Comparative genomics of the Nap2-2B clade reveals substrate partitioning, niche diversification, and reciprocal cofactor auxotrophies among uncultured hydrocarbon-degrading Peptococcaceae

Comparative genomics of the Nap2-2B clade reveals substrate partitioning, niche diversification, and reciprocal cofactor auxotrophies among uncultured hydrocarbon-degrading Peptococcaceae | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparative genomics of the Nap2-2B clade reveals substrate partitioning, niche diversification, and reciprocal cofactor auxotrophies among uncultured hydrocarbon-degrading Peptococcaceae Boonfei Tan, Christian Zafra, Charmaine Ng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9199614/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Uncultured Peptococcaceae of the Nap2-2B clade are frequently detected in methanogenic hydrocarbon-degrading environments, yet their metabolic diversity remains poorly understood. Here, we analyse 34 metagenome-assembled genomes spanning four genera within this clade. Phylogenomic analysis of 741 Desulfotomaculales genomes places Nap2-2B as a monophyletic family-level lineage. Glycyl radical enzyme phylogeny and operon context reveal strict substrate partitioning: SCADC1-2-3 encodes alkylsuccinate synthase for aliphatic hydrocarbon activation, 46–80 and UBA4053 encode benzylsuccinate synthase for aromatic activation, and JAIMBK01 lacks hydrocarbon activation genes but retains sulfate reduction. Pangenome-level pathway reconstruction identifies complementary cofactor biosynthetic potential, notably in cobalamin and pantothenate biosynthesis, consistent with possible cofactor complementation. Genome-scale metabolic modeling further indicates that the alkane-degrading lineage can support syntrophic hexane degradation, whereas the aromatic lineage cannot under the modeled conditions because it lacks pyruvate:ferredoxin oxidoreductase. Together, these data support a tightly integrated syntrophic guild in which substrate partitioning, possible cofactor complementation, and distinct electron-disposal strategies may structure community assembly, shape carbon and electron flow, and influence methanogenic hydrocarbon attenuation in anoxic tailings environments. Biological sciences/Biotechnology Biological sciences/Microbiology methanogenic hydrocarbon degradation syntrophy Peptococcaceae genome-scale metabolic modeling oil sands tailings anaerobic alkane degradation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Anaerobic hydrocarbon degradation in petroleum-impacted subsurface environments proceeds primarily through syntrophic partnerships, in which fermenting bacteria oxidize hydrocarbons to acetate, CO2, and H2/formate while methanogenic archaea consume these products to maintain thermodynamic favorability ( 1 – 5 ). The initial activation step is catalyzed by glycyl radical enzymes: alkylsuccinate synthase (AssA) for aliphatic hydrocarbons and benzylsuccinate synthase (BssA) for monoaromatics ( 6 , 7 ). Members of the family Peptococcaceae (order Desulfotomaculales, class Clostridia) are among the most frequently detected hydrocarbon degraders in methanogenic enrichment cultures from oil sands tailings ponds ( 8 , 9 ). Tan et al. ( 10 ) reported the first genome from this lineage: Peptococcaceae SCADC (GCA_000681355.2), assembled from fluorescence-activated cell-sorted cells from a short-chain alkane-degrading culture. This draft genome encoded a divergent assA (47% identity to AssA from Desulfoglaeba alkanexedens) but lacked dissimilatory sulfate reduction genes, suggesting an obligately syntrophic lifestyle. A subsequent culture study ( 9 ) showed that the enrichment degraded both linear and branched/cyclic alkanes, and that upon sulfate amendment the Peptococcaceae did not respond,and instead being displaced by sulfate-reducing Deltaproteobacteria. Since then, metagenomic sequencing has yielded dozens of additional genomes affiliated with this lineage, now designated the Nap2-2B clade within the GTDB taxonomy ( 11 ). These genomes span four GTDB-defined genera — SCADC1-2-3, 46–80, JAIMBK01, and UBA4053; none of which have standing in nomenclature. Here, we present a comparative genomic analysis of 34 Nap2-2B metagenome-assembled genomes (MAGs). We characterize the phylogenomic placement, hydrocarbon activation gene repertoire, and full metabolic potential of each genus, revealing strict substrate partitioning, a sulfate-reducing lineage within the clade, and reciprocal cofactor auxotrophies that provide a mechanistic hypothesis for why these abundant organisms have resisted cultivation. We further validate these genomic predictions through genome-scale metabolic modeling and flux balance analysis, quantifying the distinct syntrophic strategies and carbon partitioning of each lineage. Results Phylogenomic placement of the Nap2-2B clade To establish the evolutionary context of the Nap2-2B lineage, we constructed a maximum-likelihood phylogeny of 741 genomes from the order Desulfotomaculales using a concatenated alignment of 120 bacterial single-copy marker genes (bac120; 5,035 amino acid positions; Fig. 1) ( 11 ). The 34 Nap2-2B genomes formed a well-supported monophyletic clade at the family level, sister to the Desulfotomaculaceae. Within this clade, four genera were resolved with strong support: SCADC1-2-3 (n = 13), 46–80 (n = 13), JAIMBK01 (n = 6), and UBA4053 (n = 2), consistent with GTDB taxonomy assignments. The original Peptococcaceae SCADC genome (GCA_000681355.2; ( 10 )) falls within SCADC1-2-3. Pairwise average nucleotide identity (ANI) analysis using skani ( 12 ) revealed that no inter-genus genome pair exceeded the 95% ANI species boundary threshold, confirming genus-level divergence (Fig. S1 ; Supplementary Table S3). Within genera, multiple species-level clusters were identified: intra-genus ANI values ranged from 84–100% in SCADC1-2-3 and 46–80, with several genome pairs exceeding 95% ANI. Inter-genus ANI values were consistently below 82%. JAIMBK01 showed the tightest within-genus clustering (93–95% ANI), while UBA4053 comprised a single species cluster (99.6% ANI). These patterns indicate substantial species-level diversity within each genus despite shared ecological associations with hydrocarbon-impacted environments. Genome quality and composition varied across genera (Fig. S2 ). Estimated completeness (CheckM2; ( 13 )) ranged from 50–98%, with JAIMBK01 having the largest genomes (2.1–3.4 Mb; GC content ~ 55%) and SCADC1-2-3 having smaller genomes (1.2–2.6 Mb; GC content ~ 43%). Pangenome analysis using Panaroo ( 14 ) identified 15,255 gene clusters with an open pangenome architecture: no core genes were shared by > 95% of genomes, 2,541 shell genes (15–85%), and 12,714 cloud genes (< 15%), reflecting the extensive functional diversification across this family (Fig. S3). Hydrocarbon activation genes and operon architecture A total of 22 glycyl radical enzyme (GRE) sequences were identified across the 34 Nap2-2B genomes. Phylogenetic analysis (IQ-TREE 2; ( 15 )) classified 19 as bona fide assA or bssA ; three were reclassified as pyruvate formate-lyase or divergent GREs based on sequence identity, operon context, and absence of associated subunits (Fig. 2). The verified assignments revealed strict phylogenetic partitioning: SCADC1-2-3 encodes assA (8 sequences in 6 genomes) for aliphatic hydrocarbon activation, 46–80 encodes bssA (9 sequences in 8 genomes) for aromatic activation, and UBA4053 encodes bssA (2 sequences). Benzoyl-CoA reductase subunits ( bcrABCD , class I) were detected in UBA4053, JAIMBK01, and to a lesser extent 46–80, but only UBA4053 combines bssA with bcrABCD , suggesting potential for complete monoaromatic degradation. JAIMBK01 lacks assA and bssA entirely. Operon context analysis (± 7 flanking genes annotated by DIAMOND blastp against UniRef100 ( 16 )) confirmed clade-specific architectures (Fig. 3). The assA operons in SCADC1-2-3 were flanked by methylmalonyl-CoA mutase (MCM), succinate-CoA transferase (SCT), and enoyl-CoA hydratase (ECH): downstream enzymes of the methylsuccinate pathway, with the activating enzyme assD detected in 3 of 7 operons as paralogous copies (AssD1/AssD2), matching the arrangement in Desulfoglaeba alkanexedens ALDC. The bssA operons in 46–80 and UBA4053 contained bssD (9/11 operons), bssB (6/11), and downstream bbs beta-oxidation genes ( bbsEF, bbsG, bbsB ) in a subset of operons. Incomplete subunit detection in some operons likely reflects MAG contig fragmentation and sequence divergence of the small subunits (Supplementary Table S1 ). Functional clustering mirrors phylogenomic genus assignments To assess whether the phylogenomic structure of the Nap2-2B clade reflects broader metabolic differentiation beyond hydrocarbon activation genes, we performed unsupervised functional clustering using the complete set of KEGG ortholog (KO) annotations. Eggnog-mapper v2.1 ( 17 ) annotated 67,751 of 79,275 predicted proteins (85.5%), with 42,309 receiving KO assignments (53.4%). After filtering for KOs present in at least two genomes, 1,946 KOs were retained for analysis. Hierarchical clustering of KO presence/absence profiles (Ward's method on Jaccard distances) recovered three major genus-level groups (Fig. 4A): (i) SCADC1-2-3 (n = 13), (ii) JAIMBK01 (n = 6), and (iii) 46–80 + UBA4053 (n = 15), with SCADC1-2-3 further splitting into two sub-clusters at finer resolution. The grouping of UBA4053 with 46–80 is consistent with their shared bssA -based aromatic hydrocarbon activation strategy. Principal component analysis (PCA) of standardized KO profiles confirmed clear separation of all four genera along PC1 (14.7% variance) and PC2 (12.1%; Fig. 4B). PERMANOVA on the Jaccard distance matrix confirmed that genus membership explains a significant fraction of functional variance (pseudo-F = 7.42, p < 0.001, 9,999 permutations; ANOSIM R = 0.578, p < 0.001). Pairwise comparisons were significant for all major genus pairs after Bonferroni correction (SCADC1-2-3 vs 46–80: F = 11.14, p < 0.001; SCADC1-2-3 vs JAIMBK01: F = 7.94, p < 0.001; 46–80 vs JAIMBK01: F = 8.81, p = 80% prevalence in target, <=20% in others) for SCADC1-2-3, 46–80, and JAIMBK01, respectively, with JAIMBK01 carrying the most unique functions, consistent with its distinct ecological niche. A core of 654 KOs was shared by all four genera, with 59, 66, 259, and 35 unique KOs in SCADC1-2-3, 46–80, JAIMBK01, and UBA4053, respectively (Table S4). The concordance between unsupervised functional clustering and phylogenomic genus assignments derived from entirely independent data (KO annotations vs bac120 marker genes), demonstrates that each genus occupies a distinct metabolic niche. Notably, SCADC1-2-3, 46–80, and UBA4053 co-occur in the same environments: all three genera were recovered from the same same environment in at least two independent oil sands tailings samples (Syncrude MLSB, SRX211001; Athabasca oil sands enrichment, SRX559894), confirming that these functionally distinct lineages coexist in situ . In contrast, JAIMBK01 genomes were recovered exclusively from deep subsurface aquifers (Russia, Israel, Nevada) and hot springs, not from hydrocarbon-impacted environments. Carbon metabolism and fermentation strategies Pangenome-level pathway reconstruction using the maximum completeness across all genomes within each genus - to account for individual MAG incompleteness, revealed genus-specific carbon metabolism strategies (Fig. 5A): Central carbon metabolism. All four genera encode the Wood-Ljungdahl pathway for CO2 fixation/acetogenesis (formate-THF ligase, methylene-THF reductase, CO dehydrogenase/acetyl-CoA synthase). Glycolysis was variably complete: JAIMBK01 encodes a full Embden-Meyerhof-Parnas (EMP) pathway including pyruvate kinase, while SCADC1-2-3 consistently lacks pyruvate kinase, suggesting flux through the incomplete glycolysis or a non-canonical route. The TCA cycle is partially encoded in all genera but incomplete, consistent with a fermentative rather than respiratory lifestyle. Beta-oxidation. All genera encode the core beta-oxidation enzymes (acyl-CoA dehydrogenase, enoyl-CoA hydratase, 3-hydroxyacyl-CoA dehydrogenase, thiolase), consistent with their role in degrading the CoA-activated products of hydrocarbon activation. 46–80 additionally encodes long-chain acyl-CoA synthetase, suggesting capacity to activate exogenous fatty acids. Fermentation end products. 