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Methanobrevibacter smithii is the human gut's main methane-maker, and the enzyme it uses to make methane runs on a nickel cofactor called coenzyme F430. Without nickel, there is no methanogenesis.
Methanobrevibacter smithii is the dominant methanogenic archaeon of the human gut. It consumes the hydrogen that bacteria produce during fermentation and converts it to methane, which improves fermentation efficiency but is also linked to obesity and to methane-associated constipation. Its methane-making machinery is fundamentally nickel-dependent, running on the nickel cofactor coenzyme F430.
Microbiome-targeted interventions (MBTIs) are validated using a dual-evidence logical framework. First, the intervention must realign the condition’s microbiome signature by increasing beneficial taxa that are consistently depleted and reducing pathogenic taxa that are consistently enriched. Second, the intervention must demonstrate measurable clinical benefit. Concordance of these effects in the same context validates the intervention as an MBTI and supports the clinical relevance of the microbiome signature.
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease, four years before the first published case study.
Read MoreMethanobrevibacter smithii is the dominant methanogenic archaeon in the human gut, not a bacterium and not a classical pathogen. It occupies the end of the fermentation chain: it consumes the molecular hydrogen produced when gut bacteria ferment fiber and uses it to reduce carbon dioxide to methane.[1] By removing hydrogen, it makes bacterial fermentation more efficient, which raises the energy the host extracts from food and links methanogen carriage to metabolism and body weight.[1] On this database it appears as a differentially abundant taxon across many human microbiome studies.
M. smithii is a natural fit for this database's metal lens because its defining metabolism is nickel-dependent. The terminal enzyme of methanogenesis, methyl-coenzyme M reductase, carries nickel at its active site in the form of coenzyme F430, a nickel tetrapyrrole, and the archaeon also uses [NiFe] hydrogenases to take up hydrogen.[2] Nickel is therefore not a virulence accessory here but the cofactor its entire energy metabolism runs on.
M. smithii is a small, Gram-variable, non-motile coccobacillus-to-short-rod archaeon adapted to the strictly anaerobic colon.[1] It bears surface adhesin-like proteins that help it associate with bacterial fermenters, positioning it to capture their hydrogen.
M. smithii is a keystone hydrogen sink. Fermentation by gut bacteria produces hydrogen, which accumulates and thermodynamically slows further fermentation unless it is removed; by consuming that hydrogen for methanogenesis, M. smithii keeps the fermentation chain running and shapes which bacterial pathways dominate.[1] The methane it produces slows intestinal transit, which is why methane levels track with constipation-predominant symptoms.[3]
M. smithii's functions are metabolic rather than virulent.
| Feature | Description and role |
|---|---|
| Methanogenesis (MCR / coenzyme F430) | Reduces carbon dioxide with hydrogen to make methane; the terminal enzyme depends on the nickel cofactor coenzyme F430 (see Metallomics).[2] |
| Hydrogen syntrophy | Consumes bacterial hydrogen, relieving thermodynamic feedback and boosting fermentation efficiency and host energy harvest.[1] |
| [NiFe] hydrogenases | Nickel-dependent enzymes that take up molecular hydrogen to feed methanogenesis.[2] |
| Transit modulation | Methane output slows gut motility, associating higher methanogen loads with constipation.[3] |
M. smithii is one of the clearest cases where nickel is essential rather than incidental: its core energy metabolism cannot proceed without it.
| Metal / ion | Key features in M. smithii |
|---|---|
| Nickel (Ni) | The methanogenesis terminal enzyme, methyl-coenzyme M reductase, holds nickel in coenzyme F430, a nickel tetrapyrrole at its active site; [NiFe] hydrogenases add a second nickel requirement, so nickel availability gates methane production.[2] |
| Hydrogen economy | Nickel enzymes let the archaeon convert bacterial hydrogen into methane, coupling the gut's metal supply to its fermentation efficiency and gas output.[2][1] |
Because it sits at the hub of gut fermentation, M. smithii is associated with several conditions, usually as a marker of an altered fermentation or transit state rather than a direct cause.
| Association | Direction and interpretation |
|---|---|
| Energy harvest and obesity | By improving fermentation efficiency, methanogen carriage has been linked to greater energy extraction and body-weight regulation.[1] |
| Methane, constipation, and IBS | Methane slows transit; intestinal methanogen overgrowth is linked to constipation-predominant irritable bowel syndrome.[3] |
| Colorectal cancer | Multi-cohort data found M. smithii enriched in colorectal cancer while several other methanogens were depleted, and methanogens co-occur with butyrate-producing bacteria, a nuanced, direction-mixed signal.[4] |
M. smithii is a normal commensal, not an infection to clear; the entries below are classified by our validation method and are not medical advice. Modulation is relevant mainly when methane overgrowth drives constipation.
| Intervention | Class | Status |
|---|---|---|
| Methane-directed therapy for methanogen overgrowth | Clinical | Validation In Progress |
| Fermentable-substrate (dietary) modulation | Diet | Validation In Progress |
| Nickel availability as a methanogenesis lever | Concept | Validation In Progress |
| Intervention | Mechanism |
| Methane-directed therapy | When intestinal methanogen overgrowth drives constipation, reducing methanogen load lowers methane and can improve transit.[3] |
| Dietary modulation | Changing fermentable substrate alters the hydrogen supply that M. smithii depends on, indirectly shifting methane output.[1] |
| Nickel availability | Because methanogenesis requires the nickel cofactor F430, nickel supply is a theoretical lever on methane production.[2] |
Where Methanobrevibacter smithii (NCBI:txid2173) appears as a differentially abundant taxon across the Microbiome Medicine corpus. Each row aggregates every experiment in which the archaeon moved in a given condition; direction is its change in the case/exposure group, and grade is the strongest single study's methodology weight (A·D·S·C·R), the same engine that grades every signature on this site.
Across 28 conditions and 28 studies, the signal is genuinely mixed: enriched in 12, depleted in 13, and direction-conflicting in 3 (directional agreement 0.61). Because M. smithii is a variably detected archaeon whose abundance tracks fermentation and transit state, its differential signal often marks a functional shift rather than a fixed role, so the aggregate evidence tier is Low.
How to read these. M. smithii is an archaeon, so amplicon and shotgun pipelines built for bacteria can under- or over-detect it depending on their reference database. Its abundance also swings with diet, hydrogen supply, and transit time. A differential signal is best read as a change in the gut's fermentation state, which is why direction can conflict between cohorts and the aggregate tier stays Low.
