml;thig et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As key agents of deep-sea biogeochemistry, they offer insights into microbial survival strategies under extreme energy limitation (Sizikov et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNodule-associated microbes employ diverse strategies to cope with metal stress and sustain metabolism. These include metal efflux systems, extracellular polymeric substances (EPS) for detoxification and microenvironment stabilization, and metal-oxidizing metabolisms linked to energy conservation (Lian et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., 2015). In both nodules and sediments, microbes utilize organic and inorganic carbon sources via chemoautotrophic and heterotrophic pathways, with key roles in nitrification, denitrification, and sulfur cycling (Zhang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, community-level survival depends on metabolic complementarity and interspecies metabolite exchange.\u003c/p\u003e \u003cp\u003eMetabolic complementarity enables efficient energy and electron flow among taxa with distinct trophic roles. For example, hydrogenotrophs release organic acids (e.g., acetate, formate) that support heterotrophs (Jiang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while sulfate-reducing microbes generate sulfide that fuels metal oxide reduction and supports chemoautotrophs (Wu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These interactions enhance energy efficiency and stress resistance in oligotrophic settings. Life-history strategies further shape metabolic networks. The Y-A-S framework (Malik et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) describes trade-offs among growth rate (Y), resource acquisition (A), and stress tolerance (S). In nodule fields, fast-growing taxa degrade substrates and release intermediates, while stress-tolerant taxa efficiently utilize these byproducts. Moreover, \u0026ldquo;metabolic hub\u0026rdquo; microbes exhibiting mixed strategic traits can both produce key metabolites and efficiently assimilate metabolic byproducts from other taxa, serving as pivotal nodes that mediate energy flow and functional connectivity within the microbial network (Douglas et al., 2020).\u003c/p\u003e \u003cp\u003eThis study investigates microbial communities inhabiting polymetallic nodules and surrounding sediments from the eastern Pacific Ocean. Using metagenome-assembled genomes (MAGs), we aim to: (1) characterize microbial composition and metal resistance mechanisms; (2) identify metabolic adaptations to high pressure and low nutrient availability; and (3) explore how life-history strategies shape metabolite exchange and resource utilization under extreme conditions.These findings provide new insights into microbial ecological roles and adaptive strategies in deep-sea, energy-limited ecosystems.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling and DNA extraction\u003c/h2\u003e \u003cp\u003eIn October 2023, sediment and polymetallic nodule samples were collected from the Clarion\u0026ndash;Clipperton Fracture Zone (eastern Pacific; 4,983\u0026ndash;5,211 m depth) during cruise DY135-E2 (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of 17 sediment and 3 nodule samples were obtained using a custom deep-sea sampler and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until processing. Surface layers (3\u0026ndash;5 mm) of nodules were scraped and powdered for DNA extraction. Sediment samples were centrifuged to remove excess water. DNA was extracted using commercial kits (see Supplementary Methods for details), with duplicates per sample to ensure reproducibility.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMetagenomic sequencing and assembly\u003c/h3\u003e\n\u003cp\u003eDNA libraries were constructed and sequenced on an Illumina HiSeq 2500 platform (2 \u0026times; 150 bp). Raw reads were quality-filtered and assembled into contigs using MEGAHIT. Contigs\u0026thinsp;\u0026ge;\u0026thinsp;1,000 bp were retained for downstream analysis. Detailed parameters are provided in Supplementary Methods.\u003c/p\u003e\n\u003ch3\u003eGenome binning and annotation\u003c/h3\u003e\n\u003cp\u003eMetagenome-assembled genomes (MAGs) were recovered using MetaBAT2 and MaxBin2 via MetaWrap. High-quality MAGs (\u0026gt;\u0026thinsp;70% completeness, \u0026lt;\u0026thinsp;10% contamination) were dereplicated at 99% ANI using dRep. Taxonomy was assigned using GTDB-Tk. Protein-coding genes were predicted using Prodigal. Functional annotation included metal transport (TCDB), nitrogen and sulfur cycling (NCycDB/SCycDB), CAZymes (dbCAN), and small-carbon metabolism (KEGG KO). See Supplementary Methods for full annotation pipeline.\u003c/p\u003e\n\u003ch3\u003eRelative abundance and network analysis\u003c/h3\u003e\n\u003cp\u003eClean reads were mapped to MAGs using BWA to estimate relative abundance. Co-occurrence networks were constructed based on Spearman correlations and Random Matrix Theory (RMT) using the iNAP platform. Only MAGs present in \u0026gt;\u0026thinsp;50% of samples within a group were included.\u003c/p\u003e\n\u003ch3\u003eMetabolite exchange prediction\u003c/h3\u003e\n\u003cp\u003eMetabolic complementarity and potential metabolite exchange between MAGs were predicted using PhyloMint based on genome-scale metabolic models reconstructed by CarveMe. Dual interactions were defined when both co-occurrence and metabolic complementarity were detected. Metabolites were classified into eight major categories (see Supplementary Methods).