Northern peatland microbial communities exhibit resistance to warming and acquire electron acceptors from soil organic matter

Domain: earth_science
Published: 2025-07-25
Editorial outcome: major_revision

Abstract
The response of microbial communities that regulate belowground carbon turnover to climate change drivers in peatlands is poorly understood. Here, we leverage a whole ecosystem warming experiment to elucidate the key processes of terminal carbon decomposition and community responses to temperature rise. Our dataset of 697 metagenome-assembled genomes (MAGs) represents the microbial community from the surface (10 cm) to 2 m deep into the peat column, with only 3.7% of genomes overlapping with other well-studied peatlands. Community composition has yet to show a significant response to warming after 3 years, suggesting that metabolically diverse soil microbial communities are resistant to climate change. Surprisingly, abundant and active methanogens in the genus Candidatus Methanoflorens, Methanobacterium, and Methanoregula show the potential for both acetoclastic and hydrogenotrophic methanogenesis. Nonetheless, the predominant pathways for anaerobic carbon decomposition include sulfate/sulfite reduction, denitrification, and acetogenesis, rather than methanogenesis based on gene abundances. Multi-omics data suggest that organic matter cleavage provides terminal electron acceptors, which together with methanogen metabolic flexibility, may explain peat microbiome composition resistance to warming.

Introduction
Northern peatlands, which are often dominated by peat mosses (Sphagnum spp. 1) and characterized by their waterlogged organic soils 2, contain approximately half of Earth’s soil carbon 3. The peatland “carbon bank” is a consequence of the intricate balance between plant productivity and slow decomposition rates of soil organic matter (SOM). Sphagnum spp. mosses are thought to play a key role in carbon sequestration. They act as ecosystem engineers, fostering acidic, nitrogen- and phosphate-poor conditions, and retaining water to create anaerobic environments that impede SOM decomposition 4–6. Northern peatlands face mounting threats, with more pronounced warming projected at high latitudes in comparison to global averages 7–10. The heterotrophic microbial communities that mediate SOM decomposition and the release of greenhouse gases (carbon dioxide, CO2; methane, CH4) are expected to be stimulated by warming 11–15. However, our ability to predict the rates and controls of SOM decomposition in peatland soils is hindered by a limited understanding of the microbial communities involved.

Peatland soils differ profoundly in physical, chemical, and biological characteristics compared to their better understood upland forest counterparts. Upland forest soils are generally not saturated with water and oxygen is more available for microbial utilization. Thus, most SOM degradation is thought to occur under aerobic conditions and decomposition is hypothesized to be limited by carbon substrate availability 16,17. In contrast, water-saturated soils of Sphagnum-dominated peatlands are defined by anoxia, a paucity of inorganic terminal electron acceptors (TEAs), and respiratory pathways that mediate the terminal decomposition of organic matter are likely rate limiting 18–21. Carbon fixed by live vegetation is rapidly oxidized to CO2 above the water table, where oxygen is available 11,12,19. Immediately below the water table, fermentation products often accumulate in peat porewaters in temperate and high latitude peatlands 11,19, indicating that terminal electron accepting processes (TEAPs) become rate limiting in the absence of oxygen. In this zone, SOM degradation is mediated by mainly anaerobic processes 12,22. Anaerobic laboratory incubation experiments corroborate field observations, showing that amendments of peat with energetically favorable, non-fermentable electron donors such as acetate and formate do not enhance mineralization, as evidenced by minimal changes in greenhouse gas production rates 23. Conversely, a substantial increase in CO2 production is observed immediately after the introduction of nitrate 23 and sulfate 24, highlighting the limitation imposed on respiration by TEAs 23,25–27. Further, and despite TEA limitation under anaerobic conditions, several studies in temperate peatlands have consistently reported 10 to 1000 times higher CO2 production relative to CH4 11,23,28–32. Since the stoichiometry of methanogenesis predicts a 1:1 ratio of CO2 to CH4 production, higher ratios of CO2:CH4 implicate the predominance of alternative TEAPs 33,34. Fermentation products that accumulate at in situ temperatures are consumed at elevated temperatures and increase CO2 production, providing evidence that warming somewhat alleviates the limitation of terminal decomposition 23,35. This finding suggests that TEAs might be released from the degradation of organic matter itself, a hypothesis that remains to be tested.

A growing body of research has employed metagenomic approaches to uncover the community dynamics and metabolic potential of microorganisms in peat soils 36–41. The focus has been on the initial steps in SOM degradation (carbohydrate-active enzymes) in acidic, carbon-rich peats dominated by Sphagnum spp., where members of the Acidobacteriota as well as microbial groups that mediate methane cycling were shown to dominate the soil microbial communities 12,41. Genes encoding diverse hydrolases believed to be specific for Sphagnum-derived carbon compounds were uncovered and the genomic potential for respiration of fermentation products (acetate) was linked to the Acidobacteriota at the oxic–anoxic interface 36. Extensive investigations at Stordalen Mire, a Swedish peatland containing a permafrost thaw gradient, led to the recovery of over a thousand metagenome-assembled genomes (MAGs) and the identification of Methanoflorens as a keystone group of methanogens in peatlands 41–44. However, less attention has focused on terminal decomposition processes other than methanogenesis.

The Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment, combines air and peat warming in a whole-ecosystem manipulation experiment which aims to understand the impact of climate change drivers (warming and elevated atmospheric CO2) on ecosystem functioning in a forested peat bog in northern Minnesota. During the first three years of the SPRUCE experiment, warming led to significant increases in greenhouse gas production 45, and surface peat layers became more methanogenic as CH4 production rates increased more than CO2 production rates 12. Preliminary metagenomic analysis suggested that, despite the increase in CH4 production, the abundance of methanogens remained stable 12. Notwithstanding these recent insights, the influence of warming on other functional guilds, such as sulfate reducers or acetogens, has not yet been addressed, leaving a critical gap in our understanding of the effects of temperature on peat-dwelling microorganisms. In addition, the metabolic networks that drive the terminal steps of carbon turnover remain to be elucidated, hindering our ability to predict their response to climate change.

This study provides an extensive genome characterization of the microorganisms living in peatland systems for a North American site. Here, we establish a framework to understand how organic matter is processed in soils of this anaerobic, oligotrophic, carbon-rich ecosystem by examining the metabolic potential and microbe-metabolite interactions along a 2m depth gradient. Furthermore, we leverage this framework in combination with the SPRUCE experiment to begin elucidating how the community of microorganisms mediating organic matter degradation in northern peat soils can be resistant 46 to warming. Our results underscore that the wide metabolic versatility of abundant species may allow them to adapt their activity without significantly changing their abundance. The uncovered taxonomic and metabolic novelty of microbes along with our proposed conceptual model alter perceptions of anaerobic respiration pathways mediating terminal decomposition in peatlands and provide a mechanistic framework for improved predictions of the ratio of CO2:CH4 emissions.

Results
SPRUCE peat harbors a plethora of undescribed taxa structured by depth
We sequenced 2.4 terabase pairs (Tbp) of metagenomic sequence data from 131 samples collected in 2015, 2016, and 2018 (5–40 Gbp of quality-processed data per sample), which are supported by biogeochemical and metabolomic data collected in parallel (Supplementary Information for a detailed description of the metabolic data). Metagenome binning resulted in the recovery of 697 medium- to high-quality MAGs (more than 50% complete and less than 10% contaminated, Data S1) dereplicated at the species level (i.e., average nucleotide identity [ANI] >95%). The MAG dataset well represents overall microbial richness at the site as evidenced by recruitment of the total metagenomic reads that could be mapped with representative MAGs. The lowest average read recruitment was observed at the peat surface 10–20 cm layer (28.7% of total reads) whereas at the three deeper depth intervals (40–50, 100–125, and 150–175 cm) on average 65, 67.4, and 67.7% of total reads were represented in the MAG dataset, respectively (Figure S1). Depth was the main driver of variation in microbial community composition (P=0.002, adjR2=0.13, Fig. 1A, B) and functional potential (P<0.001, R2=0.623). Taxonomically, SPRUCE MAGs belong to a diverse range of bacterial (620 genomes) and archaeal phyla (77 genomes). Specifically, we recovered MAGs from 33 phyla (Fig. 1B, C), including Bacteria belonging to Acidobacteriota (222 MAGs), Actinomycetota (75), Proteobacteria (64) and Verrucomicrobiota (48), and Archaea from the Thermoproteota (45) and Halobacteriota (17). Genomes of diverse aerobic chemoorganoheterotrophs were abundant in surface peat, while the deeper depths were predominated by taxa known for their anaerobic metabolism. Acidobacteriota was the most abundant phylum at all depths, with the highest abundance observed in surface peat (10–20 cm). Similarly, Actinomycetota and Pseudomonadota were more abundant in the surface layer. At mid depth (40–50 cm) we found a peak in the relative abundance of Desulfobacterota_B and Halobacteriota while the deep peat (100–125 and 150–175 cm) was the preferred habitat for Thermoproteota, Thermoplasmatota, Verrucomicrobiota and Chloroflexota (Fig. 1B). This taxonomic stratification is a consequence of the metabolic distribution across the vertical profile in response to TEA availability and organic matter quality, as we show below.

The SPRUCE MAGs represent undescribed taxa, with the large majority (79.5%) belonging to genera that have not been formally described following the rules of either the ICNP or SeqCode. Furthermore, 20.8% and 4.6% of all MAGs were from an undescribed order and undescribed class, respectively (Fig. 1C). The novelty of the SPRUCE soil microbiome was also supported by comparison against metagenomes retrieved from a broad range of global soil types (upland forest, tropical, Antarctic, grassland, and managed agricultural ecosystems) (Fig. S2), revealing that SPRUCE peat harbors a plethora of ecosystem-specific, undescribed microbial taxa. To further examine the distinct characteristics of the dataset, we specifically compared our SPRUCE metagenomes against metagenomes from Stordalen Mire, a Swedish peatland with similar features to SPRUCE (type of soil, latitude, temperature, pH) as a well-studied global reference point with detailed, publicly available metagenomic analyses 41,47. A non-metric multi-dimensional scaling (NMDS) plot based on MASH distances of raw reads revealed clustering by peatland type (bog, fen, palsa, P=0.001, adjR2=0.31, Fig. 1D, Fig. S3) and site (SPRUCE vs Stordalen Mire, P=0.001, adjR2=0.14, Fig. 1D, Figure S3), further highlighting the novelty of these peat metagenomes with respect to previous work. In addition, only 26 of the total MAGs recovered in the present study (3.7%) shared more than 95% ANI to MAGs reported by Woodcroft, et al. 41 from Stordalen Mire. Most of these shared genomes were members of the phylum Acidobacteriota (8 MAGs), Actinomycetota (7 MAGs), and Halobacteriota (4 MAGs). Of particular interest, 5 out of the 26 MAGs belonged to Candidatus species named by Woodcroft et al. 41. For example, SPRUCE 382 was assigned to Ca. Acidiflorens clade 2, SPRUCE 682 to Ca. Acidiflorens stordalenmirensis, SPRUCE 50 to Ca. Changshengia, SPRUCE 210 to Ca. Methanoflorens crillii, and SPRUCE 510 to Ca. Methanoflorens stordalenmirensis, the dominant methanogen genome in Stordalen Mire.

