From proteins to polysaccharides: lifestyle and genetic evolution of Coprothermobacter proteolyticus

Interplay between traditional Chinese medicine polysaccharides and gut microbiota: The elusive "polysaccharides-bond-bacteria-enzyme" equation

Polysaccharides are one of the most important components of traditional Chinese medicine (TCM) and have been extensively studied for their immunomodulatory properties. The functions and effects of TCM polysaccharides are closely related to the gut microbiota, making the study of their interaction a hot topic in the field of TCM metabolism. This review follows two main inquiries: first, how the gut microbiota breaks down TCM polysaccharides to produce bioactive metabolites; and second, how TCM polysaccharides reshape the gut microbiota as a carbon source. Understanding the interaction mechanism involves a challenging equation of the structural association of TCM polysaccharides with the metabolic activities of the microbiota. This review has meticulously searched, partially organized literature spanning the past decade, that delves into the interaction mechanism between TCM polysaccharides and gut microbiota. It also gives an overview of the complex factors of the elusive "polysaccharides-bond-bacteria-enzyme" equation: the complexity of polysaccharide structures, the diversity of glycosidic bond types, the communal nature of metabolizing microbiota, the enzymes involved in functional degradation by microbiota, and the hierarchical roles of polysaccharide utilization locus and gram-positive PULs. Finally, this review aims to facilitate discussion among peers in the field of TCM microbiota and offers prospects for research in related fields, paving the way for pharmacological studies on TCM polysaccharides and gut microbiota therapeutics, and providing a reference point for further clinical research.

Identification of a deep-branching thermophilic clade sheds light on early bacterial evolution

It has been proposed that early bacteria, or even the last universal common ancestor of all cells, were thermophilic. However, research on the origin and evolution of thermophily is hampered by the difficulties associated with the isolation of deep-branching thermophilic microorganisms in pure culture. Here, we isolate a deep-branching thermophilic bacterium from a deep-sea hydrothermal vent, using a two-step cultivation strategy ("Subtraction-Suboptimal", StS) designed to isolate rare organisms. The bacterium, which we name Zhurongbacter thermophilus 3DAC, is a sulfur-reducing heterotroph that is phylogenetically related to Coprothermobacterota and other thermophilic bacterial groups, forming a clade that seems to represent a major, early-diverging bacterial lineage. The ancestor of this clade might be a thermophilic, strictly anaerobic, motile, hydrogen-dependent, and mixotrophic bacterium. Thus, our study provides insights into the early evolution of thermophilic bacteria.

Integrative meta-omics in Galaxy and beyond

BACKGROUND

'Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as "meta-omics", they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities.

RESULTS

In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum, Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO.

CONCLUSION

Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of 'omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.

Viruses direct carbon cycling in lake sediments under global change

Global change is altering the vast amount of carbon cycled by microbes between land and freshwater, but how viruses mediate this process is poorly understood. Here, we show that viruses direct carbon cycling in lake sediments, and these impacts intensify with future changes in water clarity and terrestrial organic matter (tOM) inputs. Using experimental tOM gradients within sediments of a clear and a dark boreal lake, we identified 156 viral operational taxonomic units (vOTUs), of which 21% strongly increased with abundances of key bacteria and archaea, identified via metagenome-assembled genomes (MAGs). MAGs included the most abundant prokaryotes, which were themselves associated with dissolved organic matter (DOM) composition and greenhouse gas (GHG) concentrations. Increased abundances of virus-like particles were separately associated with reduced bacterial metabolism and with shifts in DOM toward amino sugars, likely released by cell lysis rather than higher molecular mass compounds accumulating from reduced tOM degradation. An additional 9.6% of vOTUs harbored auxiliary metabolic genes associated with DOM and GHGs. Taken together, these different effects on host dynamics and metabolism can explain why abundances of vOTUs rather than MAGs were better overall predictors of carbon cycling. Future increases in tOM quantity, but not quality, will change viral composition and function with consequences for DOM pools. Given their importance, viruses must now be explicitly considered in efforts to understand and predict the freshwater carbon cycle and its future under global environmental change.