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLife-history strategy classification\u003c/h2\u003e \u003cp\u003eMAGs were assigned to life-history strategies (Y, A, S, AS, AY, SY, AVER) using an XGBoost-based framework trained on genomic features (e.g., GC content, rRNA copy number, metabolic versatility). Detailed training set and feature list are provided in Supplementary Methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRecovery of MAGs and dominant microbes in polymetallic nodule field\u003c/h2\u003e \u003cp\u003eA total of 314 high-quality, non-redundant MAGs were recovered from nodules and surrounding sediments. Sediment yielded 154 MAGs spanning 10 phyla, with 73.1% unclassified at the species level (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Nodule contained 160 MAGs across 22 phyla, and 94.3% remained unclassified, indicating substantially higher taxonomic novelty (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Table S3). Sediment-inhabiting communities were dominated by \u003cem\u003eProteobacteria\u003c/em\u003e (73.3%), followed by \u003cem\u003eNitrospirota\u003c/em\u003e and \u003cem\u003eDesulfobacterota\u003c/em\u003e_B. These included putative ammonia-oxidizing autotrophs that may contribute primary production in deep-sea sediments (Figure S3). Nodule-inhabiting MAGs displayed a distinct profile, with \u003cem\u003eProteobacteria\u003c/em\u003e (38.8%), \u003cem\u003eGemmatimonadota\u003c/em\u003e, and \u003cem\u003eAcidobacteriota\u003c/em\u003e as dominant phyla, and a notable enrichment of \u003cem\u003eThermoproteota\u003c/em\u003e archaea (9.37%), all belonging to \u003cem\u003eNitrosopumilaceae\u003c/em\u003e (Figure S3). High-abundance MAGs (top 50% of relative abundance) further highlighted habitat-specific patterns: sediments were enriched in \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eMethylomirabilota\u003c/em\u003e, and \u003cem\u003eNitrospirota\u003c/em\u003e, whereas nodules were dominated by \u003cem\u003eThermoproteota\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, and \u003cem\u003eAcidobacteriota\u003c/em\u003e (Figure S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMetal resistance in polymetallic nodule field\u003c/h2\u003e \u003cp\u003eComparative genomic analyses revealed that MAGs from both habitats encode extensive metal transport and redox-related functions, with Hg and Cu metabolism representing the largest fractions of metal-related genes in sediments (48.3% and 14.1%) and nodules (43.3% and 21.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Table S4 and S5). Their taxonomic distributions, however, differed markedly. In sediments, Hg resistance genes (\u003cem\u003emerA\u003c/em\u003e/\u003cem\u003emerB\u003c/em\u003e/\u003cem\u003emer\u003c/em\u003e) were mainly contributed by \u003cem\u003eGammaproteobacteria\u003c/em\u003e, \u003cem\u003eMethylomirabilota\u003c/em\u003e, and \u003cem\u003eNitrospirota\u003c/em\u003e, whereas nodules showed additional enrichment in \u003cem\u003eGemmatimonadota\u003c/em\u003e, occurring in 97.5% of nodule-inhabiting MAGs(Table S6 and S7). Cu-resistance functions (CopB/CopD, CueO, PcoA) were present in 155 sediment-inhabiting MAGs but were significantly abundant and widespread in nodules (1,731 genes), primarily from \u003cem\u003eAlphaproteobacteria\u003c/em\u003e and \u003cem\u003eGemmatimonadota\u003c/em\u003e. Multicopper oxidases (MCOs: \u003cem\u003emnxG\u003c/em\u003e, \u003cem\u003emofA\u003c/em\u003e, \u003cem\u003ecotA\u003c/em\u003e) (Table S6 and S7), capable of oxidizing Mn(II) and Fe(II), were strongly enriched in nodules (616 vs. 91 genes), mainly in \u003cem\u003eAlphaproteobacteria\u003c/em\u003e and \u003cem\u003eNitrospirota\u003c/em\u003e. Nodule-inhabiting MAGs also contained significantly higher numbers of As-, Cr-, and Pb-resistance functions (1,773 genes, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), including ACR3/ArsB/ArsP, AioA, ArsC, NemA/ChrR, and PbrBC/ILT, largely belonged to \u003cem\u003eAlphaproteobacteria\u003c/em\u003e and \u003cem\u003eGemmatimonadota\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFe\u0026sup2;⁺ transporters (FeoB) and vacuolar iron transporters (VIT) were detected in 163 and 60 MAGs, respectively, together with eight additional functional categories (Figure S5, Table S8). Iron regulatory and uptake systems\u0026mdash;PvdS, FecR, TonB, ExbB/D, and FbpB\u0026mdash;comprised\u0026thinsp;\u0026gt;\u0026thinsp;90% of iron-related functions. Regulatory genes accounted for 41.5% of iron functions in nodules and 30.1% in sediments. \u003cem\u003epvdS\u003c/em\u003e was more abundant in nodules (12.95% vs. 6.92%), mainly in \u003cem\u003eGammaproteobacteria\u003c/em\u003e and \u003cem\u003eGemmatimonadota\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Sediment-inhabiting MAGs showed higher abundance of \u003cem\u003efbpB\u003c/em\u003e, indicating stronger reliance on high-affinity Fe\u0026sup2;⁺ uptake under reduced sediment conditions.\u003c/p\u003e \u003cp\u003eFurthermore, our analysis of horizontal gene transfer (HGT) events within the nodule field revealed the potential genetic origins of the aforementioned metal resistance and iron transport functional genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Among 303 total HGT events (205 in sediments; 98 in nodules), only 13 involved metal-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Table S9). Sediment-inhabiting transfers (NemA, Nramp, PvdS) primarily occurred from \u003cem\u003eGammaproteobacteria\u003c/em\u003e to \u003cem\u003eAlphaproteobacteria\u003c/em\u003e. In nodules, HGT events (MerA, AioA) occurred exclusively within \u003cem\u003eProteobacteria\u003c/em\u003e, with all recipients belonging to \u003cem\u003eAlphaproteobacteria\u003c/em\u003e. The scarcity of metal resistance\u0026ndash;related HGT suggested that these traits were predominantly maintained through vertical inheritance, reflecting long-term genomic stability in deep-sea nodule environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCarbon metabolism\u003c/h2\u003e \u003cp\u003eIn both habitats, glycosyltransferases (GTs) represented the most abundant CAZyme family, followed by glycoside hydrolases (GHs), carbohydrate esterases (CEs), and auxiliary activity enzymes (AAs) (Figure S6). GT2, GT4, GT51, and GT9 were highly enriched in both sediments and nodules, indicating shared glycosyl-transfer capabilities. Although GH genes were abundant in both habitats (1,268 in sediments; 3,241 in nodules), only GH23 showed high prevalence across both environments. Sediment-inhabiting MAGs were enriched in GH102 and GH103, associated with chitin and N-acetylglucosamine polysaccharide degradation, whereas nodule-inhabiting MAGs showed higher abundances of GH1 and GH13, reflecting greater β-glucosidase and amylase activity and distinct carbon-source preferences. CE families exhibited similar profiles across habitats, with CE1, CE11, CE14, and CE4 dominating. GH23 and CE4\u0026mdash;both linked to chitin degradation\u0026mdash;were abundant in both habitats, suggesting that chitin represents an important carbon reservoir in these oligotrophic deep-sea systems.