Internal summaries of the 28 studies we reviewed in which M. smithii was a differential taxon across this corpus.
Ancient DNA (aDNA) analysis of archaeological dental calculus has provided a wealth of insights into ancient health, demography and lifestyles. However, the workflow for ancient metagenomics is still evolving, raising concerns about reproducibility. Few systematic investigations have examined how DNA extraction methods and library preparation protocols influence ancient oral microbiome recovery, despite evidence from modern populations suggesting that they do. This leaves a gap in our understanding of how wet-lab protocols impact aDNA recovery from dental calculus. In this study, we apply two DNA extraction and two library preparation methods in the aDNA field on dental calculus samples from Hungary and Niger. Samples from each context have similar chronological ages, but differences in their levels of aDNA preservation are notable, providing additional insights into how the efficacy of wet-lab protocols is impacted by sample preservation. Several metrics were employed to assess intra- and inter-sample variability, such as DNA fragment length recovery, GC content, clonality, endogenous content, DNA deamination and microbial composition. Our findings indicate that both DNA extraction and library preparation protocols can considerably impact ancient DNA recovery from archaeological dental calculus. Furthermore, no single protocol consistently outperformed the others across all assessments, and the effectiveness of specific protocol combinations depended on the preservation of the sample. These findings highlight the challenges of meta-analyses and underscore the need to account for technical variability. Lastly, our study raises the question of whether the field should strive to standardise methods for comparability or optimise protocols based on sample preservation and specific research objectives.
This study examined how ethnicity and geography each relate to fecal microbiota composition and dietary habits among Tibeto-Burman-speaking hill-tribe populations in Northern Thailand. Researchers used quantitative PCR to characterize gut microbiota and applied multivariate statistical methods, including multiple factor analysis and partial least squares discriminant analysis, to link microbiota composition with ethnicity, geographic location, dietary behaviors, and other host variables. The goal was to disentangle whether ethnic identity or regional residence is the stronger driver of gut microbiota variation, a question the abstract notes is understudied in Thailand.
The study population consisted of 102 individuals from Tibeto-Burman hill-tribe ethnic groups, specifically the Akha, Lahu, and Lisu peoples. These participants resided in two provinces of Northern Thailand, Chiang Mai and Chiang Rai, allowing comparisons both across ethnic groups and across geographic locations. The abstract does not provide further demographic detail such as age or sex distribution.
Both ethnicity and geography were associated with gut microbiota composition and dietary patterns, but geography showed a stronger association with microbiota variation than ethnicity did. Ethnicity, by contrast, was primarily linked to differences in dietary habits rather than directly to microbiota composition. Notably, microbiota profiles were more similar among different ethnic groups sharing the same location than among the same ethnic group split across different regions, and the diet-microbiota relationship itself varied by ethnic and geographic group. Host factors other than diet, ethnicity, and geography had a comparatively minor influence on microbiota composition.
The findings suggest that shared environment and geography can outweigh shared ethnic ancestry in shaping the gut microbiota, at least among closely related hill-tribe populations living in the same region. This implies that microbiome studies should account for local geographic and environmental exposures rather than treating ethnicity alone as the key explanatory variable. The results also highlight that diet, rather than ethnicity per se, may be the more direct pathway linking population identity to microbiota differences, which is relevant for designing future studies of diet-microbiome relationships in diverse populations.
Colorectal cancer (CRC) is a significant global health burden, ranking amongst the top causes of cancer-associated mortality. Emerging evidences implicate gut microbiota as a prominent mediator of cell signalling, immune, and metabolic pathways in the pathophysiology of CRC.
We analysed 16S rRNA amplicon sequencing data (PRJEB7774) from faecal samples of 46 CRC patients and 63 healthy controls to assess shifts in microbial composition, diversity, and biomarker taxa. Differential abundances of microbiota were determined using Linear Discriminant Analysis Effect Size (LEfSe) and Random Forest (RF) models. Host-microbiota interactions were explored using the Human Microbiome Affect the Host Epigenome (MIAOME) and Host Genetic and Immune Factors Shaping Human Microbiota (GIMICA) databases, with key host genes validated using Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) datasets. Functional enrichment analyses were performed to uncover associated biological processes and pathways.
CRC samples exhibited significantly reduced alpha diversity and distinct beta diversity profiles, compared to controls. Taxonomic profiling revealed an enrichment of potentially pathogenic bacteria, including Prevotella copri, Methanobrevibacter smithii, Bacteroides eggerthii, and Dialister invisus, and depletion of beneficial microbes such as Bifidobacterium animalis and Ruminococcus sp. Predicted host-microbe interactions highlighted associations between key microbial biomarkers and inflammation-related genes (CD44, CXCL8, DUSP16, FOXP3, IFNGR2, IL18), all significantly overexpressed in CRC samples. Enrichment analyses linked these genes to immune pathways, including NF-κB, TLR and cytokine signalling.
Our study reveals a distinct gut microbiota signature in CRC and suggests functional interactions between microbial dysbiosis and host inflammatory responses. These findings emphasize the potential of microbiota-based interventions and microbial metabolites as adjunctive strategies for the management of CRC.
This study analyzed a newly built resource called curatedMetagenomicData (cMD) 3, a uniformly processed collection of over 22,000 human microbiome samples with manually curated metadata. The researchers combined data across 94 studies and 42 countries to make large-scale meta-analysis possible, something that had been difficult due to a lack of standardization across public datasets. Using this resource, they searched for microbial species and functions associated with host traits and disease status. They also developed a new metric, the oral enrichment score (OES), based on the relative abundance in the gut of bacteria that are typically found in the oral cavity rather than the gut.
The analysis drew on more than 22,000 human microbiome samples aggregated from 94 separate studies conducted across 42 countries. This is a public, pooled metagenomic dataset rather than a single original cohort recruited for this study. The abstract does not give specific demographic breakdowns beyond noting that sex, age, body mass index, and disease status were among the host variables examined across this large, internationally diverse sample collection.
The meta-analysis identified hundreds of microbial species and thousands of microbial functions that were significantly associated with a person's sex, age, body mass index, and disease status. The team catalogued these associations as a reference resource for the field. Most notably, they found that a higher oral enrichment score (OES), meaning greater relative abundance of oral-type bacteria in the gut, was a consistent feature of individuals with disease. The overall patterns identified across the dataset were described as modest but widely shared across the many studies pooled together.