46–80 encodes butyrate kinase and phosphotransbutyrylase, indicating capacity for butyrate production as a fermentation end product. SCADC1-2-3 lacks both butyrate kinase and the classical acetate kinase/phosphotransacetylase pathway but likely produces acetate via CoA transferases ( e.g. , succinate-CoA transferase encoded in the assA operon context), consistent with acetyl-CoA as the terminal product of beta-oxidation. This difference in fermentation end products has implications for syntrophic partnerships: butyrate producers require additional syntrophic oxidation steps, potentially involving different partner organisms ( 18 ). Propionate metabolism. Methylmalonyl-CoA mutase (MCM) and methylmalonyl-CoA epimerase were detected predominantly in SCADC1-2-3 and JAIMBK01, with only sporadic occurrence in 46–80 (1 of 13 genomes), consistent with propionate as an intermediate in alkane degradation via the methylsuccinate pathway. The complete propionyl-CoA carboxylase (PCC, both alpha and beta subunits) was found in JAIMBK01 and SCADC1-2-3. Additional carbon substrates . Lactate dehydrogenase was widespread across all genera at the pangenome level. Pyruvate formate-lyase (PFL) was present in SCADC1-2-3, 46–80, and JAIMBK01. Aldehyde:ferredoxin oxidoreductase (AOR) was encoded by 46–80, JAIMBK01, and UBA4053 but absent from SCADC1-2-3, suggesting different capacities for aldehyde oxidation. Carbon storage via PHA synthase was predominantly found in SCADC1-2-3. Energy conservation and electron acceptor usage The most striking metabolic distinction among Nap2-2B genera lies in their energy conservation strategies (Fig. 5B): Sulfate reduction. Complete dissimilatory sulfate reduction: sulfate activation (sat), adenylyl-sulfate reductase (aprAB), and dissimilatory sulfite reductase (dsrAB) — was exclusively encoded by JAIMBK01. This genus also uniquely possessed QmoA (quinone-interacting membrane-bound oxidoreductase), which couples electron transfer from the membrane quinone pool to AprAB during sulfate reduction ( 19 – 21 ). The remaining three genera (SCADC1-2-3, 46–80, UBA4053) completely lacked sulfate reduction genes, confirming and generalizing the original observation from the single SCADC genome ( 9 , 10 ). This finding directly resolves the observation by Tan et al. ( 9 ) that Clostridia (Peptococcaceae) in methanogenic SCADC enrichment cultures did not respond to sulfate amendment. The dominant Peptococcaceae in those cultures affiliated with SCADC1-2-3 are indeed genetically incapable of sulfate reduction. However, the Nap2-2B clade as a whole does contain a sulfate-reducing lineage (JAIMBK01), indicating that this metabolic capability has been retained in one branch while being lost in the syntrophic fermenters. Terminal electron acceptors. In the absence of sulfate reduction, the syntrophic genera appear to rely primarily on fumarate reductase (frdA). Fumarate reduction regenerates succinate, the co-substrate required for fumarate addition to hydrocarbons by AssA / BssA , thus coupling electron disposal to substrate regeneration in an elegant metabolic cycle ( 22 , 23 ). Arsenate reductase ( arsC ) was common across genera, likely functioning in arsenic detoxification rather than energy conservation ( 24 ). NADH-dependent nitrite reductase ( nirB ) was restricted to JAIMBK01 and UBA4053, while cytochrome c552 nitrite reductase ( nrfA ; dissimilatory nitrite reduction to ammonium, DNRA ( 25 )) was detected in a single JAIMBK01 genome. DMSO reductase was exclusive to JAIMBK01. Notably, no nitrate reductase ( narGHI, napAB, nasAB ), denitrification enzymes ( nirK, nirS, norBC, nosZ ), iron reductase, TMAO reductase, selenate reductase, or aerobic terminal oxidases were detected in any Nap2-2B genome, consistent with obligately anaerobic lifestyles in deep, anoxic environments. Electron transfer and syntrophic machinery. All genera encoded ETF (electron-transferring flavoprotein) and butyryl-CoA dehydrogenase, suggesting ETF-based electron bifurcation as the primary mechanism for coupling endergonic oxidation reactions to exergonic ones during syntrophic growth ( 19 , 26 , 27 ). Rnf complex, Nfn transhydrogenase, and electron-bifurcating [FeFe]-hydrogenase were absent from all genomes, indicating that these alternative electron bifurcation systems are not utilized. Multiple [NiFe]- and [FeFe]-hydrogenase groups were encoded, consistent with H2 production during syntrophic oxidation ( 26 , 28 ). Formate dehydrogenase was widespread, supporting both H2 and formate as interspecies electron carriers to methanogenic partners ( 29 ). Complementary cofactor biosynthetic potential is consistent with possible cofactor complementation Analysis of cofactor and vitamin biosynthesis pathways revealed a striking pattern of complementary pathway presence and absence between the two largest genera (Fig. 5B): SCADC1-2-3 encodes the anaerobic cobalamin (vitamin B12) biosynthesis pathway, including the corrin ring assembly genes ( cbiD, cbiE, cbiG, cbiT, cobH, cobI, cobJ, cobM ; ( 30 )) and dedicated cobalt transporter ( cbiMNOQ ; ( 31 )), but lacks the ketopantoate reductase ( panE ) required for pantothenate (vitamin B5) biosynthesis ( 32 ). In contrast, 46–80 encodes the complete pantothenate pathway including panE but lacks the corrin ring biosynthesis genes essential for i B12 synthesis. The heme biosynthesis pathway is more complete in 46–80 ( hemB, hemC, hemN ) than in SCADC1-2-3 ( hemB, hemN but sporadic hemC ). Both genera encode menaquinone biosynthesis and folate (B9) biosynthesis, and both possess partial biotin (B7) biosynthesis genes ( bioB, bioF, bioA ), though neither has a fully complete pathway ( 33 ). The strongest reciprocal dependencies involve B12 and B5. Cobalamin is an essential cofactor for methylmalonyl-CoA mutase ( 34 ): a key enzyme in the alkane degradation pathway encoded by SCADC1-2-3, meaning this genus produces the very cofactor its own central metabolism requires. Pantothenate is the precursor of coenzyme A, essential for all acyl-CoA-dependent reactions including beta-oxidation ( 35 ). The co-occurrence of SCADC1-2-3 and 46–80 in the same tailings metagenomes (see above) makes these complementary cofactor profiles ecologically relevant. Together, the pathway complementarity, shared habitat occurrence, and the central metabolic roles of B12 and B5 are consistent with possible cofactor complementation between these genera: SCADC1-2-3 has the genomic potential to produce B12, whereas 46–80 has the potential to produce B5 and has a more complete heme pathway. When combined with their mutual dependence on methanogenic archaea for thermodynamic pull (H2/formate consumption)( 2 ), this supports a hypothesis in which cofactor complementation, substrate partitioning, and thermodynamic coupling may all contribute to community stability ( 2 , 5 , 36 ). This hypothesized multi-layered interdependence may help explain why these organisms, despite being abundant in tailings environments, have never been isolated in pure culture. Genome-scale metabolic modeling validates substrate partitioning and predicts distinct syntrophic strategies To test whether the genomic differences identified above translate into distinct metabolic capabilities, we reconstructed genome-scale metabolic models (GEMs) for SCADC1-2-3 and 46–80 using gapseq v1.4.0 ( 37 ). Because individual MAGs in both genera have incomplete genomes (50–98% completeness), we adopted a pangenome approach: models were reconstructed independently for each genome in a clade (10 genomes per genus, spanning > 86% ANI), and the union of all reactions was merged into a single pangenome GEM representing the complete metabolic potential of each genus. The SCADC1-2-3 pangenome comprised 1,695 reactions and 1,474 metabolites; the 46–80 pangenome comprised 1,844 reactions and 1,623 metabolites. Each pangenome GEM was placed in a simulated syntrophic consortium: hexane as the sole carbon and energy source, Widdel & Bak anaerobic mineral medium ( 38 ), and simplified methanogenesis reactions representing hydrogenotrophic (4H2 + CO2 -> CH4 + 2H2O), acetoclastic (CH3COO- + H2O -> CH4 + CO2), and formatotrophic partners. Flux balance analysis (FBA) was used to maximize biomass production while allowing the methanogen reactions to consume syntrophic intermediates (H2, acetate, formate). Table 1 Flux balance analysis of SCADC1-2-3 and 46–80 pangenome GEMs in syntrophic hexane-degrading consortia. Parameter SCADC1-2-3 46–80 46–80 + PFOR* Model reactions 1,695 1,844 1,845 Model metabolites 1,474 1,623 1,623 Manual gap filling None None PFOR only PFOR (native) Yes Absent (all 13 genomes) Added Ech hydrogenase Yes Absent Absent Growth rate (1/h) 0.108 No growth 0.033 CH4 yield (mol/mol hexane) 2.92 -- 4.04 CH4 yield (%Buswell max) ( 39 ) 61.5% -- 85.1% H2-methanogenesis 100% -- 37.7% Acetoclastic methanogenesis 0% -- 62.3% C to biomass 51.3% -- 22.6% C to CH4 48.7% -- 67.3% C to CO2 0% -- 10.1% *PFOR added artificially to enable growth as a test case for comparing electron disposal strategies. The Buswell equation prediction for complete hexane methanogenesis is 4.75 mol CH4/mol hexane. Experimental CH4 yield from SCADC enrichment cultures is approximately 40% of Buswell maximum (Tan et al. 2015). The SCADC1-2-3 pangenome achieved growth on hexane without manual gene additions (Table 1 ). In the merged pangenome, biosynthetic gaps present in the reference MAG 46–80 (GCA_002382685.1), including PFOR, prephenate dehydrogenase, and N-acetylornithine deacetylase, were supplied by genes present in other clade members. This supports the view that several apparent single-genome auxotrophies reflect MAG incompleteness rather than true lineage-level absences. In contrast, under the gapseq-reconstructed pangenome model with default parameterization ( 37 ), the 46–80 pangenome could not achieve growth on hexane even when all 10 clade genomes were pooled. The critical missing enzyme was PFOR: none of the 10 genomes in the 46–80 clade encode either of the two PFOR variants found in SCADC1-2-3. While 46–80 encodes pyruvate:formate lyase (PFL), this enzyme produces formate rather than reducing ferredoxin ( 40 ) and, unlike PFOR, cannot operate in the reverse (gluconeogenic) direction to carboxylate acetyl-CoA to pyruvate under physiological conditions ( 41 ), and therefore cannot substitute for PFOR in an alkane-degrading context. This genomic absence is consistent across all 13 genomes in the 46–80 genus, confirming that the inability to interconvert acetyl-CoA and pyruvate via ferredoxin-dependent carboxylation ( 42 ) is a genuine metabolic constraint of this lineage rather than an artifact of MAG incompleteness. This result independently corroborates the substrate partitioning inferred from GRE phylogeny (Fig. 2): SCADC1-2-3 is metabolically equipped for syntrophic alkane degradation, while 46–80 is not. The absence of PFOR in 46–80 is particularly informative because it reveals a metabolic bottleneck that would not be apparent from pathway presence/absence annotation alone. Both genera encode complete beta-oxidation pathways and could theoretically process alkylsuccinate intermediates; it is specifically the inability to route acetyl-CoA toward pyruvate for biosynthesis, a single enzymatic step, that renders 46–80 incompatible with an alkane-degrading lifestyle on mineral medium. The FBA predicted different electron-disposal strategies for the two genera. Under the modeled conditions, SCADC1-2-3 routed reducing equivalents to H 2 via Ech hydrogenase, yielding exclusively hydrogenotrophic methanogenesis, whereas the PFOR-complemented 46–80 test case exported a mixture of H 2 and acetate. These outcomes are consistent with, but do not prove, differentiated methanogen partnerships. This interpretation is compatible with the original SCADC enrichment communities, in which Peptococcaceae co-occurred with both Methanosaetaceae and Methanomicrobiaceae ( 9 , 36 ). The model therefore generates a testable hypothesis that different methanogens may preferentially consume intermediates released by different Nap2-2B partners. Carbon-balance estimates also differed between the models. SCADC1-2-3 allocated about 51% of substrate carbon to biomass and 49% to CH4, whereas the PFOR-complemented 46–80 test case allocated 23% to biomass, 67% to CH 4 , and 10% to CO 2 . These values should be interpreted comparatively rather than as exact environmental yields. At zero biomass production, the SCADC1-2-3 model slightly exceeded the Buswell maximum ( 39 ) because of residual CO 2 uptake in the consortium formulation, indicating that quantitative methane-yield predictions are approximate. During growth, the model predicted 61.5% of the Buswell maximum, compared with about 40% reported for SCADC enrichment cultures ( 9 ). The discrepancy is plausibly explained by maintenance costs, thermodynamic constraints, and community processes not represented in the simplified model. The models therefore provide a preliminary framework for how community composition may influence carbon partitioning and methane production in tailings environments, but experimental validation of these quantitative predictions remains necessary. Methods Genome dataset and quality assessment A total of 34 metagenome-assembled genomes (MAGs) affiliated with the Nap2-2B clade were retrieved from NCBI GenBank based on GTDB taxonomy assignments ( 11 ). Four RefSeq (GCF_) assemblies that duplicated GenBank (GCA_) entries were excluded to avoid double-counting. Genome quality was assessed using CheckM2 v1.0 ( 13 ). Assembly statistics including genome size, GC content, contig N50, and number of coding sequences were computed using QUAST ( 44 ). Protein-coding genes were predicted using Prodigal v2.6.3 ( 45 ) as part of the eggnog-mapper pipeline ( 17 ). Phylogenomic analysis A phylogenomic tree of 741 Desulfotomaculales genomes was constructed using the bac120 marker gene set (120 bacterial single-copy marker genes; ( 46 )). Marker genes were identified and aligned using GTDB-Tk v2 ( 11 ). The concatenated alignment (5,035 amino acid positions) was