\u003c/p\u003e \u003cp\u003eMicrobes from both habitats encoded complete Wood\u0026ndash;Ljungdahl (m00377) and phosphate acetyltransferase\u0026ndash;acetate kinase (m00579) pathways, although the K00196 component was absent in nodules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Core C₁-transfer genes (\u003cem\u003efhs, folD, metF\u003c/em\u003e) were abundant in both sediments and nodules (395 vs. 291 genes), mainly within \u003cem\u003eGamma\u003c/em\u003e-, \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, and \u003cem\u003eNitrospirae\u003c/em\u003e. Sediment-inhabiting communities exhibited stronger acetyl-CoA\u0026ndash;to\u0026ndash;acetate conversion potential, carrying 104 copies of \u003cem\u003eackA/pta\u003c/em\u003e compared to 36 in nodules. Differences also emerged in CO₂-to-formate reduction: sediment \u003cem\u003efdhA\u003c/em\u003e genes were primarily contributed by \u003cem\u003eNitrospinia\u003c/em\u003e and \u003cem\u003eGemmatimonadota\u003c/em\u003e, whereas nodule-inhabiting communities additionally contributed by \u003cem\u003eMethylomirabilia\u003c/em\u003e and \u003cem\u003eDehalococcoidia\u003c/em\u003e. Both habitats encoded reductive TCA cycle (rTCA) genes (m00173), particularly \u003cem\u003esdhA/sdhB\u003c/em\u003e and \u003cem\u003esucC/sucD\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Sediments contained more \u003cem\u003esdh\u003c/em\u003e genes than nodules (441 vs. 262), predominantly from \u003cem\u003eGammaproteobacteria\u003c/em\u003e and \u003cem\u003eNitrospinia\u003c/em\u003e. Finally, acetyl-CoA synthase (ACS) was significantly more abundant in sediments (264 vs. 139), indicating a greater capacity for acetate utilization in sediment communities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNitrogen cycling and Sulfur metabolism\u003c/h2\u003e \u003cp\u003eNitrogen cycling genes were detected in both habitats, but their distributions differed markedly, indicating distinct ecological roles in nitrogen transformations. Nitrogen fixation genes (\u003cem\u003enifKH\u003c/em\u003e) occurred only in two nodule-inhabiting MAGs and were absent from sediments. Nitrification potential was limited: sediments contained 6 MAGs with \u003cem\u003eamo/pmo\u003c/em\u003e and four MAGs with \u003cem\u003ehao\u003c/em\u003e (mainly \u003cem\u003eGammaproteobacteria\u003c/em\u003e and \u003cem\u003eAcidobacteriae\u003c/em\u003e), whereas nodules contained only one \u003cem\u003ehao\u003c/em\u003e-bearing MAG. No nitrite oxidation genes were detected, suggesting that complete nitrification is rare. Organic nitrogen metabolism (\u003cem\u003enmo\u003c/em\u003e) occurred in 6 sediment- and 12 nodule-inhabiting MAGs, primarily \u003cem\u003eGammaproteobacteria\u003c/em\u003e and \u003cem\u003eAlphaproteobacteria\u003c/em\u003e. Dissimilatory nitrate reduction genes (\u003cem\u003enarGHI/napAB; nirBD/nrfACH\u003c/em\u003e) were detected in 48.7% of sediment-inhabiting MAGs and 18.7% of nodule-inhabiting MAGs, while assimilatory nitrate reduction (\u003cem\u003enarB/nasAB/NR; nirA\u003c/em\u003e) occurred in 33.9% and 26.8%, respectively, indicating that both communities can utilize nitrate as an electron acceptor. Genes for NO₂⁻ reduction to NO (\u003cem\u003enirK/nirS\u003c/em\u003e) were significantly enriched in sediments (30.1% vs. 18.1% in nodules; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Sediments also showed stronger potential for NO reduction to N₂O, with 32 MAGs carrying \u003cem\u003enorBC\u003c/em\u003e, mainly \u003cem\u003eGammaproteobacteria\u003c/em\u003e, \u003cem\u003eNitrospinia\u003c/em\u003e, and \u003cem\u003eGemmatimonadota\u003c/em\u003e, compared to 4 MAGs in nodules. Complete denitrification was limited, with \u003cem\u003enosZ\u003c/em\u003e detected in only 2 sediment- and 9 nodule-inhabiting MAGs. Overall, sediment-inhabiting communities exhibited greater potential for denitrification and ammonia oxidation and may contribute NO to the overlying seawater. The scarcity of nitrification and nitrite oxidation genes suggests that nitrate in this system is largely supplied by overlying water rather than produced in situ.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGenes associated with both dissimilatory sulfite reduction (dsr) and the reverse pathway (rdsr)\u0026mdash;including \u003cem\u003esat, aprAB, dsrB\u003c/em\u003e, and \u003cem\u003eqmoABC\u003c/em\u003e\u0026mdash;were detected in sediments and nodules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), indicating the potential for both sulfate/sulfite reduction and sulfur oxidation. Sulfate-reduction genes were abundant in both habitats (933 in sediments; 684 in nodules). \u003cem\u003esat\u003c/em\u003e occurred in 103 sediment- and 91 nodule-inhabiting MAGs, mainly belonged to \u003cem\u003eGammaproteobacteria\u003c/em\u003e, \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, and \u003cem\u003eMethylomirabilia\u003c/em\u003e. \u003cem\u003eaprAB\u003c/em\u003e was also enriched, with significantly higher abundance in sediments (86 vs. 25). Assimilatory sulfate reduction genes (\u003cem\u003ecys\u003c/em\u003e) were widespread, occurring in 96.7% of sediment-inhabiting MAGs and 89.3% of nodule MAGs (780 vs. 568 genes). Only 6 sediment-inhabiting MAGs carried \u003cem\u003edsrB\u003c/em\u003e, and \u003cem\u003edsrD\u003c/em\u003e was absent from all samples, suggesting that sulfur oxidation may proceed through the reverse Dsr pathway, supported by the presence of \u003cem\u003efccAB\u003c/em\u003e in 10 MAGs. For thiosulfate metabolism, the SOX system was more prevalent and diverse in sediments\u0026mdash;especially \u003cem\u003esoxB\u003c/em\u003e and the full \u003cem\u003esox\u003c/em\u003e gene cluster\u0026mdash;whereas nodules showed enrichment of tetrathionate-pathway genes (\u003cem\u003etsdA\u003c/em\u003e and tetrathionate reductase), indicating a habitat-specific preference for S₂O₃\u0026sup2;⁻ metabolism via tetrathionate intermediates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenomic clues to microbial metabolic synergies