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Bilophila wadsworthia is a 'bile-loving' gut microbe that eats the sulfur in taurine-conjugated bile acids. A diet high in saturated animal fat can make it bloom and produce hydrogen sulfide, a gas that is genotoxic to the colon lining.
Bilophila wadsworthia is a bile-loving, sulfite-reducing gut pathobiont. It turns the organic sulfur in taurine-conjugated bile acids into hydrogen sulfide, and a diet high in saturated animal fat can make it bloom, linking it to inflammation, colitis, and metabolic dysfunction.
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 MoreBilophila wadsworthia is a Gram-negative, strictly anaerobic rod and a bile-loving member of the human gut microbiota. Its name means “bile-loving,” and it was first recovered from gangrenous and perforated appendicitis specimens, and from normal stool, at the Wadsworth anaerobe laboratory.[1] It is a low-abundance but widespread gut resident in the family Desulfovibrionaceae (Deltaproteobacteria). On this database it surfaces as a differentially abundant taxon across dozens of human microbiome studies, where its meaning is tied closely to one thing: the host's supply of organic sulfur.
What makes B. wadsworthia distinctive is how it earns its living. Rather than fermenting sugars, it respires the sulfonate group of taurine-conjugated bile acids, reducing it to hydrogen sulfide.[2][3] A diet high in saturated (milk-derived) animal fat shifts the liver toward taurine-conjugated bile acids, delivering that organic sulfur to the distal gut and letting B. wadsworthia bloom, a shift linked to inflammation and colitis in susceptible hosts.[4] That diet → bile-acid → sulfur → sulfide axis is exactly the nutritional immunity lens this database reads it through: B. wadsworthia is less a fixed marker of any one disease than a readout of the gut's fat-and-bile environment.
A Gram-negative, strictly anaerobic, non-fermentative (asaccharolytic) rod that grows slowly on blood-based anaerobic media and is stimulated by bile, the trait behind its name.[1] It is strongly catalase-positive and urease-positive, reduces nitrate, and has a DNA G+C content of about 39 to 40 mol%.[1] It belongs to the Desulfovibrionaceae, the family of intestinal sulfur-respiring bacteria, but unlike classic sulfate reducers it does not reduce sulfate; its electron sinks are sulfite, thiosulfate, and the organic sulfonates of bile.[1][2]
B. wadsworthia's defining feature is anaerobic respiration of organic sulfur. It takes up taurine (2-aminoethanesulfonate) and related sulfonates, deaminates taurine to sulfoacetaldehyde, reduces that to isethionate, then cleaves isethionate with a glycyl-radical enzyme (isethionate sulfite-lyase) to release sulfite; a dissimilatory sulfite reductase then reduces the sulfite to hydrogen sulfide.[3][2] The pathway runs on an iron-dependent respiratory chain (menaquinone and cytochrome c), so the organism's core chemistry is a metalloenzyme cascade that turns dietary and bile-derived sulfur into H₂S.[2] Because taurine-conjugated bile acids are the richest gut source of that organic sulfur, the size of the B. wadsworthia population tracks the host's bile-acid conjugation state.[4]
B. wadsworthia is a pathobiont: a normal low-abundance resident that becomes harmful when it blooms. It was first isolated as a major anaerobe of perforated and gangrenous appendicitis,[1] and it can cause abscesses and bacteremia. Its microbiome-medicine relevance, though, is the bloom. In genetically susceptible (Il10-deficient) mice, a saturated-fat diet expanded B. wadsworthia and triggered a T helper type 1 inflammatory response and colitis, an effect reproduced simply by feeding taurocholic acid.[4] A bloom also aggravates high-fat-diet metabolic dysfunction, worsening inflammation, gut-barrier integrity, glucose control, and hepatic steatosis.[5] As with any differential-abundance signal, an appearance in a gut study is an ecological association shaped by diet, not evidence of infection.