used to infer a maximum-likelihood phylogeny with FastTree 2 ( 47 ) under the WAG+GAMMA model with SH-like support values (1,000 resamples). The tree was rooted at the midpoint. Average nucleotide identity Pairwise ANI values were computed using skani v0.3.1 ( 12 ) for all 34 Nap2-2B genomes. Pangenome analysis Pangenome analysis was performed using Panaroo v1.3 ( 14 ) with moderate clean mode and a core threshold of 0.95. Gene accumulation curves were computed from 50 random genome permutations. GRE phylogenetic analysis and verification Glycyl radical enzyme (GRE) sequences were identified using eggnog-mapper v2.1, KO assignments and BLAST searches against characterized AssA/BssA reference sequences ( 17 ). Candidate GRE sequences were aligned using MAFFT ( 48 ) together with canonical reference sequences (AssA from D. alkenivorans AK-01, ABH11460; AssA from D. alkanexedens ALDC, ADJ51097; BssA from A. toluolicum , AAK50372; BssA from D. toluolica Tol2, CCK78310; MasD from Azoarcus sp. HxN1, CAO03074; PFL from E. coli , P09373) and the 17 closest independent BLAST hits from NCBI nr. The phylogeny was inferred using IQ-TREE 2 ( 15 ) under the LG + G4 substitution model with 1,000 ultrafast bootstrap replicates. Operon context was assessed by annotating ± 7 flanking genes around each GRE anchor using DIAMOND blastp v2.1.9 ( 16 ) against UniRef100 (e-value < = 1e-5). Sequences classified as PFL (K00656), CutC , or other non- assA/bssA GREs based on phylogenetic placement, sequence identity, and operon context were excluded from the verified set. Functional annotation and metabolic pathway analysis All predicted proteins were annotated using eggnog-mapper v2.1 ( 17 ) against the eggNOG 5.0 database ( 49 ). KEGG ortholog (KO) assignments were used for metabolic pathway reconstruction. Pathway completeness was calculated as the fraction of KO markers detected per pathway per genome, using curated pathway definitions for 105 metabolic pathways spanning hydrocarbon activation, beta-oxidation, central carbon metabolism, TCA cycle, Wood-Ljungdahl pathway, carbon fixation, fermentation, sulfate reduction, terminal electron acceptors, hydrogenases, electron transfer, pili/motility, ATPases, nitrogen metabolism, cofactor/vitamin biosynthesis, oxidative stress, metal homeostasis, and sporulation. Unsupervised functional clustering was performed on the full KO presence/absence matrix (34 genomes x 1,946 KOs present in > = 2 genomes) using Ward's hierarchical clustering on Jaccard distances. PCA was performed on standardized (z-scored) KO profiles. Statistical significance of functional differentiation among genera was tested using PERMANOVA and ANOSIM on Jaccard distances with 9,999 permutations. Pairwise comparisons were Bonferroni-corrected for multiple testing. Genus-specific indicator KOs were identified as those present in > = 80% of one genus and < = 20% of all others. Genome-scale metabolic model reconstruction and flux balance analysis Genome-scale metabolic models (GEMs) were reconstructed for individual MAGs using gapseq v1.4.0 ( 37 ) with default parameters and the ModelSEED biochemistry database ( 50 ). No manual gap filling or reaction additions were performed on individual models; all reactions in each model were derived solely from the genome sequence by gapseq's automated reconstruction pipeline. Because individual MAGs have incomplete genomes (50–98% completeness), pangenome GEMs were constructed for the SCADC1-2-3 and 46–80 clades by merging the union of all reactions from 10 genomes per genus (spanning > 86% ANI) into a single model, using the reference genome as the base (GCA_002382685.1 for SCADC1-2-3; GCA_002382755.1 for 46–80). Exchange reactions were retained from the base model and added for new extracellular metabolites introduced by clade members. The pangenome merging strategy takes the union of reactions across clade members; no reactions were removed or modified during merging. For consortium FBA, each pangenome GEM was provided with hexane as the sole carbon source (1 mmol/gDW/h uptake), Widdel & Bak anaerobic mineral medium (NH 4+ , PO 4 , SO 4 , CO 2 , H 2 O, trace metals) ( 38 ), and coupled to simplified methanogenesis reactions representing hydrogenotrophic (4H 2 + CO 2 -> CH 4 + 2H 2 O), acetoclastic (CH 3 COO − + H 2 O -> CH 4 + CO 2 ), and formatotrophic (4HCOO- + 4H + -> CH 4 + 3CO 2 + 2H 2 O) partners. Hexane activation (fumarate addition via AssA) and beta-oxidation of hexylsuccinate to acetyl-CoA were added as custom reactions representing the known biochemistry of alkylsuccinate synthase-mediated alkane degradation ( 51 ), as these substrate-specific reactions are not included in the ModelSEED database. The biomass reaction was maximized using FBA as implemented in COBRApy v0.29 ( 52 ). Methane yields were compared to the Buswell equation prediction for hexane (C 6 H 14 + 4.5 H 2 O -> 4.75 CH 4 + 1.25 CO 2 ) as a stoichiometric consistency check ( 39 ). All scripts for model reconstruction, pangenome merging, and consortium FBA are provided in Supplementary Data. Discussion The original Peptococcaceae SCADC genome ( 10 ) provided the first glimpse into the metabolism of uncultured hydrocarbon-degrading Clostridia from oil sands tailings. Our comparative analysis of 34 genomes spanning four genera reveals that this single genome represented just one facet of a metabolically diverse family-level lineage. The Nap2-2B clade encompasses at least three distinct ecological strategies: (i) syntrophic aliphatic hydrocarbon degradation (SCADC1-2-3), (ii) syntrophic aromatic hydrocarbon degradation (46–80 and UBA4053), and (iii) sulfate-reducing metabolism with broad carbon substrate range but without hydrocarbon activation (JAIMBK01). The strict phylogenetic partitioning of assA (alkane activation) in SCADC1-2-3 and bssA (aromatic activation) in 46–80/UBA4053 suggests that substrate specialization was an early evolutionary event in the diversification of this clade. The acquisition of different GRE variants, presumably through horizontal gene transfer, as suggested by the deep phylogenetic distances between Nap2-2B GREs and those of characterized organisms, defined the ecological trajectory of each lineage. Once committed to either aliphatic or aromatic substrates, the downstream metabolic pathways diverged accordingly: the methylsuccinate pathway and MCM-dependent propionate metabolism in alkane degraders vs the bbs/benz beta-oxidation pathway and bcrABCD benzoyl-CoA reductase in aromatic degraders. Genome-scale metabolic modeling independently validates this partitioning: the SCADC1-2-3 pangenome model is metabolically self-sufficient for syntrophic hexane degradation, while the 46–80 pangenome model cannot achieve growth on hexane under default FBA parameterization due to the clade-wide absence of PFOR - a single enzymatic bottleneck that would not be apparent from pathway completeness analysis alone. Tan et al . ( 9 ) reported that Peptococcaceae in methanogenic SCADC enrichment cultures did not respond to sulfate addition, while Deltaproteobacteria proliferated and became dominant sulfate reducers. This was puzzling given that the Peptococcaceae are phylogenetically nested within a family rich in sulfate-reducing genera ( Desulfotomaculum , Desulfosporosinus ). Our analysis provides a genomic explanation: the SCADC1-2-3 lineage that dominates these cultures genuinely lacks the entire dissimilatory sulfate reduction pathway ( sat, aprAB, dsrAB, dsrMKJOP, QmoABC ). This is unlikely to be an artifact of MAG incompleteness - the absence is consistent across 13 genomes with completeness estimates ranging from 50–98%, though the lower-completeness genomes cannot definitively exclude the possibility. The identification of complete sulfate reduction exclusively in JAIMBK01 adds nuance to this picture. The Nap2-2B clade has not entirely abandoned sulfate reduction; rather, this capability has been retained in one lineage while being lost in the three syntrophic fermenting lineages. The phylogenomic tree suggests that sulfate reduction may represent the ancestral state, with loss occurring independently in the common ancestor of the syntrophic clades, a pattern consistent with the evolutionary trajectory proposed for Pelotomaculum and other obligate syntrophs within the Desulfotomaculales ( 43 ). Tan et al. ( 9 ) detected fumarate-addition metabolites for 2-methylpentane and methylcyclopentane, but not for n -alkanes, despite depletion of both substrate classes. This pattern is consistent either with preferential activation of branched and cyclic alkanes by the divergent SCADC1-2-3 AssA or with rapid turnover of n -alkane-derived intermediates below detection limits. Upon sulfate amendment, sulfate reducers such as Desulfoglaeba would be expected to outcompete syntrophic Peptococcaceae for overlapping substrates because they can couple hydrocarbon oxidation directly to sulfate reduction. The genomic absence of sulfate-reduction genes in SCADC1-2-3 explains why this lineage cannot access that energetic advantage. This contrast is relevant for tailings ponds, where methanogenic syntrophs are expected to dominate mainly in sulfate-depleted zones. The complementary cofactor biosynthetic profiles of SCADC1-2-3 (encodes B12 biosynthesis; lacks B5) and 46–80 (encodes B5 biosynthesis; lacks B12) are consistent with complementary gene loss. Similar vitamin dependencies have been reported in other microbial communities, where they are interpreted as potential stabilizers of mutualistic interactions ( 36 ). In Nap2-2B, these complementary profiles may add another layer of interdependence beyond classic syntrophic electron transfer. A plausible community model is that SCADC1-2-3 supplies alkane activation and potentially B12, whereas 46–80 or another partner supplies B5, with methanogens removing H2, formate, and acetate. Whether cofactor exchange actually occurs remains to be tested. The FBA results are compatible with that broader hypothesis because they suggest distinct electron sinks for different partners, but they do not by themselves demonstrate partner specificity. The coexistence of aliphatic and aromatic hydrocarbon degraders within the same family-level clade suggests substrate partitioning rather than generalism in tailings environments. This organization could reduce direct competition while permitting metabolic complementarity among co-occurring partners. Differences in predicted fermentation-linked strategies may further diversify these interactions. SCADC1-2-3 is more closely associated with H 2 -linked syntrophy, whereas 46–80 shows gene-content support for butyrate-linked metabolism and, in the PFOR test case, mixed H 2 /acetate export. If such differences operate in situ , they could shift the balance between hydrogenotrophic and aceticlastic methanogenesis across tailings environments. The carbon-balance results likewise suggest that a substantial fraction of alkane-derived carbon may be diverted to biomass rather than methane, although the exact proportions remain model dependent. Sulfate amendment would be expected to alter this balance by favoring sulfate-reducing competitors over methanogenic syntrophs. The ecological separation of JAIMBK01 from the hydrocarbon-degrading genera is notable. While SCADC1-2-3, 46–80, and UBA4053 consistently co-occur in oil sands tailings, all six JAIMBK01 genomes were recovered from deep subsurface aquifers and hot springs - environments with available sulfate but without significant hydrocarbon inputs. This geographic and ecological separation, combined with the absence of hydrocarbon activation genes and the exclusive presence of dissimilatory sulfate reduction in JAIMBK01, suggests that the evolutionary divergence within the Nap2-2B clade reflects adaptation to fundamentally different ecological niches: syntrophic hydrocarbon degradation in tailings (SCADC1-2-3, 46–80, UBA4053) versus sulfate-dependent heterotrophy in the deep subsurface (JAIMBK01). Taken together, the genomic and modeling results support a Nap2-2B guild structured by substrate partitioning, possible cofactor complementation, and distinct syntrophic strategies. The apparent fragility of this network may influence hydrocarbon attenuation in oil sands tailings and related anaerobic environments. Declarations Competing Interests The authors declare no competing interests. Additional Information The authors declare that they have no competing interests. Funding Funding was provided by the Manila Central University Institutional Office. Author Contribution BT contributed to the conceptualization of the study, led data curation, methodology development, investigation, formal analysis, validation, and visualisation, and drafted the original manuscript. CZ contributed to data curation, methodology development, investigation, formal analysis and validation, and reviewing of the manuscript. CN contributed to conceptualization, methodology, participated in the investigation and formal analysis, and contributed to writing the original draft as well as reviewing and editing the manuscript. Acknowledgements We thank Manila Central University Institutional Research Office for supporting this research. Data Availability All genome sequences analyzed in this study are publicly available in NCBI GenBank under the accession numbers listed in Supplementary Table S2. The original Peptococcaceae SCADC genome is deposited under accession JJNX00000000.2. Custom analysis scripts are provided in https://doi.org/10.5281/zenodo.19163780 References Callaghan, A. V. Metabolomic investigations of anaerobic hydrocarbon-impacted environments. Curr. Opin. Biotechnol. 