Microbial community composition remains stable in response to warming despite changes in gas fluxes
The MAG composition-based NMDS and db-RDA analyses of samples collected in 2016 and 2018 did not reveal any significant effects of warming or eCO2 on overall microbial community composition at any of the sampled depths (Fig. 1A). There were no significant changes in the relative abundance of any of the functional groups with temperature at any depth (Fig. 2). Furthermore, the abundance of only a small subset of genomes (6.7%) appeared to correlate with the temperature treatments (Data S2). Broadly the community does not respond to warming, i.e. it is resistant to this disturbance (Allison & Martiny, 2008) despite the sensitivity of CO2 and CH4 emission rates with warming 11,12. This could be explained by the slow generation time of soil bacteria 48, which may not complete a generation in ecosystems with short growing seasons 49.

Biogeochemical data from previous studies at SPRUCE (that is, using the same experimental design) have shown that the ratio of CO2 to CH4 in the peat declined with warming at the surface because of increased CH4 over CO2 emissions, indicating that the peatland ecosystem is becoming more methanogenic 11,12. Rate measurements of acetogenesis and methanogenesis conducted in summer of 2018 at SPRUCE, coinciding in space and time with our metagenomic dataset, also showed that rates of homoacetogenesis increased with temperature 50. Broadly, metabolite composition remained stable along the warming and eCO2 treatments through the depth profile (Fig. S4). However, more thorough examinations of organic matter molecular composition at SPRUCE have revealed that the aerobic, surface layer of peat is degraded more readily with warming 51,52, possibly due to interactions between plants and their root associated microbes 53. Thus, the response of peat microbes to warming at the community level must be (if any) smaller than the response in their activity. To identify potential mechanisms underlying the discrepancy between stable microbial community composition and increased gas emissions under warming, we explored the potential metabolic abilities of peat microbes and developed a conceptual model. This model illustrates the main microbial metabolic mechanisms driving carbon decomposition and CH4 and CO2 emissions across the peat vertical profile.

Previously undescribed and abundant peat genomes reveal a broad metabolic diversity
We first focus on the most abundant MAGs, which exhibited a versatile metabolic potential, with the ability to perform two or more of the energy-generating pathways described above (Fig. S5). Metabolic potential was confirmed when at least half of the genes in the KEGG pathway, along with key genes (e.g., fdh for the Wood–Ljungdahl pathway), were annotated in the MAG. We present a few metabolic models before delving into our conceptual model to illustrate this metabolic flexibility.

A small but growing body of evidence reveals that members of the Acidobacteriota, a ubiquitous soil lineage, are capable of sulfate respiration in peat soils 54. Recent work based on lab incubations confirmed and extended these observations, showing the potential for both aerobic respiration and sulfate reduction in the same Acidobacteriota genome 55. Acidobacteriota MAGs in our dataset further corroborate these findings. For example, SPRUCE 682, with an average 3.2% relative abundance at the 40-50 cm, shows the potential to respire oxygen and/or sulfate. This genome, which belongs to the genus Ca. Acidiflorens (Sba1), also shows the potential to obtain sulfate by cleaving organic-sulfur compounds, such as choline-O-sulfate (Fig. 3).

SPRUCE 586 is another interesting genome within the Acidobacteriota phylum, increasing in abundance with depth and reaching 2.9% relative abundance in the 100–125 cm layer. Encoding the Wood-Ljungdahl pathway, SPRUCE 586 is a potential acetogen. Furthermore, norB was detected in this MAG, demonstrating its potential to produce N2O, a potent greenhouse gas (Fig. 3). The family UBA7540, that contains SPRUCE 586, is among the most diverse families in the SPRUCE peat, with 17 species that are especially abundant in the deep peat (100–125 and 150–175 cm depths). However, only 35 UBA7540 genomes are found in GTDB and almost no information about this group is available in the literature. Thus, here we provide insights into the metabolic potential of this uncharacterized microbial group that abounds in the deep peat.

The Bathyarchaeia have been estimated to be among the most abundant soil microorganisms on Earth 56. While more information is available from members of the phylum in marine sediments, the Bathyarchaeia were detected in a range of anoxic sediments, including peatlands (Woodcroft et al., 2018). Here we show, for the first time, that the Bathyarchaeia have the metabolic potential for sulfite reduction. Two MAGs belonging to the family FEN-987, SPRUCE 533 (0.07% mean relative abundance at 150–175 cm) and 422 (0.13% mean relative abundance at 100–125 cm), encode the corresponding hallmark genes. Interestingly, we observed the potential for oxygen, sulfite and nitrite respiration in SPRUCE 533, which highlights the adaptability of this species to variations in TEA availability (Fig. 3).

Similar to other well-studied peatlands in Europe, including Stordalen Mire 41, our previous work shows that genomes characterized as Candidatus Methanoflorens (Bog-38) are by far the most abundant methanogen genus present in SPRUCE soils 12. The present study reveals that methanogens such as Ca. Methanoflorens may have a much more versatile metabolic potential than was previously perceived. For example, Ca. Methanoflorens stordalenmirensis (SPRUCE 510), the most abundant Ca. Methanoflorens species in our dataset (2.8% average relative abundance at 40–50 cm), encodes the potential for both acetoclastic and hydrogenotrophic methanogenesis (Fig. 3). This dual potential was observed in 89.1% of the Ca. Methanoflorens MAGs (n=156) recovered from both SPRUCE and Stordalen Mire, indicating that it is not an exclusive trait of SPRUCE 510 or assembly/binning error (chimeric genome), but a general feature of this genus. To date, members of Methanosarcina, a genus that is largely considered as acetotrophic, are the only methanogens known to utilize both hydrogen and acetate as substrates to produce methane 57.

Conceptual framework for depth-stratified metabolic potential in a temperate northern peatland
The primary environmental forcing we observe is the distinct depth stratification of microbial community composition (Fig. 1A, PERMANOVA p-value < 0.001) and functional potential (Fig. 4A), which closely mirrors changes in redox conditions, gas emissions, and soil organic matter composition (Fig. 4B, S4, S5). Redox chemistry drives microbial energy conservation pathways and is closely coupled to oxygen supply 58 linked to fluctuations in the water table, which rarely penetrates to below 30 cm depth at SPRUCE 45,59. This strong depth stratification of microbial communities and processes is in agreement with our past studies at the SPRUCE site 28,32,60,61, those of other peatland systems 62–64 and as in our conceptual framework below, can be interpreted as a response of metabolic potential to changing terminal acceptor availability and soil organic matter quality.

In SPRUCE peat, sulfate reduction is an important metabolic trait throughout the depth profile, detected in 34 MAGs identified as sulfate reducers across Acidobacteriota (11 MAGs), Desulfobacterota (including Desulfobacterota_B and Desulfobacterota_G, 13 MAGs), and Thermoplasmatota (2 MAGs, in the genus UBA184). Sulfite reduction, on the other hand, is attributed to 20 MAGs distributed among Acidobacteriota (9 MAGs), Desulfobacterota (6 MAGs), and Thermoproteota (2 MAGs in the family Fen-987). Within the Acidobacteriota, a substantial proportion, 5% of this taxonomic group, have the potential to mediate sulfate reduction, while 4% have the capability to perform sulfite reduction. Overall, the keystone functional guild of methanogens constitutes 3% of all MAGs, and notably, 20 MAGs within this group (n=21) exhibit the capability to execute multiple methanogenic pathways, further categorized into hydrogenotrophic (17 MAGs), acetoclastic (20 MAGs), and methylotrophic (6 MAGs) pathways (Data S3). The Wood-Ljungdahl pathway is common and phylogenetically diverse within our dataset; 58 bacterial MAGs, primarily from the phylum Desulfobacterota (15 MAGs), Planctomycetota (7 MAGs), Acidobacteriota (6 MAGs) and Chloroflexota (6 MAGs), show this potential. In addition, a large portion of the recovered Thermoproteota genomes contained the potential for homoacetogenesis (54.5% of the 33 identified MAGs). Note, however, that some species, such as sulfate-reducing bacteria, may use the reverse Wood-Ljungdahl pathway to generate energy through the oxidation of acetate to H2 and CO2 65. Denitrification pathways are encoded by 174 MAGs, representing 25% of all MAGs, with Bacteria and Archaea contributing 160 and 14 of these MAGs, respectively. Members of the Acidobacteriota are significant players in denitrification, with 48 MAGs (21.6% of all Acidobacteriota MAGs, Fig. S6) containing this potential, while Desulfobacterota, including Desulfobacterota_B and Desulfobacterota_G, contribute 23 MAGs, making up 46.7% of all Desulfobacterota involved in denitrification (Fig. S7).

The acrotelm (10–20 cm): fluctuations in conditions lead to a diverse aerobic and fermentative microbial community
Peat mosses (Sphagnum spp.) carpet the soil surface in many temperate peatlands, including the S1 bog at SPRUCE sampled here. Beneath the living Sphagnum layer and above the water table, the 10-20 cm depth interval stands out as a hotspot for decomposition (Fig. S8), as SOM derived from decaying plant biomass and labile organic compounds from root exudates are decomposed primarily via aerobic respiration. Our previous work showed that molecular oxygen is available at this depth 61. Accordingly, MAGs encoding for pathways of aerobic respiration dominate at this depth (Fig. S8). Fungi, which are either strictly aerobic or yeast capable of fermentation, have been also shown to peak in abundance 61 and richness in this oxic/anoxic zone 66. We did not identify clear trends in the overall response of aerobes to warming (Fig. 2A) and only a few aerobic genomes showed a positive (2 MAGs) and negative (4 MAGs) response to warming (Data S2), indicating a lack of response of this functional group to warming treatment.

The S1 bog water table occasionally reaches the 10 to 20 cm layer below surface and our previous work shows that oxygen is depleted where the soil is saturated with water 61. Thus, the surface peat is ephemerally anoxic, and we find a high metabolic potential for acetate and alcoholic fermentation in the acrotelm (Fig. 4A, S8), indicating heterotrophy is mainly driven by fermentation under anoxic conditions. In agreement with this observation, St James et al. (2020) detected the potential for acetate fermentation in most MAGs present in the 10–40 cm layer from the Sphagnum-dominated MacLean bog (New York State). In the present study, acetate production via fermentation is encoded mainly in the phyla Verrucomicrobiota (14 MAGs) and Actinomycetota (12 MAGs), while the potential for alcoholic fermentation was present in Acidobacteriota (165 MAGs), Actinomycetota (48 MAGs) and Pseudomonadota (46 MAGs). The total relative abundance of MAGs with the potential to ferment to acetate and alcohol did not respond to warming, and the response of MAGs capable of fermentation in the surface was mixed (Data S2).