in polymetallic nodule field\u003c/h2\u003e \u003cp\u003eMicrobial functional potential in the nodule field was largely shaped by metabolite exchange and metabolic complementarity. Across all MAGs, eight major metabolite categories were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). C-, A-, and I-type metabolites showed the highest transfer frequencies in both habitats, underscoring their central roles in community-level metabolic integration. OC-type metabolites were also widely exchanged, consistent with the transformation and turnover of complex organic matter. In contrast, L- and N-type metabolites were infrequently transferred, while OA- and S-type metabolites showed localized distributions, likely linked to taxa with specialized metabolic roles. Nodule-inhabiting communities exhibited higher overall transfer activity\u0026mdash;particularly for A, C, I, and OC metabolites\u0026mdash;indicating more intensive interactions related to energy metabolism and organic matter processing. Moreover, nodules showed relatively higher percent transfer of L and N metabolites, suggesting enhanced basal metabolism and nucleic acid turnover.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of high-abundance metabolite subclasses further highlighted habitat-specific metabolic preferences. C-type metabolites, key intermediates in β-oxidation and TCA-cycle\u0026ndash;related pathways, differed strongly between habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Sediments were dominated by long-chain acyl-CoA and aromatic intermediates (e.g., stearoyl-CoA), reflecting active lipid catabolism and biosynthesis. Nodules were enriched in short-chain CoA derivatives and more oxidized intermediates (e.g., octanoyl-CoA), indicating distinct lipid metabolic strategies. Within A-type metabolites, sediments primarily exchanged amino acid and pyrimidine biosynthesis intermediates (e.g., L-homoserine, L-aspartate), consistent with metabolite-assisted nitrogen metabolism. Nodules accumulated specific amino acid derivatives, suggesting selective amino acid cycling or rapid intracellular regulation. I-type metabolites showed similar subclass compositions in both habitats and mainly comprised Mg\u0026sup2;⁺, Ca\u0026sup2;⁺, Zn\u0026sup2;⁺, and Co\u0026sup2;⁺ ions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLife history strategies influence metabolite delivery associations\u003c/h2\u003e \u003cp\u003eUsing an XGBoost-based framework, all MAGs were classified into life-history strategy groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In sediments, MAGs were dominated by SY (25.1%), AY (22.6%), and AS (18.8%), whereas the S group was least abundant (4.4%). In contrast, nodule MAGs were enriched in AS (26.2%), AY (20%), and Y (18.1%), followed by smaller fractions of SY and AVER (6.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Table S11 and S12). Life-history strategies exhibited strong, habitat-specific influences on metabolite exchange (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In sediments, AY exerted the greatest effects on carbohydrate (gain\u0026thinsp;=\u0026thinsp;56.6), CoA-derivative (39.5), and inorganic metabolite exchange (34.7). AS strongly influenced inorganic compounds and CoA derivatives (296.1 and 153.4, respectively), but contributed minimally to other categories (gain\u0026thinsp;\u0026lt;\u0026thinsp;5). In nodules, AY impacted organic compounds, CoA derivatives, carbohydrates, and organic acids, while AS primarily affected organic compounds, carbohydrates, and nucleoside-related metabolites (43.4, 36.7, 34.4). Habitat-specific dominant groups also showed contrasting roles. In sediments, SY strongly affected amino acid (101) and inorganic metabolite exchange (90), whereas in nodules SY was more involved in nucleic acid exchange. Conversely, the Y group in nodules mainly influenced amino acid and inorganic-ion exchange, while in sediments it contributed most to nucleoside and organic acid exchange. Although AVER was low in abundance across both habitats, it consistently influenced CoA derivatives, organic acids, and organic compounds, suggesting a generalist rather than specialized metabolic role.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eMicrobes from the tubercle zone exhibiting high taxonomic novelty and niche specificity.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMicrobial communities in deep-sea polymetallic nodule fields exhibited pronounced taxonomic novelty and niche specificity. Genome-resolved analyses recovered numerous previously uncharacterized lineages, underscoring nodule ecosystems as underexplored hotspots of microbial diversity (Zhou et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The clear compositional separation between sediments and nodules (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) indicates functional divergence shaped by contrasting microenvironments. As solid substrates, nodules create micro-oxic to anoxic interfaces and steep chemical gradients, supporting higher phylogenetic richness and broader ecological niches (Molari et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sediments were dominated by \u003cem\u003eProteobacteria\u003c/em\u003e (73.3%) alongside taxa central to anaerobic nitrogen and sulfur cycling, including \u003cem\u003eNitrospirota\u003c/em\u003e (12.3%) and \u003cem\u003eDesulfobacterota\u003c/em\u003e_B (10.3%), consistent with active ammonia oxidation and sulfate reduction. In contrast, nodule microbiomes showed reduced \u003cem\u003eProteobacteria\u003c/em\u003e abundance and greater diversity due to enrichment of \u003cem\u003eGemmatimonadetes\u003c/em\u003e and \u003cem\u003eAcidobacteriota\u003c/em\u003e, as well as archaeal \u003cem\u003eNitrosopumilaceae\u003c/em\u003e\u0026mdash;key ammonia-oxidizing autotrophs that support carbon fixation and nitrogen cycling on oligotrophic nodule surfaces (Wright et al., 2023). The presence of \u003cem\u003eAcidobacteriota\u003c/em\u003e further suggests adaptation to acidic or metal-rich microhabitats, potentially mediating Fe\u0026ndash;Mn redox transformations (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sediment communities relied largely on heterotrophic carbon degradation and anaerobic methane oxidation (e.g., \u003cem\u003eMethylomirabilota\u003c/em\u003e), whereas nodule habitats were characterized by stronger inorganic chemolithoautotrophic activity, driven by AOA and putative Fe\u0026ndash;Mn\u0026ndash;metabolizing \u003cem\u003eAcidobacteriota\u003c/em\u003e (Mujakić et al., 2022; Wang et al., 2021). Together, these taxonomic and functional distinctions reflect divergent energy acquisition strategies and highlight the complementary metabolic roles that may contribute to nodule formation and deep-sea ecosystem stability.