Catabolism and interactions of uncultured organisms shaped by eco-thermodynamics in methanogenic bioprocesses

Current understanding of the carbon cycle in methanogenic environments involves trophic interactions such as interspecies H2 transfer between organotrophs and methanogens. However, many metabolic processes are thermodynamically sensitive to H2 accumulation and can be inhibited by H2 produced from co-occurring metabolisms. Strategies for driving thermodynamically competing metabolisms in methanogenic environments remain unexplored. To uncover how anaerobes combat this H2 conflict in situ, we employ metagenomics and metatranscriptomics to revisit a model ecosystem that has inspired many foundational discoveries in anaerobic ecology—methanogenic bioreactors. Through analysis of 17 anaerobic digesters, we recovered 1343 high-quality metagenome-assembled genomes and corresponding gene expression profiles for uncultured lineages spanning 66 phyla and reconstructed their metabolic capacities. We discovered that diverse uncultured populations can drive H2-sensitive metabolisms through (i) metabolic coupling with concurrent H2-tolerant catabolism, (ii) forgoing H2 generation in favor of interspecies transfer of formate and electrons (cytochrome- and pili-mediated) to avoid thermodynamic conflict, and (iii) integration of low-concentration O2 metabolism as an ancillary thermodynamics-enhancing electron sink. Archaeal populations support these processes through unique methanogenic metabolisms—highly favorable H2 oxidation driven by methyl-reducing methanogenesis and tripartite uptake of formate, electrons, and acetate. Integration of omics and eco-thermodynamics revealed overlooked behavior and interactions of uncultured organisms, including coupling favorable and unfavorable metabolisms, shifting from H2 to formate transfer, respiring low-concentration O2, performing direct interspecies electron transfer, and interacting with high H2-affinity methanogenesis. These findings shed light on how microorganisms overcome a critical obstacle in methanogenic carbon cycles we had hitherto disregarded and provide foundational insight into anaerobic microbial ecology. 7-KWLZT2hLDf6jAPRuutn9 Video Abstract Video Abstract


Background
Methanogenic bioprocesses are capable of converting municipal and industrial waste to methane and, thus, are paramount for achieving a sustainable environment [1,2]. These processes have also served as model ecosystems throughout the history of anaerobic microbiology, including the discovery of syntrophic bacteria [3][4][5], isolation of model H 2 - [6] and acetate-utilizing [7][8][9] methane-generating archaea, and characterization of novel modes of bacteria-archaea symbiosis [10,11]. Such pioneering studies generated our current understanding of how methanogenic microbial communities mineralize organic matter in both natural and engineered ecosystems (Fig. 1a)-(i) polymer hydrolysis to monomers, (ii) monomer (e.g., sugars and amino acids [AAs]) decomposition to H 2 , acetate, and other fatty acids (FAs; "acidogenesis"), (iii) FA degradation to H 2 and acetate, (iv) interconversion of H 2 and acetate ("acetogenesis"/ syntrophic acetate oxidation), and (v) transformation of H 2 and acetate to CH 4 and CO 2 ("methanogenesis") [1,[12][13][14][15]. However, the majority of microbial populations in methanogenic bioprocesses/ecosystems has eluded cultivation and characterization [16,17], suggesting we are far from fully comprehending the intricacies of the microbial ecology driving methanogenic decomposition. Uncovering the ecophysiology of the uncultured organisms, their ecological interactions, and the carbon and electron flow they create as a community is essential for advancing anaerobic microbiology, furthering our comprehension of the anaerobic sector of Earth's biogeochemical cycles, and inspiring innovation in anaerobic biotechnology.
In the well-accepted scheme of methanogenic carboncycling, carbohydrate, AA, and FA degraders, all generate and transfer H 2 to methanogens (Fig. 1a) due to the lack of favorable electron acceptors. This H 2 transfer is a critical component of methanogenic decomposition as many processes cannot proceed without H 2 being maintained at low concentrations, an interaction known as "syntrophy" [18]. However, the co-existence of the above diverse H 2 -generating processes is paradoxical. Many H 2 -generating metabolic processes are thermodynamically favorable and can produce H 2 at high concentrations (H 2 -tolerant, HT, [H 2 ] max ≥ 100 Pa) (e.g., 1020 Pa for glucose degradation; Fig. 1b and S1), whilst others can be inhibited by much lower H 2 concentrations (H 2 -sensitive, HS, [H 2 ] max < < 100 Pa) (e.g., 2.8 Pa H 2 for butyrate degradation; Fig. 1b and S1) [18][19][20]. (The concentration threshold was set at 100 Pa due to the large observed gap in H 2 tolerances between 16 and 119 Pa; Fig. S1 and Table S1.) Although H 2 -scavenging methanogens can maintain low H 2 concentrations to symbiotically support organisms performing HS metabolism (an interaction known as "syntrophy"), the high abundance and activity of organisms performing HT metabolism may generate high H 2 concentrations above which HS metabolism cannot function. For example, the H 2 concentration in co-cultures of H 2 -producing organotrophic bacteria and hydrogenotrophic methanogens can vary significantly depending on the substrates (12 Pa for fatty acids [21],~20 Pa for aromatic compounds [22],~60 Pa for select amino acids [23], > 700 Pa for lactate and ethanol [24]). Thus, in the presence of organisms performing HT metabolisms, partner methanogens may not maintain H 2 concentrations sufficiently low