24 (3), 506–515. 10.1016/j.copbio.2012.08.012 (2013). Dolfing, J., Larter, S. R. & Head, I. M. Thermodynamic constraints on methanogenic crude oil biodegradation. ISME J. 2 (4), 442–452. 10.1038/ismej.2007.111 (2007). Head, I. 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Supplementary Files PeptococcaceaeSCADSupplementarySubmission.docx SupplementaryTables.xlsx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 19 May, 2026 Reviews received at journal 16 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Editor invited by journal 06 Apr, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 27 Mar, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9199614","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622511182,"identity":"e7c64007-5a57-49c6-b9c4-947e0d0f8a07","order_by":0,"name":"Boonfei Tan","email":"","orcid":"","institution":"Manila Central University","correspondingAuthor":false,"prefix":"","firstName":"Boonfei","middleName":"","lastName":"Tan","suffix":""},{"id":622511183,"identity":"3c4af301-75c5-4bbb-a071-b69638a28a0f","order_by":1,"name":"Christian Zafra","email":"","orcid":"","institution":"Manila Central University","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Zafra","suffix":""},{"id":622511184,"identity":"3f33b9ed-ec5b-451f-a107-52e1c01cbd76","order_by":2,"name":"Charmaine Ng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYLCCDxDKAIgPEKeDcQbJWph5SNLCP+3s4de2OXaJDezN2yQYd9whrEXidl6ade625MQGnmNlEoxnnhHWYiCdY2acu+1AYoNEjpkEY9thIrVYgrTIvyFei/FjRrAtPERqkbidY8bYuy3ZuI0nrdgi8QwRWvhn5xh/+LnNTraf/fDGGx93EKEFCNgkwCSISGwgSgcD8wc4k5FILaNgFIyCUTCyAAAMjjj6GT4tvgAAAABJRU5ErkJggg==","orcid":"","institution":"Manila Central University","correspondingAuthor":true,"prefix":"","firstName":"Charmaine","middleName":"","lastName":"Ng","suffix":""}],"badges":[],"createdAt":"2026-03-23 11:08:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9199614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9199614/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960630,"identity":"9939f487-d86a-450b-834d-bda52ec8bb22","added_by":"auto","created_at":"2026-04-15 09:22:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":481466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenomic tree of 741 Peptococcaceae-order genomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaximum-likelihood phylogeny of 741 genomes from order Desulfotomaculales inferred from a concatenated alignment of 120 bacterial single-copy marker genes (bac120; 5,035 amino acid positions) using FastTree 2 (47) under the WAG+GAMMA model with SH-like support values (1,000 resamples). The tree is rooted at the midpoint. Non-Nap2-2B clades are collapsed into labeled triangles, colored by dominant GTDB family assignment; labels indicate family name(s) and genome count. The Nap2-2B clade (n=34 genomes) is expanded with branch lengths magnified (25x; scale bar = 0.05 substitutions/site) and highlighted with red background shading and bracket. Nap2-2B tips are colored by GTDB genus assignment: blue = SCADC1-2-3 (n=13), red = 46-80 (n=13), black = JAIMBK01 (n=6), green = UBA4053 (n=2). These are GTDB placeholder names with no formal binomial published (*). GCA_000681355.2 is the original Peptococcaceae SCADC single-amplified genome (10). SH-like support values \u0026gt;=0.70 are shown as black dots at internal nodes (large \u0026gt;=0.95, medium \u0026gt;=0.80, small \u0026gt;=0.70). GCA and GCF accessions for the same genome (e.g., GCA_000740865.1 / GCF_000740865.1) represent GenBank and RefSeq assemblies, respectively.\u003c/p\u003e","description":"","filename":"Fig1phylogenomictree.png","url":"https://assets-eu.researchsquare.com/files/rs-9199614/v1/2e52847187e1b310e7b3df39.png"},{"id":106961199,"identity":"0b18df1b-cf13-4c81-8fd1-7b9ae663748f","added_by":"auto","created_at":"2026-04-15 09:24:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1366734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMaximum-likelihood phylogeny of glycyl radical enzyme (GRE) sequences identified in Nap2-2B MAGs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhylogenetic tree of 40 GRE amino acid sequences comprising 19 verified assA/bssA sequences from 15 Nap2-2B MAGs, 6 canonical references, and 15 independent BLAST hits from NCBI nr. Sequences were aligned with MAFFT (48) and inferred with IQ-TREE 2 (15) under LG+G4 with 1,000 ultrafast bootstraps; the tree is midpoint rooted. Bootstrap support \u0026gt;=70% is shown at internal nodes (red \u0026gt;=95%, orange \u0026gt;=80%, grey \u0026gt;=70%). Nap2-2B sequences are colored by GTDB genus assignment: blue, SCADC1-2-3; red, 46-80; green, UBA4053. \"SCADC ref\" denotes JJNX00000000.2. Filled circles indicate independent BLAST hits; black labels indicate canonical references for AssA, BssA, MasD, and the PFL outgroup. Source environment and sequence type (MAG or isolate) are shown as metadata columns. GCA_054600955.1 contains three assA paralogs on two contigs and GCA_003485325.1 contains two bssA copies on separate contigs. Sequences excluded after curation included one PFL-like GRE from 46-80 (GCA_002421505.1), one PFL from JAIMBK01 (GCA_040755975.1), one highly divergent edge-of-contig GRE from 46-80 (GCA_001508995.1), nmsA sequences, amplicon/clone sequences, and one duplicate BLAST hit identical to GCA_003445555.1. Reference annotations were also corrected for MasD, AssA, and BssA entries as described in Supplementary Table S1.\u003c/p\u003e","description":"","filename":"Fig2GREtree.png","url":"https://assets-eu.researchsquare.com/files/rs-9199614/v1/051b05ac5ce6a9bce4f527fb.png"},{"id":106961091,"identity":"38d67ce3-2378-47b6-9adf-e8c38604666a","added_by":"auto","created_at":"2026-04-15 09:24:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":700729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene neighborhood (synteny) diagrams of assA/bssA operons in Nap2-2B MAGs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSynteny diagrams showing +/-7 flanking genes around each assA or bssA anchor gene (blue and red arrows) across 18 Nap2-2B operons and two canonical references (\u003cem\u003eD. alkanexedens\u003c/em\u003e ALDC for \u003cem\u003eass\u003c/em\u003e; \u003cem\u003eA. toluolicum\u003c/em\u003e for \u003cem\u003ebss\u003c/em\u003e). Genes are colored by DIAMOND blastp annotation against UniRef100 (e-value \u0026lt;=1e-5) (16), and ribbons connect homologous genes between adjacent tracks. Panel A shows seven SCADC1-2-3 \u003cem\u003eassA\u003c/em\u003eoperons, including three \u003cem\u003eassA \u003c/em\u003eparalogs from GCA_054600955.1. Conserved flanking genes include methylmalonyl-CoA mutase, enoyl-CoA hydratase, succinate-CoA transferase, and transporter functions. AssD activating enzymes were detected in 3 of 7 \u003cem\u003eassA\u003c/em\u003e operons. Panel B shows eleven \u003cem\u003ebssA\u003c/em\u003eoperons from 46-80 (9) and UBA4053 (2), including two \u003cem\u003ebssA\u003c/em\u003e copies in GCA_003485325.1. Downstream \u003cem\u003ebbs\u003c/em\u003e-pathway genes were detected in a subset of operons. \u003cem\u003eBssD/HbsD\u003c/em\u003e homologs were detected in 9 of 11 operons, \u003cem\u003ebssB\u003c/em\u003ein 6 of 11, and \u003cem\u003ebssC\u003c/em\u003e in 1 of 11. The incomplete recovery of small subunits may reflect sequence divergence or MAG fragmentation. Full DIAMOND results are provided in Supplementary Table S1.\u003c/p\u003e","description":"","filename":"Fig3operonsynteny.png","url":"https://assets-eu.researchsquare.com/files/rs-9199614/v1/d8ceb8fc37d08e58aa929e53.png"},{"id":106906627,"identity":"83733726-c0a2-4e1d-9153-d97d3f2b3a5a","added_by":"auto","created_at":"2026-04-14 15:49:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":245324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional clustering of 34 Nap2-2B genomes based on full KEGG ortholog profiles.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Hierarchical clustering based on the full complement of 1,946 KEGG orthologs (KOs) detected across all 34 Nap2-2B genomes via eggnog-mapper v2.1 (16). KO presence/absence profiles were clustered using Ward's method on Jaccard distances. Genome labels are colored by GTDB genus assignment. Unsupervised clustering at k=4 recovers four clusters corresponding to JAIMBK01 (n=6), 46-80 + UBA4053 (n=15), and two SCADC1-2-3 sub-clusters (n=15 and n=2), with the latter two grouping together at a higher distance threshold. At the genus level, three major functional groups emerge: SCADC1-2-3, JAIMBK01, and 46-80 + UBA4053, the latter grouping consistent with their shared\u003cem\u003e bssA\u003c/em\u003e-based aromatic hydrocarbon activation. (B) PCA of standardized KO profiles shows clear separation of all four genera along PC1 (14.7%) and PC2 (12.1%). The concordance between unsupervised functional clustering and phylogenomic genus assignments demonstrates that each genus occupies a distinct metabolic niche. Colors as in Fig. S1.\u003c/p\u003e","description":"","filename":"Fig4KOclustering.png","url":"https://assets-eu.researchsquare.com/files/rs-9199614/v1/be6507695497fe0c4f38a52b.png"},{"id":106906624,"identity":"a62164a4-7ab1-43cc-9b20-457c72cca92c","added_by":"auto","created_at":"2026-04-14 15:49:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1500925,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePangenome metabolic pathway completeness for four Nap2-2B genera.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathway completeness is shown at the pangenome level (maximum completeness across genus members) to reduce the impact of MAG incompleteness. White cells therefore represent absences not recovered from any genome in the genus. Sample sizes are SCADC1-2-3 (n=13), 46-80 (n=13), JAIMBK01 (n=6), and UBA4053 (n=2). Percent values are shown only for partial pathway completeness. (A) Carbon and energy metabolism. Categories include hydrocarbon activation, beta-oxidation, central carbon metabolism, TCA cycle, Wood-Ljungdahl pathway, carbon fixation/gluconeogenesis, butyrate/acetate metabolism, propionate metabolism, other carbon substrates, and carbon storage. (B) Energy conservation, cofactors, and cellular processes. Categories include sulfate reduction, terminal electron acceptors, hydrogenases, electron transfer, H2/formate interspecies transfer, ATPases, nitrogen metabolism, cofactor/vitamin biosynthesis, oxidative stress, metal homeostasis, and sporulation. Key pangenome-level features include assA, PFOR, and methylmalonyl-CoA mutase in SCADC1-2-3; \u003cem\u003ebssA\u003c/em\u003e and butyrate-production capacity in 46-80; complete sulfate reduction in JAIMBK01; and bcrABCD in UBA4053. Ech hydrogenase is exclusive to SCADC1-2-3. SCADC1-2-3 encodes cobalamin biosynthesis but lacks pantothenate biosynthesis, whereas 46-80 shows the reciprocal pattern, consistent with possible cofactor complementation. AssA and BssA assignments are from the verified GRE tree (Fig. 2), not KEGG KO calls. Colors as in Fig. S1.\u003c/p\u003e","description":"","filename":"Fig5metabolicheatmap.png","url":"https://assets-eu.researchsquare.com/files/rs-9199614/v1/5ce577f5fdf549976c5a3f22.png"},{"id":106961367,"identity":"31ceed88-60e9-47bf-8f4a-0cb1bc1fbc8b","added_by":"auto","created_at":"2026-04-15 09:25:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":631517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothesized interaction model for co-occurring Nap2-2B lineages in methanogenic hydrocarbon-impacted environments.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchematic summarizing genomic and modeling-based inferences for the two dominant hydrocarbon-associated Nap2-2B genera. SCADC1-2-3 (blue) encodes alkylsuccinate synthase (assA) and is predicted to activate aliphatic hydrocarbons, whereas 46-80 (red) encodes benzylsuccinate synthase (bssA) and is predicted to activate aromatic substrates. Comparative pathway reconstruction suggests complementary cofactor biosynthetic capacities, with SCADC1-2-3 retaining cobalamin biosynthesis and 46-80 retaining pantothenate biosynthesis; the dashed orange arrows indicate putative cofactor complementation rather than experimentally demonstrated exchange. Flux balance analysis further suggests different electron-disposal strategies under the modeled conditions: SCADC1-2-3 favors H\u003csub\u003e2\u003c/sub\u003e-linked syntrophy, whereas the PFOR-complemented 46-80 test case exports a mixture of H2 and acetate. The absence of pyruvate:ferredoxin oxidoreductase (PFOR) from 46-80 genomes is one candidate explanation for the failure of the 46-80 pangenome model to grow on alkane substrates in mineral medium. Methanogen boxes indicate plausible hydrogenotrophic and aceticlastic sinks for intermediates, but specific partner assignments and metabolite exchange remain hypotheses requiring experimental validation.