B. wadsworthia's harms come less from classical toxins than from its metabolic output and its tolerance of the bile-rich distal gut. Several factors below (taurine respiration, the sulfite-to-sulfide step) are the same sulfur machinery detailed under Metabolism, which ties its chemistry directly to its pathology.
| Virulence factor | Description and role |
|---|---|
| Hydrogen sulfide (H₂S) | The organism's main respiratory product. At the concentrations found in the large intestine, H₂S is genotoxic to colonic epithelial cells and erodes the protective mucus barrier, and it is implicated in ulcerative colitis and colorectal cancer.[3][6] |
| Bile tolerance | Grows in concentrations of bile that inhibit most anaerobes, letting it colonize the bile-rich distal small bowel and colon and exploit taurine-conjugated bile acids as a nutrient.[1][4] |
| Taurine / sulfonate respiration | The isethionate sulfite-lyase (glycyl-radical enzyme) pathway converts bile-derived taurine into sulfite and then H₂S, the metabolic engine behind its inflammatory effects.[3] |
| Lipopolysaccharide / Th1 activation | As a Gram-negative pathobiont, a bloom drives a pro-inflammatory T helper type 1 immune response, the proximal cause of diet-induced colitis in susceptible hosts.[4] |
Read through the nutritional-immunity lens, B. wadsworthia's dependence on host-supplied organic sulfur is also its weakness. Where a metal-scavenging pathogen is starved by withholding metals, this organism is fed by the diet; cut the supply line and the bloom collapses.
| Weak point | Why it is exploitable |
|---|---|
| Saturated-fat dependence | The bloom is driven by milk-derived saturated fat acting through taurine-conjugated bile acids; lowering that fat load reduces the taurocholate that feeds it.[4] |
| Bile-acid conjugation | Taurine (not glycine) conjugation supplies the organic sulfur, so shifting hepatic conjugation away from taurocholate starves the H₂S pathway. This is the mechanistic lever behind the diet effect.[4][7] |
| Competitive suppression | In high-fat-fed mice, the probiotic Lactobacillus rhamnosus CNCM I-3690 limited B. wadsworthia expansion and reduced the associated inflammation and barrier dysfunction, a proof of concept for crowding it out.[5] |
For a pathobiont, an “intervention” is anything that constrains its bloom or blunts its harm. Everything below is research-stage or dietary and is not medical advice; each is classified by our validation method as Validated, Promising Candidate, or Validation In Progress. The through-line is the sulfur supply line: starve B. wadsworthia of the taurine-conjugated bile acids it feeds on.
| Intervention | Class | Status |
|---|---|---|
| Saturated / animal-fat restriction | Diet | Validation In Progress |
| Bile-acid conjugation modulation | Concept | Validation In Progress |
| Lactobacillus rhamnosus CNCM I-3690 | Probiotic | Validation In Progress |
| Intervention | Mechanism |
| Saturated / animal-fat restriction | Fewer taurine-conjugated bile acids reach the distal gut, cutting the organic sulfur B. wadsworthia respires and the H₂S it makes.[4] |
| Bile-acid conjugation modulation | Shifting hepatic conjugation away from taurocholate removes the taurine-derived sulfur that fuels the pathway.[4] |
| Lactobacillus rhamnosus CNCM I-3690 | Limits B. wadsworthia expansion and reduces inflammation and gut-barrier dysfunction in high-fat-fed mice.[5] |
In susceptible hosts, chronic high intake of saturated (milk-derived) animal fat is the exposure most consistently linked to a B. wadsworthia bloom, because it drives the taurocholic acid that feeds it.[4] The direct causal evidence is from mouse models, so this is a mechanism to weigh, not a clinical prescription.
Where B. wadsworthia (NCBI:txid35833) appears as a differentially abundant taxon across the Microbiome Medicine corpus. Each row aggregates every experiment in which the organism 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 40 conditions and 48 studies, the signal is genuinely mixed: enriched in 13, depleted in 18, and direction-conflicting in 9 (directional agreement 0.54). Because B. wadsworthia is a diet-responsive pathobiont, its direction in any one condition often reflects the cohort's saturated-fat and bile-acid environment as much as the disease itself, so the aggregate evidence tier is Low.