In the acrotelm, aerobic respiration is coupled to canonical methanotrophy at the oxic-anoxic interface where oxygen and methane are supplied from above and below, respectively. Anaerobic respiration pathways are less pronounced at this depth, with the genomic potential for methanogenesis, sulfate respiration, and acetogenesis at their lowest abundances (Fig. 4A). Nevertheless, these anaerobic pathways are still notably active (Fig. 5A), and the increase in porewater gas concentration in response to temperature 12 indicates that these pathways are sensitive to climate change drivers. We find evidence that the dominant methanogen at SPRUCE, Ca. Methanoflorens, is actively performing both acetoclastic and hydrogenotrophic methanogenesis at the surface of the bog (Fig. 5B). Other active methanogens, such as members of the genus Methanobacterium and Methanoregula, are also capable of flexibility in their substrate use. Considering the widespread metabolic flexibility of methanogens at SPRUCE, the significant increase in ribosomal protein transcription (P=0.009) in the most active acetogens of the surface layer (Fig. 5C) suggests that the mechanism by which elevated temperatures promote methanogenesis in the bog is through enhanced availability of substrates (e.g., acetate).

The mesotelm (40–50 cm): hotspot of anaerobic respiration via cleavage of TEAs from organic matter
Peatlands store the majority of their carbon in thick peat layers below the water table, where soil organic matter decomposition is dominated by anaerobic pathways 20,21,28,58. Within the 40–50 cm depth range at SPRUCE, the potential for aerobic processes diminishes, giving way to anaerobic respiration as the primary driver of organic matter degradation (Fig. 4A and S8).

The paradigm in freshwater wetlands is that methanogenesis predominates over the TEA pathways coupled to the decomposition of SOM 20,21,33,35,58, thereby regulating the production of greenhouse gases (CO2, CH4). Here, however, despite a peak in methanogen abundance at 40-50 and 100-125 cm, the potential for other anaerobic respiration pathways (as reflected by relative abundance of the corresponding hallmark genes), such as sulfate reduction, exceeds that of methanogenesis (Fig. 4A). We find that sulfate-reducers are less active individually, but unlike methanogens, which are almost monospecific in their activity, a diverse set of sulfate-reducers are active (Fig. 5A). The stoichiometry of methanogenesis predicts a 1:1 ratio of CO2 to CH4 production and higher ratios of CO2:CH4 implicate alternate carbon oxidation pathways 23,28,33. Despite the scarcity of inorganic TEAs in peat, numerous field and incubation studies in northern peatlands, including those conducted at SPRUCE, have consistently reported 10 to 1,000 times higher CO2 production than CH4, indicating the dominance of alternative TEAPs in SOM decomposition 11,23,28–32. Our results provide further support for these observations (Fig. 4B) but raise the question of the source of TEAs supporting anaerobic respiration, which are typically detected at very low concentrations in peat 67,68.

We propose that inorganic TEAs are obtained from the cleavage of the organic matter itself. Indeed, of the sulfate- and sulfite-reducers detected, 46 out of 55 MAGs demonstrate the genomic potential to cleave inorganic sulfur (sulfate, sulfite) from organic-sulfur compounds (Figure S9). This ability is widespread throughout the total microbial community, with 31% of our MAGs encoding genes for inorganic sulfur cleavage. Sulfur cleavage from organic matter is independent of oxygen regime 69 and persists throughout the depth profile (Fig. 4A), suggesting that TEAs are cleaved from organic matter. Further, our data suggest that choline-O-sulfate, shown to be a major metabolite produced by Sphagnum 70, is likely an important source of sulfate for anaerobic respiration, as the abundance of betC genes, responsible for cleaving SO4 2- from choline-O-sulfate, correlates with the ratio choline/choline-O-sulfate as well as porewater SO4 2- concentrations through the depth profile. Specifically, betC decreases in relative abundance in parallel with the abundance of detected choline-O-sulfate within our companion metabolite dataset and a relative increase in the byproduct, choline (Fig. 4C). Similarly, porewater SO4 2- concentrations follow the decrease in betC abundance supporting the link between them. Throughout the growing season, we observed that porewater SO4 2- concentration appears to be mainly driven by microbial activity regulated by water table rather than by the amount of rainwater (Fig. S10). Under oxic conditions (low water table), porewater SO4 2- concentration increases, likely because of a higher degradation activity, while under anoxic conditions (high water table), SO4 2- concentration decreases due to the activity of sulfate-reducers. It should be noted that micromolar concentrations of SO4 2- are sufficient to support sulfate reduction activity in peat soils 71. Gene expression of betC in the acrotelm confirm the activity of the gene and its involvement in sulfur cycling (Fig. S11). BetC releases SO4 2- transiently, as betC expression and SO4 2- are not significantly correlated, but gene expression is intricately related to the intensity of its substrate, choline-O-sulfate and its other product, choline. The expression of dsrA is significantly negatively correlated with SO4 2- (Estimate=-0.005, P=0.005) and choline concentrations (Estimate=-0.025, P=0.001), indicating that when SO4 2- accumulates under oxic conditions, dsrA transcripts decrease and choline accumulates. Choline-o-sulfate, however, does not significantly relate to the rate of dsrA expression (0.096), since only some MAGs respond to choline-O-sulfate in the peat (Fig. S11). These lines of evidence suggest that bacteria capable of sulfate cleavage from organic matter are supplying sulfate as an alternative source of TEA for respiration. Thus, our findings provide a possible explanation for the long-standing question of the mechanisms behind the unexpectedly high CO2 production in TEA-poor peatlands.

Our previous determinations of microbial activity corroborate the observations of genomic functional potential reported here, revealing a mid-depth hotspot in organic matter decomposition activity at 40–50 cm in the peat column, as evidenced by rates and ratios of greenhouse gas production, the accumulation of gases in soil porewaters, and the transformation of SOM (metabolomes) with depth 11,12,32,39,60. While relative gene abundance provides compelling evidence for the importance of the metabolic potential of anaerobic respiration pathways at depth, our hypotheses must be confirmed by process-specific rate measurements in the future. Nevertheless, this potential mechanism of organic matter processing has implications for warming peatlands, and could explain increases in porewater CO2 observed below the water table 12. Although warming did not impact the total relative abundance of MAGs with the potential to reduce sulfate, sulfite (Fig. 4B), or cleave sulfate from organic matter, among the five genomes that significantly increase in relative abundance with warming at 40–50 cm depth, two are Acidobacteriota involved in sulfur cycling (Data S2). The first, SPRUCE 490 in the family Koribactericeae (Coefficient=1.084, P=0.035), has the genomic potential to reduce sulfate and sulfite and ferment to alcohol. The second, SPRUCE 69, in the family Bryobacteraceae (Coefficient=1.650, P=0.45), can potentially cleave sulfate from choline-O-sulfate.

The catotelm (100–125 and 150–175 cm): interactions between methanogens and acetogens as well as high potential for denitrification and methylotrophic methanogenesis
Below the mid-depth peak (40–50 cm) in anaerobic respiration activity, the genomic potential for acetogenesis via the Wood-Ljungdahl pathway continues to increase, resulting in the highest concentrations of acetate in the deep peat (Fig. 4A, Fig. S12). While metaproteomic data from the catotelm is not available, key enzymes in the Wood-Ljungdahl pathway, such as acetyl-CoA synthase and 5-methyltetrahydrofolate corrinoid/iron sulfur protein methyltransferase, were consistently detected between 20–70 cm depth, providing evidence for the activity of this pathway. The pathways of methanogenesis are not partitioned equally throughout the depth profile, and although methanogens with the potential to produce methane via the acetoclastic and hydrogenotrophic pathways dominate throughout the peat profile, methylotrophic methanogens increase noticeably in the deep peat (Fig. S13). Methylaminated organic compounds, such as 1-methyladenine and 1-methylguanine are negatively correlated (r=-0.84, and r=-0.84) with the abundance of the gene mttB, which encodes for a trimethylamine co-methyltransferase. The depletion of metabolites in the presence of key genes in the pathway of methylaminotrophic methanogenesis suggests that the pathway is active and links the deep peat to the complex, organic bound nitrogen cycle of peatlands. Known substrates for methylotrophic methanogenesis include trimethylamines, dimethylamines, monomethylamines, glycine betaine, dimethyl sulfide (DMS), methanol, and choline, among others 72. In SPRUCE peat, methanol and choline are likely derived from the degradation of lignin and choline-O-sulfate, respectively, providing key substrates for methylotrophic methanogenesis, as recently proposed for the Stordalen Mire 44. Furthermore, the observed negative correlation between methylnucleotides and mttB suggests that methylated nucleotides may potentially act as precursors for methylamines, a hypothesis that warrants further investigation. Of the few genomes that significantly respond to warming, SPRUCE 117 in the order Methanomassiliicoccales, which has the potential to produce methane using acetate, methanol, or methylamine as substrate, is negatively correlated with temperature in the deep peat (100-125 cm: Coefficient=-0.380, P=0.042, and 150-175 cm: Coefficient=-2.206, P=0.020). Recently, methylotrophic methanogenesis has emerged as an important process in peatland soils, with reports of methylotrophic orders accounting for up to half of the transcription 44. In the bog where our experiment was conducted, 13C-labeled methylated substrates including methanol and monomethylamine were readily converted to CH4 by methylotrophic methanogens 73. The negative correlation of SPRUCE 117 relative abundance with warming signals that methanogenesis pathways at SPRUCE are shifting with warming, but the decrease in metabolic potential for the demethylation of methylaminated organic compounds cannot explain why the bog is becoming more methanogenic with warming.