Biomethane production from waste activated sludge promoted by sludge incineration bottom ash: The distinctive role of metal cations and inert fractions

Sludge incineration bottom ash (SA), a solid waste generated by incineration of waste activated sludge (WAS), has been demonstrated as an inexpensive additive to increase biomethane production from anaerobic digestion (AD) of WAS. However, how SA improved methanogenic performance of a WAS digester remains elusive. Here, we addressed this question by fractionating the SA into accessible leachate (SA-L) and inert residue (SA-R) and investigating their individual effects. The cumulative biomethane production was increased by 6.7%, 20.2% and 39.6% with addition of SA-L, SA-R and SA, respectively. Mechanistic study showed that release of organic-binding metals (Ca and Fe) from SA dissolution suppressed volatile fatty acids production by increasing the apparent activation energy (AAE) and decreasing the surface binding sites for hydrolytic/acidogenic enzymes during WAS hydrolysis-acidogenesis, while trace elements in SA-L promoted metabolism of methanogens (Methanothermobacter and Methanosarcina). In contrast, the gypsum/silicate-cored SA-R facilitated hydrolysis-acidogenesis with reduced AAE but drastically inhibited methanogenesis due to competition of sulfate-reducing bacteria Thermodesulfovibrio. The comparative analysis of KEGG-based functional genes indicated that the enhanced methane metabolism and reductive CO₂ fixation pathways with SA addition could result from the release of trace elements to support key enzyme activities.

Mechanistic insights into consumption of the food additive xanthan gum by the human gut microbiota

Processed foods often include food additives such as xanthan gum, a complex polysaccharide with unique rheological properties, that has established widespread use as a stabilizer and thickening agent. Xanthan gum's chemical structure is distinct from those of host and dietary polysaccharides that are more commonly expected to transit the gastrointestinal tract, and little is known about its direct interaction with the gut microbiota, which plays a central role in digestion of other dietary fibre polysaccharides. Here we show that the ability to digest xanthan gum is common in human gut microbiomes from industrialized countries and appears contingent on a single uncultured bacterium in the family Ruminococcaceae. Our data reveal that this primary degrader cleaves the xanthan gum backbone before processing the released oligosaccharides using additional enzymes. Some individuals harbour Bacteroides intestinalis that is incapable of consuming polymeric xanthan gum but grows on oligosaccharide products generated by the Ruminococcaceae. Feeding xanthan gum to germfree mice colonized with a human microbiota containing the uncultured Ruminococcaceae supports the idea that the additive xanthan gum can drive expansion of the primary degrader Ruminococcaceae, along with exogenously introduced B. intestinalis. Our work demonstrates the existence of a potential xanthan gum food chain involving at least two members of different phyla of gut bacteria and provides an initial framework for understanding how widespread consumption of a recently introduced food additive influences human microbiomes.

Biosolids for safe land application: does wastewater treatment plant size matters when considering antibiotics, pollutants, microbiome, mobile genetic elements and associated resistance genes?

Soil fertilization with wastewater treatment plant (WWTP) biosolids is associated with the introduction of resistance genes (RGs), mobile genetic elements (MGEs) and potentially selective pollutants (antibiotics, heavy metals, disinfectants) into soil. Not much data are available on the parallel analysis of biosolid pollutant contents, RG/MGE abundances and microbial community composition. In the present study, DNA extracted from biosolids taken at 12 WWTPs (two large-scale, six middle-scale and four small-scale plants) was used to determine the abundance of RGs and MGEs via quantitative real-time PCR and the bacterial and archaeal community composition was assessed by 16S rRNA gene amplicon sequencing. Concentrations of heavy metals, antibiotics, the biocides triclosan, triclocarban and quaternary ammonium compounds (QACs) were measured. Strong and significant correlations were revealed between several target genes and concentrations of Cu, Zn, triclosan, several antibiotics and QACs. Interestingly, the size of the sewage treatment plant (inhabitant equivalents) was negatively correlated with antibiotic concentrations, RGs and MGEs abundances and had little influence on the load of metals and QACs or the microbial community composition. Biosolids from WWTPs with anaerobic treatment and hospitals in their catchment area were associated with a higher abundance of potential opportunistic pathogens and higher concentrations of QACs.

Mantis: flexible and consensus-driven genome annotation

Background

The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources.

Results

We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations.

Conclusions

Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis.