How to read these. B. wadsworthia is a genuine, if low-abundance, member of the gut microbiota, so these signals are ecologically real rather than contamination artifacts. The caveat is interpretive: it is a diet-responsive pathobiont whose abundance tracks saturated-fat intake and taurine-conjugated bile acids, so an enrichment in a given condition may reflect the cohort's diet as much as the disease. Species-level 16S assignments and very low abundances still warrant caution. This is why the direction can conflict between cohorts and the aggregate tier stays Low.
Internal summaries of the 50 studies we reviewed in which B. wadsworthia was a differential taxon across this corpus.
This pilot study examined sarcopenia, the age-related decline in muscle mass and strength, using an integrative multi-omics approach. Researchers combined plasma metabolomics (308 metabolites), lipidomics (295 lipids), clinical measures of muscle function, and gut microbiome profiling via 16S rRNA sequencing to identify signatures associated with the condition. A support vector machine model with recursive feature elimination was used to pinpoint discriminative metabolites, and microbiome profiles were then correlated with these metabolite patterns. The aim was to integrate metabolic, inflammatory, and microbiome-related dimensions that have typically been studied separately.
The cohort consisted of forty community-dwelling older adults aged 60 to 87 years. Participants were classified as sarcopenic (n = 15) or non-sarcopenic (n = 25) using the EWGSOP2 diagnostic criteria. Classification incorporated dominant hand grip strength, chair rise time, psoas muscle cross-sectional area measured by CT, and SARC-F questionnaire scores. The study is described by the authors as a pilot within an Indian population.
Dominant hand grip strength was the strongest clinical predictor of sarcopenia, achieving an AUROC of 0.93. Sarcopenic subjects showed higher systemic inflammation, reflected in an elevated neutrophil-to-lymphocyte ratio, and elevated plasma arachidonic acid. Thirteen lipid species, primarily lysophosphatidylcholines, lysophosphatidylethanolamines, hexosylceramides, and acylcarnitines, were significantly associated with sarcopenia. Twenty-four metabolites, including spermidine, were also identified as part of the discriminative signature, alongside gut microbiome profiles correlated with these metabolite patterns.
The findings suggest that sarcopenia in older adults is accompanied by a coordinated shift across grip strength, systemic inflammation, lipid metabolism, and gut microbiome composition, not a single isolated marker. Grip strength combined with metabolite and lipid signatures may offer a practical, multi-dimensional approach to identifying at-risk individuals. The involvement of inflammatory and polyamine-related metabolites such as spermidine points to potential mechanistic pathways worth further investigation. Because this is a small pilot study, larger cohorts are needed before these signatures can be considered validated biomarkers.
This study examined how diet shapes the human gut microbiome at species-level resolution, using app-based diet logs paired with shotgun metagenomic sequencing. The researchers modeled associations between specific foods, broader dietary patterns (including degree of food processing), and microbial diversity, species composition, and functional pathways. They also tested whether these diet-microbiome associations held up over a multi-year period and explored whether predicted microbiome shifts could inform personalized dietary interventions.
The analysis drew on 10,068 participants from the Human Phenotype Project, each contributing app-based dietary logs and shotgun metagenomic data. The abstract does not specify demographic details such as age range, sex distribution, or geographic location beyond identifying the cohort as part of this project. This represents one of the largest paired diet-microbiome datasets described in the abstract, but no further population characteristics are given.