The deep peat exhibited significant metabolic potential for denitrification, with a peak occurring at 100-125 cm depth, which coincided with a consistent depletion of NO3- at this depth (Fig. 4A, E). Metaproteomic data throughout the depth profile and transcripts from the acrotelm confirmed the expression of denitrification genes. We observed a distinct distribution of genes involved in denitrification, with norB showing by far the highest relative abundance (Fig. S14). This gene encodes the nitric oxide reductase, an enzyme that catalyzes the reduction of nitric oxide (NO) to the potent greenhouse gas nitrous oxide (N2O), pointing to the catotelm as a potential source of N2O. Given the limited availability of oxidized nitrogen compounds in peat, the origin of the terminal electron acceptors for denitrification remains unclear. We propose that inorganic nitrogen forms could be derived from the organic matter itself, akin to sulfur compounds. Several lines of evidence from our metagenomic and metabolomic data support this hypothesis. Genes related to the hydrolysis and deamination of organic nitrogen compounds correlate positively with norB, while the respective compounds (e.g., nucleosides, peptides and amino acids) correlate negatively, suggesting consumption (Fig. 4D). In agreement with this observation, we found peptidases encoded close to the norB genes (i.e., within 10 genes upstream or downstream) in 54% of all norB-encoding MAGs, which suggests that these genes may act on the same pathway. In addition, previous metabolomic studies at SPRUCE reported an enrichment of N compounds with depth 32. However, the potential for reoxidation or recycling of inorganic nitrogen in the deep, anoxic peat remains enigmatic. Mineralized nitrogen is generally released in a reduced form as ammonium, and accordingly, porewater ammonium concentration in SPRUCE increases with depth (Fig. 4E). Possible explanations for the oxidation of ammonium in anaerobic peat soils include Fe(III)-mediated anaerobic ammonium oxidation (Feammox) 74,75, or ammonium oxidation by methanotrophs 76. While little information is available, biogeochemical evidence indicates Feammox is favored under acidic, anoxic conditions 75, which match the chemical environment in SPRUCE peat. Although Fe(III) concentrations at SPRUCE are low in comparison to mineral soils, a substantial pool of Fe(III) is observed that decreases with depth, reaching a minimum concentration at the 150–175 cm depth interval 77. Indeed, we observed a strong correlation in the relative abundance of norB and ferritin genes (Pearson’s correlation=0.83; p-value=0), supporting a closer relationship between them (Fig. S15). Thus, these trends are consistent with our hypothesis that Fe(III) could be used as the electron acceptor in the oxidation of ammonium. Decomposition rates are shown to decline dramatically between 50 and 100 cm depth 22,32, and an alternate explanation is simply that the cleavage of some oxidized organic S and N compounds supplies sufficient inorganic TEAs to support the lower rates of anaerobic respiration, and the replenishment of electron acceptor through reoxidation is not necessary.

Our ability to predict the impact of climate change on the vast carbon stores of peatlands is hampered by a limited understanding of the microbial dynamics and metabolic pathways that regulate carbon turnover. Here, we contribute to filling this gap by providing a conceptual framework for the functional potential of microbial communities extending 2m into the peat column. The genome-resolved findings presented here provide an explanation for field observations that peatlands generally emit much more CO2 than CH4 under anaerobic conditions and propose mechanisms by which warming can impact the metabolism of the microbial community. Our conclusions on microbial dynamics in the belowground peat are supported by over 10 years of biogeochemical and metabolomics data collected from the SPRUCE site 11,12,23,39,60,61,77,78. From our conceptual framework, we describe two potential mechanisms that could explain why microbial community composition remains stable despite increasing greenhouse gas emissions in the face of warming temperatures (Fig. 6). First, the wide metabolic versatility of abundant species, may allow them to adapt their activity without significantly changing their abundance. For example, Ca. Methanoflorens (Bog-38) species, exhibit a genomic repertoire capable of performing both acetoclastic and hydrogenotrophic methanogenesis, and Ca. Methanoflorens is actively performing both acetoclastic and hydrogenotrophic methanogenesis at the surface of the bog. Thus, these versatile methanogens can take advantage of the available acetate, or if acetoclastic methanogenesis is not energetically favorable, use the CO2 and H2 produced from the oxidation of energetically rich substrates for hydrogenotrophic methanogenesis (Fig. 6) 79. This metabolic versatility combined with the slow growth rate of methanogens 80,81 and the influence of temperature on microbial activity, may explain why biogeochemical data show that methanogenic activity increases with warming, but community composition remains stable. A transition between methanogenic pathways with increasing temperature is indeed expected, based on previous isotopic measurements in the SPRUCE peat that indicated a shift from hydrogenotrophic to acetoclastic methanogenesis 12. However, validation of such a transition in response to climate change drivers necessitates further experimental evidence from activity-based (metatranscriptomic) studies.

Second, the increased input of organic matter resulting from higher plant productivity at warmer temperatures may replenish the TEA pool available for anaerobic respiration, given the potential capability of peat microbes to obtain TEA from organic compounds (Fig. 6). This mechanism could thus be facilitating the respiration of organic matter via alternative pathways to methanogenesis and thus, acting as a counterbalance to limit the growth and activity of methanogens by fostering the activity of other anaerobic groups (e.g., sulfate-reducers) and thus, the release of CO2. The genomic evidence reported here in support of this mechanism is further complemented by previous studies that showed that TEA limitation is alleviated at high temperatures 23,35. Further experimental data will be required in the future to assess the existence and extent of this mechanism. For example, the activity of enzymes responsible for the cleavage of TEA from organic compounds, such choline-sulfatase (betC), would be expected to correlate with the release of CO2. Culture studies will also be needed to verify these physiological mechanisms.

To conclude, we have assessed the impact of warming on keystone functional guilds, providing evidence that microbial community composition in northern peatlands is resistant despite the sensitivity of gas production 46. Therefore, the previously reported increase in CH4 and CO2 is likely due to changes in microbial activity rather than community changes. The uncovered taxonomic and metabolic novelty of microbes inhabiting peat soils along with our proposed conceptual model alter perceptions of anaerobic respiration pathways mediating terminal decomposition in peatlands and provide a mechanistic framework for improved predictions of the ratio of CO2:CH4 emissions and their likely response to climate drivers.

Methods
Study site
The Spruce and Peatland Responses Under Changing Environments (SPRUCE) is a whole ecosystem warming experiment located in the ombrotrophic, acidic S1 Bog of the Marcell Experimental Forest, north of Grand Rapids, MN (47°30.4760 N; 93°27.1620 W). The experiment consists of 17 open-top chambers that control the peat and air temperature (ambient, +0, +2.25, +4.5, +6.75 and +9 °C) as well as atmospheric CO2 concentration (ambient and 900 ppm). In June 2014, an array of heating rods 3m in depth were established and began warming the belowground peat. A subsurface corral hydrologically isolates each enclosure but allow natural lateral water flow. In August 2015, aboveground air warming was initiated. The air within the enclosure is heated by a propane heater outside the chamber and reintroduced to achieve desired temperature levels, with monitoring conducted at the target control point located at +2 meters in the center of the plot. In 2016, CO2 additions began. The pure CO2 is vaporized and warmed before being distributed and homogenized within the enclosure 82. Environmental variables such as temperature, precipitation and gas emissions are constantly monitored. The readers are referred to the official webpage for further information on the experiment and a comprehensive list of project measurements (https://mnspruce.ornl.gov).

The outflow chemistry from the enclosures indicates that, in August 2016 and 2018, on average the pH was 3.42 and total organic carbon was 85.83 mg C/L. Porewater chemistry data and environmental monitoring data was accessed on the SPRUCE repository 83,84. The S1 Bog is dominated by peat mosses of the genus Sphagnum, including S. fallax and S. divinum (previously magellanicum), and sparsely populated by Black spruce (Picea mariana) and larch (Larix laricina). Ericaceous shrubs such as Rhododendron groenlandicum and Chamaedaphne calyculata are commonly found throughout the peatland and accompanied by the herbaceous Maianthemum trifolium. In the absence of severe drought conditions, the water table fluctuates at a depth of between 0 and 30 cm of peat depth 45,61,78,85,86.

Physicochemical measurements
Porewater concentrations of SO4, NH4 and NO3 were collected from the SPRUCE data repository 84. This data was collected at regular intervales of twice or three times per month during the ice-free period of the year. Daily precipitation data was obtained from Sebestyen et al. 87. Acetate concentration along the depth profile was obtained from Zalman et al. 73.

DNA and RNA extraction and sequencing
Peat samples were collected in June of 2015 and 2016 and August of 2018 from the hollows of each of the 10 whole-ecosystem warming chambers with a serrated knife at the surface and a Russian corer at depth. The samples were separated into depth increments and six 0.35g subsamples of homogenized peat from the 10–20 cm, 40–50 cm, 100–125 cm, and 150–175 cm depth increments were collected and extracted for DNA using the MoBio PowerSoil Pro Kit (QIAGEN) and resuspended in 50 μL of 10mM Tris buffer. Illumina TruSeq metagenome libraries were generated by the JGI using their standard protocol. Metagenomes are publicly available on the JGI Genome Portal and NCBI Sequence Read Archive (SRA) (Data S4). We obtained peat samples in August 2020 from the hollows of each of the 10 whole-ecosystem warming chambers from the 10–20 cm depth increment. Sample was homogenized, and subsamples of 20 mL were flash frozen and kept at -80 °C for RNA extractions. We extracted RNA from six subsets of 30 mg of soil from the 10–20 cm depth using the Powermicrobiome RNeasy kit (QIAGEN), purified the samples using the OneStep PCR Inhibitor Removal Kit (ZYMO), digested the DNA using TURBO DNA-free Kit (Invitrogen), and pooled the samples with RNAClean & Concentrator-5 (ZYMO).

Metagenome preprocessing, binning and annotations
Metagenomic reads were quality filtered using bbduk.sh v38.18 (qtrim=w,3 trimq=17 minlength=70 tbo tossjunk=t cardinalityout=t) 88 and then, coverage was normalized using bbnorm.sh v38.18 (target=30 min=5 prefilter=t tossbadreads=t). Both original and normalized metagenomic reads were assembled independently with idba_ud v1.1.3 (--maxk 120) 89 and SPAdes.py v3.15.5 (-k 21,33,55,77,99,127 --meta --only-assembler) 90. Then, contigs longer than 1kb were used to recovery metagenome-assembled genomes (MAGs) with Maxbin v2.2.7 91 and metaBAT2 v2.15 92. MAGs were first dereplicated (within samples) with miga v1.3.8.3 (derep_wf --fast) 93 yielding a total of 5,963 MAGs that were further dereplicated (between samples) using dRep v3.4.3 (-comp 50 -conn 15 -sa 0.95 --S_algorithm fastANI) 94. As a result, the final dataset was composed of 697 unique genomospecies. CheckM v1.2.2 (lineage_wf) 95 was employed to assess MAG quality and GTDB-tk v2.1.0 (classify_wf) 96 to classify them taxonomically. MAG annotation was performed with DRAM v1.4.6 using default parameters 97 and KEGG module completeness was evaluated using a custom R script. Pathways were then manually curated for each MAG, using pathway-specific criteria to determine if a MAG had the metabolic potential for the pathway (see Data S5 for criteria used to define presence/absence of each metabolic pathway). In general, pathways were considered as detected when at least half of the genes in the KEGG pathway and a key gene in the pathway (e.g. dsrA for sulfate reduction, fdh for the Wood–Ljungdahl pathway) were annotated. The potential metabolic pathways for each MAG are available in Data S7. We determined MAG active function using the same definition but replaced MAG gene presence with metatranscriptomic coverage of MAG genes. To determine MAG-specific activity, we calculated average ribosomal protein activity, and excluded MAGs that did not express at least half the ribosomal proteins listed in Data S6. CoverM v0.6.1 98 was employed to calculate the truncated average sequencing depth at 80% (TAD80) for each MAG (-p bwa-mem --min-read-aligned-length 100 --min-read-percent-identity 95 --min-read-aligned-percent 70 --exclude-supplementary -m trimmed_mean --trim-min 10 --trim-max 90). TAD80 was then normalized by genome equivalent values obtained from MicrobeCensus v1.1.0 99 to normalize coverage by the sequencing depth while accounting for microbial genome size within the sample. MAGs analyzed in this manuscript were deposited in NCBI under BioProject PRJNA1084886.