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBroad-spectrum, integrative, and vertically transferred anti-metal strategy\u003c/h2\u003e \u003cp\u003eMetal toxicity is a major selective force shaping microbial genome evolution in polymetallic nodule fields. Consistent with the coexistence of multiple metals, genes conferring resistance to Hg, Cu, As, Fe, and Cr were abundant across both habitats, indicating that microbes employ broad-spectrum and integrated strategies to withstand complex metal stressors (Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; S\u0026aacute;nchez-Corona et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These strategies operate at two levels. First, functional interconnections promote co-enrichment of resistance systems; for example, multicopper oxidases (MCOs), central to Cu homeostasis, can also oxidize Fe(II) and Mn(II), driving parallel enrichment of Fe- and Mn-related genes (Pooalai et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, detoxification pathways share conserved oxidative stress responses, enabling enzymes such as MerA, ArsC, and ChrR to contribute to cross-resistance (Yang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Metal resistance functions were further integrated through community-level metabolic networks (Sharma et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Core lineages\u0026mdash;including \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eMethylomirabilota\u003c/em\u003e, and \u003cem\u003eGemmatimonadota\u003c/em\u003e\u0026mdash;recurrently contributed functions such as MerAB, CopBD, PcoA, and ArsC, while nodule-associated \u003cem\u003eNitrosopumilaceae\u003c/em\u003e showed notable enrichment of Cu- and Hg-related genes, suggesting key roles in metal transport and redox metabolism.\u003c/p\u003e \u003cp\u003eIron acquisition strategies were similarly diverse. The widespread presence of the TonB\u0026ndash;ExbB/D system highlights the dominance of TonB-dependent Fe\u0026sup3;⁺\u0026ndash;siderophore uptake in deep-sea Gram-negative microbes (Klebba et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Co-occurrence of FecR (Fe\u0026sup3;⁺ uptake regulation) and FbpB (high-affinity Fe\u0026sup2;⁺ transport) indicates flexible adaptation to fluctuating redox conditions. The higher abundance of FbpB in sediments suggests enhanced suitability for low oxygen, strongly reducing environments (Payne et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Garber et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the extensive repertoire of metal resistance genes, their dissemination appears strongly dominated by vertical inheritance. Only limited HGT events were detected, consistent with the deep sea\u0026rsquo;s stable, chronic metal exposure regime, which favors long-term genomic conservation rather than frequent lateral gene acquisition (Gillard et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Aminov et al., 2011; Yang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The slow-growth and low-metabolism lifestyles typical of deep-sea microbes, together with defense systems such as CRISPR and restriction\u0026ndash;modification pathways (Zhu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), likely further constrain foreign DNA uptake. Collectively, these features support a conservative evolutionary strategy in which vertical inheritance maintains functional stability under persistent metal stress. Deep-sea microorganisms have developed an ecological\u0026ndash;evolutionary stable strategy (ESS) shaped by evolutionary pressures over million-year timescales. The adaptive mechanisms within deep-sea nodule ecosystems follow a logic closer to \u0026ldquo;genome consolidation driven by slow variables\u0026rdquo; rather than a \u0026ldquo;rapid-response mobile gene pool.\u0026rdquo; This insight is essential for understanding the long-term evolutionary trajectories of deep-sea microorganisms and for assessing the environmental impacts of deep-sea mining.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdaptation strategies drive the acquisition of different carbon substrates.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe CAZyme composition and small-carbon metabolic potential of deep-sea nodule field microbiomes reflect clear adaptive differentiation between habitats. GTs were the most abundant CAZy class, with GT2 particularly enriched in nodule-inhabiting microbes, consistent with enhanced EPS and polysaccharide biosynthesis (Kaur et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). On nodule surfaces\u0026mdash;where mineral-bound organic matter is limited\u0026mdash;EPS likely supports biofilm formation, mitigates metal toxicity, and adsorbs scarce organic substrates, forming a localized carbon reservoir (Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kayoumu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similar EPS-driven adaptations are documented in other oligotrophic deep-sea systems such as vent chimneys (Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, sediment-inhabiting microbiomes were enriched in degradative GH families, especially GH102 and GH103, indicating specialization toward chitin and GlcNAc-based polysaccharide degradation (Jiang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Together with high GH23 and CE4 abundances, these patterns show that chitin represents a major bioavailable carbon pool derived from particulate organic matter and cellular debris. Thus, nodule-inhabiting communities favor carbon acquisition via EPS production and complex polysaccharide synthesis, whereas sediments rely on organic matter breakdown.