The findings suggest that OES can serve as a simple, quantifiable signal of altered gut microbiome health, since oral bacteria showing up in the gut appears to track with disease status across many different conditions and populations. Because cMD 3 is described as reproducible and readily updatable, it offers an ongoing reference dataset that other researchers can use to validate microbiome-disease associations. This kind of large, standardized meta-analysis approach could help establish more generalizable, cross-study biomarkers of microbiome health rather than relying on findings from single, smaller cohorts.
This study tested whether Parkinson's disease alters the periodontitis-associated oral microbiome. Researchers collected unstimulated saliva samples and stool samples and profiled microbial communities using next-generation sequencing of the 16S ribosomal RNA gene (V1-V3 regions). Clinical, periodontal, and neurological parameters were recorded, including the severity of Parkinson's disease motor dysfunction.
Three groups were enrolled: patients with periodontitis and Parkinson's disease (PA+P), patients with periodontitis but without Parkinson's disease (P), and systemically and periodontally healthy individuals used as controls (HC). The abstract does not give exact group sizes. The PA+P group had mild to moderate motor dysfunction, and plaque scores were comparable between the PA+P and P groups, indicating similarly effective oral hygiene.
Beta diversity in saliva differed significantly between HC and PA+P, between HC and P, and between P and PA+P groups, showing that both periodontitis and the presence of Parkinson's disease reshape the oral microbial community. Saliva and fecal microbial profiles were distinct from each other. Mycoplasma faucium, Tannerella forsythia, Parvimonas micra, and Saccharibacteria (TM7) were increased in the P group, while Prevotella pallens, Prevotella melaninogenica, and Neisseria multispecies were more abundant in the PA+P group. In fecal samples from the P group, Ruthenibacterium lactatiformans, Dialister succinatiphilus, Butyrivibrio crossotus, and Alloprevotella tannerae were detected.
The findings support the hypothesis that Parkinson's disease is associated with a distinct periodontitis-related oral microbial signature, separate from periodontitis alone. Because oral and gut microbial profiles diverged between groups despite similar oral hygiene, the results suggest disease-associated shifts rather than simple hygiene differences drive these community changes. This points to the oral-gut microbiome axis as a potential area for further investigation in Parkinson's disease and periodontitis.
This study examined the gut microbiome's association with type 2 diabetes (T2D) by analyzing shotgun metagenomic sequencing data. Researchers looked beyond species-level associations to strain-level and phylogenetic diversity within species, aiming to identify specific microbial features and functional pathways linked to T2D. The analysis also explored community-level functional changes, such as perturbations in glucose metabolism, and mechanisms like horizontal gene transfer that could explain strain-specific effects on metabolic risk.
The study drew on 8,117 shotgun metagenomes pooled from 10 cohorts spanning the United States, Europe, Israel, and China. These cohorts included individuals with type 2 diabetes, prediabetes, and normoglycemic (non-diabetic) status. The abstract does not provide individual-level demographic details, but the analysis represents a large, multi-national, cross-cohort metagenomic dataset.
Dysbiosis in 19 phylogenetically diverse species was associated with T2D at a false discovery rate below 0.10, including enrichment of Clostridium bolteae and depletion of Butyrivibrio crossotus. These microorganisms contributed to community-level functional changes, such as perturbations in glucose metabolism, that may underlie T2D pathogenesis. The study further identified within-species phylogenetic diversity across 27 species, such as Eubacterium rectale, that explained inter-individual differences in T2D risk, with some effects attributable to strain-specific gene carriage involved in horizontal gene transfer and other novel biological processes.
By resolving microbial associations with T2D down to the strain level, this work helps explain why prior species-level findings have been inconsistent across studies. Identifying strain-specific gene carriage and functional pathways, including those affecting glucose metabolism, offers a clearer mechanistic basis for how gut microbes may contribute to T2D pathogenesis. This strain-resolved approach could inform future efforts to develop microbiome-based biomarkers or targeted interventions for metabolic disease risk.
This study examined how environmental, socioeconomic, and health factors relate to gut microbiome composition at both the species and strain level. Researchers used deeply sequenced metagenomic data to identify associations between bacterial species and a range of host phenotypes and situational factors. They also performed a meta-analysis of species-level profiles across multiple datasets to look for consistent patterns, such as links to body mass index.
The study drew on a community-based cohort of 1,871 people living in 19 isolated villages in the Mesoamerican highlands of western Honduras. This is a non-industrialized, geographically isolated population, a setting the authors note remains uncommon in deep gut microbiome sequencing studies. Additional comparisons were made using species-level profiles from other, unspecified datasets as part of a meta-analysis.
Socioeconomic factors accounted for 51.44% of all associations found between the gut microbiome and human phenotypes, making them the dominant category of influence. Meta-analysis across datasets identified several bacterial species associated with body mass index, consistent with prior research. Incorporating strain-level phylogenetic information changed the overall picture of host-microbiome relationships, especially for factors like household wealth, where wealthier individuals were found to harbor different strains of Eubacterium rectale than less wealthy individuals.
The findings suggest that socioeconomic circumstances are a major driver of gut microbiome variation, potentially more so than many other individual health factors. The demonstration that strain-level differences (not just species presence) track with wealth indicates that species-level analysis alone can miss biologically meaningful variation. The authors conclude that gut microbiome surveillance in such populations could help illuminate broader patterns relevant to both individual and public health.
The human gut microbiome composition has been linked to Parkinson's disease (PD). However, knowledge of the gut microbiota on the genome level is still limited. Here we performed deep metagenomic sequencing and binning to build metagenome-assembled genomes (MAGs) from 136 human fecal microbiomes (68 PD samples and 68 control samples). We constructed 952 non-redundant high-quality MAGs and compared them between PD and control groups. Among these MAGs, there were 22 different genomes of Collinsella and Prevotella, indicating high variability of those genera in the human gut environment. Microdiversity analysis indicated that Ruminococcus bromii was statistically significantly (p < 0.002) more diverse on the strain level in the control samples compared to the PD samples. In addition, by clustering all genes and performing presence-absence analysis between groups, we identified several control-specific (p < 0.05) related genes, such as speF and Fe-S oxidoreductase. We also report detailed annotation of MAGs, including Clusters of Orthologous Genes (COG), Cas operon type, antiviral gene, prophage, and secondary metabolites biosynthetic gene clusters, which can be useful for providing a reference for future studies.