Diet significantly predicted microbial diversity, with correlations of 0.26 for richness and 0.24 for the Shannon Index, and it predicted the relative abundance of 669 of 724 species tested (92.4 percent) and 313 of 320 functional pathways (97.8 percent), all at a false discovery rate below 0.05. Feature attribution revealed specific food-microbe links, including coffee with Lawsonibacter asaccharolyticus (r = 0.43), yogurt with Streptococcus thermophilus (r = 0.42), and milk with Bifidobacterium species (r = 0.31 to 0.36). Degree of food processing emerged as a broader dietary pattern predictor of microbial diversity and composition, and 82.5 percent of species showed significant longitudinal tracking between predicted and observed abundances over four years.
The scale and consistency of these diet-microbiome associations, holding for the large majority of species and pathways tested and persisting over four years, suggest diet is a robust and durable lever for shaping gut microbial ecology. The identification of specific, reproducible food-microbe pairings (such as coffee, yogurt, and milk with particular taxa) and the role of food processing level point toward concrete dietary targets rather than vague nutritional advice. The exploratory framework for simulating personalized dietary interventions with predicted microbiome shift effects moves this work toward practical, individualized nutrition strategies rather than one-size-fits-all recommendations.
This study examined whether bacteria native to the oral cavity continuously translocate into the lower gastrointestinal tract or instead become locally adapted, persistent residents once they arrive there. The researchers used cross-sectional shotgun metagenomic sequencing of saliva, gastric, duodenal, and fecal samples, paired with isolation and whole-genome sequencing of Streptococcus salivarius strains, to trace strain-level movement of oral bacteria along the gut. This approach let them distinguish ongoing seeding from the mouth versus established colonization within each gut compartment.
The study population was 44 healthy and stunted children from Bangui, Central African Republic. From these children, the researchers analyzed metagenomic samples across four body sites (saliva, stomach, duodenum, and feces) and isolated and sequenced 87 Streptococcus salivarius isolates. The design directly compared stunted children to healthy children within this cohort.
Members of the genera Streptococcus, Veillonella, Rothia, and Haemophilus were shown to translocate from the oral cavity into the lower gastrointestinal tract. Fecal S. salivarius isolates were more closely related to oral isolates from the same child than to isolates from other children, and saliva showed higher S. salivarius nucleotide diversity than other compartments, consistent with frequent, ongoing intraindividual translocation rather than static colonization. In stunted children, overrepresentation of oral bacteria in the duodenum tracked with increased microbial biomass, whereas in the colon it was linked to an overall depletion of biomass, including reduced levels of butyrate-producing strains.
The findings support a model of continuous, individual-specific translocation of oral bacteria into the lower gut rather than one-time colonization, which could help explain ectopic oral bacterial overgrowth seen in gastrointestinal disease. In stunted children, the divergent biomass effects, expansion in the duodenum but depletion, including of butyrate producers, in the colon, suggest oral bacterial translocation may contribute differently to small intestinal and colonic dysfunction in undernutrition. This strain-level view offers a basis for further investigating how oral-gut microbial exchange relates to childhood stunting and gastrointestinal disorders more broadly.
This study examined whether alterations in the gut microbiome track the development of Parkinson's disease (PD) in people who carry GBA1 gene variants but have not (yet) developed PD symptoms. The researchers combined clinical data with fecal metagenomics and used an analysis method that assessed both differential abundance of microbial species and the coherence of that abundance variation across groups, measured with Cliff's delta. The goal was to determine whether microbiome composition could serve as an early marker of PD risk in genetically at-risk individuals.
The primary cohort included 271 patients with PD, 43 carriers of GBA1 variants who had not developed PD symptoms (GBA-NMC), and 150 healthy controls. Findings were then checked against three independent cohorts from the United States, Korea, and Turkey, together comprising 638 additional PD patients and 319 additional healthy controls. In total, the study drew on clinical and fecal metagenomic data from close to 1,400 individuals across four countries.
About 25% of the gut microbiome in GBA-NMC individuals showed a composition that was intermediate between healthy controls and patients with PD. This intermediate microbiome signature was strongly correlated with disease progression in patients who already had PD, and with prodromal symptoms suggestive of future PD in both GBA-NMC and healthy individuals. Similar microbiome alterations were reproduced across the three independent international cohorts, strengthening confidence that the pattern is not specific to one population.