for Fig. 1 General scheme of methanogenic organic compound degradation and the "H 2 conflict." a Scheme for the degradation of organic macromolecules and the major intermediates, including AAs (blue or purple [see below]), sugars (red), FAs (green), and H 2 (orange). b Gibbs free energy change for the degradation of representative AAs with low (isoleucine; blue) and high (glutamine; purple) calculated H 2 tolerance, sugar (glucose), and fatty acid (FA) (butyrate) and H 2 -oxidizing CO 2 -reducing methanogenesis with varying H 2 partial pressures. The vertical dotted lines indicate each pathway's threshold H 2 concentration at which ΔG becomes 0 kJ/ mol. ΔG values are calculated as the ΔG reaction + (mol ATP generated/mol reaction)*ΔG ATPsynthesis (see details below). The H 2 partial pressure range at which each metabolism is thermodynamically favorable is shown at the top (horizontal bars with corresponding colors). Hydrogen partial pressures that overlap with those for H 2 -oxidizing CO 2 -reducing methanogenesis are indicated (solid colors) and would be permissive for that reaction. Metabolisms with [H 2 ] max less than 100 Pa and greater than 100 Pa are respectively defined as H 2 -sensitive and H 2 -tolerant. The following conditions were used for calculations-10 μM butyrate, 300 μM acetate, 0.1 μM amino acids and sugars, 1 mM NH 4 + , 50 mM HCO 3 -, 50 kPa CH 4 , pH of 7, and 37°C. ΔG ATPsynthesis is assumed to be 60 kJ/mol. For butyrate, isoleucine, glutamine, glucose, and H 2 /CO 2 methanogenesis, ATP yields of 0.33, 1, 1.33, 4.67, and 0.2 were assumed. The ATP yields are calculated as follows: ATP generated -ATP consumedx*(NADH generated -NADH consumed ) + x*(FdH 2generated -FdH 2consumed ) -2x*(ETFH 2generated -ETFH 2consumed ) -2x*(quinol generatedquinol consumed ), where x is the ATP synthase ATP:H + ratio (assumed to be 1:3 for organotrophy and 1:5 for methanogenesis in this figure). Abbreviations: NADH-reduced nicotinamide adenine dinucleotide; FdH 2 -reduced ferredoxin; ETF-reduced electron transfer flavoprotein HS metabolisms. Moreover, anaerobic digester H 2 concentrations can exceed the theoretical maximum H 2 concentration threshold for many HS syntrophic metabolisms (up to 20 Pa [25][26][27]). Therefore, for HS and HT metabolisms to proceed concurrently, organisms performing HS metabolism must either spatially segregate based on thermodynamic properties or be able to transfer and/or dispose of electrons through alternative routes to circumvent thermodynamic inhibition. In the context of anaerobic bioreactors, spatial segregation has thus far only been observed in reactors that allow extensive biofilm formation (e.g., upflow anaerobic sludge blanket reactors) [28], suggesting the latter may be an especially important metabolic strategy in anaerobic digestion. This "hydrogen conflict" may be an overlooked component of the ecology in both natural and engineered methanogenic ecosystems. We suspect the absence of this selective pressure in conventional cultivation strategies could be a major factor contributing to why a significant proportion of the predominant microbial diversity in methanogenic ecosystems remains uncultured.
This thermodynamic paradox of HS and HT reactions co-occurring in proximity to each other is further exacerbated by the extremely low substrate concentrations available in situ. In many methanogenic engineered and natural ecosystems, the energy input is a complex mixture of macromolecules (i.e., detritus derived from dead bodies/cells of animals, plants, and microorganisms). Hydrolysis of detritus macromolecules releases diverse soluble monomers and oligomers that are subsequently absorbed and catabolized with little to no accumulation (10 -7 to 10 -6 M based on AA transporter K d values [29]). Similarly, monomer-derived FAs only accumulate to micromolar levels [30,31]. Such substrate concentrations, orders of magnitude lower than conventional cultivation media, can impede HS metabolism. For example, a three-order lower butyrate availability (e.g., 10 mM to 10 μM) can decrease the maximum tolerable H 2 concentration for butyrate degradation by 30-fold (e.g., 88 Pa to 2.8 Pa; assuming conditions in Fig. 1b).
In the conventional scheme of methanogenic degradation, organotrophic metabolisms converge into a shared pool of H 2 , yet many metabolisms are only thermodynamically possible at low H 2 concentrations and may be inhibited by activity of other concurrent H 2 -generating metabolisms. How organisms thrive under such thermodynamic restrictions remains unknown as most populations abundant in methanogenic ecosystems remain uncultured, possibly due to contrasting in situ and in vitro conditions. To characterize these organisms without cultivation, metagenomics and metatranscriptomics are effective tools that allow direct recovery of genomes (or metagenome-assembled genomes-MAGs) and gene expression profiles from the target ecosystem [32]. Although such "omics"-based methods predict rather than prove biological phenomena, rigorous analyses can provide valuable insight into potentially novel microbial processes taking place in situ. Moreover, given challenges associated with tracking the fate and turnover of H 2 [33], a gene expression-based approach is one of the few strategies available for effectively tracing related behavior. In this study, we integrate omics analyses across multiple bioreactors, rigorous anaerobiosistailored metabolic reconstruction, and thermodynamics to unveil the ecophysiology and metabolic strategies of uncultured microbial populations tailored to driving thermodynamically sensitive metabolic processes in a model methanogenic ecosystem, anaerobic digesters.