This study investigated the relationship between periodontitis and the oral-gut axis in first-trimester pregnant women using integrative microbiome and metabolome profiling. Researchers combined 16S rRNA sequencing of subgingival plaque, saliva, and stool with untargeted metabolomics of serum and other sample types, alongside clinical traits. The goal was to characterize how oral dysbiosis linked to periodontitis translates into distal gut microbial and metabolic changes during early pregnancy.
The cohort consisted of 54 Chinese pregnant women sampled at the first trimester. Of these, 31 women had maternal periodontitis (the Perio group) and 23 women served as Non-Perio controls. Subgingival plaque, saliva, serum, and stool samples were collected from each participant for multi-omics analysis.
The study identified a novel bacterial distinguisher, Coprococcus, in the feces of women with periodontitis, and this genus was associated with subgingival periodontopathogens. Notably, Coprococcus behaved differently from other fecal genera within the Lachnospiraceae family. The ratio of fecal Coprococcus to Lachnoclostridium was able to discriminate between the Perio and Non-Perio groups, indicating a measurable gut-level signature tied to oral disease status.
The findings support the existence of a functional oral-gut axis through which periodontitis in early pregnancy is reflected in distinct gut microbial and metabolic alterations. Identifying the fecal Coprococcus to Lachnoclostridium ratio as a discriminating feature suggests potential translational value as a biomarker linking oral and gut health in pregnant women. This integrative multi-omics approach may help clarify how periodontitis contributes to adverse pregnancy outcomes via systemic, gut-mediated pathways.
This study examined whether exercise intensity changes gut microbiome composition and function in low active people with type 2 diabetes. It compared two 8-week supervised exercise programs: combined aerobic and resistance moderate intensity continuous training (C-MICT) versus combined aerobic and resistance high-intensity interval training (C-HIIT). Faecal samples were collected before and after the intervention and analyzed with metagenome shotgun sequencing to assess microbial taxa, metabolic pathways, and short-chain fatty acids.
The participants were low active adults with type 2 diabetes enrolled in a sub-study of the Exercise for Type 2 Diabetes Study, a single centre, prospective, randomised controlled trial. A total of 12 participants completed the 8-week intervention, randomised to either the C-MICT or C-HIIT group. This is a small clinical cohort rather than a large population sample.
Post-exercise alpha-diversity differed between the two intensity groups, as did the relative abundance of specific taxa (p < .05). Lower exercise intensity (C-MICT) was associated with higher post-exercise relative abundance of Bifidobacterium, Akkermansia muciniphila, and the butyrate-producers Lachnospira eligens, Enterococcus spp., and Clostridium Cluster IV. The abstract also indicates that other butyrate-producers, from the orders Eryspelothrichales and Oscillospirales, along with a methane producer, showed a different pattern, though the specific direction of that difference is cut off in the provided text.
The findings suggest that exercise intensity, not just exercise participation, can shape gut microbiome composition in people with type 2 diabetes. Because lower intensity training was linked to higher levels of several butyrate-producing commensals, exercise prescription choices may carry distinct implications for gut microbial and metabolic health in this population. Larger studies would be needed to confirm these intensity-specific effects and their functional consequences.
Non-alcoholic fatty liver disease (NAFLD) is the most common cause of liver disease. Increasing evidence indicates that the gut microbiota can play an important role in the pathophysiology of NAFLD. Recently, several studies have tested the predictive value of gut microbiome profiles in NAFLD progression; however, comparisons of microbial signatures in NAFLD or non-alcoholic steatohepatitis (NASH) have produced discrepant results, possibly due to ethnic and environmental factors. Thus, we aimed to characterize the gut metagenome composition of patients with fatty liver disease.
Gut microbiome of 45 well-characterized patients with obesity and biopsy-proven NAFLD was evaluated using shot-gun sequencing: 11 non-alcoholic fatty liver controls (non-NAFL), 11 with fatty liver, and 23 with NASH.
Our study showed that Parabacteroides distasonis and Alistipes putredenis were enriched in fatty liver but not in NASH patients. Notably, in a hierarchical clustering analysis, microbial profiles were differentially distributed among groups, and membership to a Prevotella copri dominant cluster was associated with a greater risk of developing NASH. Functional analyses showed that although no differences in LPS biosynthesis pathways were observed, Prevotella-dominant subjects had higher circulating levels of LPS and a lower abundance of pathways encoding butyrate production.
Our findings suggest that a Prevotella copri dominant bacterial community is associated with a greater risk for NAFLD disease progression, probably linked to higher intestinal permeability and lower capacity for butyrate production.
This study examined how the gut microbiota changes with age and how those age-related changes relate to colorectal cancer (CRC). The researchers analyzed 11 metagenomic data sets, correcting for batch effects, then compared species composition and abundance across three age groups in both healthy individuals and CRC samples. They used LEfSe analysis to identify bacteria whose relative abundance differed by age group, then built age-prediction and CRC-risk-prediction models from those age-differentiated species.
The abstract does not report a single original cohort with a specific sample size. Instead, the study population consisted of previously published metagenomic samples drawn from 11 combined data sets accessed through the curatedMetagenomicData R package, covering both healthy individuals and people with colorectal cancer. These samples were stratified into three age groups for comparison.
The structure and composition of the gut microbiota differed significantly across the three age groups in both healthy and CRC samples. Bacteroides vulgatus abundance was lower in the older group compared to the other two groups, while Bacteroides fragilis abundance increased with aging. The researchers also identified seven bacterial species whose abundance rose with age, and found that abundance of pathogenic bacteria, including Escherichia coli, increased as well.
By linking specific age-associated shifts in gut microbiota, such as declining Bacteroides vulgatus and rising Bacteroides fragilis and Escherichia coli, to both healthy aging and CRC samples, this work suggests the microbiome could serve as a biomarker for biological aging and CRC risk. The construction of age-prediction and CRC-risk-prediction models based on these age-differentiated bacteria points toward potential microbiota-based tools for estimating cancer risk as people age. This approach could inform future screening or risk-stratification strategies that account for age-related microbial changes.