The findings suggest gut microbiome changes can flag both genetically at-risk (GBA1 carriers) and non-genetically at-risk people in the general population who may be on a trajectory toward developing PD. This positions the microbiome as a potential early, non-invasive marker during the premanifest phase of disease, before clinical symptoms appear. Such a marker could eventually help identify candidates for early monitoring or intervention trials aimed at delaying or preventing PD onset.
This study examined whether gut microbiome composition and microbial metabolite profiles differ between lean and obese youth with type 1 diabetes (T1D). Researchers used metagenomic shotgun sequencing of stool samples to characterize bacterial community structure and taxa abundance. They also measured short-chain fatty acids (SCFAs) as microbial metabolite outputs. The goal was to describe obesity-associated gut microbial changes in a T1D population, a group already at elevated risk for complications.
The pilot study included T1D youth divided into a lean group (BMI 5th to under 85th percentile, n = 27) and an obese group (BMI at or above the 95th percentile, n = 21). Participants had a mean age of 15.3 years, mean glycated hemoglobin A1c of 7.8%, and mean diabetes duration of 5.1 years. The combined sample was 42.0% female and 94.0% White.
Bacterial community composition (beta-diversity) differed significantly between BMI groups. The obese group showed a significantly higher ratio of Prevotella to Bacteroides and increased relative abundance of Prevotella copri, along with other taxa that differed between lean and obese groups. Functional profiling also revealed upregulation of branched-chain amino acid (BCAA) biosynthesis pathways in the obese group, pointing to a metabolic signature accompanying the taxonomic shifts.
These findings suggest that obesity in T1D youth is accompanied by measurable, structured changes in gut microbial ecology and function, not just body composition differences. The Prevotella to Bacteroides shift and BCAA biosynthesis upregulation echo patterns reported in obesity research more broadly, raising the possibility of shared microbial mechanisms across metabolic conditions. Because this was a pilot study, the findings support further investigation into the gut microbiome as a potential contributor to, or biomarker for, obesity-related complications in T1D.
This study examined how exposure to toxic metals and metalloids, specifically arsenic, lead, mercury, and cadmium, relates to gut microbiota composition and cardiometabolic risk. Researchers compared gut microbiome taxa between people with high versus low levels of each metal. They also assessed how metal-associated taxa related to clinical measures such as BMI, fasting blood glucose, and blood pressure, and to diagnoses of hypertension, obesity, and type 2 diabetes. A metabolic pathway analysis identified which microbial functions were enriched under higher metal exposure.
The study included 178 adults of African origin, 52 percent female, with a mean age of 43.0 years, drawn from five countries: Ghana, South Africa, Jamaica, Seychelles, and the United States. Metal exposure levels (arsenic, lead, mercury, cadmium) were measured in these participants alongside stool-based gut microbiome profiling. This multi-country design allowed comparisons across diverse African-origin populations rather than a single national cohort.
High versus low lead and arsenic levels significantly affected gut microbiome beta diversity. Seventy-one taxa were associated with high lead levels, including 30 linked to elevated BMI, 22 to type 2 diabetes, and 23 to elevated fasting blood glucose. A much larger set of 115 taxa were associated with high arsenic levels, including 32 linked to elevated BMI, 33 to type 2 diabetes, and 26 to elevated blood glucose. Porphyrin metabolism emerged as the most enriched metabolic pathway among taxa associated with higher lead and arsenic exposure.
These findings provide the first evidence in African-origin adults linking gut microbiome composition to lead and arsenic exposure and to obesity and type 2 diabetes risk. The enrichment of porphyrin metabolism suggests a possible microbial mechanism connecting toxic metal exposure to metabolic dysregulation. The results position the gut microbiome as a potential biological link between environmental metal exposure and cardiometabolic disease risk in understudied populations. This underscores the need for further research into microbiome-mediated pathways as targets for reducing metal-associated metabolic disease burden.