\u003c/p\u003e \u003cp\u003eBoth habitats encoded complete Wood\u0026ndash;Ljungdahl and acetogenesis pathways, demonstrating capacity for C₁-based carbon\u0026ndash;energy coupling. However, sediments showed significantly higher abundances of \u003cem\u003eackA/pta\u003c/em\u003e and ACS, indicating stronger potential for acetate assimilation and fermentative metabolism, likely reflecting anaerobic or micro-oxic niches (Sch\u0026uuml;tze et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The broad taxonomic distribution of \u003cem\u003efdhA\u003c/em\u003e\u0026mdash;notably in \u003cem\u003eMethylomirabilia, Dehalococcoidia\u003c/em\u003e, and \u003cem\u003eNitrospiria\u003c/em\u003e\u0026mdash;further suggests that formate oxidation contributes to energy generation, potentially linked to metal reduction (Al-Bassam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nodule-inhabiting communities showed notable activity in the reductive TCA (rTCA) cycle, supported by widespread \u003cem\u003esdhA/B\u003c/em\u003e distribution. This strategy enables CO₂ assimilation and reductive metabolism under metal-rich, redox-fluctuating, and low-organic conditions (Leng et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such metabolic flexibility\u0026mdash;using endogenous small-carbon intermediates or inorganic carbon fixation\u0026mdash;provides a competitive advantage in oligotrophic nodule microhabitats (Chen et al., 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAdaptive Diversity and Ecological Strategy Differentiation in Nitrogen and Sulfur Cycling\u003c/h2\u003e \u003cp\u003eSediment-inhabiting microbiomes exhibited greater nitrogen-cycle gene diversity and more complete pathways, whereas nodule-inhabiting communities reflected the distinct metabolic constraints of mineral\u0026ndash;microbe interfaces (Cerqueira et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wasmund et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The limited nitrogen-cycling capacity in nodules suggests that mineral phases provide alternative electron-transfer routes, shaping microbial metabolic strategies. Both habitats lacked nitrogen fixation and complete nitrification pathways, indicating that microorganisms rely primarily on seawater-derived nitrate as an electron acceptor. The absence of ammonia-oxidation genes further supports the external origin of nitrate. Thus, sediment-inhabiting microbes function largely as \u0026ldquo;nitrate processors\u0026rdquo;, regulating nitrogen transformation and release rather than replenishing endogenous nitrogen pools (Zhang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sediments habitats possessed both dissimilatory (\u003cem\u003enarGHI/napAB\u003c/em\u003e) and assimilatory (\u003cem\u003enarB/nasAB\u003c/em\u003e) nitrate reduction genes, reflecting a dual nitrate-utilization strategy that enables flexible metabolic switching under carbon-limited, redox-variable deep-sea conditions (Jiang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, nitrate-reduction genes were substantially less abundant in nodules, consistent with a greater reliance on metal redox processes for energy generation. This divergence highlights clear ecological niche separation between the two habitats. Denitrification potential also differed markedly. Sediments showed frequent \u003cem\u003enirK/nirS\u003c/em\u003e, indicating enhanced NO production capacity, whereas this step was strongly attenuated in the more oxidizing nodule microenvironment. Genes encoding \u003cem\u003enorBC\u003c/em\u003e and \u003cem\u003enosZ\u003c/em\u003e were extremely rare in nodules. Rather than representing functional loss, this genomic \u0026ldquo;incompleteness\u0026rdquo; likely reflects an energy-conserving strategy adapted to metal-rich substrates (Oba et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Because NO and N₂O reduction yield substantially less energy than oxygen or metal-oxide reduction, nodule-associated microbes appear to adopt partial denitrification, truncating energetically costly steps. This streamlined strategy facilitates rapid adjustment to fluctuating electron acceptors at mineral interfaces and represents a mineral-regulated survival mode in polymetallic nodule-inhabiting ecosystems (Ma et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to the incomplete nitrogen cycle, sulfur pathways displayed greater ecological complexity and adaptability. In sediments, the widespread presence of \u003cem\u003edsrB\u003c/em\u003e and \u003cem\u003easrA\u003c/em\u003e, together with the absence of \u003cem\u003edsrD\u003c/em\u003e, indicates the operation of the reverse Dsr (rDSR) pathway, a key strategy for oxidizing reduced sulfur substrates (e.g., H₂S, S\u0026sup2;⁻) under oxidizing conditions (Nagar et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the dark, metal oxide\u0026ndash;rich deep sea, the rDSR pathway channels electrons toward sulfate formation, supporting efficient and stable energy generation and enabling microbes to respond flexibly to redox fluctuations (Neukirchen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Whaley-Martin et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The co-occurrence of \u003cem\u003efccAB\u003c/em\u003e and \u003cem\u003esoxB\u003c/em\u003e further suggests a multistep oxidation cascade from H₂S \u0026rarr; S⁰ \u0026rarr; S₂O₃\u0026sup2;⁻. Nodule-inhabiting communities, however, primarily utilized \u003cem\u003esoxB\u003c/em\u003e and \u003cem\u003esudAB\u003c/em\u003e-mediated pathways, targeting the oxidation of intermediate sulfur compounds. This pattern is consistent with habitat-level resource differences: reduced sulfur donors in sediments derive largely from organic matter degradation and sulfate reduction, whereas nodule surfaces\u0026mdash;low in reduced sulfur\u0026mdash;provide mainly thiosulfate and other intermediates as available substrates (Zhou et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a result, sediments support a more metabolically active and flexible sulfur-cycling network, while nodule-inhabiting microbiomes operate a more streamlined configuration constrained by the solid mineral matrix and limited electron acceptor availability. This contrast highlights a broader ecological trade-off: microbial communities in polymetallic nodule fields do not evolve toward maximal functional breadth, but toward resource-adapted energetic optimization (Malik et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), balancing substrate availability, redox structure, and metabolic efficiency.