Phylogenomic tree
Proteins encoded on each MAG were first predicted using Prodigal v2.6.3 (-p meta) 100. Then, PhyloPhlAn v3.0.67 was used to build a phylogenomic tree based on the alignment of 400 universal gene markers 101 (--diversity high --fast). The tree was finally drawn and annotations added using ggtree 102.

Metatranscriptomic read processing
We quality filtered the metatranscriptome using JGI’s BBTools toolkit (Bushnell B., 2015) and removed rRNA reads using sortMeRNA v.4.3.4 103. We assessed DNA contamination in metatranscriptomic samples using custom scripts as in Johnston, et al. 104. We used CoverM v0.6.1 98 to calculate the truncated average metatranscriptomic sequencing depth at 80% of each gene and normalized by sampled depth as kilobase per million mapped reads using quality filtered metatranscript library size (TADKM).

Comparison to genomes retrieved from representative global soil types
The largest metagenomic dataset from northern peatlands published to date was obtained from Stordalen Mire in Sweden 47. To assess how different or similar our metagenomes are to Stordalen Mire metagenomes, approaches at both the read level and the MAG level were performed. At the read level, MASH v1.1 105 distances were calculated (sketch -s 10000 -r -m 2) and plotted on an Non-metric Multi-Dimensional Scaling (NMDS) plot on R using the vegan 106 and ggplot packages 107. At the MAG level, all MAGs publicly available from Stordalen Mire 47 were retrieved and dereplicated using dRep, as explained above. Then, ANI between dereplicated Stordalen Mire and SPRUCE MAGs (1,645 and 697 MAGs, respectively), was calculated with fastANI v1.33 108.

To compare SPRUCE genomes to those from a variety of representative soils around the world, metagenomes from upland forest (Hubbard Brook Experimental Forest) 109, Antarctic 110, tropical 110, grassland 111, agricultural soils 112 as well as peat incubation samples from Ward reservation soils (Massachusetts, USA) 38 were retrieved from the SRA (Data S8). MASH distances were calculated, as explained above, and 3D NMDS plots were drawn with the plotly R package 113.

Gene abundances
Genes and proteins were predicted from all contigs longer than 1kb recovered from the 131 metagenomes using Prodigal v2.6.3 (-p meta) 100. MMseqs2 v13.45111 114 was employed to obtain a non-redundant gene database (easy-cluster -c 0.8 --cov-mode 0 --min-seq-id 0.95) which was then used by CoverM v0.6.1 to calculate gene sequencing depth (-p bwa-mem --min-read-percent-identity 95 --min-read-aligned-percent 50 --exclude-supplementary -m mean --min-covered-fraction 80 --contig-end-exclusion 20). The non-redundant gene ids were extracted from the protein file using FastA.filter.pl from the enveomics package 115 to get the non-redundant protein database which was annotated with kofamscan 116, and the output filtered to keep only hits with e-value < 1e-15 and scores > 90% of the precomputed scores. Gene sequencing depth and annotation data was merged with Table.merge.pl and the table parsed with custom R scripts to get the KO sequencing depth per sample. Finally, KO relative abundances were obtained by dividing the KO sequencing depth by genome equivalents.

Metabolomics
Metabolomics analysis was performed on 40 wet peat samples (10 enclosures, 4 depth per enclosure, 1 replicate per depth and enclosure) collected in 2018 that were also used for metagenomic analysis. To dry samples and ensure uniform starting weight for extraction, peat samples were first lyophilized using a Labconco FreeZone, Benchtop freeze dryer for 48h. The freeze-dried peat samples (0.2g) were extracted by adding 20 mL of an 80:20 solution of MeOH: sterile MilliQ water. Samples were briefly vortexed and sonicated in a water bath for 2hr at 20 °C (FisherBrand CPX3800). The supernatant was filtered through a 0.45 um filter to remove cellular debris and plant material. Of this extract, 7 mL were transferred to two glass autosampler vials (3.5 mL each), dried in a vacuum centrifuge (Eppendorf Vacufuge plus), and stored at −80 °C. Prior to CL-MS/MS analysis, samples were reconstituted in 80:20 water:methanol for reverse phase (RP), and 50:50 water:acetonitrile for hydrophilic interaction liquid chromatography (HILIC).

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)
Liquid chromatography was conducted using a Thermo Scientific Vanquish Duo ultra-high performance liquid chromatography system (UHPLC). For reverse phase (RP) separation, extracts were separated on a Waters ACQUITY HSS T3 C18 column, while hydrophilic interaction liquid chromatography (HILIC) separation utilized a Waters ACQUITY BEH amide column. Samples were injected into the column with a volume of 1 microliter. The elution conditions were as follows: for RP, a gradient from 99% mobile phase A (0.1% formic acid in H2O) to 95% mobile phase B (0.1% formic acid in methanol) was employed for 16 minutes. For HILIC, the gradient went from 99% mobile phase A (0.1% formic acid, 10 mM ammonium acetate, 90% acetonitrile, 10% H2O) to 95% mobile phase B (0.1% formic acid, 10 mM ammonium acetate, 50% acetonitrile, 50% H2O). Both columns were operated at a temperature of 45 °C, with a flow rate of 300 microliters/minute.

Spectral data collection was carried out using a Thermo Scientific Orbitrap Exploris 480 mass spectrometer. For RP, a spray voltage of 3500V was applied in positive mode, while for HILIC, 2500V was used in negative mode with the H-ESI source. The ion transfer tube and vaporizer temperature were maintained at 350 °C. Compound fragmentation was achieved through data-dependent MS/MS with HCD collision energies set at 20, 40, and 80.

Metabolite processing and annotation
Data analysis was conducted using the Compound Discoverer 3.3 software by Thermo Fisher Scientific, employing an untargeted metabolomics workflow. The initial steps of the analysis involved spectral alignment and peak picking. Putative elemental compositions of unknown compounds were predicted using the exact mass, isotopic pattern, fine isotopic pattern, and MS/MS data using the builtin HighChem Fragmentation Library of reference fragmentation mechanisms. Metabolite annotation was performed using an in-house database built from 1200 reference standards, spectral libraries and compound databases. First, fragmentation scans, retention time and ion mass of unknown compounds were compared with those in the in-house database. Second, fragmentation scans (MS2) searches in mzCloud were performed, which is a curated database of MSn spectra containing more than 9 million spectra and 20000 compounds. Third, predicted compositions were obtained based on mass error, matched isotopes, missing number of matched fragments, spectral similarity score (calculated by matching theoretical and measured isotope pattern), matched intensity percentage of the theoretical pattern, the relevant portion of MS, and the MS/MS scan. The mass tolerance used for estimating predicted composition was 5 ppm. Finally, annotation was complemented by searching MS1 scans on different online databases with ChemSpider (using either the exact mass or the predicted formula). To enhance annotation coverage and add compound classes, SIRIUS, CSI:FingerID, and CANOPUS were used 117. Compounds chemical taxonomy data based on chemical structure was assigned using ClassyFire 118. Furthermore, metabolite annotation was further enhanced using CMM-RT which employs machine learning, artificial intelligence, and neural networks for more accurate predictions of Retention Times (RTs) 119. Compounds level of annotation was assigned according to the Metabolomics Standards Initiative 120 as follows: Level 1: compounds with exact match to a standard reference compound in our in-house library, Level 2: compounds with full match to online spectral databases using mzCloud database (based on MS2 spectra matching), Level 2.1 for compounds with a full match to databases such as ChemSpider and/or CMMRT, relying on mass, molecular formula, and/or retention time, combined with annotations from SIRIUS using MS2 fragmentation patterns, Level 2.2 applied to compounds with a full match to databases (ex. ChemSpider) and CMMRT based on mass, molecular formula, and retention time, Level 2.3 encompassed compounds matching any single annotation source, Level 3.1: for matches based on molecular formulas generated through Predicted Compositions node and/or CMMRT, combined with annotations from SIRIUS using MS2 fragmentation patterns, Level 3.2 indicated matches based on molecular formulas generated through Predicted Compositions in conjunction with CMMRT. Finally, Level 3.3 represented matches based on chemical formulas from any annotation source. Following metabolite annotation, poorly annotated features were eliminated by performing manual QC on the features. QC correction was applied in Compound Discoverer using a linear regression model that retains features with a QC area RSD < 30% and was limited to a maximum correction of < 25%. Mass spectra and chromatography from duplicate annotations (for a single compound) were manually inspected for spectral quality, peak shape, and retention time following data processing with Compound Discoverer to assess if one of the multiple annotations was correct. When a single confident annotation was identified, all other duplicate feature annotations were removed. Further annotations were inspected for removal of in-source fragments.

Metaproteomics
A total of 36 samples collected in August 2018 at 10-20, 40-50 and 75-100 cm, coinciding in time and space with our metagenomic sampling were sent to the Environmental Molecular Sciences Laboratory (Richland, WA) and processed for metaproteomic analysis. In brief, proteins were extracted following lyophilization using the MPLEx extraction protocol 121 and prepared for digestion using the Filter-Aided-Sample-Preparation (FASP) method 122 demonstrated for soil 123. Peptides were normalized to a final concentration of 0.1 μg/mL prior to injection (5 μL) to an Orbitrap Lumos Fusion Tribrid mass spectrometer (Thermo Scientific, San Jose, CA) outfitted with an in-house constructed nano-electrospray ionization interface 124. Spray voltage was 1.8 kV with an ion transfer tube temperature of 250 °C. Fourier transform mass spectra (FT-MS) were acquired from 350-1800 m/z at a resolution of 60k (automatic gain control, AGC, target 4×105) and the top 12 FT higher-energy collisional dissociation tandem mass spectra (FT-HCD-MS/MS) were acquired in data dependent mode using an isolation window of 0.7 m/z (normalized collision energy of 30 and a 45 s. exclusion time) with a resolution of 50k (AGC target 1×105). Peptides were separated inline to the mass spectrometer using a Waters nanoACQUITY UPLC (Waters Corporation, Milford, MA). Ultra-performance liquid chromatography (UPLC) conditions have previously been described 123,125. Measured MS/MS were searched against sample specific metagenomes (NCBI BioProject repository) to assign peptide sequences using the MS-GF+ algorithm 126 with a parameter file set to 20 ppm parent ion mass tolerance, allowing for partial tryptic peptides, and dynamic modification of oxidized methionine (+15.9949 Da). Resulting peptide data was filtered to retain peptides with a false-discovery rate less than 5% using a targeted-decoy approach 127 with adjustment of the MS-GF+ spectral probability generating function to 1.13×10−10. The identified peptides were then aligned against the proteins predicted from the 697 MAGs using BLASTp v2.14.0 (-task blastp-short). Hits were filtered by best hit using BlastTab.best_hit_sorted.pl from the enveomics collection 115 and only hits with a maximum of 1 mismatch and 1 gap open were considered.