This study examined how gut microbiome composition differs across three common diet patterns: omnivore, vegetarian, and vegan. The researchers built metagenomic profiles to determine whether diet pattern leaves a detectable, diet-specific signature in the gut microbiome. They also looked at whether these microbial signatures relate to host cardiometabolic health markers and whether diet-associated gut microbes overlap with microbes found in food itself, including dairy and soil sources.
The analysis drew on 21,561 individuals pooled from five independent, multinational human cohorts. The abstract does not give further demographic detail (age, sex, or specific countries) for this pooled population. This scale and multinational scope let the researchers test whether diet-microbiome associations held consistently across different populations rather than in a single study group.
Gut microbial profiles distinguished omnivore, vegetarian, and vegan diets with strong accuracy, achieving a mean AUC of 0.85. Red meat intake was a strong driver of the omnivore microbiome signature, with microbes such as Ruminococcus torques, Bilophila wadsworthia, and Alistipes putredinis enriched in omnivores and negatively correlated with cardiometabolic health. In contrast, vegan-associated signature microbes correlated with more favorable cardiometabolic markers and were also found enriched in omnivores who ate more plant-based foods. Diet-specific gut microbes partly overlapped with microbes found in food itself, such as the dairy organism Streptococcus thermophilus and typical soil microbes detected in vegans.
These diet-associated microbial signatures, including the link between Bilophila wadsworthia and poorer cardiometabolic outcomes in omnivores, suggest gut microbiome profiling could help explain why plant-based diets are associated with better cardiometabolic health. The findings support using microbiome signatures as objective, diet-pattern-specific biomarkers rather than relying solely on self-reported dietary intake. The authors state that these signatures of common Western diet patterns can inform future nutritional interventions and epidemiological research.
Only the title of this study was available, not an abstract, so this summary is based solely on that title. The title indicates the researchers examined how diet exerts selective pressure on the human gut microbiota. The scope appears to span two levels of biological organization: which bacterial species are present (species composition) and the genetic makeup within individual bacterial strains (strain-level variation). No specific methods, sequencing approach, or statistical analyses can be confirmed from the title alone.
The abstract was not available, so no cohort size, demographic details, or recruitment setting can be stated. The title's reference to "the human gut microbiota" indicates the subjects were humans, most plausibly a study population or public dataset with dietary and gut metagenomic data. Without further detail, the sample should be understood only as human gut microbiome data linked to dietary information, not a defined patient group. No age, sex, geography, or health status can be honestly inferred.
Because no abstract text was provided, no specific results, effect sizes, or organism names can be reported. The title itself is the only available signal, and it asserts that dietary selective effects are detectable at multiple levels of microbial organization. This implies the study found diet-associated differences both in which species are present and in the genetic variants carried by strains within those species. Beyond this general claim embedded in the title, no further findings can be stated without fabricating detail.
If diet shapes the gut microbiota down to the strain-genetic level, dietary interventions could in principle drive evolutionary or selective changes in resident bacterial populations, not just shifts in which species are present. This would suggest that assessing diet's effect on the microbiome requires strain-level genomic analysis, not species-level profiling alone. Such a finding could inform how nutrition-based interventions are designed and monitored for microbiome-targeted therapies. Because only the title was available, these implications are inferred from the title's framing and should be confirmed against the full study before being treated as established.
This study examined whether adding a legume-enriched, multicomponent diet to a calorie-restricted eating plan could improve metabolic health beyond calorie restriction alone. It was designed as a 16-week, single-blind, parallel-group randomized controlled trial comparing an intervention diet against a calorie-restricted control diet. The primary outcomes tracked were markers of glycemia, with measurements taken at two- or four-week intervals throughout the trial. The researchers also examined whether gut microbiome changes, particularly shifts in fiber-degrading species and related metabolites, mediated any observed benefits.