\u003c/p\u003e \u003cp\u003eSediments and nodules highly differentiated in terms of electron acceptor utilization, substrate conversion efficiency, and energy budgeting strategies. Sediments fulfill the system-level role of \u0026ldquo;functional completeness and redundancy,\u0026rdquo; whereas nodules represent \u0026ldquo;high-efficiency, low-energy extreme adaptation units.\u0026rdquo; This complementary structure suggested that the functional stability of deep-sea nodule regions arises from the coordinated division of labor among distinct ecological units during energy\u0026ndash;matter transformation, rather than from the metabolic capacity of any single environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLife strategies regulated along dual axes shape the transmission of distinct metabolites\u003c/h2\u003e \u003cp\u003eMetabolite exchange between nodule- and sediment-inhabiting communities differed markedly, emphasizing the influence of mineral\u0026ndash;microbe interfaces on metabolic cooperation (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nodule-associated microbes formed a more dynamic network centered on energy-related intermediates, with both habitats showing frequent exchange of acyl-CoA derivatives, indicating syntrophic interactions via fatty-acid metabolism. Sediments exchanged predominantly long-chain acyl-CoA and aromatic intermediates (e.g., stearoyl-CoA), consistent with energy storage and complex organic matter degradation (Garay et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, nodules were enriched in short-chain acyl-CoA (e.g., octanoyl-CoA), reflecting rapid, efficient lipid catabolism adapted to oligotrophic, episodically supplied conditions (Ma et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sediment communities, supported by relatively higher organic matter inputs, exhibited complex metabolic division of labor. The extensive exchange of succinate not only underscored its role as a TCA-cycle intermediate but also suggested potential interspecies signaling functions that reinforce metabolic synergy. Carbohydrate transport patterns further differentiated the two habitats: sediments showed frequent exchange of phosphorylated sugars, indicating an active carbohydrate co-metabolic network involving coordinated polysaccharide degradation, monosaccharide conversion, and phosphorylation (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, nodule microbes exchanged far fewer carbohydrate metabolites, likely due to the physicochemical inaccessibility of organic matter bound to mineral surfaces, which limits cooperative degradation (Kleber et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMicrobial life-history strategies govern the allocation of resources to growth, acquisition, and stress tolerance, thereby shaping ecological roles and the structure of metabolite-exchange networks (Malik et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Distinct strategies exhibit characteristic resource-use preferences and metabolic specializations, which in turn determine the direction and intensity of metabolite exchange. Applying the Y\u0026ndash;A\u0026ndash;S framework revealed clear functional differentiation among strategic groups. In resource-rich sediments, communities were dominated by SY, AY, and AS strategists, collectively forming a multifunctional assemblage adapted to fluctuating niches (Peng et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). AY and AS groups primarily mediated carbohydrate and inorganic compound exchange, respectively, while SY lineages played key roles in amino-acid and ion exchange, supporting nitrogen cycling and ionic homeostasis. These complementary functions help maintain ecosystem stability through rapid responses to resource pulses and enhanced resilience (Pascual-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In nutrient-limited nodule habitats, a different equilibrium emerged. AS strategists dominated the exchange of organic compounds and nucleosides, AY mediated carbohydrate and organic-acid turnover, and Y specialized in amino-acid and inorganic-ion exchange. These patterns indicate streamlined, efficiency-oriented metabolism under intensified stress and limited carbon supply. Strategic groups also displayed pronounced functional plasticity across environments: SY microbes shifted from amino-acid/ion exchange in sediments to nucleic-acid exchange in nodules, while Y-type lineages reversed their roles across habitats. AVER populations, although low in abundance, consistently exchanged CoA derivatives, organic acids, and complex organic compounds, functioning as cross-environment metabolic hubs (Loos et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIntegrating life-history traits with metabolite-exchange networks revealed a dual-axis regulatory framework driven by resource availability and environmental stress intensity. Sediments, with higher resource inputs and niche heterogeneity, favored multifunctional SY\u0026ndash;AY\u0026ndash;AS assemblages, promoting functional redundancy, metabolic plasticity, and ecological stability. Nodules, characterized by nutrient scarcity and metal-induced stress, selected for streamlined AS- or AY\u0026ndash;Y-type combinations that maximize acquisition efficiency and survival under energy-limited regimes. These contrasting but complementary strategies maintain the balance between community stability and biogeochemical efficiency. This study proposes a perspective on microbial metabolic organization in deep-sea ecology: microbial functions are not simply additive but are structured by an \u0026ldquo;ecological niche\u0026ndash;energy model\u0026rdquo; determined by life-history strategies. Accordingly, it offers a basis for understanding how deep-sea ecosystems construct sustainable metabolic networks.