Updates on metaQuantome Software for Quantitative Metaproteomics

metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Comparison of microbial communities during anaerobic digestion of kitchen waste: Effect of substrate sources and temperatures

In this study, batch experiments were conducted to compare the effect of temperature and substrate source on microbial communities in the anaerobic digestion of kitchen waste. The results showed that the microbial communities of anaerobic digestion were not sensitive to varied sources of waste, but shifted with the change in operating temperatures. In the reactors operated at mesophilic conditions, Levilinea, Syntrophomonas, Methanothrix, and Methanosphaerula, etc. were the dominant microbes during the process. While in thermophilic reactors, Levilinea, Ornatilinea, Methanosphaerula and Methanomassiliicoccus, etc. prevailed. Meanwhile, an enrichment in Coprothermobacter, Defluviitoga, Defluviitalea, Tepidimicrobium, Lutispora and Fonticella were observed as the temperature changed from mesophilic to thermophilic, suggesting these genera could be selectively enriched at thermophilic conditions. The results provided fundamental understanding of the microbiology that could support the scale up of food waste anaerobic digestion.

Blackwater biomethane recovery using a thermophilic upflow anaerobic sludge blanket reactor: Impacts of effluent recirculation on reactor performance

Thermophilic anaerobic digestion is a promising process for high-solid blackwater (BW) treatment due to improved hydrolysis rates, high methanogenesis efficiency, and pathogen removal, when compared with mesophilic treatment. In the present work, the effects of effluent recirculation (i.e., mixing) on thermophilic blackwater treatment were studied. A laboratory-scale thermophilic upflow anaerobic sludge blanket reactor was operated with and without effluent recirculation. The methanogenesis efficiency of the BW treatment increased from 45.0 ± 2.9% when effluent recirculation was applied to 56.7 ± 5.5% without effluent recirculation. Without effluent recirculation, the COD accumulation in the bioreactor was reduced from 17.2 to 3.8% and the effluent volatile fatty acids (VFA) concentration was reduced from 0.64 ± 0.18 to 0.15 ± 0.10 g/L. Further, both acetoclastic and hydrogenotrophic methanogenic activity increased from 101.3 ± 10.8 and 93.9 ± 6.1 to 120.4 ± 9.4 and 118.2 ± 13.2 mg CH₄-COD/(gVSS⋅d), respectively, after effluent recirculation was discontinued. The predominant methanogens changed from Methanothermobacter (67%) with effluent recirculation to Methanosarcina (62%) without effluent recirculation. As compared to the effluent recirculation conditions, the enhanced biomethane recovery and treatment performance without effluent recirculation can be attributed to the close proximity of bacteria and archaea groups and the reduced VFA accumulation. Predicted functional gene comparison showed higher prevalence of function for intermediate metabolite transportation (transporters, ATP-binding cassette (ABC) transporters, and two-component system) after discontinuing effluent circulation, which contributed to improved syntrophic propionate oxidation and syntrophic acetate oxidization and enhanced hydrogenotrophic methanogenesis.

Thermogemmata fonticola gen. nov., sp. nov., the first thermophilic planctomycete of the order Gemmatales from a Kamchatka hot spring

A novel aerobic moderately thermophilic bacterium, designated strain 2918^T, was isolated from a terrestrial hot spring of Kamchatka, Russian Federation. Gram-negative, motile, spherical cells were present singly, in pairs, or aggregates, and reproduced by budding. The strain grew at 25-60°C and within a pH range of 5.0-8.0 with an optimum at 54-60°C and pH 7.5. Strain 2918^T did not require sodium chloride or yeast extract for growth. It was a chemoorganoheterotroph, growing on mono-, di- and polysaccharides (starch, lichenan, galactan, arabinan, xanthan gum, beta-glucan). No growth was observed under anaerobic conditions neither in the presence of sulfur, nitrate, or thiosulfate nor without adding any electron acceptor. Major cellular fatty acids were C_18:0 and C_20:0. The respiratory quinone was MK-6. The size of the genome of strain 2918^T was 4.81 Mb. Genomic DNA G+C content was 60.4mol%. According to the 16S rRNA gene sequence and conserved protein sequences phylogenies, strain 2918^T represented a distinct lineage of the order Gemmatales within Planctomycetes. Based on phylogenetic analysis and phenotypic features, the novel isolate was assigned to a novel genus in the Gemmatales for which the name Thermogemmata gen. nov. is proposed. Strain 2918^T (=KCTC 72012^T =VKM B-3161^T) represents its first species Thermogemmata fonticola sp. nov.

Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes

While the field of microbiology has adapted to the study of complex microbiomes via modern meta-omics techniques, we have not updated our basic knowledge regarding the quantitative levels of DNA, RNA and protein molecules within a microbial cell, which ultimately control cellular function. Here we report the temporal measurements of absolute RNA and protein levels per gene within a mixed bacterial-archaeal consortium. Our analysis of this data reveals an absolute protein-to-RNA ratio of 10²–10⁴ for bacterial populations and 10³–10⁵ for an archaeon, which is more comparable to Eukaryotic representatives’ humans and yeast. Furthermore, we use the linearity between the metaproteome and metatranscriptome over time to identify core functional guilds, hence using a fundamental biological feature (i.e., RNA/protein levels) to highlight phenotypical complementarity. Our findings show that upgrading multi-omic toolkits with traditional absolute measurements unlocks the scaling of core biological questions to dynamic and complex microbiomes, creating a deeper insight into inter-organismal relationships that drive the greater community function.

Here, the authors perform a temporal multi-omic analysis of a minimalistic cellulose-degrading and methane-producing consortium at the strain level and estimate protein-to-RNA ratios and RNA-protein dynamics of the community simultaneously over time.

Superior resolution characterisation of microbial diversity in anaerobic digesters using full-length 16S rRNA gene amplicon sequencing

In the past decade, the characterisation of the microbial community in anaerobic digestion was primarily done by using high-throughput short-read amplicon sequencing. However, the short-read approach has inherent primer bias and low phylogenetic resolution. Our previous study using Illumina MiSeq suggested that the heterogeneity of AD microbiome was operation-driven. To advance our knowledge towards the complexity of the AD microbiome, we performed full-length 16S rRNA gene amplicon sequencing using PacBio Sequel for a more accurate phylogenetic identification. To this end, purified DNA samples from 19 global anaerobic digesters were sequenced. Sixteen methanogenic archaea were identified at the species level. Among them, Methanosarcina horonobensis and Methanosarcina flavescens had significant presence under specific operating conditions. Methanothrix concilii presented in all digesters sequenced. Unexpectedly, over 90% of the Smithella detected were closely related to alkane-degrading Smithella strains D17 and M82, not Smithella propionica. Using LEfSe and network analysis, the interspecies relationship between the fermentative and syntrophic bacteria was addressed. Comparison of the short- and long-read sequencing results were performed and discussed. From sample preparation to data analysis, this work characterised the digester microbiomes in a superior resolution.

A Methanogenic Consortium Was Active and Exhibited Long-Term Survival in an Extremely Acidified Thermophilic Bioreactor

Acid crisis characterized by acid accumulation and/or low pH is a common reason for the failure of anaerobic digestion (AD), which is usually applied for wastewater and waste treatment. Acid-tolerant methanogens are rarely reported to be active in the artificial anaerobic digester. In this study, we observed that the thermophilic methanogenesis by a consortium in the form of flocs and not granules could still be recovered during long-term operation at acetate concentration of up to 104 mM and pH 5.5 by adjusting the pH gradually or directly to pH 5.5 or 5.0. The acclimation process involving the gradual decrease in pH could enhance the resistance of the consortium against extreme acidification. The stable isotopic signature analysis of biogas revealed that Methanosarcina, which produced methane through acetoclastic methanogenesis (AM) pathway, was the predominant methane producer when the pH was decreased gradually to 5.0. Meanwhile, the abundance of Coprothermobacter increased with a decrease in pH. Contrastingly, when directly subjected to an environment of pH 5.5 and 104 mM acetate (15.84-mM free acetic acid) after a 42-day lag phase, Methanothermobacter was the predominant methanogen. Methanothermobacter initiated methane production through the hydrogenotrophic pathway and formed syntrophic relationship/consortium with the potential acetate-oxidizing bacteria, Thermacetogenium and Coprothermobacter. Comparative metagenomic and metatranscriptomic analysis on this self-adapted and acid-tolerant consortium revealed that the genes, such as GroEL, DnaK, CheY, and flagellum-related genes (FlaA, FlgE, and FliC) from Anaerobaculum, Thermacetogenium, and Coprothermobacter were highly overexpressed in response to system acidification. Microbial self-adaptation patterns (community structure adjustment, methanogenesis pathway shift, and transcriptional regulation) of thermophilic methanogenic consortium to gradual and sudden acidification were evaluated by integrated stable isotopic signature and comparative meta-omic approaches. The study elucidated the acid-resistant mechanism of thermophilic methanogenic consortium and deepened our knowledge of the function, interaction, and microbial characteristics of Methanosarcina, Methanothermobacter, and Coprothermobacter under extreme acidic environment.