The trial enrolled 127 Chinese participants with prediabetes living in Singapore, randomized into an intervention group (n = 63) and a control group (n = 64). Participants had a mean age of 62.2 years and a mean BMI of 23.8 kg/m2, indicating an older adult population that was not obese by standard classification. Both groups received calorie-restricted diets, with the intervention group's diet additionally enriched with legumes as part of a multicomponent approach.
Both groups significantly reduced their BMI after 16 weeks compared with baseline, with minimal difference between the two diets on this measure. However, the legume-enriched intervention group showed significantly greater reductions in LDL cholesterol, total cholesterol, and HbA1c compared with the calorie-restricted control group. These improvements were accompanied by increases in fiber-degrading bacterial species in the intervention group, with the effects appearing to be mediated through metabolites such as bile acids and amino acids.
The findings suggest that enriching a calorie-restricted diet with legumes provides added metabolic benefits for people with prediabetes beyond calorie restriction alone, particularly for lipid and glycemic markers. The mediating role of fiber-degrading gut bacteria and metabolites like bile acids and amino acids points to the gut microbiome as a mechanistic link between legume intake and improved metabolic outcomes. This supports legume-enriched dietary patterns as a practical, food-based strategy for managing prediabetes, with the gut microbiome as a potential target for further intervention research.
This study examined whether gut microbiota structure and function relate to outcomes of immune checkpoint inhibitor (ICI) therapy in non-melanoma skin cancers. The researchers performed 16S rRNA V1-V2 gene amplicon sequencing on fecal samples collected longitudinally, then ran tumor-dependent differential analyses of bacterial composition alongside untargeted fecal metabolomics. The goal was to identify bacterial genera, species, and metabolic pathways associated with response versus non-response to ICI treatment.
The analysis drew on 68 fecal samples collected longitudinally from individuals with basal cell carcinoma (n = 5), Merkel cell carcinoma (n = 5), or cutaneous squamous cell carcinoma (CSCC, n = 11). All participants were undergoing ICI therapy for a non-melanoma skin cancer. This is a small, exploratory cohort spanning three distinct tumor types rather than a single large patient population.
Across all tumor types combined, the researchers identified 10 differential bacterial genera between ICI responders and non-responders. Within the CSCC subgroup specifically, 10 genera and 20 species distinguished responders from non-responders, and predicted functional pathway analyses found 8 pathways enriched in non-responders and 12 enriched in responders. Untargeted fecal metabolomics further identified nine KEGG pathways associated with ICI efficacy in CSCC, pointing to microbial metabolic activity as a correlate of treatment response.
The findings suggest that gut microbiota composition and function are linked to ICI therapy outcomes in non-melanoma skin cancers, extending observations previously made mainly in melanoma to other skin cancer types. Because this is described as an exploratory study with a small sample size, the specific genera, species, and pathways identified should be viewed as hypothesis-generating rather than confirmed predictors. Larger studies are needed to validate these microbiome features before they could inform strategies to predict or improve ICI response in non-melanoma skin cancer.
This study developed a machine learning prediction model for postprandial glycemic response (PPGR) in pregnant women, comparing models built from continuous glucose monitoring (CGM) data, food diaries, lifestyle factors, and biochemical parameters, with and without gut microbiota data. Microbiota composition was assessed using 16S rRNA gene sequencing of stool samples. The central question was whether adding microbiome data meaningfully improves the accuracy of predicting how a woman's blood glucose responds to meals.
The study involved 105 pregnant women, including 77 with diet-treated gestational diabetes mellitus (GDM) and 28 healthy pregnant women. Each participant underwent 7 days of continuous glucose monitoring, kept food diaries, and provided stool samples for microbiome analysis.
Adding microbiome data increased the variance explained in peak glycemic levels (GLUmax) from 34% to 42%, and in incremental area under the glycemic curve (iAUC120) from 50% to 52%. The final model, which incorporated microbiota data, correlated more strongly with measured PPGRs than a model based on carbohydrate count alone (r = 0.72 versus r = 0.51 for iAUC120). Despite this improvement, the abstract notes that the microbiome's overall contribution to model performance was modest.