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe deep-sea polymetallic nodule field, as an extreme low-energy and metal-enriched habitat, hosts microbial communities with remarkable diversity and functional specialization. Our findings show that nodule- and sediment-associated microbiomes form a spatially stratified and functionally complementary system. Nodule communities are enriched in taxa with strong metal resistance and low-energy metabolic capacities, relying on metabolic complementarity and substrate exchange to sustain activity under chronic stress. In contrast, sediment communities exhibit higher taxonomic diversity, functional redundancy, and broader metabolic potential, acting as a functional reservoir that buffers environmental fluctuations and supplements nodule-surface processes. This spatial-functional partitioning illustrates how microorganisms maintain ecosystem stability through synergistic interactions between extreme (nodule) and moderate (sediment) microenvironments. The sustained cycling of key elements (C, N, S) under severe energy limitation emerges from this differentiated yet interconnected structure. We propose that such division of labor underpins the long-term persistence of polymetallic nodule ecosystems and serves as a representative model for energy-deprived deep-sea biomes. More broadly, this layered organization highlights how microbial life exploits spatial heterogeneity and cross-community cooperation to maintain functional stability under extreme conditions, providing a conceptual framework for adaptive strategies in other resource-limited ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank the professors of the Third Institute of Oceanography, Ministry of Natural Resources, for sharing data and offering insightful advice during the study. We further acknowledge the officers, technicians, and crew members of the DY79 deep-sea expedition for their professional support in sampling operations and logistical coordination.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Key Research and Development Program of China (Grant No. 2023YFC2811402 awarded to X.Y. Guan).\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe metagenomic sequencing data generated in this study have been deposited in the NCBI BioProject database under the accession number PRJNA1403823. The data are publicly available and accessible for peer review. No metabolomic data were generated or analysed in this study.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: Not applicable.Consent for publication: Not applicable.Ethics declaration: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAl-Bassam, M. M., Kim, J. N., Zaramela, L. S., Kellman, B. P., Zuniga, C., Wozniak, J. M., ... \u0026amp; Zengler, K. (2018). Optimization of carbon and energy utilization through differential translational efficiency. \u003cem\u003eNature communications\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 4474.\u003c/li\u003e\n \u003cli\u003eAminov, R. I. 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Microorgan isms 8: 1874\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polymetallic nodules, life-history strategies, Metabolic exchange networks, metagenomics","lastPublishedDoi":"10.21203/rs.3.rs-8281434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8281434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDeep-sea polymetallic nodule fields are among the most oligotrophic and metal-stressed ecosystems on Earth, where microbial communities play critical roles in driving elemental cycling and maintaining ecosystem stability. However, how environmental stress and resource limitation shape microbial life-history strategies and metabolic interactions in these systems remains poorly understood.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe investigated the community structure, metabolic potential, and life-history strategies of microbial assemblages inhabiting polymetallic nodules and surrounding sediments by reconstructing 314 high-quality, non-redundant metagenome-assembled genomes (MAGs). Compared to sediment communities, nodule-associated microbes exhibited higher taxonomic novelty and niche specificity, with enrichment of archaeal lineages such as Nitrosopumilaceae. Both habitats harbored broad-spectrum metal resistance genes, predominantly maintained through vertical inheritance, while nodule-inhabiting microbes showed significant enrichment of resistance genes targeting arsenic, chromium, and lead. Functional analyses revealed a spatial division of labor characterized by a \u0026ldquo;specialization\u0026ndash;buffering\u0026rdquo; relationship, with differentiated contributions to carbon, nitrogen, and sulfur cycling. Metabolic exchange network analysis indicated that sediment microbes engaged in more active exchange of energetic metabolites, including acyl-CoA and amino acids. Taxa with multifunctional life-history strategies (SY, AY, and AS) enhanced energy flow and network stability through intensified organic carbon and amino acid transfer, reflecting functional redundancy and ecological resilience. In contrast, nodule-associated communities, dominated by AS and Y strategies, primarily exchanged small organic acids and inorganic ions, consistent with adaptations toward resource efficiency and environmental persistence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results demonstrate that metabolic strategic differentiation in deep-sea nodule ecosystems reflects a dynamic trade-off between resource availability and environmental stress. Our dual-axis life-history framework provides a new ecological perspective on how functional stability is maintained in deep-sea extreme environments.\u003c/p\u003e","manuscriptTitle":"Dual-Axis Life-History Framework Explains Metabolite Exchange and Functional Differentiation in Nodule Microbiomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 11:13:41","doi":"10.21203/rs.3.rs-8281434/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbaa84ec-c879-45e4-8cbe-9cb415c39bb0","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-09T15:10:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-09T10:18:17+00:00","index":37,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T15:24:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 11:13:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8281434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8281434","identity":"rs-8281434","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}