This study investigated whether gut bacteria that produce endogenous ethanol contribute to nonalcoholic steatohepatitis (NASH). Researchers measured fecal ethanol, glucose, total proteins, triglyceride and total cholesterol using high-performance liquid chromatography. They also characterized the gut microbiota using microbial culturomics and 16S rRNA metagenomics targeting the V3V4 hypervariable region to identify which viable bacteria and genetic signatures were enriched in NASH.
The study compared fecal samples from 41 patients with NASH to 24 controls without the disease. This case-control design allowed direct comparison of biochemical parameters and microbial composition between diseased and healthy states. No further demographic details are given in the abstract.
Fecal ethanol and glucose were significantly elevated in NASH patients compared to controls, while triglyceride, total cholesterol and total protein levels did not differ. Culturomics identified enrichment of the ethanol-producing bacteria Enterocloster bolteae and Limosilactobacillus fermentum in NASH samples. 16S rRNA sequencing confirmed enrichment of ethanol-producing bacteria including L. fermentum, corroborating the culture-based findings with independent genetic evidence.
The findings support endogenous ethanol production by specific gut bacteria as a plausible mechanistic contributor to NASH, independent of dietary alcohol intake. By combining culturomics with 16S metagenomics, the study strengthens the case that microbially derived ethanol, rather than only enterobacteria or yeasts previously implicated, may drive liver injury in NASH. This suggests ethanol-producing bacteria such as L. fermentum and E. bolteae could become targets for diagnostic or therapeutic strategies aimed at reducing hepatic damage in NASH patients.
This study examined whether gut microbiota composition influences the return of estrus in post-weaning sows by regulating the metabolism of sex steroid hormones. The researchers used 16S rRNA gene sequencing, metagenomic sequencing, and fecal metabolome analysis to link microbial community changes to hormone-related outcomes. They specifically looked at how shifts in gut bacterial species affect the functional capacity for steroid hormone biosynthesis within the gut microbiome.
The study analyzed 207 fecal samples from well-phenotyped sows using 16S rRNA gene sequencing to find associations between gut microbes and estrus return. A subset of 85 fecal samples underwent metagenomic sequencing to identify specific bacterial species tied to estrus return status. The findings were then confirmed in a separate validation cohort of sows.
Metagenomic analysis identified 37 bacterial species significantly associated with estrus return after weaning. Sows that returned to normal estrus had increased abundances of L. reuteri and P. copri, and decreased abundances of B. fragilis, S. suis, and B. pseudolongum, compared to non-returning sows. These microbial shifts significantly altered the gut microbiome's functional capacity for steroid hormone biosynthesis, and metabolome analysis found significant differences in sex steroid hormones and related compounds between normal and non-return sows.
By integrating differential bacterial species, metagenomic function, and fecal metabolome data, the study provides evidence that gut microbiota, including reduced B. fragilis abundance, is linked to normal post-weaning estrus return through effects on sex steroid hormone metabolism. This suggests that specific gut bacteria could serve as biomarkers or targets for improving reproductive efficiency in sows. The findings point toward potential microbiome-based strategies to address delayed or absent estrus return, a costly problem in swine production.
We explored the fecal microbiota in pediatric patients <18 years of age with treatment-naïve IBD (80 Crohn’s disease (CD), 27 ulcerative colitis (UC)), in 50 non-IBD patients with gastrointestinal symptoms without inflammation and in 75 healthy children. Using a targeted qPCR approach, the quantities of more than 100 different bacterial species were measured. Results: The bacterial abundance was statistically significantly reduced in the IBD and non-IBD patients compared to the healthy children for several beneficial species. The CD patients had a lower abundance of Bifidobacterium species compared to the UC patients, and the IBD patients in need of biologic therapy had a lower abundance of butyrate producing bacteria. Based on the abundance of bacterial species at diagnosis, we constructed Diagnostic, Phenotype and Prognostic Indexes. Patients with a high Diagnostic Index had 2.5 times higher odds for having IBD than those with a lower index. The CD patients had a higher Phenotype Index than the UC patients. Patients with a high Prognostic Index had 2.1 higher odds for needing biologic therapy compared to those with a lower index. Conclusions: The fecal abundance of bacterial species can aid in diagnosing IBD, in distinguishing CD from UC and in identifying children with IBD in need of biologic therapy.
This study examined the gut microbiome in Parkinson's disease (PD) using large-scale, high-resolution shotgun metagenomic sequencing of fecal DNA. The researchers applied uniform, standardized methods throughout, followed by metagenome-wide association studies requiring agreement between two independent statistical methods (ANCOM-BC and MaAsLin2) before declaring a disease association. They also conducted network analysis to identify clusters of co-occurring microbial species and functional profiling to characterize microbial genes and pathways.
The study enrolled 490 individuals with Parkinson's disease and 234 control individuals. Fecal samples from this cohort underwent deep shotgun sequencing to generate the metagenomic data analyzed in the study. The abstract does not provide further demographic detail on the participants.
Over 30 percent of the species, genes, and pathways tested showed altered abundances in Parkinson's disease, indicating widespread dysbiosis. PD-associated species organized into polymicrobial clusters that grew, shrank, or competed together rather than acting independently. The PD microbiome was disease permissive: it showed overabundance of pathogens and immunogenic components, dysregulated neuroactive signaling, an excess of molecules that induce alpha-synuclein pathology, and overproduction of toxicants, alongside a reduction in anti-inflammatory and neuroprotective factors that would otherwise support recovery.
By validating in human PD patients findings previously seen only in experimental models, this study strengthens the case that the gut microbiome contributes to multiple disease mechanisms in Parkinson's disease. The reconciliation of prior human PD microbiome literature helps resolve inconsistencies across earlier studies and establishes a more standardized foundation for future research. The reduction in anti-inflammatory and neuroprotective microbial factors points to a loss of protective capacity that may limit the body's ability to counteract disease processes, suggesting the microbiome as a potential target for future mechanistic and therapeutic investigation.
This study examined the interaction between the gut microbiota and colorectal cancer (CRC) using metagenomic data retrieved from the GMrepo database. Researchers analyzed differences in gut microbiota distribution between CRC cases and controls at the species level, built a co-occurrence network, and assessed microbial interactions with environmental factors. Random forest models were then used to identify significant microbial biomarkers capable of differentiating CRC samples from control samples.