\u003c/p\u003e","description":"","filename":"Fig6syntrophicmodel.png","url":"https://assets-eu.researchsquare.com/files/rs-9199614/v1/cd7e3336d6a93a541c76acb8.png"},{"id":106964743,"identity":"b287d916-29fe-4b08-ba1f-063f299cae2d","added_by":"auto","created_at":"2026-04-15 09:51:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5342901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9199614/v1/8aa04403-ae2f-4833-a1cc-75b37d3b45cd.pdf"},{"id":106906620,"identity":"febaf009-564f-460b-b9ca-65e1bcceb6d0","added_by":"auto","created_at":"2026-04-14 15:49:23","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":951992,"visible":true,"origin":"","legend":"","description":"","filename":"PeptococcaceaeSCADSupplementarySubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-9199614/v1/e2966e0a1e952053d047c9f3.docx"},{"id":106961526,"identity":"b3153836-520c-4dd3-8cd3-3371fb0e5f82","added_by":"auto","created_at":"2026-04-15 09:25:54","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":102581,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9199614/v1/605e067547ba1be39f512c97.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative genomics of the Nap2-2B clade reveals substrate partitioning, niche diversification, and reciprocal cofactor auxotrophies among uncultured hydrocarbon-degrading Peptococcaceae","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnaerobic hydrocarbon degradation in petroleum-impacted subsurface environments proceeds primarily through syntrophic partnerships, in which fermenting bacteria oxidize hydrocarbons to acetate, CO2, and H2/formate while methanogenic archaea consume these products to maintain thermodynamic favorability (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The initial activation step is catalyzed by glycyl radical enzymes: alkylsuccinate synthase (AssA) for aliphatic hydrocarbons and benzylsuccinate synthase (BssA) for monoaromatics (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMembers of the family Peptococcaceae (order Desulfotomaculales, class Clostridia) are among the most frequently detected hydrocarbon degraders in methanogenic enrichment cultures from oil sands tailings ponds (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Tan \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) reported the first genome from this lineage: Peptococcaceae SCADC (GCA_000681355.2), assembled from fluorescence-activated cell-sorted cells from a short-chain alkane-degrading culture. This draft genome encoded a divergent \u003cem\u003eassA\u003c/em\u003e (47% identity to AssA from Desulfoglaeba alkanexedens) but lacked dissimilatory sulfate reduction genes, suggesting an obligately syntrophic lifestyle. A subsequent culture study (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) showed that the enrichment degraded both linear and branched/cyclic alkanes, and that upon sulfate amendment the Peptococcaceae did not respond,and instead being displaced by sulfate-reducing Deltaproteobacteria.\u003c/p\u003e \u003cp\u003eSince then, metagenomic sequencing has yielded dozens of additional genomes affiliated with this lineage, now designated the Nap2-2B clade within the GTDB taxonomy (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These genomes span four GTDB-defined genera \u0026mdash; SCADC1-2-3, 46\u0026ndash;80, JAIMBK01, and UBA4053; none of which have standing in nomenclature. Here, we present a comparative genomic analysis of 34 Nap2-2B metagenome-assembled genomes (MAGs). We characterize the phylogenomic placement, hydrocarbon activation gene repertoire, and full metabolic potential of each genus, revealing strict substrate partitioning, a sulfate-reducing lineage within the clade, and reciprocal cofactor auxotrophies that provide a mechanistic hypothesis for why these abundant organisms have resisted cultivation. We further validate these genomic predictions through genome-scale metabolic modeling and flux balance analysis, quantifying the distinct syntrophic strategies and carbon partitioning of each lineage.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenomic placement of the Nap2-2B clade\u003c/h2\u003e \u003cp\u003eTo establish the evolutionary context of the Nap2-2B lineage, we constructed a maximum-likelihood phylogeny of 741 genomes from the order Desulfotomaculales using a concatenated alignment of 120 bacterial single-copy marker genes (bac120; 5,035 amino acid positions; Fig.\u0026nbsp;1) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The 34 Nap2-2B genomes formed a well-supported monophyletic clade at the family level, sister to the Desulfotomaculaceae. Within this clade, four genera were resolved with strong support: SCADC1-2-3 (n\u0026thinsp;=\u0026thinsp;13), 46\u0026ndash;80 (n\u0026thinsp;=\u0026thinsp;13), JAIMBK01 (n\u0026thinsp;=\u0026thinsp;6), and UBA4053 (n\u0026thinsp;=\u0026thinsp;2), consistent with GTDB taxonomy assignments. The original Peptococcaceae SCADC genome (GCA_000681355.2; (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)) falls within SCADC1-2-3.\u003c/p\u003e \u003cp\u003ePairwise average nucleotide identity (ANI) analysis using skani (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) revealed that no inter-genus genome pair exceeded the 95% ANI species boundary threshold, confirming genus-level divergence (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table S3). Within genera, multiple species-level clusters were identified: intra-genus ANI values ranged from 84\u0026ndash;100% in SCADC1-2-3 and 46\u0026ndash;80, with several genome pairs exceeding 95% ANI. Inter-genus ANI values were consistently below 82%. JAIMBK01 showed the tightest within-genus clustering (93\u0026ndash;95% ANI), while UBA4053 comprised a single species cluster (99.6% ANI). These patterns indicate substantial species-level diversity within each genus despite shared ecological associations with hydrocarbon-impacted environments.\u003c/p\u003e \u003cp\u003eGenome quality and composition varied across genera (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Estimated completeness (CheckM2; (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)) ranged from 50\u0026ndash;98%, with JAIMBK01 having the largest genomes (2.1\u0026ndash;3.4 Mb; GC content\u0026thinsp;~\u0026thinsp;55%) and SCADC1-2-3 having smaller genomes (1.2\u0026ndash;2.6 Mb; GC content\u0026thinsp;~\u0026thinsp;43%). Pangenome analysis using Panaroo (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) identified 15,255 gene clusters with an open pangenome architecture: no core genes were shared by \u0026gt;\u0026thinsp;95% of genomes, 2,541 shell genes (15\u0026ndash;85%), and 12,714 cloud genes (\u0026lt;\u0026thinsp;15%), reflecting the extensive functional diversification across this family (Fig. S3).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHydrocarbon activation genes and operon architecture\u003c/h3\u003e\n\u003cp\u003eA total of 22 glycyl radical enzyme (GRE) sequences were identified across the 34 Nap2-2B genomes. Phylogenetic analysis (IQ-TREE 2; (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)) classified 19 as \u003cem\u003ebona fide assA\u003c/em\u003e or \u003cem\u003ebssA\u003c/em\u003e; three were reclassified as pyruvate formate-lyase or divergent GREs based on sequence identity, operon context, and absence of associated subunits (Fig.\u0026nbsp;2). The verified assignments revealed strict phylogenetic partitioning: SCADC1-2-3 encodes \u003cem\u003eassA\u003c/em\u003e (8 sequences in 6 genomes) for aliphatic hydrocarbon activation, 46\u0026ndash;80 encodes \u003cem\u003ebssA\u003c/em\u003e (9 sequences in 8 genomes) for aromatic activation, and UBA4053 encodes \u003cem\u003ebssA\u003c/em\u003e (2 sequences). Benzoyl-CoA reductase subunits (\u003cem\u003ebcrABCD\u003c/em\u003e, class I) were detected in UBA4053, JAIMBK01, and to a lesser extent 46\u0026ndash;80, but only UBA4053 combines \u003cem\u003ebssA\u003c/em\u003e with \u003cem\u003ebcrABCD\u003c/em\u003e, suggesting potential for complete monoaromatic degradation. JAIMBK01 lacks \u003cem\u003eassA\u003c/em\u003e and \u003cem\u003ebssA\u003c/em\u003e entirely.\u003c/p\u003e \u003cp\u003eOperon context analysis (\u0026plusmn;\u0026thinsp;7 flanking genes annotated by DIAMOND blastp against UniRef100 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)) confirmed clade-specific architectures (Fig.\u0026nbsp;3). The \u003cem\u003eassA\u003c/em\u003e operons in SCADC1-2-3 were flanked by methylmalonyl-CoA mutase (MCM), succinate-CoA transferase (SCT), and enoyl-CoA hydratase (ECH): downstream enzymes of the methylsuccinate pathway, with the activating enzyme assD detected in 3 of 7 operons as paralogous copies (AssD1/AssD2), matching the arrangement in \u003cem\u003eDesulfoglaeba alkanexedens\u003c/em\u003e ALDC. The \u003cem\u003ebssA\u003c/em\u003e operons in 46\u0026ndash;80 and UBA4053 contained \u003cem\u003ebssD\u003c/em\u003e (9/11 operons), \u003cem\u003ebssB\u003c/em\u003e (6/11), and downstream \u003cem\u003ebbs\u003c/em\u003e beta-oxidation genes (\u003cem\u003ebbsEF, bbsG, bbsB\u003c/em\u003e) in a subset of operons. Incomplete subunit detection in some operons likely reflects MAG contig fragmentation and sequence divergence of the small subunits (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eFunctional clustering mirrors phylogenomic genus assignments\u003c/h3\u003e\n\u003cp\u003eTo assess whether the phylogenomic structure of the Nap2-2B clade reflects broader metabolic differentiation beyond hydrocarbon activation genes, we performed unsupervised functional clustering using the complete set of KEGG ortholog (KO) annotations. Eggnog-mapper v2.1 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) annotated 67,751 of 79,275 predicted proteins (85.5%), with 42,309 receiving KO assignments (53.4%). After filtering for KOs present in at least two genomes, 1,946 KOs were retained for analysis.\u003c/p\u003e \u003cp\u003eHierarchical clustering of KO presence/absence profiles (Ward's method on Jaccard distances) recovered three major genus-level groups (Fig.\u0026nbsp;4A): (i) SCADC1-2-3 (n\u0026thinsp;=\u0026thinsp;13), (ii) JAIMBK01 (n\u0026thinsp;=\u0026thinsp;6), and (iii) 46\u0026ndash;80\u0026thinsp;+\u0026thinsp;UBA4053 (n\u0026thinsp;=\u0026thinsp;15), with SCADC1-2-3 further splitting into two sub-clusters at finer resolution. The grouping of UBA4053 with 46\u0026ndash;80 is consistent with their shared \u003cem\u003ebssA\u003c/em\u003e-based aromatic hydrocarbon activation strategy. Principal component analysis (PCA) of standardized KO profiles confirmed clear separation of all four genera along PC1 (14.7% variance) and PC2 (12.1%; Fig.\u0026nbsp;4B). PERMANOVA on the Jaccard distance matrix confirmed that genus membership explains a significant fraction of functional variance (pseudo-F\u0026thinsp;=\u0026thinsp;7.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 9,999 permutations; ANOSIM R\u0026thinsp;=\u0026thinsp;0.578, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Pairwise comparisons were significant for all major genus pairs after Bonferroni correction (SCADC1-2-3 vs 46\u0026ndash;80: F\u0026thinsp;=\u0026thinsp;11.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; SCADC1-2-3 vs JAIMBK01: F\u0026thinsp;=\u0026thinsp;7.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 46\u0026ndash;80 vs JAIMBK01: F\u0026thinsp;=\u0026thinsp;8.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Indicator KO analysis identified 8, 10, and 81 genus-specific KOs (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;80% prevalence in target, \u0026lt;=20% in others) for SCADC1-2-3, 46\u0026ndash;80, and JAIMBK01, respectively, with JAIMBK01 carrying the most unique functions, consistent with its distinct ecological niche. A core of 654 KOs was shared by all four genera, with 59, 66, 259, and 35 unique KOs in SCADC1-2-3, 46\u0026ndash;80, JAIMBK01, and UBA4053, respectively (Table S4).\u003c/p\u003e \u003cp\u003eThe concordance between unsupervised functional clustering and phylogenomic genus assignments derived from entirely independent data (KO annotations vs bac120 marker genes), demonstrates that each genus occupies a distinct metabolic niche. Notably, SCADC1-2-3, 46\u0026ndash;80, and UBA4053 co-occur in the same environments: all three genera were recovered from the same same environment in at least two independent oil sands tailings samples (Syncrude MLSB, SRX211001; Athabasca oil sands enrichment, SRX559894), confirming that these functionally distinct lineages coexist \u003cem\u003ein situ\u003c/em\u003e. In contrast, JAIMBK01 genomes were recovered exclusively from deep subsurface aquifers (Russia, Israel, Nevada) and hot springs, not from hydrocarbon-impacted environments.\u003c/p\u003e\n\u003ch3\u003eCarbon metabolism and fermentation strategies\u003c/h3\u003e\n\u003cp\u003ePangenome-level pathway reconstruction using the maximum completeness across all genomes within each genus - to account for individual MAG incompleteness, revealed genus-specific carbon metabolism strategies (Fig.\u0026nbsp;5A):\u003c/p\u003e \u003cp\u003e \u003cb\u003eCentral carbon metabolism.