Statistical analyses
Statistical analyses were done in R version 4.2.3 (R Core Team, 2023). When testing for the impact of warming and eCO2 on MAG relative abundance, only samples from 2016 and 2018 were used since warming was initiated in 2015, and eCO2 in 2016.

Bray-Curtis and Unifrac beta diversity were calculated from the MAG relative abundance (distance(), phyloseq) 128. The resulting matrices were used as the response variable of a distance based redundancy analysis (dbrda(), vegan), with the full model including sampling read and depth. Stepwise model selection was used to select the best fit model (ordiR2step(), vegan). Depth specific models were created to determine if warming and eCO2 influenced community composition. This resulted in a total of four models, and temperature was calculated from the average over the month of August during the year of collection at the probe closest to the depth fraction. For the samples from the 20–30 cm depth fraction we used the temperature at 20 cm, for samples from the 40–50 cm depth fraction we used the temperature at 40 cm, for samples from the 100–125 cm depth fraction we used the temperature at 100 cm, and for samples from the 150–175 cm depth fraction we used the temperature at 200 cm.

To target specific hypotheses, we fitted multiple linear regressions for the aggregated relative abundance of MAGs in 2016 and 2018 that had the metabolic potential for pathways of interest as the response variable (lmer, lme4) 129. Pathways of interest included homoacetogenesis via the Wood-Ljungdahl pathway, acetogenesis via fermentation, sulfate and sulfite reduction, and methylotrophic, acetoclastic, and hydrogenotrophic methanogenesis. Response variables included peat temperature and eCO2, and we included year as a random factor, the step function (lmerTest) was used to eliminate non-significant predictors 130. We employed Maaslin2 131 to determine multivariable association between temperature and MAG-specific abundances (min_prevalence=0) in 2018. We did not include 2016 since year-to-year effects could not be accounted for. Fixed effects included warming only. For both hypothesis-based models and Maaslin2, each depth was processed separately, and temperature was calculated as for the distance-based redundancy analysis.

LC-MS/MS feature peak abundance values were normalized through mean normalization. Distance matrices were calculated via Bray-Curtis dissimilarity and used for permutational multivariate ANOVA (PERMANOVA) in the vegan package, with visualization in ggplot2. Log2 fold change (L2FC) values were computed for each identified feature to identify meaningful differences in compound expression across different depth. T-tests were performed to determine statistical significance between depths. The Benjamini-Hochberg procedure was used to control the false discovery rate, which provided adjusted p-values (Padj) to account for multiple testing 132. Adjustments were made separately for RP and HILIC datasets. Compounds were then filtered based on associated Padj. values < 0.05, categorizing them as upregulated in the sample when L2FC > 0, or downregulated in the sample when L2FC < 0.