To accurately reconstruct the metabolic behavior of individual species, we annotate metabolic pathways with strict criteria by taking advantage of the thermodynamic and energetic restrictions of anaerobic life. Due to the cavernous gap in electron acceptor redox potentials (e.g., O 2 to H 2 O [E°' of 1.23 V] vs H + to H 2 [E°' of − 0.42 V]), aerobic degradation is highly exergonic and massive energy recovery occurs from O 2 reduction (e.g.,~32 ATP per glucose), while anaerobic degradation is much more thermodynamically limited and often requires energy investment for disposing electrons. Moreover, certain anaerobic metabolisms can become endergonic (i.e., ΔG > 0 kJ mol -1 ) with only slight byproduct (e.g., H 2 ) accumulation and require intimate cross-feeding with byproduct-consuming partners, a symbiosis known as syntrophy [3]. To thrive at this thermodynamic edge of life, anaerobes must employ unique metabolic strategies for coupling substrate oxidation with electron disposal and optimizing energy input and recovery during this process [40][41][42]. Pioneering efforts in isolating and characterizing syntrophic metabolizers and their enzymes was paramount for obtaining this foundational knowledge [4,5,[43][44][45]. Capitalizing on these unique insights into syntrophic metabolism, we identified for each active species metabolic pathways that (i) have electron transfer enzymes that account for all predicted oxidative and reductive reactions, (ii) provided net positive energy Fig. 2 Phylogenetic distribution of MAGs recovered and species (MAG clusters) associated with a high metatranscriptome-based activity. The phylogenetic classification was determined using GTDBtk (left). The number of MAGs associated with a cultured genus or uncultured lineages (at different taxonomic levels) is shown (right). Bacterial and archaeal species respectively associated with metatranscriptome-based activities ≥ 0.4% or ≥ 0.3% of the mapped transcriptomes in at least one reactor are shown conservation either by ATP synthesis and/or by the generation of an ion motive force, (iii) are exergonic in situ, and (iv) have all necessary genes highly expressed (see details in methods section; Fig. S4 as an example; and supplementary tables for a summary of capacities [ Table  S4], summary of H 2 /formate-generating electron transfer capacities [ Table S6], summary of metabolic behavior across digesters [Table S7-S9], metabolic behavior in individual reactors [Table S10-S18; and Table S19 for all collected together]). The maximum metabolic capacity observed within a species cluster and the total metabolic capacity of that cluster were similar (Table S4 and Fig.  S5), suggesting consistent ecological roles across digesters.

Thermodynamic conundrum
The co-existence of the above processes is puzzling in terms of thermodynamics. Most forms of organotrophy in methanogenic ecosystems are presumed to dispose electrons by reducing H + to H 2 . However, some types of organotrophy can produce H 2 to levels that can thermodynamically inhibit other types if H 2 accumulates to sufficient levels. The question then is how does H 2mediated interspecies electron transfer from organotrophic bacteria to methanogenic archaea, which is a core process in methanogenic ecosystems, proceed in these ecosystems? Based on our calculations, the maximum tolerable H 2 concentration varies significantly among substrates and pathways involved (Fig. 3b). Degradation of sugars and many AAs is highly exergonic and HT, while the degradation of FAs and certain AAs is HS and may require exceptionally low H 2 concentrations (≤ 16 Pa). Despite the rapid H 2 consumption by partner methanogens, the high activity and abundance of organisms performing HT metabolism (44~70% of mapped metatranscriptomes) may generate localized high H 2 concentrations that can inhibit organisms performing HS metabolism. Moreover, the estimated maximum H 2 concentration thresholds for HS AA degradation (1.1~10.3 Pa H 2 ) and FA degradation (1.2 2.8 Pa H 2 ) are very close to the minimum hydrogen threshold that conventional H 2 -utilizing CO 2 -reducing methanogenesis can use (1.7 Pa H 2 ), which is often lower than bulk H 2 concentrations detected in reactors (< 10 Pa) [25,47]. Thus, we expect that the species performing HS metabolism may have unique strategies to circumvent these thermodynamic obstacles.

Coupling H 2 -tolerant (HT) and H 2 -sensitive (HS) metabolisms
To identify ancillary metabolic pathways supporting HS metabolisms in situ, we compared catabolic capacities across the 192 high-activity species. Pearson correlation revealed correspondence between the number of HS AA metabolisms per species cluster and the number of The maximum H 2 concentration that each degradation pathway can tolerate is shown (i.e., ΔG reaction + x*ΔG ATPsynthesis = 0, where x is the amount of ATP synthesized per substrate degraded). The ATP yield for each pathway was based on the sum of (i) the ATP consumption/ generation in the main carbon transformation pathway and (ii) vectorial H + translocation associated with membrane-based electron transfer (e.g., Rnf, Hyb, Fdn), assuming the shortest electron flow route from substrate oxidation to H 2 /formate generation that involves electron bifurcation and reverse electron transport where possible; all of this was based on pathways that were observed to be expressed in this study. Reactions that would either lose much energy as heat (e.g., cytosolic Fd red -oxidizing H 2 generation) or require energy input under in situ conditions (e.g., cytosolic NADH-oxidizing H 2 generation) were not considered. For substrates whose degradation proceeds through pyruvate or acetyl-CoA, maximum H 2 concentrations for oxidation to acetate are shown (see Supplementary Table S1 for a list of reactions). Note that fermentation pathways (e.g., acetyl-CoA reduction to butyrate) would increase the maximum H 2 but reduce ATP yield. The Gibbs free energy yield at standard conditions and pH 7 (ΔG°') and estimated ATP yields are also shown. See Fig. 1 for details for calculating ATP yield and maximum tolerable H 2 concentration. For each pathway, ΔG reaction was calculated assuming 300 μM acetate, 10 μM for other FAs, 1 mM NH 4 + , 50 kPa CH 4 , 50 mM HCO 3 -, 37°C, 3.9 × 10 -4 atm H 2 S, and 0.1 μM for all other compounds. ΔG ATPsynthesis was assumed to be 60 kJ/mol. *Although more exergonic alternative pathways exist for these HS AA degradation pathways (e.g., through butyrate fermentation), species only expressing the HS pathway(s) were identified in situ, indicating that HS metabolism of these AAs is relevant in situ. 1 For isovalerate degradation, an ATP synthase ATP:H + ratio of 1:4 was assumed. 