\u003c/b\u003e All four genera encode the Wood-Ljungdahl pathway for CO2 fixation/acetogenesis (formate-THF ligase, methylene-THF reductase, CO dehydrogenase/acetyl-CoA synthase). Glycolysis was variably complete: JAIMBK01 encodes a full Embden-Meyerhof-Parnas (EMP) pathway including pyruvate kinase, while SCADC1-2-3 consistently lacks pyruvate kinase, suggesting flux through the incomplete glycolysis or a non-canonical route. The TCA cycle is partially encoded in all genera but incomplete, consistent with a fermentative rather than respiratory lifestyle.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBeta-oxidation.\u003c/b\u003e All genera encode the core beta-oxidation enzymes (acyl-CoA dehydrogenase, enoyl-CoA hydratase, 3-hydroxyacyl-CoA dehydrogenase, thiolase), consistent with their role in degrading the CoA-activated products of hydrocarbon activation. 46\u0026ndash;80 additionally encodes long-chain acyl-CoA synthetase, suggesting capacity to activate exogenous fatty acids.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFermentation end products.\u003c/b\u003e 46\u0026ndash;80 encodes butyrate kinase and phosphotransbutyrylase, indicating capacity for butyrate production as a fermentation end product. SCADC1-2-3 lacks both butyrate kinase and the classical acetate kinase/phosphotransacetylase pathway but likely produces acetate via CoA transferases (\u003cem\u003ee.g.\u003c/em\u003e, succinate-CoA transferase encoded in the assA operon context), consistent with acetyl-CoA as the terminal product of beta-oxidation. This difference in fermentation end products has implications for syntrophic partnerships: butyrate producers require additional syntrophic oxidation steps, potentially involving different partner organisms (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePropionate metabolism.\u003c/b\u003e Methylmalonyl-CoA mutase (MCM) and methylmalonyl-CoA epimerase were detected predominantly in SCADC1-2-3 and JAIMBK01, with only sporadic occurrence in 46\u0026ndash;80 (1 of 13 genomes), consistent with propionate as an intermediate in alkane degradation via the methylsuccinate pathway. The complete propionyl-CoA carboxylase (PCC, both alpha and beta subunits) was found in JAIMBK01 and SCADC1-2-3.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdditional carbon substrates\u003c/b\u003e. Lactate dehydrogenase was widespread across all genera at the pangenome level. Pyruvate formate-lyase (PFL) was present in SCADC1-2-3, 46\u0026ndash;80, and JAIMBK01. Aldehyde:ferredoxin oxidoreductase (AOR) was encoded by 46\u0026ndash;80, JAIMBK01, and UBA4053 but absent from SCADC1-2-3, suggesting different capacities for aldehyde oxidation. Carbon storage via PHA synthase was predominantly found in SCADC1-2-3.\u003c/p\u003e\n\u003ch3\u003eEnergy conservation and electron acceptor usage\u003c/h3\u003e\n\u003cp\u003eThe most striking metabolic distinction among Nap2-2B genera lies in their energy conservation strategies (Fig.\u0026nbsp;5B):\u003c/p\u003e \u003cp\u003e \u003cb\u003eSulfate reduction.\u003c/b\u003e Complete dissimilatory sulfate reduction: sulfate activation (sat), adenylyl-sulfate reductase (aprAB), and dissimilatory sulfite reductase (dsrAB) \u0026mdash; was exclusively encoded by JAIMBK01. This genus also uniquely possessed QmoA (quinone-interacting membrane-bound oxidoreductase), which couples electron transfer from the membrane quinone pool to AprAB during sulfate reduction (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The remaining three genera (SCADC1-2-3, 46\u0026ndash;80, UBA4053) completely lacked sulfate reduction genes, confirming and generalizing the original observation from the single SCADC genome (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This finding directly resolves the observation by Tan \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) that Clostridia (Peptococcaceae) in methanogenic SCADC enrichment cultures did not respond to sulfate amendment. The dominant Peptococcaceae in those cultures affiliated with SCADC1-2-3 are indeed genetically incapable of sulfate reduction. However, the Nap2-2B clade as a whole does contain a sulfate-reducing lineage (JAIMBK01), indicating that this metabolic capability has been retained in one branch while being lost in the syntrophic fermenters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTerminal electron acceptors.\u003c/b\u003e In the absence of sulfate reduction, the syntrophic genera appear to rely primarily on fumarate reductase (frdA). Fumarate reduction regenerates succinate, the co-substrate required for fumarate addition to hydrocarbons by \u003cem\u003eAssA\u003c/em\u003e/\u003cem\u003eBssA\u003c/em\u003e, thus coupling electron disposal to substrate regeneration in an elegant metabolic cycle (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Arsenate reductase (\u003cem\u003earsC\u003c/em\u003e) was common across genera, likely functioning in arsenic detoxification rather than energy conservation (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). NADH-dependent nitrite reductase (\u003cem\u003enirB\u003c/em\u003e) was restricted to JAIMBK01 and UBA4053, while cytochrome c552 nitrite reductase (\u003cem\u003enrfA\u003c/em\u003e; dissimilatory nitrite reduction to ammonium, DNRA (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)) was detected in a single JAIMBK01 genome. DMSO reductase was exclusive to JAIMBK01.\u003c/p\u003e \u003cp\u003eNotably, no nitrate reductase (\u003cem\u003enarGHI, napAB, nasAB\u003c/em\u003e), denitrification enzymes (\u003cem\u003enirK, nirS, norBC, nosZ\u003c/em\u003e), iron reductase, TMAO reductase, selenate reductase, or aerobic terminal oxidases were detected in any Nap2-2B genome, consistent with obligately anaerobic lifestyles in deep, anoxic environments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eElectron transfer and syntrophic machinery.\u003c/b\u003e All genera encoded ETF (electron-transferring flavoprotein) and butyryl-CoA dehydrogenase, suggesting ETF-based electron bifurcation as the primary mechanism for coupling endergonic oxidation reactions to exergonic ones during syntrophic growth (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Rnf complex, Nfn transhydrogenase, and electron-bifurcating [FeFe]-hydrogenase were absent from all genomes, indicating that these alternative electron bifurcation systems are not utilized. Multiple [NiFe]- and [FeFe]-hydrogenase groups were encoded, consistent with H2 production during syntrophic oxidation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Formate dehydrogenase was widespread, supporting both H2 and formate as interspecies electron carriers to methanogenic partners (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComplementary cofactor biosynthetic potential is consistent with possible cofactor complementation\u003c/h2\u003e \u003cp\u003eAnalysis of cofactor and vitamin biosynthesis pathways revealed a striking pattern of complementary pathway presence and absence between the two largest genera (Fig.\u0026nbsp;5B): SCADC1-2-3 encodes the anaerobic cobalamin (vitamin B12) biosynthesis pathway, including the corrin ring assembly genes (\u003cem\u003ecbiD, cbiE, cbiG, cbiT, cobH, cobI, cobJ, cobM\u003c/em\u003e; (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)) and dedicated cobalt transporter (\u003cem\u003ecbiMNOQ\u003c/em\u003e; (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)), but lacks the ketopantoate reductase (\u003cem\u003epanE\u003c/em\u003e) required for pantothenate (vitamin B5) biosynthesis (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In contrast, 46\u0026ndash;80 encodes the complete pantothenate pathway including panE but lacks the corrin ring biosynthesis genes essential for i B12 synthesis. The heme biosynthesis pathway is more complete in 46\u0026ndash;80 (\u003cem\u003ehemB, hemC, hemN\u003c/em\u003e) than in SCADC1-2-3 (\u003cem\u003ehemB, hemN\u003c/em\u003e but sporadic \u003cem\u003ehemC\u003c/em\u003e). Both genera encode menaquinone biosynthesis and folate (B9) biosynthesis, and both possess partial biotin (B7) biosynthesis genes (\u003cem\u003ebioB, bioF, bioA\u003c/em\u003e), though neither has a fully complete pathway (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The strongest reciprocal dependencies involve B12 and B5. Cobalamin is an essential cofactor for methylmalonyl-CoA mutase (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e): a key enzyme in the alkane degradation pathway encoded by SCADC1-2-3, meaning this genus produces the very cofactor its own central metabolism requires. Pantothenate is the precursor of coenzyme A, essential for all acyl-CoA-dependent reactions including beta-oxidation (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe co-occurrence of SCADC1-2-3 and 46\u0026ndash;80 in the same tailings metagenomes (see above) makes these complementary cofactor profiles ecologically relevant. Together, the pathway complementarity, shared habitat occurrence, and the central metabolic roles of B12 and B5 are consistent with possible cofactor complementation between these genera: SCADC1-2-3 has the genomic potential to produce B12, whereas 46\u0026ndash;80 has the potential to produce B5 and has a more complete heme pathway. When combined with their mutual dependence on methanogenic archaea for thermodynamic pull (H2/formate consumption)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), this supports a hypothesis in which cofactor complementation, substrate partitioning, and thermodynamic coupling may all contribute to community stability (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This hypothesized multi-layered interdependence may help explain why these organisms, despite being abundant in tailings environments, have never been isolated in pure culture.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenome-scale metabolic modeling validates substrate partitioning and predicts distinct syntrophic strategies\u003c/h3\u003e\n\u003cp\u003eTo test whether the genomic differences identified above translate into distinct metabolic capabilities, we reconstructed genome-scale metabolic models (GEMs) for SCADC1-2-3 and 46\u0026ndash;80 using gapseq v1.4.0 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Because individual MAGs in both genera have incomplete genomes (50\u0026ndash;98% completeness), we adopted a pangenome approach: models were reconstructed independently for each genome in a clade (10 genomes per genus, spanning\u0026thinsp;\u0026gt;\u0026thinsp;86% ANI), and the union of all reactions was merged into a single pangenome GEM representing the complete metabolic potential of each genus. The SCADC1-2-3 pangenome comprised 1,695 reactions and 1,474 metabolites; the 46\u0026ndash;80 pangenome comprised 1,844 reactions and 1,623 metabolites.\u003c/p\u003e \u003cp\u003eEach pangenome GEM was placed in a simulated syntrophic consortium: hexane as the sole carbon and energy source, Widdel \u0026amp; Bak anaerobic mineral medium (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), and simplified methanogenesis reactions representing hydrogenotrophic (4H2\u0026thinsp;+\u0026thinsp;CO2 -\u0026gt; CH4\u0026thinsp;+\u0026thinsp;2H2O), acetoclastic (CH3COO- + H2O -\u0026gt; CH4\u0026thinsp;+\u0026thinsp;CO2), and formatotrophic partners. Flux balance analysis (FBA) was used to maximize biomass production while allowing the methanogen reactions to consume syntrophic intermediates (H2, acetate, formate).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFlux balance analysis of SCADC1-2-3 and 46\u0026ndash;80 pangenome GEMs in syntrophic hexane-degrading consortia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCADC1-2-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u0026ndash;80\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u0026ndash;80\u0026thinsp;+\u0026thinsp;PFOR*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel reactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel metabolites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManual gap filling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePFOR only\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFOR (native)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbsent (all 13 genomes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdded\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEch hydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth rate (1/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH4 yield (mol/mol hexane)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH4 yield (%Buswell max) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2-methanogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetoclastic methanogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC to biomass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC to CH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC to CO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*PFOR added artificially to enable growth as a test case for comparing electron disposal strategies. The Buswell equation prediction for complete hexane methanogenesis is 4.75 mol CH4/mol hexane. Experimental CH4 yield from SCADC enrichment cultures is approximately 40% of Buswell maximum (Tan et al. 2015).