The analysis drew on 709 metagenomic samples from six projects in the GMrepo database. After matching, the study population consisted of 86 CRC patients and 86 matched healthy controls from six countries. A total of 484 microbial species and 166 related genera were analyzed across these paired samples.
The study confirmed previously recognized associations between Fusobacterium nucleatum and species within the genera Peptostreptococcus, Porphyromonas, and Prevotella with colorectal cancer. It also identified new associations involving the novel species Parvimonas micra and Collinsella. These findings, generated through a paired-sample design and machine learning models, point to an expanded panel of species-level microbial signals tied to CRC status.
By quantifying and visualizing microbiota-CRC interactions across a multi-country dataset, this work supports the development of a more precise, species-level microbiota panel for CRC diagnosis. The identification of novel associated species such as Parvimonas micra and Collinsella suggests additional candidate biomarkers beyond the well-established Fusobacterium nucleatum signal. This paired-sample, network-based approach offers a template for refining microbial diagnostic panels in colorectal cancer research.
This study examined the gut microbial composition and structure associated with colorectal cancer (CRC) across populations from different geographic regions. Researchers used whole-genome sequencing (WGS) data, annotated with MetaPhlAn2, to determine species and genus level relative abundance. They applied PCA and LEfSe analysis to compare microbial differences between regional datasets and used Spearman correlation analysis to examine relationships among CRC-associated differential species. The ultimate goal was to build and verify CRC risk prediction models based on these regional microbial differences.
The analysis drew on a metagenomic dataset of 601 samples collected from six countries, sourced from the GMrepo and NCBI databases. This represents a secondary analysis of previously generated whole-genome sequencing data rather than a newly recruited clinical cohort. The abstract does not specify individual patient demographics such as age or sex, only the multi-country, multi-sample composition of the dataset.
The composition of the intestinal bacterial community varied by region, and the specific differential intestinal bacteria linked to CRC were inconsistent from country to country. Despite this regional variability, the researchers identified a common diversity of bacteria shared across all six countries, including Peptostreptococcus. These findings indicate that CRC-associated microbiota show both a conserved core signature and considerable geographic variation.
The findings suggest that CRC risk prediction models based on gut microbiota may need to account for regional differences in microbial composition rather than assuming a universal signature. Identifying bacteria that are consistently associated with CRC across diverse populations, such as Peptostreptococcus, could support more broadly generalizable diagnostic or risk-assessment tools. At the same time, the region-specific differences highlight the importance of validating any microbiome-based CRC model within the population it will be applied to.
This study examined whether the intestinal microbiome influences clinical outcome and treatment side effects in early breast cancer. Researchers used shotgun metagenomics to characterize fecal microbiota composition and paired this with plasma metabolomics. They looked at associations between the gut microbiota, measured at baseline and after chemotherapy, and both breast cancer prognosis and therapy-induced side effects. Findings were then tested for clinical relevance in an immunocompetent mouse model colonized with patient microbiota and challenged with mouse breast cancer and chemotherapy.
The human cohort consisted of 76 early breast cancer patients contributing 121 fecal specimens, with 45 patients providing paired samples collected before and after chemotherapy. These patients were enrolled in the CANTO prospective study, which was designed to record side effects associated with clinical management of breast cancer. The findings were further validated in immunocompetent mice colonized with breast cancer patient microbiota.
Specific gut commensals were found to be overabundant in breast cancer patients compared with healthy individuals. These overabundant commensals were associated with worse breast cancer prognosis. Chemotherapy modulated the abundance of these gut microbes, and the same microbes appeared to influence weight gain and neurological side effects linked to breast cancer therapies.
The results suggest that gut microbiota composition could serve as a modifiable factor affecting both cancer prognosis and treatment tolerability in early breast cancer. Because chemotherapy itself reshapes these microbial communities, monitoring or targeting the microbiome during treatment may offer a way to improve outcomes and reduce side effects. The authors note that these findings, obtained in adjuvant and neoadjuvant settings, warrant prospective validation before any clinical application.
This study examined whether the composition of the gut microbiota differs between children with acute lymphoblastic leukemia (ALL) and healthy children. Fecal samples were analyzed using 16S rRNA quantitative arrays combined with bioinformatics analysis. The researchers compared overall community structure using Principal Coordinates Analysis (PCoA) and Non-metric Multidimensional Scaling (NMDS), and then looked for individual bacterial species that distinguished the two groups.
The study included 81 subjects total, comprising 58 pediatric patients with acute lymphoblastic leukemia and 23 healthy children serving as controls. All participants provided fecal samples for microbiota analysis. The abstract does not specify additional demographic details such as age range, sex distribution, or geographic location.
PCoA and NMDS both showed that the microbial composition of ALL patients deviated from the tight cluster formed by healthy controls, indicating a distinct gut microbiota profile in disease. Multiple bacterial species showed significant changes in abundance in ALL samples, including Roseburia faecis, Edwardsiella tarda, and Fusobacterium naviforme. Some of these differentially abundant taxa were correlated with interleukin-10 levels, suggesting a link between microbiota shifts and immune signaling. A random forest model built on these differential species distinguished ALL cases from healthy controls with good accuracy (area under the ROC curve of 0.843).
The findings suggest that childhood ALL is accompanied by a characteristic, measurable alteration in the gut microbiota rather than a random or negligible shift. The correlation between specific taxa and interleukin-10 raises the possibility that these microbial changes are connected to immune regulation in ALL patients. The strong classification performance of the random forest model suggests gut microbiota profiling could eventually contribute to distinguishing ALL cases from healthy children, supporting further investigation into microbiota-based biomarkers for this disease.
This study asked whether gut microbiome signatures linked to colorectal cancer (CRC) hold up reliably across different patient cohorts and populations. The researchers meta-analyzed fecal metagenomic sequencing data to identify microbial taxa and functional pathways that consistently distinguish CRC from controls. They also examined the microbiome's functional potential, comparing metabolic pathways such as gluconeogenesis, putrefaction, fermentation, and choline degradation between CRC and control samples. Finally, they built and tested predictive microbiome signatures for CRC diagnosis.
The analysis drew on 969 fecal metagenomes assembled from five publicly available datasets plus two newly collected cohorts, with findings further validated on two additional independent cohorts. The abstract does not specify demographic details such as age, sex, or geographic origin of participants. This design represents a large-scale, multi-population pooling of existing and new CRC and control stool metagenome datasets rather than a single defined patient group.