International chemical identifier keys (InChIKeys), which provide standardized, condensed text representations of chemical compounds, were extracted. These InChIKeys were utilized to classify compounds into standard chemical taxonomy categories using ClassyFire 118. Integration of metabolomic and metagenomic data (gene abundances) was performed with the DIABLO workflow of the R package mixOmics 133 using clr-transformed data (clr, compositions R package) 134. For the correlation analysis, the function circosPlot was used and a minimum correlation score of 0.8 or -0.8 was set to call positively and negatively correlated variables.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study “Northern peatland microbial networks exhibit resilience to warming and acquire electron acceptor from soil organic matter” by Duchesneau et al. addresses how microbial functioning regulate carbon turnover in repsonse to climate change drivers in peatland ecosystem. The authors describe the views of an ecosystem warming experiment to understand the metabolic potential and microbe-metabolite interactions. More specifically they highlight (i) that microbial community is resilient to climate warming and (ii) anaerobic carbon decomposition involved sulfur and nitrogen metabolisms as well as acetogenesis but not methanogenesis. Gene abundance and metabolic analyses were used to support these conclusions.\nThe manuscript is well-organized and results are presented in a clear manner.\nThese findings are overall interesting and somewhat novel. However, few questions need to be addressed to improve the quality of the results and their interpretations. These are:\n1-Authors have extensively used the expression “microbial networks”. However, the data presented is of microbial composition based on metagenome-assembled genomes (MAGs). For microbial networks some form of association between key members will need to be included. So, author can either replace this phrase “microbial networks” with microbial composition or include some microbial network data.\n2-Most of the claims of this study rely on the release of sulfate during carbon decomposition which allows it to function as TEA. Two lines of evidences are presented to support this claim shifts in the ratios of (i) choline/choline-O-sulfate and (ii) betC gene abundance. To strengthen these claims regarding sulfate as TEA, it will be important to establish that the release sulfate from choline is substantial in amount to produce significant effects. Similarly, for betC some form of transcript abundance data can be included. As a suggestion, these could be included in Fig 4C. Strongest evidence would be some sort of betC-sulfate correlations.\n3-Likewise, for the claim regarding denitrification and acetogenesis, their associated metabolites and gene transcript levels as well as correlation will be required to validate the claims. These can be added to Fig 4D.\nTaken together above-mentioned additional data will support the hypothesis and claims in the title. More quantitative estimations of metabolites and association with gene expression and pathways need to be established.", "reviewer_2": "Northern peatland microbial networks exhibit resilience to warming and acquire electron\nacceptor from soil organic matter\nFirst I would like to express my enthusiasm over the proposed mechanism of organic matter cleavage contributing electron acceptors and associated data, potentially helping to explain high CO2 to CH4 production and emission ratios. This is a very interesting concept.\nI also very much like the observation of negative correlations of methylated compounds with mttB – as the decomposition pathways leading to methylamine production are not well resolved.\nAlso, this is overall a very nicely written manuscript and metagenomic analyses are well done and detailed.\nMy concerns are related to the focus on warming and metabolic diversity in functional groups. On these topics, the knowledge basis is pretty strong and pressing questions are at a level requiring combined analysis of metabolic activity (metabolites and transcripts, enzyme assays), metabolic potential and process rates to obtain new mechanistic insight. In this manuscript, much of the mentioned key data from the SPRUCE project seems to have been published already, judging from the referencing, while the insights from this new dataset – Stable community and broad metabolic potential in microbial taxa – do not offer a substantial advance in our understanding.\nIm aware that this manuscript contains substantial work. However, my overall impression, depending on the impact the authors are aiming for, is that it would be better to tone down the attention to the broader metabolic potential of the microbial community, but describe these aspects more briefly. Then, provide more detail and target the manuscript more towards the electron acceptors and CO2 production, and pathways of methylamine formation. The latter seems to be connected to the overall shifts in decomposition with warming and altered ratio of CH4 to CO2, so exploring more the shifts in pathways of methanogenesis and the pathways leading to their substrates would be of great value. I think the observation of no community shifts, is important and gets the attention it needs.\nTo provide more detail in these two directions, it would be important to provide some support experiments (for example metatranscriptomic analyses on select samples + some measurement of the levels of TEA’s in the soil in addition to the already interesting metabolic data).\nSpecific comments:\nTitle:\nIm not sure about the “exhibit resilience to warming” in the title, as according to previous work there are clear effects of warming on decomposition rates and routes. I understand it is about the lack of community shifts – can that be said instead?\nAbstract:\n34-35: I think many things are known by now, so I would be more specific.\n39-41: I would not say that lack of community change in response to warming is unexpected.\n41-42: Also, I wouldn’t say that it is surprising to find a high potential for both acetoclastic and hydrogenotrophic methanogenesis.\n44-47: I was not sure about “multi-omics”.. Are there multi-omics data presented? I would highlight the electron acceptor and methylotrophic aspects more in the abstract, but I see that more data might be required to do so.\nIntroduction:\nThe introduction I overall enjoyed very much.\n96-98: More specificity needed - I think that an additional possibility is that methanogens favors from warming to consume intermediates faster and that assimilation of for example acetate into biomass increases.\nResults and discussion:\n152-153: I would prefer to have some of the metabolomic data presented in the main manuscript, and rather sacrifice some of the metagenomics.\n183-198: Im not convinced about the relevance of this comparison for the manuscript.\n212-214: Be more specific about production vs emission and would also be good to say something about changes in rates, not only ratios.\n217-222: I think the metabolomics deserve more details and explanations here. It comes across as a bit shallow like this.\n223-228: Something about this section is just very hard to read. Its like I don’t understand what you mean.\n232-234: I would not call this remarkable\n234-236: could be cut\n286-288: Be more specific – how does it correlate?\n312-313: Mention how the potential was identified as homoacetogenic.\n368-374: I think the point is good, but nitrate and sulfate reduction genes are very common, also among microbes that can transition into fermentation, so this might not be the best argument that there has been too much focus on methanogenesis over TEA driven anaerobic decomposition.\n378-381: Just checking, is this only production, or also emission data measured from in-situ surfaces.\n383-384: Very interesting, but it would be so important to have some transcriptional data on this.\n390-399: possible to add sulfate, nitrate and other TEA concentrations?\n407-408: How would you do that hypothesis testing?\n427-430: This is interesting, would be so nice to see more data in this direction.\n431-432: Say something more about where the methylamines come from? Rather that just the complex organic bound nitrogen cycle?\n432-438: What does this actually indicate? If one factor in the potential sources of methylamines?\n441-443: activity evaluation, for example based on metatranscriptomics, would be necessary to say anything here.\n449-450: any measures of compounds of the nitrogen cycle – emissions or concentrations?\n484-487: Emissions are more tricky to discuss in terms of CO2 and TEA – methanotrophs can oxidize large percentage of produced methane.\n494-503: Section is nice, but im not sure if im convinced that either you need community shifts or metabolic shift within versatile methanogens to go along with increased methanogenic activity with warming. What if the methanogens that are there simply become more or less active without changing significantly in relative abundance.\n509-515: How do you explain increased methane to CO2 ratio with warming while also the release of TEA’s should be facilitated by warming?"}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thanks for the diligent preparation of the requested data. It makes the arguments presented in the manuscript stronger.", "reviewer_2": "41-42: Also, I wouldn’t say that it is surprising to find a high potential for both acetoclastic and\nhydrogenotrophic methanogenesis.\nWe respectfully disagree with the reviewer on this point. To date, Methanosarcina spp., is the only\nknown genus with the genomic potential for both acetoclastic and hydrogenotrophic\nmethanogenesis within a single genome. Therefore, our finding that most methanogen genomes at\nSPRUCE, including the dominant Bog-38 species, encode this dual metabolic potential is both\nsurprising and uncommon in nature based on existing knowledge.\nI understand this when you say \"most methanogen genomes\" but in the abstract you say the \"dominant methanogens\",\nwhich for all we know at that point in the manuscript could be two different strains, and then it wouldnt be surprising if those\ntwo would be methanosarcina (as it is quite common genus), so maybe it would be an idea to change the wording?\n183-198: Im not convinced about the relevance of this comparison for the manuscript.\nThis comparison is contextualizing our SPRUCE metagenomes, providing readers with insight\ninto how similar or different the SPRUCE microbial communities are compared to the well-studied\ncommunities from Stordalen Mire. Furthermore, we meant to highlight the importance of Ca.\nMethanoflorens stordalenmirensis as a dominant methanogen in peatlands. Although this\ncomparison may not directly bear on our main conclusions, we believe it offers valuable insight to\nplace our results into context within existing knowledge on the global distribution of peatland\nmicrobial communities.\nAgain, it is understandable when you explain it in the context of the site being well-studied (it is maybe among the most well\nstudied world-wide?) and Ca. M. stordalenmirensis being a dominant methanogen in peatlands, but in the text you dont say\nany of these things - you say the most detailed metagenomic analyses which doesnt really demonstrate the importance of\nthese sites as a reference point.\n234-236: could be cut\nWe would like to keep as it is to ensure the meaning is not lost\nCould you explain why you think it has meaning so that i can better understand your decision?\n312-313: Mention how the potential was identified as homoacetogenic.\nIn the Methods section, we refer readers to Supplementary Table S5 for the criteria used to\ndetermine the presence or absence of each metabolic pathway.\nOr you can just briefly tell me in the rebuttal how you did it since i asked and also consider to direct the reader in the text,\nsince this is something that i obviously thought about when i was reading (as identifying homoacetogens is not trivial)\nand so maybe others will to.\n431-432: Say something more about where the methylamines come from? Rather that just the\ncomplex organic bound nitrogen cycle?\nThe reviewer is raising an interesting point here. However, with our data in hand we were not able\nto confidently identify the potential sources of methylamines\nI was thinking literature here, but maybe you already checked and there is no knowledge about this in literature?"}, "author_response": "We sincerely appreciate your thoughtful feedback and constructive suggestions to improve the quality of our manuscript. We have carefully considered each of your points and are committed to addressing them.\n1. On the use of ‘Microbial networks’: We acknowledge your concern regarding the use of the term “microbial networks.” To clarify, we have revised our terminology to refer to “microbial composition” throughout the manuscript.\n2. On sulfate as a Terminal Electron Acceptor (TEA): We agree that strengthening our evidence regarding the role of sulfate as a TEA is crucial. In response to your suggestion, we added additional data quantifying SO 2- concentration throughout the depth profile. We included these results in Figure 4. Additionally, we have added an analysis of other potential input sources of SO 2- to the bog via rainwater and SO 2- concentration dynamics with changes in water table (Figure S11) as additional lines of evidence suggesting that the sulfur cycle is regulated by the microbial community in the S1 Bog at SPRUCE rather than by abiotic (rainwater) factors. Metaproteomic and metatranscriptomic data further confirmed the expression of betC and dissimilatory sulfite reductase. Additionally, we added transcript abundance data for betC and examine correlations between betC, activity of sulfate-reducers, and sulfate levels to provide a more robust basis for our claims (Figure S10).\n3. On denitrification and acetogenesis: To validate our claims concerning denitrification, we added NH + and NO - porewater concentrations to Figure 4E, as you suggested. Peptides associated with denitrification enzymes were also detected in the metaproteomic data supporting the active state of this pathway. To validate our claims on acetogenesis, we included data on gene transcript levels at the surface of the bog, metaproteomic results between 20-70 cm depth and the acetate concentration through the depth profile.\nWe appreciate your insightful suggestions, which have undoubtedly strengthened our manuscript, and are committed to revising our work accordingly.\nThank you once again for your valuable input.\n\nWe thank the reviewer for their enthusiasm over the proposed mechanism of organic matter cleavage contributing electron acceptors and associated data, potentially helping to explain high CO2 to CH4 production and emission ratios. Furthermore, we greatly appreciate the constructive feedback provided by the reviewer which has undoubtedly helped us to clarify and underpin the main conclusions of our manuscript. We agree with the reviewer that combined analyses of multiple data types is essential to obtain new mechanistic insights from reconstructed genomes. In the new version, we have added metatranscriptomic and metaproteomic data along with measurements of terminal electron acceptors (SO 2-, NO -) and a donor (acetate), to support the mechanistic metabolic model we propose to explain the response of the peat microbial communities to warming. **Please note that line numbers refer to the PDF version of the manuscript with track changes.**\n\nWe appreciate the reviewer's interest in methylotrophic methanogenesis and affirmation of our observations regarding no community shifts with warming. Since this is the first report of deep (2m) peat microbiomes, we were also interested in providing novel insight into the depth-stratified distribution of methanogenesis pathways in bogs. However, since our results to date indicate that methylotrophic methanogens respond negatively to warming, this observation cannot account for the observed increase in CH₄ emissions, which is one of the primary questions we wanted to address in this manuscript. Rather, in response to the reviewer’s comments (along with those of reviewer 1), in the revised version of the manuscript we have added metatranscriptomic, metaproteomic and measurements of terminal electron acceptors (SO 2-, NO -) and a donor (acetate) to support our conclusions regarding the microbial mechanism(s) by which warming increases methane production. We used the metatranscriptomic data to confirm the simultaneous expression of different methanogenic pathways in the same genome and explored the effect of this metabolic diversity in methanogens on increases in methane production with warming by exploring changes in acetogenesis activity (Figure 5 and Line 353-362). We observed increased activity of acetogenic bacteria with warming that may boost methanogenic activity through higher substrate (acetate) availability.\n\nWe agree. Based on the reviewer’s comments, we have changed the title in the revised version of the manuscript to: “Northern peatland microbial communities exhibit resistance to warming and acquire electron acceptors from soil organic matter”. We have replaced the term “resilience” with “resistance” according to Shade et al., (2012).\n\nAgreed. We modified the sentence to specify that the response of the microbial communities in peatlands to climate drivers is poorly understood.\n\nWe agree and removed “Unexpectedly” from the abstract to keep objectivity.\n\nWe respectfully disagree with the reviewer on this point. To date, Methanosarcina spp., is the only known genus with the genomic potential for both acetoclastic and hydrogenotrophic methanogenesis within a single genome. Therefore, our finding that most methanogen genomes at SPRUCE, including the dominant Bog-38 species, encode this dual metabolic potential is both surprising and uncommon in nature based on existing knowledge.