2 For H 2 -oxidizing CO 2 -reducing methanogenesis, two H 2 concentrations for two ATP yields assuming different ATP synthase ATP:H + ratios. 3 For propionate and acetate degradation, an ATP synthase ATP:H + ratio of 1:5 was assumed. † Pathways whose directionality cannot be determined by sequence data alone. c For each phylum, the percentage of species expressing individual degradation pathways are shown hydrolytic exoenzyme families (glycosylhydrolases [p = 6.8 × 10 -5 ] and proteases [p = 1.3 × 10 -20 ]), pathways for HT AA metabolism (p = 7.4 × 10 -76 ), sugar degradation pathways (p = 4.5 × 10 -6 ), and types of both [FeFe] and [NiFe] hydrogenases (p = 2.3 × 10 -11 and 0.033, respectively) (see Table S4 for categories and values used for all Pearson correlation calculations). This suggests an interaction between hydrolysis of a wide range of polymers, simultaneous catabolism of multiple types of polymerderived monomers, and diverse H 2 generation pathways. Comparison of phyla showed that Bacteroidota encoded significantly more pathways for HS and HT AA metabolism (p = 0.016 and 0.018 respectively; Student's t test), glycolsylhydrolases (0.023), and proteases (0.003) than other phyla. Correlation analyses were not possible for other phyla with fewer species, but the principal component analysis also suggested a qualitative association of Fermentibacterota, Marinisomatota, Verrucomicrobiota, and KSB1 with these features (Fig. 4a, b). We found that many species of these phyla (25 out of 38 species in Bacteroidota, 1 out of 1 in Fermentibacterota, 1 out of 1 in Marinisomatota, 1 out of 5 in Verrucomicrobiota, and 1 out of 1 in KSB1) expressed genes for both HS and HT metabolism of AAs (e.g., HS and HT AA degradation with H 2 formation) (see Tables S7-S9 for overviews and S10-S18 for individual reactors). Of these, 24 Bacteroidota, 1 Fermentibacterota, 1 Verrucomicrobiota, and 1 KSB1 species were confirmed to consistently perform the above metabolism based on the following criteria: expressing the complete metabolic pathway(s) in at least 50% of the studied reactors where this species comprised ≥ 0.05% of the mapped metatranscriptome (herein referred to as ECM50 species; Table S8). We detected HS sensitive pathways for lysine (Bacteroidota), isoleucine/ leucine/valine (Bacteroidota and Marinisomatota), arginine (Bacteroidota and KSB1), glutamate (Bacteroidota, KSB1, and Fermentibacterota), glycine (all five phyla), and alanine (all five phyla). (Note that, with rigorous annotation as outlined in the methods [see Fig. S4 as an example], we can determine the directionality of most AA metabolism pathways, exceptions being alanine, cysteine, glutamate, and aspartate metabolism [ Table S1]). Although HS metabolism would be thermodynamically inhibited by H 2 generated from HT degradative processes in proximal cells or in the same cell, HS, and HT metabolism pathways intersect at shared metabolic intermediates (e.g., NAD[H], NADP[H], and/or ferredoxin) that could potentially be coupled enzymatically to provide for thermodynamically favorable redox reactions.
Though the hydrolytic organisms could theoretically focus on performing HT metabolism, we suspect, based on our analysis of the pathways present in diverse metagenomes, that these organisms degrade wide ranges of substrates (both HS and HT AA metabolism) to maximize energy recovery from the heterogeneous pool of monomers generated from polymer hydrolysis, thereby compensating for the high energy cost associated with producing extracellular hydrolytic enzymes [48]. It is important to note that HS and HT AA metabolism generally have similar ATP yields despite thermodynamic differences in substrate degradation. We suspect this energy compensation is important for the above phyla as they express a wide range of hydrolytic enzymes. For protein hydrolysis, many species clusters associated with the above phyla were in the top 30% of all active species for the average number of protease families expressed when active (i.e., in reactors they displayed ≥ 0.5% metatranscriptome-based activity) (> 7.8 families)-35 Bacteroidota (34 of which were ECM50 species), 1 Fermentibacterota (1 ECM50 species), 1 Marinisomatota (1 ECM50 species), 4 Verrucomicrobiota (4 ECM50 species), and 1 KSB1 (1 ECM50 species) respectively) (Table S19). Similarly, 21 (21 ECM50 species), 0, 1 (1), 5 (5), and 1 (1) species cluster(s) respectively for carbohydrate hydrolysis (> 7.6 glycosylhydrolase families) and 5 (5 ECM50), 0, 1 (1), 1 (1), and 0 species cluster(s) respectively for lipid hydrolysis (> 1.2 lipase families). In addition, Pearson correlation revealed an association between the numbers of families encoded for each exoenzyme type (all p ≤ 3.0 × 10 -7 ). Thus, these versatile anaerobes hydrolyze a broad range of polymers, generate diverse monomers in the process, and use thermodynamically favorable monomer degradation reactions to drive the concomitant degradation of other monomers whose degradation would be otherwise thermodynamically unfavorable. Nearly all species (96.7% or 88 out of 91) that were predicted to perform HS metabolism couple HS and HT AA degradation in at least one reactor (Table S9), suggesting this is the predominant strategy to accomplish HS AA degradation.