The gut microbiome in CRC showed reproducibly higher richness than in controls (P < 0.01), partly driven by expansions of species normally derived from the oral cavity. Functional meta-analysis linked gluconeogenesis and putrefaction/fermentation pathways to CRC, while stachyose and starch degradation pathways were associated with controls. A predictive microbiome signature trained across multiple datasets achieved consistently high accuracy in datasets and independent validation cohorts it had not been trained on, with an average area under the curve of 0.84. Pooled raw metagenome analysis also found the choline trimethylamine-lyase gene overabundant in CRC samples (P = 0.001), linking microbiome choline metabolism to CRC.
By validating microbial richness increases, specific functional pathway shifts, and a diagnostic signature across multiple independent cohorts, this study strengthens the case that gut microbiome-based biomarkers for CRC can generalize beyond a single population. The identification of an overabundant choline trimethylamine-lyase gene points to microbiome-driven choline degradation as a mechanistic link worth further investigation in CRC. The high, cross-cohort predictive accuracy (AUC 0.84) supports the feasibility of microbiome-based tools as non-invasive adjuncts for CRC screening or risk stratification.
Background: There is accumulating evidence suggesting a connection between the gut and Parkinson's disease (PD). Gut microbiota may play an important role in the intestinal lesions in PD patients. Objective: This study aims to determine whether gut microbiota differs between PD patients and healthy controls in Northeast of China, and to identify the factors that influence the changes in the gut microbiota. Methods: We enrolled 51 PD patients and 48 healthy controls in this study. Microbial species in stool samples were determined through 16S-rRNA gene sequencing. Dietary intakes were collected from a subset of 42 patients and 23 controls using a food frequency questionnaire (FFQ). Gut microbiota species richness, diversity, differential abundance of individual taxa between PD patients and controls, and the relationship between the gut microbiota abundance and the dietary and clinical factors were analyzed. Results: PD patients showed decreased species richness, phylogenetic diversity, β- diversity, and altered relative abundance in several taxa compared to the controls. PD- associated clinical scores appeared to be the most influential factors that correlated with the abundance of a variety of taxa. The most consistent findings suggested by multiple analyses used in this study were the increase of Akkermansia and the decrease of Lactobacillus in PD patients in Northeast China. Conclusion: Gut microbiota significantly differed between a group of PD patients and healthy controls in Northeast China, with decreased species richness, phylogenetic diversity, β-diversity, and altered relative abundance in several taxa compared to the controls.
This study investigated the gut microbiome and metabolome in colorectal cancer (CRC) to test whether host-microbiome associations found in prior research, mostly from developed countries, also hold in a distinct population. Researchers performed metagenomic and metabolomic analyses of fecal samples, then compared their results with CRC microbiome data available from other populations. The focus was on identifying bacterial taxa and metabolic pathways linked to CRC in a setting where the disease has historically been rare.
The study analyzed fecal samples from 30 colorectal cancer patients and 30 healthy controls recruited from two different locations in India. This population was chosen specifically because India has a low incidence of colorectal cancer and a distinct diet, lifestyle, and gut microbiome compared to other global populations. Data from this Indian cohort were also compared against previously published CRC microbiome datasets from other countries.
The researchers confirmed that Bacteroides and other bacterial taxa already linked to CRC in earlier studies were also associated with CRC in this Indian cohort. A novel finding was the association of Flavonifractor plautii, a flavonoid-degrading bacterium, with CRC in these patients. This association correlated with enzymes and metabolic modules involved in flavonoid degradation, suggesting a link between the breakdown of beneficial anticarcinogenic flavonoids and the disease. The team also identified 20 potential microbial taxonomic markers and 33 potential microbial gene markers that distinguished CRC from healthy microbiomes with high accuracy using machine learning.
The findings suggest that loss of beneficial, flavonoid-degrading control (via F. plautii) may contribute to cancer progression in this Indian cohort, expanding the known microbial players beyond previously identified taxa like Bacteroides. Because India has unusually low CRC incidence alongside a distinct gut microbiome, these cohort-specific biomarkers may not generalize globally and highlight the need for population-specific microbiome research. The taxonomic and gene markers identified could also support development of noninvasive, microbiome-based diagnostic tools for CRC in diverse populations.
Anorexia nervosa (AN) is a psychiatric disease with devastating physical consequences, with a pathophysiological mechanism still to be elucidated. Metagenomic studies on anorexia nervosa have revealed profound gut microbiome perturbations as a possible environmental factor involved in the disease. In this study we performed a comprehensive analysis integrating data on gut microbiota with clinical, anthropometric and psychological traits to gain new insight in the pathophysiology of AN. Fifteen AN women were compared with fifteen age-, sex- and ethnicity-matched healthy controls. AN diet was characterized by a significant lower energy intake, but macronutrient analysis highlighted a restriction only in fats and carbohydrates consumption. Next generation sequencing showed that AN intestinal microbiota was significantly affected at every taxonomic level, showing a significant increase of Enterobacteriaceae, and of the archeon Methanobrevibacter smithii compared with healthy controls. On the contrary, the genera Roseburia, Ruminococcus and Clostridium, were depleted, in line with the observed reduction in AN of total short chain fatty acids, butyrate, and propionate. Butyrate concentrations inversely correlated with anxiety levels, whereas propionate directly correlated with insulin levels and with the relative abundance of Roseburia inulinivorans, a known propionate producer. BMI represented the best predictive value for gut dysbiosis and metabolic alterations, showing a negative correlation with Bacteroides uniformis (microbiota), with alanine aminotransferase (liver function), and with psychopathological scores (obsession-compulsion, anxiety, and depression), and a positive correlation with white blood cells count. In conclusion, our findings corroborate the hypothesis that the gut dysbiosis could take part in the AN neurobiology, in particular in sustaining the persistence of alterations that eventually result in relapses after renourishment and psychological therapy, but causality still needs to be proven.
To compare gut microbiota in impoverished children versus children of high socioeconomic status living in the same urban area in Brazil.
A cross-sectional study was conducted to evaluate 100 children living in a slum and 30 children from a private school, ages between 5 and 11 years old, in Sao Paulo State, Brazil. To characterize the groups, data based on socioeconomic status, sanitation, and housing conditions were collected. Anthropometric measurements and neonatal data were obtained from both groups. Gut microbiota were quantified in fecal samples by real-time polymerase chain reaction.