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe SCADC1-2-3 pangenome achieved growth on hexane without manual gene additions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the merged pangenome, biosynthetic gaps present in the reference MAG 46\u0026ndash;80 (GCA_002382685.1), including PFOR, prephenate dehydrogenase, and N-acetylornithine deacetylase, were supplied by genes present in other clade members. This supports the view that several apparent single-genome auxotrophies reflect MAG incompleteness rather than true lineage-level absences.\u003c/p\u003e \u003cp\u003eIn contrast, under the gapseq-reconstructed pangenome model with default parameterization (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), the 46\u0026ndash;80 pangenome could not achieve growth on hexane even when all 10 clade genomes were pooled. The critical missing enzyme was PFOR: none of the 10 genomes in the 46\u0026ndash;80 clade encode either of the two PFOR variants found in SCADC1-2-3. While 46\u0026ndash;80 encodes pyruvate:formate lyase (PFL), this enzyme produces formate rather than reducing ferredoxin (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and, unlike PFOR, cannot operate in the reverse (gluconeogenic) direction to carboxylate acetyl-CoA to pyruvate under physiological conditions (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), and therefore cannot substitute for PFOR in an alkane-degrading context. This genomic absence is consistent across all 13 genomes in the 46\u0026ndash;80 genus, confirming that the inability to interconvert acetyl-CoA and pyruvate via ferredoxin-dependent carboxylation (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) is a genuine metabolic constraint of this lineage rather than an artifact of MAG incompleteness.\u003c/p\u003e \u003cp\u003eThis result independently corroborates the substrate partitioning inferred from GRE phylogeny (Fig.\u0026nbsp;2): SCADC1-2-3 is metabolically equipped for syntrophic alkane degradation, while 46\u0026ndash;80 is not. The absence of PFOR in 46\u0026ndash;80 is particularly informative because it reveals a metabolic bottleneck that would not be apparent from pathway presence/absence annotation alone. Both genera encode complete beta-oxidation pathways and could theoretically process alkylsuccinate intermediates; it is specifically the inability to route acetyl-CoA toward pyruvate for biosynthesis, a single enzymatic step, that renders 46\u0026ndash;80 incompatible with an alkane-degrading lifestyle on mineral medium. The FBA predicted different electron-disposal strategies for the two genera. Under the modeled conditions, SCADC1-2-3 routed reducing equivalents to H\u003csub\u003e2\u003c/sub\u003e via Ech hydrogenase, yielding exclusively hydrogenotrophic methanogenesis, whereas the PFOR-complemented 46\u0026ndash;80 test case exported a mixture of H\u003csub\u003e2\u003c/sub\u003e and acetate. These outcomes are consistent with, but do not prove, differentiated methanogen partnerships.\u003c/p\u003e \u003cp\u003eThis interpretation is compatible with the original SCADC enrichment communities, in which Peptococcaceae co-occurred with both Methanosaetaceae and Methanomicrobiaceae (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The model therefore generates a testable hypothesis that different methanogens may preferentially consume intermediates released by different Nap2-2B partners.\u003c/p\u003e \u003cp\u003eCarbon-balance estimates also differed between the models. SCADC1-2-3 allocated about 51% of substrate carbon to biomass and 49% to CH4, whereas the PFOR-complemented 46\u0026ndash;80 test case allocated 23% to biomass, 67% to CH\u003csub\u003e4\u003c/sub\u003e, and 10% to CO\u003csub\u003e2\u003c/sub\u003e. These values should be interpreted comparatively rather than as exact environmental yields. At zero biomass production, the SCADC1-2-3 model slightly exceeded the Buswell maximum (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) because of residual CO\u003csub\u003e2\u003c/sub\u003e uptake in the consortium formulation, indicating that quantitative methane-yield predictions are approximate. During growth, the model predicted 61.5% of the Buswell maximum, compared with about 40% reported for SCADC enrichment cultures (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The discrepancy is plausibly explained by maintenance costs, thermodynamic constraints, and community processes not represented in the simplified model.\u003c/p\u003e \u003cp\u003eThe models therefore provide a preliminary framework for how community composition may influence carbon partitioning and methane production in tailings environments, but experimental validation of these quantitative predictions remains necessary.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eGenome dataset and quality assessment\u003c/h2\u003e\u003cp\u003eA total of 34 metagenome-assembled genomes (MAGs) affiliated with the Nap2-2B clade were retrieved from NCBI GenBank based on GTDB taxonomy assignments (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e). Four RefSeq (GCF_) assemblies that duplicated GenBank (GCA_) entries were excluded to avoid double-counting. Genome quality was assessed using CheckM2 v1.0 (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e). Assembly statistics including genome size, GC content, contig N50, and number of coding sequences were computed using QUAST (\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e). Protein-coding genes were predicted using Prodigal v2.6.3 (\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e) as part of the eggnog-mapper pipeline (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003ePhylogenomic analysis\u003c/h2\u003e\u003cp\u003eA phylogenomic tree of 741 Desulfotomaculales genomes was constructed using the bac120 marker gene set (120 bacterial single-copy marker genes; (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e)). Marker genes were identified and aligned using GTDB-Tk v2 (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e). The concatenated alignment (5,035 amino acid positions) was used to infer a maximum-likelihood phylogeny with FastTree 2 (\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e) under the WAG+GAMMA model with SH-like support values (1,000 resamples). The tree was rooted at the midpoint.\u003c/p\u003e\u003ch2\u003eAverage nucleotide identity\u003c/h2\u003e\u003cp\u003ePairwise ANI values were computed using skani v0.3.1 (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e) for all 34 Nap2-2B genomes.\u003c/p\u003e\u003ch2\u003ePangenome analysis\u003c/h2\u003e\u003cp\u003ePangenome analysis was performed using Panaroo v1.3 (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e) with moderate clean mode and a core threshold of 0.95. Gene accumulation curves were computed from 50 random genome permutations.\u003c/p\u003e\u003ch2\u003eGRE phylogenetic analysis and verification\u003c/h2\u003e\u003cp\u003eGlycyl radical enzyme (GRE) sequences were identified using eggnog-mapper v2.1, KO assignments and BLAST searches against characterized AssA/BssA reference sequences (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e). Candidate GRE sequences were aligned using MAFFT (\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e) together with canonical reference sequences (AssA from \u003cem\u003eD. alkenivorans\u003c/em\u003e AK-01, ABH11460; AssA from \u003cem\u003eD. alkanexedens\u003c/em\u003e ALDC, ADJ51097; BssA from \u003cem\u003eA. toluolicum\u003c/em\u003e, AAK50372; BssA from \u003cem\u003eD. toluolica\u003c/em\u003e Tol2, CCK78310; MasD from \u003cem\u003eAzoarcus\u003c/em\u003e sp. HxN1, CAO03074; PFL from \u003cem\u003eE. coli\u003c/em\u003e, P09373) and the 17 closest independent BLAST hits from NCBI nr. The phylogeny was inferred using IQ-TREE 2 (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e) under the LG + G4 substitution model with 1,000 ultrafast bootstrap replicates.\u003c/p\u003e\u003cp\u003eOperon context was assessed by annotating ± 7 flanking genes around each GRE anchor using DIAMOND blastp v2.1.9 (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e) against UniRef100 (e-value \u0026lt; = 1e-5). Sequences classified as \u003cem\u003ePFL\u003c/em\u003e (K00656), \u003cem\u003eCutC\u003c/em\u003e, or other non-\u003cem\u003eassA/bssA\u003c/em\u003e GREs based on phylogenetic placement, sequence identity, and operon context were excluded from the verified set.\u003c/p\u003e\u003ch2\u003eFunctional annotation and metabolic pathway analysis\u003c/h2\u003e\u003cp\u003eAll predicted proteins were annotated using eggnog-mapper v2.1 (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e) against the eggNOG 5.0 database (\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e). KEGG ortholog (KO) assignments were used for metabolic pathway reconstruction. Pathway completeness was calculated as the fraction of KO markers detected per pathway per genome, using curated pathway definitions for 105 metabolic pathways spanning hydrocarbon activation, beta-oxidation, central carbon metabolism, TCA cycle, Wood-Ljungdahl pathway, carbon fixation, fermentation, sulfate reduction, terminal electron acceptors, hydrogenases, electron transfer, pili/motility, ATPases, nitrogen metabolism, cofactor/vitamin biosynthesis, oxidative stress, metal homeostasis, and sporulation.\u003c/p\u003e\u003cp\u003eUnsupervised functional clustering was performed on the full KO presence/absence matrix (34 genomes x 1,946 KOs present in \u0026gt; = 2 genomes) using Ward's hierarchical clustering on Jaccard distances. PCA was performed on standardized (z-scored) KO profiles. Statistical significance of functional differentiation among genera was tested using PERMANOVA and ANOSIM on Jaccard distances with 9,999 permutations. Pairwise comparisons were Bonferroni-corrected for multiple testing. Genus-specific indicator KOs were identified as those present in \u0026gt; = 80% of one genus and \u0026lt; = 20% of all others.\u003c/p\u003e\u003ch2\u003eGenome-scale metabolic model reconstruction and flux balance analysis\u003c/h2\u003e\u003cp\u003eGenome-scale metabolic models (GEMs) were reconstructed for individual MAGs using gapseq v1.4.0 (\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e) with default parameters and the ModelSEED biochemistry database (\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e). No manual gap filling or reaction additions were performed on individual models; all reactions in each model were derived solely from the genome sequence by gapseq's automated reconstruction pipeline. Because individual MAGs have incomplete genomes (50–98% completeness), pangenome GEMs were constructed for the SCADC1-2-3 and 46–80 clades by merging the union of all reactions from 10 genomes per genus (spanning \u0026gt; 86% ANI) into a single model, using the reference genome as the base (GCA_002382685.1 for SCADC1-2-3; GCA_002382755.1 for 46–80). Exchange reactions were retained from the base model and added for new extracellular metabolites introduced by clade members. The pangenome merging strategy takes the union of reactions across clade members; no reactions were removed or modified during merging.\u003c/p\u003e\u003cp\u003eFor consortium FBA, each pangenome GEM was provided with hexane as the sole carbon source (1 mmol/gDW/h uptake), Widdel \u0026amp; Bak anaerobic mineral medium (NH\u003csup\u003e4+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e, SO\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003eO, trace metals) (\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e), and coupled to simplified methanogenesis reactions representing hydrogenotrophic (4H\u003csub\u003e2\u003c/sub\u003e + CO\u003csub\u003e2\u003c/sub\u003e -\u0026gt; CH\u003csub\u003e4\u003c/sub\u003e + 2H\u003csub\u003e2\u003c/sub\u003eO), acetoclastic (CH\u003csub\u003e3\u003c/sub\u003eCOO\u003csup\u003e−\u003c/sup\u003e + H\u003csub\u003e2\u003c/sub\u003eO -\u0026gt; CH\u003csub\u003e4\u003c/sub\u003e + CO\u003csub\u003e2\u003c/sub\u003e), and formatotrophic (4HCOO- + 4H\u003csup\u003e+\u003c/sup\u003e -\u0026gt; CH\u003csub\u003e4\u003c/sub\u003e + 3CO\u003csub\u003e2\u003c/sub\u003e + 2H\u003csub\u003e2\u003c/sub\u003eO) partners. Hexane activation (fumarate addition via AssA) and beta-oxidation of hexylsuccinate to acetyl-CoA were added as custom reactions representing the known biochemistry of alkylsuccinate synthase-mediated alkane degradation (\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e), as these substrate-specific reactions are not included in the ModelSEED database. The biomass reaction was maximized using FBA as implemented in COBRApy v0.29 (\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e). Methane yields were compared to the Buswell equation prediction for hexane (C\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e14\u003c/sub\u003e + 4.5 H\u003csub\u003e2\u003c/sub\u003eO -\u0026gt; 4.75 CH\u003csub\u003e4\u003c/sub\u003e + 1.25 CO\u003csub\u003e2\u003c/sub\u003e) as a stoichiometric consistency check (\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e). All scripts for model reconstruction, pangenome merging, and consortium FBA are provided in Supplementary Data.