Shifting to interspecies formate transfer
Unlike polymer/monomer catabolism, the number of syntrophic FA degradation pathways encoded in a species cluster had a negative correlation with the number of [FeFe] hydrogenases (Pearson correlation p = 0.044; Fig. 4a, b). This suggests that the FA-degrading syntrophic metabolizers likely employ an alternative route for the re-oxidation of their reduced carriers. While H 2 exchange is the most well-recognized mode of interspecies electron transfer, CO 2 -reducing formate generation also serves as an important mechanism for electron disposal and transfer [33,42,44,49]. FA catabolism indeed had a unique positive correlation with both Fdh-H type (cytosolic) and Fdh-N type (membrane-associated) formate dehydrogenases (Pearson correlation p = 2.1 × 10 -12 and 1.5 × 10 -35 ) not observed for AA and sugar metabolism. Nearly all Desulfobacterota species (12 total across uncultured Desulfomonalia order UBA1602, Syntrophales families UBA8958 and UBA2192, and Smithellaceae) actively performing syntrophic FA metabolism in at least one reactor expressed genes for CO 2 -reducing formate generation (11 ECM50 species out of 12 total or 91.7%) and, of these, most only expressed genes for formate generation and not for H 2 generation (82.0% ECM50 species; Table S9). In agreement, Desulfobacterota had significantly higher numbers of FA degradation (Student's t test p = 0.045) and Fdh-H/Fdh-N type formate dehydrogenases (p = 0.044 and 0.032) compared to other phyla. Most Spirochaeotota (uncultured class UBA4802) populations expressing syntrophic butyrate degradation also expressed genes for formate generation (two out of three FA-degrading Spirochaeotota species ECM50). We also observed a correlation between FA metabolism and Fdh-N type formate dehydrogenases with the number of intracellular energyconserving electron transport enzyme complexes (see Table  S6 for list) (Pearson correlation p = 6.4 × 10 -5 and 2.7 × 10 -4 , respectively), indicating the importance of possessing multiple energy conservation routes for syntrophic FA degradation. Through comparing the presence/absence of individual functions (based on automatic emapper-based annotations) across all active species clusters ( Fig. 4c and Table S20), we also identified correlation (Student's t test; p < 0.05) in Desulfobacterota and Spirochaetota between the FA-degrading enzyme acyl-CoA dehydrogenase, electron transfer flavoprotein:quinone oxidoreductase, and formate dehydrogenases Fdh-H and Fdh-N, which plots out the route of electron flow for the most thermodynamically difficult redox reaction involved in syntrophic FA metabolism-the generation of formate or H 2 from electrons proteases and glycosylhydrolases (GHs) as the number of families encoded in the genome; FA, AA, and sugar degradation as the number of pathways encoded in the genome; electron transfer/ energy conservation pathways (i.e., Rnf, Nfn, Fix, Efd, and FloxHdr) as the number of pathways encoded in the genome; H 2 and formate generation as presence/absence; and cytochrome bd oxidasemediated O 2 respiration as presence/absence. Individual species (points) and metabolic capacities (vectors) are shown. Confidence ellipses (95%) are shown for MAGs belonging to specific phyla. b PCA of active species and the metabolic behavior they expressed: proteases and glycosylhydrolases (GHs) as the number of families expressed in at least one reactor; FA, AA, and sugar degradation as the number of complete pathways expressed in at least one reactor; electron transfer/energy conservation pathways (i.e., Rnf, Nfn, Fix, Efd, and FloxHdr) as the number of pathways expressed in at least one reactor; H 2 and formate generation as the highest hydrogenase/ formatted dehydrogenase subunit expression level (calculated as RPKM normalized to specie's non-zero median expression level); and cytochrome bd oxidase-mediated O 2 respiration as the highest oxidase subunit expression level. Individual species (points) and metabolic capacities (vectors) are shown. c PCA of active species and their functional profiles predicted through eggNOG. Functions that are detected at a significantly higher frequency in Desulfobacterota and Spirochaetota than other phyla (p < 0.05) are shown as vectors. The functions associated with these vectors are shown in Table S16 derived from acyl-CoA oxidation. Earlier proteomic studies implied these enzyme systems for H 2 or formate production from electrons derived from acyl-CoA oxidation in Syntrophomonas wolfei [45,50]. The finding that this same enzyme system is used in diverse bacteria suggests that this may be the common mechanism for the difficult redox reaction. Remarkably, many populations lacked hydrogenases (53.3% or 8 out of 15 species; see Table S6 for hydrogenases surveyed). This observation is in stark contrast with what is known about isolated syntrophic organisms, which all possess hydrogenases and employ H 2 as an interspecies electron carrier [19,51]. However, further proteomic studies are necessary to verify the absence of hydrogenases in these novel syntrophic populations. We also identified three putative syntrophic acetate-degrading species clusters in Thermotogota (Pseudothermotoga and an uncultured Thermotogae order) expressing a previously proposed glycinemediated acetate degradation pathway (two acetatedegrading Thermotogota ECM50 species) [17]. Two coupled this with formate generation (no H 2 generation) in at least one reactor (one out of two acetate-degrading Thermotogota species ECM50).
Unlike hitherto characterized syntrophs, which are cultured in the absence of other H 2 -generating processes (i.e., only one substrate in the culture medium), these newly discovered uncultured organisms may thrive in the presence of highly thermodynamically favorable H 2generating processes. We propose that organisms that perform HS FA catabolism avoid thermodynamic conflict with those that use HT-catabolism by completely or partially forgoing H 2 generation and relying on formate transfer to efficiently transport electrons to physically distant metabolic partners [33,49,50]. In contrast with H 2 , formate concentrations are unlikely to accumulate locally in situ as formate-producing activity is absent or low in most polymer/monomer-degrading species (i.e., most of the active community) based on our analyses and formate has a higher diffusion rate than H 2 [33,49]. Although formate is challenging to detect in anaerobic digesters, it is estimated to be at concentrations around 2.5 μM (equivalent to 4.5 Pa H 2 at 37°C, pH 7, and 50 mM HCO 3 -) [33]. Moreover, formate transfer would allow FA degraders to recover additional energy via iontranslocating, formate transporters [42] (expressed by 88.9% of FA-degrading species).