The children in the private school group had higher rates of cesarean delivery and premature birth than the children in the slum group. Staphylococcus aureus (90% vs 48.0%) and Clostridium difficile (100% vs 43.0%) were more commonly found in the children from the private school than in the impoverished children (P < 0.0001). C perfringens was most frequently identified in the group of children from the slum (92.0% vs 80%; P = 0.064). Higher counts of total eubacteria, Firmicutes and Bacteroidetes phyla organisms, Escherichia coli, Lactobacillus spp., and Methanobrevibacter smithii were found in the children living in poverty, whereas higher counts of Salmonella spp., C difficile, and C perfringens were observed in the children living in satisfactory housing conditions (P < 0.05).
Important differences were observed between the gut microbiota of children living under distinct socioeconomic and environmental conditions within the same city. Our findings suggest that children of high socioeconomic status have less favorable gut microbiota than do children who live in poverty.
This study examined whether human gut microbial enterotypes, defined by the ratio of Prevotella to Bacteroides abundance (P/B ratio), are a stable and biologically meaningful way to classify individuals. The researchers used quantitative PCR to measure the P/B ratio and 35 selected bacterial taxa. They then tested whether a 6-month controlled dietary intervention, comparing the new Nordic diet (NND) to the average Danish diet (ADD), could shift these microbial groupings or the underlying taxa.
The study included 62 subjects between 18 and 65 years old who had central obesity and components of metabolic syndrome. Participants were grouped into two discrete clusters based on their P/B ratio, then followed through the randomized 6-month dietary intervention comparing NND and ADD.
Subjects could be reliably divided into two discrete groups using only their P/B ratio, and this grouping remained stable across the 6-month diet intervention. Neither the P/B-based groups nor the broader cohort showed significant changes in the 35 quantified bacterial taxa when comparing the ADD and NND diets. Despite this microbial stability, the high-P/B group had higher total plasma cholesterol than the low-P/B group after the intervention.
The findings suggest that P/B-based enterotyping identifies a stable, diet-resistant trait of the gut microbiota rather than a state that shifts readily with short-term dietary change. Because the high-P/B group showed higher plasma cholesterol after intervention, stratifying individuals by P/B ratio could help identify subgroups with differing metabolic or cardiovascular risk responses to diet. This supports using P/B ratio as a simple stratification tool for future studies assessing individualized responses to dietary interventions.
Genus and species level analysis is the best way to characterize alterations in the human gut microbiota that are associated with obesity, because the clustering of obese and lean microbiotas increases with the taxonomic depth of the analysis. Bifidobacterium genus members have been associated with a lean status, whereas different Lactobacillus species are associated both with a lean and an obese status. We analyzed the fecal concentrations of Bacteroidetes, Firmicutes, Methanobrevibacter smithii, the genus Lactobacillus, five other Lactobacillus species previously linked with lean or obese populations, Escherichia coli and Bifidobacterium animalis in 263 individuals, including 134 obese, 38 overweight, 76 lean and 15 anorexic subjects to test for the correlation between bacterial concentration and body mass index (BMI). Of these subjects, 137 were used in our previous study.
Firmicutes were found in >98.5%, Bacteroidetes in 67%, M. smithii in 64%, E. coli in 51%, Lactobacillus species between 17 and 25% and B. animalis in 11% of individuals. The fecal concentration of Lactobacillus reuteri was positively correlated with BMI (coefficient=0.85; 95% confidence interval (CI) 0.12-0.58; P=0.02) in agreement with what was reported for Lactobacillus sakei. As reported, B. animalis (coefficient=-0.84; 95% CI -1.61 to -0.07; P=0.03) and M. smithii (coefficient=-0.43, 95% CI -0.90 to 0.05; P=0.08) were negatively associated with the BMI. Unexpectedly, E. coli was found here for the first time to negatively correlate with the BMI (coefficient=-1.05; 95% CI -1.60 to -0.50; P<0.001).
Our findings confirm the specificity of the obese microbiota and emphasize the correlation between the concentration of certain Lactobacillus species and obesity.
Obesity is associated with increased health risk and has been associated with alterations in bacterial gut microbiota, with mainly a reduction in Bacteroidetes, but few data exist at the genus and species level. It has been reported that the Lactobacillus and Bifidobacterium genus representatives may have a critical role in weight regulation as an anti-obesity effect in experimental models and humans, or as a growth-promoter effect in agriculture depending on the strains. To confirm reported gut alterations and test whether Lactobacillus or Bifidobacterium species found in the human gut are associated with obesity or lean status, we analyzed the stools of 68 obese and 47 controls targeting Firmicutes, Bacteroidetes, Methanobrevibacter smithii, Lactococcus lactis, Bifidobacterium animalis and seven species of Lactobacillus by quantitative PCR (qPCR) and culture on a Lactobacillus-selective medium.
In qPCR, B. animalis (odds ratio (OR)=0.63; 95% confidence interval (CI) 0.39-1.01; P=0.056) and M. smithii (OR=0.76; 95% CI 0.59-0.97; P=0.03) were associated with normal weight whereas Lactobacillus reuteri (OR=1.79; 95% CI 1.03-3.10; P=0.04) was associated with obesity.
The gut microbiota associated with human obesity is depleted in M. smithii. Some Bifidobacterium or Lactobacillus species were associated with normal weight (B. animalis) while others (L. reuteri) were associated with obesity. Therefore, gut microbiota composition at the species level is related to body weight and obesity, which might be of relevance for further studies and the management of obesity. These results must be considered cautiously because it is the first study to date that links specific species of Lactobacillus with obesity in humans.
2026-07-04
Methanobrevibacter smithii majorTaxon page created: biology (morphology, ecological role, hydrogen syntrophy), the nickel-dependent methanogenesis metallome, clinical associations, the data-derived Conditions table across 28 conditions, and the full research feed.
Chen H, Gan Q, Fan C.
Methyl-coenzyme M reductase and its post-translational modifications.Front Microbiol. 2020
Takakura W, Pimentel M.
Small intestinal bacterial overgrowth and irritable bowel syndrome - an update.Front Psychiatry. 2020
Li T, Coker OO, Sun Y, Li S, Liu C, Lin Y, Wong SH, Miao Y, Sung JJY, Yu J.
Multi-cohort analysis reveals altered archaea in colorectal cancer fecal samples across populations.Gastroenterology. 2024
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