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe original Peptococcaceae SCADC genome (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e) provided the first glimpse into the metabolism of uncultured hydrocarbon-degrading Clostridia from oil sands tailings. Our comparative analysis of 34 genomes spanning four genera reveals that this single genome represented just one facet of a metabolically diverse family-level lineage. The Nap2-2B clade encompasses at least three distinct ecological strategies: (i) syntrophic aliphatic hydrocarbon degradation (SCADC1-2-3), (ii) syntrophic aromatic hydrocarbon degradation (46–80 and UBA4053), and (iii) sulfate-reducing metabolism with broad carbon substrate range but without hydrocarbon activation (JAIMBK01).\u003c/p\u003e \u003cp\u003eThe strict phylogenetic partitioning of \u003cem\u003eassA\u003c/em\u003e (alkane activation) in SCADC1-2-3 and \u003cem\u003ebssA\u003c/em\u003e (aromatic activation) in 46–80/UBA4053 suggests that substrate specialization was an early evolutionary event in the diversification of this clade. The acquisition of different GRE variants, presumably through horizontal gene transfer, as suggested by the deep phylogenetic distances between Nap2-2B GREs and those of characterized organisms, defined the ecological trajectory of each lineage. Once committed to either aliphatic or aromatic substrates, the downstream metabolic pathways diverged accordingly: the methylsuccinate pathway and MCM-dependent propionate metabolism in alkane degraders vs the bbs/benz beta-oxidation pathway and bcrABCD benzoyl-CoA reductase in aromatic degraders. Genome-scale metabolic modeling independently validates this partitioning: the SCADC1-2-3 pangenome model is metabolically self-sufficient for syntrophic hexane degradation, while the 46–80 pangenome model cannot achieve growth on hexane under default FBA parameterization due to the clade-wide absence of PFOR - a single enzymatic bottleneck that would not be apparent from pathway completeness analysis alone. Tan \u003cem\u003eet al\u003c/em\u003e. (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e) reported that Peptococcaceae in methanogenic SCADC enrichment cultures did not respond to sulfate addition, while Deltaproteobacteria proliferated and became dominant sulfate reducers. This was puzzling given that the Peptococcaceae are phylogenetically nested within a family rich in sulfate-reducing genera (\u003cem\u003eDesulfotomaculum\u003c/em\u003e, \u003cem\u003eDesulfosporosinus\u003c/em\u003e). Our analysis provides a genomic explanation: the SCADC1-2-3 lineage that dominates these cultures genuinely lacks the entire dissimilatory sulfate reduction pathway (\u003cem\u003esat, aprAB, dsrAB, dsrMKJOP, QmoABC\u003c/em\u003e). This is unlikely to be an artifact of MAG incompleteness - the absence is consistent across 13 genomes with completeness estimates ranging from 50–98%, though the lower-completeness genomes cannot definitively exclude the possibility.\u003c/p\u003e \u003cp\u003eThe identification of complete sulfate reduction exclusively in JAIMBK01 adds nuance to this picture. The Nap2-2B clade has not entirely abandoned sulfate reduction; rather, this capability has been retained in one lineage while being lost in the three syntrophic fermenting lineages. The phylogenomic tree suggests that sulfate reduction may represent the ancestral state, with loss occurring independently in the common ancestor of the syntrophic clades, a pattern consistent with the evolutionary trajectory proposed for \u003cem\u003ePelotomaculum\u003c/em\u003e and other obligate syntrophs within the Desulfotomaculales (\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTan \u003cem\u003eet al.\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e) detected fumarate-addition metabolites for 2-methylpentane and methylcyclopentane, but not for \u003cem\u003en\u003c/em\u003e-alkanes, despite depletion of both substrate classes. This pattern is consistent either with preferential activation of branched and cyclic alkanes by the divergent SCADC1-2-3 AssA or with rapid turnover of \u003cem\u003en\u003c/em\u003e-alkane-derived intermediates below detection limits. Upon sulfate amendment, sulfate reducers such as \u003cem\u003eDesulfoglaeba\u003c/em\u003e would be expected to outcompete syntrophic Peptococcaceae for overlapping substrates because they can couple hydrocarbon oxidation directly to sulfate reduction. The genomic absence of sulfate-reduction genes in SCADC1-2-3 explains why this lineage cannot access that energetic advantage.\u003c/p\u003e \u003cp\u003eThis contrast is relevant for tailings ponds, where methanogenic syntrophs are expected to dominate mainly in sulfate-depleted zones. The complementary cofactor biosynthetic profiles of SCADC1-2-3 (encodes B12 biosynthesis; lacks B5) and 46–80 (encodes B5 biosynthesis; lacks B12) are consistent with complementary gene loss. Similar vitamin dependencies have been reported in other microbial communities, where they are interpreted as potential stabilizers of mutualistic interactions (\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Nap2-2B, these complementary profiles may add another layer of interdependence beyond classic syntrophic electron transfer. A plausible community model is that SCADC1-2-3 supplies alkane activation and potentially B12, whereas 46–80 or another partner supplies B5, with methanogens removing H2, formate, and acetate. Whether cofactor exchange actually occurs remains to be tested. The FBA results are compatible with that broader hypothesis because they suggest distinct electron sinks for different partners, but they do not by themselves demonstrate partner specificity.\u003c/p\u003e \u003cp\u003eThe coexistence of aliphatic and aromatic hydrocarbon degraders within the same family-level clade suggests substrate partitioning rather than generalism in tailings environments. This organization could reduce direct competition while permitting metabolic complementarity among co-occurring partners. Differences in predicted fermentation-linked strategies may further diversify these interactions. SCADC1-2-3 is more closely associated with H\u003csub\u003e2\u003c/sub\u003e-linked syntrophy, whereas 46–80 shows gene-content support for butyrate-linked metabolism and, in the PFOR test case, mixed H\u003csub\u003e2\u003c/sub\u003e/acetate export. If such differences operate \u003cem\u003ein situ\u003c/em\u003e, they could shift the balance between hydrogenotrophic and aceticlastic methanogenesis across tailings environments. The carbon-balance results likewise suggest that a substantial fraction of alkane-derived carbon may be diverted to biomass rather than methane, although the exact proportions remain model dependent. Sulfate amendment would be expected to alter this balance by favoring sulfate-reducing competitors over methanogenic syntrophs.\u003c/p\u003e \u003cp\u003eThe ecological separation of JAIMBK01 from the hydrocarbon-degrading genera is notable. While SCADC1-2-3, 46–80, and UBA4053 consistently co-occur in oil sands tailings, all six JAIMBK01 genomes were recovered from deep subsurface aquifers and hot springs - environments with available sulfate but without significant hydrocarbon inputs. This geographic and ecological separation, combined with the absence of hydrocarbon activation genes and the exclusive presence of dissimilatory sulfate reduction in JAIMBK01, suggests that the evolutionary divergence within the Nap2-2B clade reflects adaptation to fundamentally different ecological niches: syntrophic hydrocarbon degradation in tailings (SCADC1-2-3, 46–80, UBA4053) versus sulfate-dependent heterotrophy in the deep subsurface (JAIMBK01).\u003c/p\u003e \u003cp\u003eTaken together, the genomic and modeling results support a Nap2-2B guild structured by substrate partitioning, possible cofactor complementation, and distinct syntrophic strategies. The apparent fragility of this network may influence hydrocarbon attenuation in oil sands tailings and related anaerobic environments.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAdditional Information\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFunding\u003c/strong\u003e \u003cp\u003eFunding was provided by the Manila Central University Institutional Office.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBT contributed to the conceptualization of the study, led data curation, methodology development, investigation, formal analysis, validation, and visualisation, and drafted the original manuscript. CZ contributed to data curation, methodology development, investigation, formal analysis and validation, and reviewing of the manuscript. CN contributed to conceptualization, methodology, participated in the investigation and formal analysis, and contributed to writing the original draft as well as reviewing and editing the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe thank Manila Central University Institutional Research Office for supporting this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll genome sequences analyzed in this study are publicly available in NCBI GenBank under the accession numbers listed in Supplementary Table S2. The original Peptococcaceae SCADC genome is deposited under accession JJNX00000000.2. Custom analysis scripts are provided in https://doi.org/10.5281/zenodo.19163780\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCallaghan, A. V. 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Biol.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1752-0509-7-74\u003c/span\u003e\u003cspan address=\"10.1186/1752-0509-7-74\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"methanogenic hydrocarbon degradation, syntrophy, Peptococcaceae, genome-scale metabolic modeling, oil sands tailings, anaerobic alkane degradation","lastPublishedDoi":"10.21203/rs.3.rs-9199614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9199614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUncultured Peptococcaceae of the Nap2-2B clade are frequently detected in methanogenic hydrocarbon-degrading environments, yet their metabolic diversity remains poorly understood. Here, we analyse 34 metagenome-assembled genomes spanning four genera within this clade. Phylogenomic analysis of 741 Desulfotomaculales genomes places Nap2-2B as a monophyletic family-level lineage. Glycyl radical enzyme phylogeny and operon context reveal strict substrate partitioning: SCADC1-2-3 encodes alkylsuccinate synthase for aliphatic hydrocarbon activation, 46\u0026ndash;80 and UBA4053 encode benzylsuccinate synthase for aromatic activation, and JAIMBK01 lacks hydrocarbon activation genes but retains sulfate reduction. Pangenome-level pathway reconstruction identifies complementary cofactor biosynthetic potential, notably in cobalamin and pantothenate biosynthesis, consistent with possible cofactor complementation. Genome-scale metabolic modeling further indicates that the alkane-degrading lineage can support syntrophic hexane degradation, whereas the aromatic lineage cannot under the modeled conditions because it lacks pyruvate:ferredoxin oxidoreductase. Together, these data support a tightly integrated syntrophic guild in which substrate partitioning, possible cofactor complementation, and distinct electron-disposal strategies may structure community assembly, shape carbon and electron flow, and influence methanogenic hydrocarbon attenuation in anoxic tailings environments.\u003c/p\u003e","manuscriptTitle":"Comparative genomics of the Nap2-2B clade reveals substrate partitioning, niche diversification, and reciprocal cofactor auxotrophies among uncultured hydrocarbon-degrading Peptococcaceae","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 15:49:18","doi":"10.21203/rs.3.rs-9199614/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-19T09:47:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-16T16:58:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T21:09:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74407080507672618383802758362568016935","date":"2026-05-06T02:57:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286674094013501611759120557165684010544","date":"2026-04-13T18:31:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T15:11:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T15:09:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T14:53:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T11:21:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-27T11:15:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"719861de-301d-48f6-8fc7-b200a2236f09","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-19T09:47:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-16T16:58:43+00:00","index":90,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T21:09:13+00:00","index":85,"fulltext":""},{"type":"reviewerAgreed","content":"74407080507672618383802758362568016935","date":"2026-05-06T02:57:27+00:00","index":80,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66245489,"name":"Biological sciences/Biotechnology"},{"id":66245490,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-05-19T09:54:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 15:49:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9199614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9199614","identity":"rs-9199614","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}