Aerobic respiration by obligate anaerobes
Beyond the coupling of HS metabolism with HT metabolism or formate generation, we found a positive correlation between the number of HS AA and FA metabolism pathways with the presence of a cytochrome bd oxidase (Pearson correlation p = 4.3 × 10 -4 and 2.3 × 10 -8 ), a terminal oxidase for aerobic respiration (Fig. 4a, b) Fig. 4c), supporting the functionality of cytochrome bd oxidase. Although the anaerobic digestion ecosystem is considered to be strictly anaerobic, minute amounts of O 2 can enter the system through the influent wastewater [54][55][56]. This is analogous to gas or water percolation from an aerobic zone to a neighboring anaerobic zone in natural ecosystems. Moreover, cytochrome bd oxidase can function even at nanomolar concentrations of O 2 [57]. Using this low-concentration O 2 as an alternative electron disposal route can reduce the dependence on H 2 or formate production, which is thermodynamically sensitive to the accumulation of these byproducts and increases the thermodynamic favorability of their overall catabolism.  [58] and anaerobic digestion can benefit from controlled microaeration [56,59]. Thus, organisms encountering kinetic and thermodynamic bottlenecks (i.e., hydrolysis and HS AA/FA degradation) of methanogenic organic matter mineralization may depend on O 2 for optimal activity.

New routes of electron flow in methanogens
To better understand interspecies electron transfer, we investigated the metabolic behavior of methanogenic archaea. As expected, most Euryarchaeota and Halobacterota expressed H 2 -and/or formate-driven CO 2 -reducing methanogenesis genes, syntrophically supporting electron disposal of organotrophic activity (Table S3). We also discovered high activity (gene expression) in Ca. Methanofastidiosa (previously known as class WSA2), an archaeon previously proposed to utilize methylated thiols as a carbon source for methanogenesis rather than CO 2 [60]. Metatranscriptomics provided further evidence that Ca. Methanofastidiosum indeed performs H 2 -oxidation coupled to methylated thiolreduction to methane in situ (all methyl-reducing Methanofastidiosum were ECM50 species) (Table S7). Based on thermodynamics, such methanogens can theoretically tolerate much lower H 2 concentrations than those that use conventional H 2 /CO 2 methanogenesis (0.1 Pa versus 1.7 Pa, respectively; assuming conditions in Fig. 3). This would mean that in the presence of methylated thiols (generated from the degradation of methylated compounds such as methionine), Ca. Methanofastidiosum can pull H 2 concentrations to much lower levels than conventional methanogens and more effectively support H 2 generation from HS metabolism. Thus, methylated compounds likely play an important role in overcoming thermodynamically challenging metabolisms in anaerobic digestion and other methanogenic ecosystems.
Although interspecies electron transfer in methanogenic ecosystems is often simplified as H 2 exchange, such microbial interactions are clearly more complex. In addition to the exchange of metabolites such as H 2 or formate, microorganisms can also directly transfer electrons to each other, a process called direct interspecies electron transfer (DIET) [11]. Yet, the prevalence and importance of DIET in anaerobic digestion are unclear. Among methanogens detected in situ, Methanothrix is the only lineage known to be capable of utilizing extracellular electrons to drive CO 2 -reducing methanogenesis [11], although it is most well known for its capacity to use acetate for methanogenesis. We identified three Methanothrix species expressing DIET-driven CO 2 reduction and acetoclastic methanogenic pathways (all acetate-degrading Methanothrix were ECM50 species; Table S7-S9), indicating the presence of "exoelectrogenic" organisms in situ. Inspection of the transcriptomes revealed that 13 and 18 bacterial phyla may perform DIET respectively through multiheme c-type cytochromes (including members of uncultured phyla Omnitrophota, KSB1, and Krumholzibacterota) and conductive pili (including members of uncultured phyla Cloacimonadota, Omnitrophota, Patescibacteria, Krumholzibacterota, and WOR-3). Expression of multiheme c-type cytochromes was observed for Desulfobacterota and Spirochaeota performing syntrophic FA degradation (53.0% of FA-degrading Desulfobacterota and Spirochaetota species; 46.7% ECM50 species) and versatile polymer/monomer-degrading Bacteroidota, Verrucomicrobiota, and KSB1 (54.2%; 40.0% ECM50 species). For conductive pili, we found positive correlation for the presence of conductive pili with syntrophic FA degradation (Pearson correlation p = 8.0 × 10 -7 ) and other capacities associated with in situ FA degraders (Fdh-N type formate dehydrogenase [p = 1.9 × 10 -8 ], electron transfer complexes [p = 2.3 × 10 -4 ], and cytochrome bd oxidase [p = 5.2 × 10 -3 ]), while no correlation was observed with hydrolytic enzymes and AA/sugar degradation. We further confirmed that many FA-degrading Desulfobacterota and Spirochaetota species express conductive pili (60.0%; 46.7% ECM50 species), but only three populations of the versatile hydrolytic HS/HT AA degraders expressed putative conductive pili in at least one reactor (5.7% ECM50 species). Diverse phyla and niches likely take advantage of DIET because exoelectrogenic metabolism can theoretically much more thermodynamically favorable than H 2 generation due to the high reduction potential of c-type cytochromes (E°' of − 220 to + 180 mV [61]). The reason for the difference in the distribution of multi-heme cytochromes (all studied niches) and putative conductive pili (preferentially found in syntrophic FA degraders) remains unclear. Though the necessary physical proximity between syntrophs and electron-accepting partners may allow more opportunities for conductive pili to transfer electrons, further investigation is required. In total, hydrolysis, monomer degradation, and FA degradation by uncultured organisms across 20 phyla may rely on Methanothrix species for H 2 -independent extracellular electron transfer, though different niches may use different routes.