\n\nMetagenomics along with metabolomics data were incorporated into the original submission. In the revised version of the manuscript, we have added metatranscriptomic and metaproteomic data.\n\nThank you. We’re pleased that you like it!\n\nWe agree entirely! This is something we address in the discussion as well and include as part of our conceptual framework (when we discuss the metabolic versatility of methanogens as an explanation for the increase in methane emissions via alternative substrate supply). However, this paragraph relates to the increase in CO emissions with warming, which can only be explained by 2 higher respiration rates of processes that depend on alternative TEAs (besides methanogenesis and aerobic respiration). We changed the text to clarify this, and we hope that we include enough discussion of acetate uptake by methanogens in the rest of the text to satisfy the reviewer.\n\nDetailed metabolomic data from SPRUCE peat have previously been published in Tfaily et al., 2014 (https://doi.org/10.1002/2013JG002492) or Wilson et al., 2021 (https://doi.org/10.1073/pnas.2004192118). However, metagenomic data from SPRUCE peat is still scarce in the literature and we prefer to give more weight to the metagenomics in this manuscript to further complement previous literature.\n\nThis comparison is contextualizing our SPRUCE metagenomes, providing readers with insight into how similar or different the SPRUCE microbial communities are compared to the well-studied communities from Stordalen Mire. Furthermore, we meant to highlight the importance of Ca. Methanoflorens stordalenmirensis as a dominant methanogen in peatlands. Although this comparison may not directly bear on our main conclusions, we believe it offers valuable insight to place our results into context within existing knowledge on the global distribution of peatland microbial communities.\n\nIn the revised version we added a sentence about emission rates (see line 206) and replaced “production” with “emission” to be more specific and consistent.\n\nAs mentioned above, previous publications have already focused on the impacts of warming on peat metabolites and we prefer to keep the focus of the manuscript on the metagenomics and use the metabolomics, metatranscriptomics and metabolomics to support our conclusions.\n\nWe appreciate this comment. We have tried to simplify and clarify these sentences in the revised version.\n\nWe agree and removed the term in the revised manuscript.\n\nWe would like to keep as it is to ensure the meaning is not lost.\n\nIn Figure 1A we show the clustering of samples by depth which is further supported by a NMDS stress value of 0.0691 and a PERMANOVA test (p-value < 0.001). We have added the PERMANOVA p-value to the main text.\n\nIn the Methods section, we refer readers to Supplementary Table S5 for the criteria used to determine the presence or absence of each metabolic pathway.\n\nThe reviewer raises a very good point. To address his/her comment, in the new version of the manuscript, we provide evidence from metatranscriptomic and metaproteomic data together with porewater chemical constituents (SO 2-, NO - and NH +) to support the existence of an active S 4 3 4 and N cycle driven by microbial activity (see new Figure 4, 5 and S10 and S11).\n\nBoth depending on the citation, but the disproportionate production of CO over CH remains 2 4 despite ombrotrophic conditions. For example, Song et al., 2023 reports greenhouse gas production rates from SPRUCE via incubations, whereas Hopple et al., 2020 reports emissions from the surface peat using flux collars as well as depth-resolved gas production rates at SPRUCE. Wilson et al. (2021) reports on a long-term dataset from the depth-resolved accumulation of gases in porewater that supports parallel production rate and flux (emission) measurements. These robust datasets are all consistent in reporting that both CO and CH production are increasing with 2 4 warming.\n\nTranscriptomic data provided in the revised version reveal that betC expression, choline, choline-O-sulfate, and sulfate are intricately interconnected, with complex and likely dynamic interactions. See new Figure S11.\n\nWe have added these data to Figure 4.\n\nDeep peat incubations to look at rates of methane production in the deep peat have been performed by Tfaily et al. 2014 (https://doi.org/10.1002/2013JG002492) indicating that production occurs at that depth. Further testing with substrate additions should be used to test this hypothesis.\n\nIt would certainly be great to have some transcriptomic data on methylotrophic methanogenesis. Unfortunately, our efforts to extract RNA from the deepest samples, where this pathway is mostly found, were unsuccessful and remains a technical challenge to solve in the future.\n\nThe reviewer is raising an interesting point here. However, with our data in hand we were not able to confidently identify the potential sources of methylamines.\n\nDecreased availability of methylamines derived from higher degradation activity in upper layers could certainly be a factor underlying the decrease in the relative abundance of SPRUCE 117. However, despite our efforts, we were not able to get conclusive insights from the metabolomic data to explain this response.\n\nAs mentioned above, our efforts to extract RNA from the deepest samples were unsuccessful so we are unable to address questions about the activity of methylotrophic methanogenesis at depth. Nevertheless, the drop in their relative abundance suggest that they are being outcompeted by other members of the community and thus, likely the activity of methylotrophic methanogens drops with warming.\n\nIn Figure 4E we have added the NO - and NH + concentrations and in Figure 4D the abundance of 3 4 substrates and products from deamination reactions.\n\nIn our experience methanotrophs represent a minor fraction of the microbial community as shown in Figure 4A. In fact, we found no transcripts of the methane monooxygenase components in our surface peat dataset. Thus, the amount of methane oxidized by methanotrophs seems to be low. Further, rate measurements of methane oxidation performed in static chambers inside the SPRUCE enclosures support our omics data (Bridgham et al., unpublished).\n\nYes, we agree. That is in fact what we see, they become more active, but don’t change their abundance. In our Hopple et al. (2020) paper, we performed qPCR of mcrA in peat samples as a proxy for methanogen abundance, showing no change in absolute abundance with warming. In this section we want to highlight that the metabolic versatility displayed by methanogens might be helping them to respond and adapt to warming and varying substrate availability.\n\nThis is an excellent question. While it's not easy to answer, it's important to consider that CO₂ produced by anaerobic respiration processes (other than methanogenesis) can serve as a substrate for hydrogenotrophic methanogens and acetogens via the Wood-Ljungdahl pathway. In other words, increased CO₂ production also provides more substrate for hydrogenotrophic methanogens and acetogens, which ultimately generate more acetate, a substrate for acetoclastic methanogens. Thus, it seems plausible that a positive feedback loop within these metabolic networks could lead to greater substrate availability for methanogens while consuming part of the CO₂ produced. In addition, please keep in mind that both CO and CH production are increasing with warming, 2 4 although CH moreso than CO . Thus, it could be that this partitioning of decomposition pathways 4 2 is not yet reflected in the stable microbial communities at the DNA level.\n\nWe thank the reviewer for their positive response and are happy that the reviewer feels that we have strengthened our conclusions.\n\nThank you for your thoughtful and detailed feedback on this point, which has helped us to improve the manuscript. We understand your concern and appreciate your clarification. In response, we have revised the wording in the abstract to specify the abundant and activate methanogens (lines 41-43), providing clarity on their identity and relevance.\n\nThank you for clarifying the importance of presenting this comparison more effectively. We see how the text could be more precise in emphasizing the relevance of the Stordalen Mire site as a well-studied global reference point (Line 183-184) and the significance of Ca. Methanoflorens stordalenmirensis as a dominant methanogen in peatlands (Line 196 and 268). We have revised this section to be more explicit on these points, ensuring that the importance of the Stordalen Mire site and its role as a reference for our contextual comparison are clear. The reviewer’s feedback has greatly improved the framing of this discussion, and we genuinely appreciate their patience and attention to detail in helping us strengthen this part of the manuscript. Thank you again for the valuable input.\n\nWe initially felt this text added meaning, but upon further reflection, we agree that it can be streamlined and replaced with more meaningful information about our functional annotation methods (see comments below). We’ve revised this section to reduce wordiness, and we appreciate your suggestion for improving the manuscript’s conciseness—thank you!\n\nThis should be clearer in the text. To address this, we have added a sentence to the results/discussion section (Line 234-236) that replaces the sentence we decided to remove (above), providing a clearer explanation of the functional assignment in our MAGs. Additionally, we have expanded the methods section (Lines 646-655) to include more detailed information about our functional assignment approach and reorganized some content for clarity.\n\nThank you for your feedback on this relevant point. We have reviewed several articles on the topic, including a recent review (https://doi.org/10.1128/mmbr.00024-22). To the best of our knowledge, methylamines could be obtained from a variety of molecules, including abundant compounds in SPRUCE peat, such as choline. We also speculate that methylated nucleotides, such as methyladenine and methylguanine, could serve as potential precursors of methylamines in peat, given the negative correlation between these compounds and mttB. However, more research is needed to confirm the existence of such a metabolic pathway. In addition to methylamines, methanol could also be produced during the degradation of lignin, which is an abundant component of peat soils. We have added several sentences to the main text (Lines 465-472) to briefly summarize the current knowledge on the sources of methylamines and our hypothesis for SPRUCE peat."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The study “Northern peatland microbial networks exhibit resilience to warming and acquire\nelectron acceptor from soil organic matter” by Duchesneau et al. addresses how microbial\nfunctioning regulate carbon turnover in response to climate change drivers in peatland\necosystem. The authors describe the views of an ecosystem warming experiment to understand\nthe metabolic potential and microbe-metabolite interactions. More specifically they highlight (i)\nthat microbial community is resilient to climate warming and (ii) anaerobic carbon\ndecomposition involved sulfur and nitrogen metabolisms as well as acetogenesis but not\nmethanogenesis. Gene abundance and metabolic analyses were used to support these\nconclusions. The manuscript is well-organized and results are presented in a clear manner.\nThese findings are overall interesting and somewhat novel. However, few questions need to be\naddressed to improve the quality of the results and their interpretations. These are:\n1-Authors have extensively used the expression “microbial networks”. However, the data\npresented is of microbial composition based on metagenome-assembled genomes (MAGs). For\nmicrobial networks some form of association between key members will need to be included. So,\nauthor can either replace this phrase “microbial networks” with microbial composition or include\nsome microbial network data.\n2-Most of the claims of this study rely on the release of sulfate during carbon decomposition\nwhich allows it to function as TEA. Two lines of evidences are presented to support this claim\nshifts in the ratios of (i) choline/choline-O-sulfate and (ii) betC gene abundance. To strengthen\nthese claims regarding sulfate as TEA, it will be important to establish that the release sulfate\nfrom choline is substantial in amount to produce significant effects. Similarly, for betC some\nform of transcript abundance data can be included. As a suggestion, these could be included in\nFig 4C. Strongest evidence would be some sort of betC-sulfate correlations.\n3-Likewise, for the claim regarding denitrification and acetogenesis, their associated metabolites\nand gene transcript levels as well as correlation will be required to validate the claims. These can\nbe added to Fig 4D.\nTaken together above-mentioned additional data will support the hypothesis and claims in the\ntitle. More quantitative estimations of metabolites and association with gene expression and\npathways need to be established.", "reviewer_2": "Northern peatland microbial networks exhibit resilience to warming and acquire electron\nacceptor from soil organic matter\nFirst I would like to express my enthusiasm over the proposed mechanism of organic matter\ncleavage contributing electron acceptors and associated data, potentially helping to explain high\nCO2 to CH4 production and emission ratios. This is a very interesting concept.\nI also very much like the observation of negative correlations of methylated compounds with\nmttB – as the decomposition pathways leading to methylamine production are not well resolved.\nAlso, this is overall a very nicely written manuscript and metagenomic analyses are well done\nand detailed.\nMy concerns are related to the focus on warming and metabolic diversity in functional groups.\nOn these topics, the knowledge basis is pretty strong and pressing questions are at a level\nrequiring combined analysis of metabolic activity (metabolites and transcripts, enzyme assays),\nmetabolic potential and process rates to obtain new mechanistic insight. In this manuscript, much\nof the mentioned key data from the SPRUCE project seems to have been published already,\njudging from the referencing, while the insights from this new dataset – Stable community and\nbroad metabolic potential in microbial taxa – do not offer a substantial advance in our\nunderstanding."}, "author_response": "We sincerely appreciate your thoughtful feedback and constructive suggestions to improve the\nquality of our manuscript. We have carefully considered each of your points and are committed to\naddressing them.\n1. On the use of ‘Microbial networks’: We acknowledge your concern regarding the use of\nthe term “microbial networks.” To clarify, we have revised our terminology to refer to\n“microbial composition” throughout the manuscript.\n2. On sulfate as a Terminal Electron Acceptor (TEA): We agree that strengthening our\nevidence regarding the role of sulfate as a TEA is crucial. In response to your suggestion,\nwe added additional data quantifying SO 2- concentration throughout the depth profile. We\n4\nincluded these results in Figure 4. Additionally, we have added an analysis of other\npotential input sources of SO 2- to the bog via rainwater and SO 2- concentration dynamics\n4 4\nwith changes in water table (Figure S11) as additional lines of evidence suggesting that the\nsulfur cycle is regulated by the microbial community in the S1 Bog at SPRUCE rather than\nby abiotic (rainwater) factors. Metaproteomic and metatranscriptomic data further\nconfirmed the expression of betC and dissimilatory sulfite reductase. Additionally, we\nadded transcript abundance data for betC and examine correlations between betC, activity\nof sulfate-reducers, and sulfate levels to provide a more robust basis for our claims (Figure\nS10).\n3. On denitrification and acetogenesis: To validate our claims concerning denitrification,\nwe added NH + and NO - porewater concentrations to Figure 4E, as you suggested.\n4 3\nPeptides associated with denitrification enzymes were also detected in the metaproteomic\ndata supporting the active state of this pathway. To validate our claims on acetogenesis, we\nincluded data on gene transcript levels at the surface of the bog, metaproteomic results\nbetween 20-70 cm depth and the acetate concentration through the depth profile.\nWe appreciate your insightful suggestions, which have undoubtedly strengthened our manuscript,\nand are committed to revising our work accordingly.\nThank you once again for your valuable input.\n\nWe thank the reviewer for their enthusiasm over the proposed mechanism of organic matter\ncleavage contributing electron acceptors and associated data, potentially helping to explain high\nCO2 to CH4 production and emission ratios. Furthermore, we greatly appreciate the constructive\nfeedback provided by the reviewer which has undoubtedly helped us to clarify and underpin the\nmain conclusions of our manuscript. We agree with the reviewer that combined analyses of\nmultiple data types is essential to obtain new mechanistic insights from reconstructed genomes.\nIn the new version, we have added metatranscriptomic and metaproteomic data along with\nmeasurements of terminal electron acceptors (SO 2-, NO -) and a donor (acetate), to support the\n4 3\nmechanistic metabolic model we propose to explain the response of the peat microbial\ncommunities to warming. **Please note that line numbers refer to the PDF version of the\nmanuscript with track changes.**"}]

Decision letter