Further inspection of the transcriptomes revealed the possible involvement of Methanothrix in formate degradation. Although the ability of Methanothrix to degrade formate has been controversial [7,62,63], we detected consistent expression of a formate dehydrogenase complex in two out of three Methanothrix species in all reactors they were active (Table S7-S9). Based on gene organization of the formate dehydrogenase in the most active Methanothrix species JPASx098 (fdhA with hdrABC and ferredoxins; ≥ 99% similarity to M. soehngenii genes MCON_3277-83), Methanothrix may oxidize formate and funnel electrons into methanogenesis (via HS-CoM/HS-CoB and ferredoxin). We suspect that Methanothrix primarily performs acetate-driven methanogenesis but, in parallel, can uptake formate and electrons from extracellular pili and cytochromes to drive CO 2 -reducing methanogenesis. Therefore, Methanothrix likely plays an essential role in supporting multiple H 2independent electron disposal routes for organisms performing HS metabolism.

Temperature-based differences
The coupling of HS catabolism with hydrolysis/HT catabolism, formate generation, oxygen respiration, and DIET was observed across all reactors, despite variation in temperature (Table S7). This indicates that the described phenomena may support HS metabolism at a wide temperature range. Across all studied temperatures, we observed Desulfobacterota and Spirochaetota FA degradation coupled with the expression of formate generation, oxygen respiration, and DIET (with the exception of Desulfobacterota at thermophilic temperature). Strict reliance on formate-generating FA degradation was only observed at mesophilic temperatures. Coupled expression of HS and HT AA degradation (with complementary formate/H 2 generation) was also observed across all temperatures. However, some of these AAdegrading species were only observed to have high activity and express complete pathways at specific temperature ranges-UBP6, KSB1, Cloacimonadota, Fermentibacterota, Marinisomatota, Chloroflexota, Firmi-cutes_A, Firmicutes (~35°C); WOR-3_A, Firmicutes_E (> 50°C), Krumholtzibacterota (≤ 30°C); Coprothermobacterota (> 40°C); Myxococcota, Spirochaetota, Planctomycetota (< 50°C), Caldisericota (~35°C and > 50°C). For methanogenesis, CO 2 -reducing methanogenesis by Methanothrix (potentially driven by DIET) was detected across all temperatures, but formate oxidation by Methanothrix and methyl reduction by Methanofastidiosa was only observed at temperatures below 50°C. Thus, based on the available data, strategies for supporting HS metabolism and organisms that perform these challenging reactions differ between anaerobic digesters operated at different temperatures. However, analyses of more samples at non-standard temperatures (~35°C) are necessary to better characterize temperature-based variation.

Conclusion
In methanogenic ecosystems, degradation of organic matter generates H 2 as a central byproduct and necessitates microbial interactions between H 2 -generating organotrophic bacteria and H 2 -consuming methanogenic archaea. However, organotrophic metabolisms have diverse thermodynamic properties and many processes (i.e., HT catabolism) can generate H 2 concentrations much beyond the thermodynamic limit of others (i.e., HS catabolism), which has not been addressed in previous models. Through metagenomic and metatranscriptomic analyses of multiple anaerobic digesters, we predict that uncultured organisms may employ unique strategies to drive thermodynamically competing metabolisms (Fig. 5)-parallel and broad-range HS and HT metabolism, a shift (often complete) from H 2 to formate as a soluble electron carrier, respiration of lowconcentration O 2 , DIET and formate exchange with Methanothrix, and interaction with high H 2 -affinity methanogenesis by Ca. Methanofastidiosum. The observed metabolic behaviors are likely tailored to the thermodynamic conditions in situ and quite distinct from cultured organisms. With such omics-based insights, future cultivation-based studies can be designed to verify and further characterize organisms that perform thermodynamically challenging catabolism under the in situ selective pressures (e.g., enrichment/cultivation of syntrophic degraders in the presence of both methanogens and H 2 -producing fermenters). The newly discovered metabolic strategies and ecology driving organic matter mineralization improve our understanding of carbon cycling in methanogenic ecosystems and foundational knowledge for innovation in biotechnology.

Sample collection and sequencing
Anaerobic digesters in 17 full-scale municipal wastewater treatment plants were selected for metagenomic and nine digesters were selected for metatranscriptomic sequencing to cover a wide range of operation temperature within the sequencing capacity (Table S2). As described previously [38], most digesters had an activated sludge process upstream while one analyzed reactor only had primary treatment upstream (USRA). Likewise, most digesters were operated at mesophilic temperatures (~35°C), but JPHG and JPTR were operated at a slightly elevated temperature (~40°C), JPHW and USRA at slightly lower temperatures (< 30°C), and USOA and JPMR at thermophilic temperatures (> 50°C). Several digesters (JPHW, JPNA, and USDV) were operated in series (same retention time) with the first digester treating primary/secondary clarifier sludge and the second treating sludge produced by the first. ADurb, JPHG, JPNA, USST, and USCA also treating other non-sewagederived waste, including food waste and sludge from other sources. Waste treated by HKST had high salinity and sulfate content and were dosed with ferric chloride to suppress the release of sulfide (4000 to 6000 mg/L chloride concentration). HKYL also treated tannery industry wastewater containing high zinc and chromium concentrations. Sludge samples for DNA and RNA sequencing were collected simultaneously (same day and same reactor). Different sludge samples were taken at separate time points (e.g., 1 month apart), as documented previously [38]. Genomic DNA was extracted using the FastDNA SPIN Kit for Soil (MP Biomedicals, Carlsbad, CA, USA). RNA was extracted using acidphenol/chloroform/isoamyl alcohol (125:24:1) and chloroform, precipitated by cold ethanol, and purified by DNase treatments [41]. DNA and RNA samples were dispensed in a barcoded plate and shipped on dry ice to the Joint Genomic Institute (JGI) in the Department of Energy for sequencing using the Illumina HiSeq-2500 1 TB platform and HiSeq-2000 1 TB platform for DNA and RNA sequencing respectively (2 × 151 bp). See the following Department of Energy Joint Genome Institute standard operating procedures for metagenomics and metatranscriptomics: Metagenome SOP 1064 and Metatranscriptome SOP 1066.1.

Gene annotation and metabolic reconstruction
All genomes were annotated through a combination of Prokka v1.13 (kingdom = Bacteria or Archaea chosen based on phylogeny defined by GTDBtk) [71] and further manual curation. We specifically examined sugar degradation (18 types), amino acid degradation (20 types), electron transduction mechanisms (e.g., NADH: quinone oxidoreductase), respiration (O 2 and nitrogen species), H 2 metabolism, formate metabolism, and polymer hydrolysis (glycosylhydrolase, extracellular peptidase, and extracellular lipase families). The curation involved functional domain analysis through CD-Search with its corresponding conserved domain database [72,73]; signal peptide and transmembrane domain prediction through SignalP v4.1 (default parameters) [74]; carbohydrate-active enzyme, peptidase, and lipase prediction through dbCAN 5.0 [75], MEROPS [76], and lipase engineering database [77]; and hydrogenase annotation with assistance from HydDB [78] with default parameters. In addition, to further verify the function, we compared the sequence similarity of each gene to a database containing enzymes with experimentally verified catalytic activity and genes with extensive genetic, phylogenetic, and/or genomic characterizations with a 40% amino acid similarity cutoff. For enzymes that have divergent functions even with a 40% similarity cutoff (e.g., [ , and electron transduction complexes (e.g., Rnf and Nqr) that are composed of multiple subunits and tend to colocalize in the genome, we only annotated the function of the complex if all subunits were identified in an operon or the operon appeared to be divided onto two contigs (i.e., two ends of an operon on the ends of two contigs). Pili were annotated to be conductive for pilA genes containing many aromatic residues (≥ 9% of total peptide length) relatively evenly distributed across the length of the protein (every 20 amino acids) as described in a previous study [79]. Membrane-bound or extracellular multi-heme cytochromes were annotated for proteins encoding transmembrane or N-terminal signal peptides respectively and multiple heme-binding sites.
In addition to gene annotation, metabolic capacities and traits (e.g., sugar and AA catabolism) were predicted based on the strict criteria that all enzymes necessary for the pathway could be identified. It is critical to be cautious in annotating anaerobic metabolism due to (i) the difficulty in the annotation of enzymes and pathways in specialized anaerobic metabolisms and (ii) the ambiguous directionality of catabolic enzymes and pathways. For example, genes and pathways for propionate catabolism are nearly indistinguishable from those for propionate fermentation. Similarly, many amino acid degradation genes and pathways can also be used for biosynthesis. Thus, an anaerobic catabolic pathway was included in the analysis when the target genome harbored a complete pathway for substrate oxidation and electron transfer reactions compatible with the reoxidation of all electron carriers involved. For example, if an organism encodes oxidation of an AA that produces one NADH and one NADPH per substrate and has a ferredoxin-dependent hydrogenase, the AA catabolism is only predicted if the organism also encodes oxidoreductases/dehydrogenases that can transfer electrons from both NADH and NADPH to ferredoxin. To further confirm the directionality, we determine whether the predicted pathway (i) can also recover energy (e.g., generate ATP or proton motive force) and (ii) is catabolismspecific in biochemically characterized isolates or involves enzymes that are known to be used in the catabolic direction for steps (see Table S1). For some pathways, the directionality cannot be determined by sequence data alone (noted in Table S1 and Fig. 3). Based on the metabolic capacities predicted as above, we also define the total metabolic capacity for each species. Organisms from the same species can have different metabolic capacities, so metabolic capacities were predicted for each MAG prior to clustering into species to avoid creating "chimeric" metabolic reconstructions.
Metatranscriptomic-based activity of each metabolic pathway was predicted with strict criteria-expression of all genes involved in the pathway at a normalized expression level (RPKM of target gene divided by the median RPKM of all genes belonging to the target MAG) ≥ 1 by the target species-level MAG cluster in a single reactor averaged over the triplicate metatranscriptomes. Although a species-level cluster of MAGs can contain more metabolic capacities than the individual MAGs, the false prediction is not anticipated as only genes that are present would be detected and reads were mapped with high stringency (99% similarity).
To identify potential correlations between metabolic capacities, principal correspondence analysis was performed using R and the R packages FactoMineR and ggplot2 [80][81][82]. For this, a matrix containing the species-level MAG clusters with their corresponding the presence (value of 1), absence (value of 0), or diversity (see the following sentence) of each metabolic capacity was constructed. For diversity, the number of protein families (proteases and glycosyl hydrolases) or the number of pathways (fatty acid, AA, and sugar degradation) present in the target MAG cluster were used as values. Confidence ellipses (95%) were also plotted using the ggplot2 package (stat_ellipse). Similarly, to further identify relationships between metabolic activities, principle correspondence analysis was conducted for a matrix containing the species-level MAG clusters with expression levels of representative genes from individual pathways or diversity of pathways expressed for a particular category of metabolism. The following values were employed-for proteases and glycosylhydrolases, the number of protein families expressed in at least one reactor; for fatty acid, AA, and sugar degradation, the number of pathways expressed in at least one reactor; for electron transfer/ energy conservation pathways (Rnf, Nfn, Fix, Efd, and FloxHdr), the number of pathways expressed in at least one reactor; for H 2 and formate generation, the highest normalized expression level (RPKM normalized to species' nonzero median expression level) detected for hydrogenase and formate dehydrogenase catalytic subunits across all reactors; and, for O 2 respiration, the highest normalized expression level of any cytochrome bd oxidase subunit across all reactors. Although the principal correspondence analysis for the metatranscriptome-based metabolic activity was based on values spanning across all analyzed reactors, the observed and discussed correlations were further verified based on activities in individual reactors (see Table S7 and S16). Pearson correlation and Student's t test calculations were performed using Microsoft Excel functions Pearson() and T.DIST().