These findings suggest that gut microbiota data can enhance personalized glycemic response prediction for pregnant women, including those with GDM, beyond what meal content and lifestyle factors alone provide. Because the microbiome's added value was modest, it may be best used as a supplementary feature layered onto CGM- and diet-based models rather than a primary driver of prediction accuracy. This approach could support more individualized dietary guidance for glycemic control during pregnancy in women with GDM.
This study examined the gut microbiome's relationship to colorectal cancer (CRC) by pooling stool metagenomic data across 18 cohorts. It combined 12 existing metagenomic datasets with 6 new cohorts that added detailed information on cancer stage and the anatomic location of tumors within the colon and rectum. The analysis aimed to improve microbiome-based prediction of CRC and to identify species- and strain-level signatures associated with disease presence, tumor location, and progression.
The pooled analysis drew on stool metagenomes from 3,741 samples across 18 cohorts. This included 930 patients with colorectal cancer, 210 with adenomas, and 976 healthy control individuals from 12 prior datasets, expanded with 6 new cohorts contributing 1,625 additional samples with granular staging and tumor-location data. The population therefore spans multiple international studies of CRC patients, adenoma patients, and healthy controls rather than a single clinical trial cohort.
The expanded analysis improved CRC prediction accuracy based solely on gut metagenomics, reaching an average area under the curve of 0.85, aided by 19 newly profiled species and distinct clades of Fusobacterium nucleatum. Specific gut species distinguished left-sided from right-sided CRC (area under the curve of 0.66), with right-sided tumors showing enrichment of oral-typical microbes. Strain-level analysis also identified CRC-associated signatures within the commensals Ruminococcus bicirculans and Faecalibacterium prausnitzii, with particular subclades linked to late-stage disease.
The findings support the gut microbiome as a viable clinical target for CRC screening, given the improved prediction accuracy achieved from stool metagenomics alone. The identification of tumor-location-specific and strain-level signatures suggests the microbiome can also serve as a biomarker for CRC progression, not just detection. These results point toward more refined, strain- and site-aware microbiome diagnostics that could complement or extend current CRC screening approaches.
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 examined how two contrasting diets, a vegan diet and a ketogenic diet, affect human immunity and the gut microbiota. Researchers performed a post hoc analysis of a clinical trial in which participants sequentially followed each diet for two weeks. They used a multiomics approach combining multidimensional flow cytometry, transcriptomics, proteomics, metabolomics, and metagenomics to capture changes in host immune cells and the microbiome. The design allowed direct comparison of each diet's impact, as well as the effect of switching from one diet to the other.
The cohort consisted of 20 participants enrolled in a registered clinical trial (NCT03878108). Each participant consumed both diets sequentially, serving as their own control across the vegan and ketogenic phases. The abstract does not give further demographic details such as age, sex distribution, or health status, so no additional characteristics can be stated beyond the sample size and crossover design.
A ketogenic diet significantly upregulated pathways and enriched cell populations associated with the adaptive immune system. In contrast, a vegan diet had a significant impact on the innate immune system, including upregulation of antiviral immunity pathways. Both diets differentially altered the microbiome and host-associated amino acid metabolism, with the ketogenic diet producing a strong downregulation of most microbial pathways compared with both baseline and the vegan diet. Despite variation among participants, the researchers found a tightly connected network across the different omics datasets, driven largely by compounds related to amino acids, lipids, and immune markers.
The findings suggest that diet composition can selectively steer the immune system toward either adaptive or innate and antiviral immune programs, depending on whether the diet is ketogenic or vegan. This implies that short-term dietary interventions could be used strategically to modulate specific arms of human immunity. The tight coupling between microbial amino acid and lipid metabolism and immune signaling also points to diet-driven microbiome changes as a plausible mechanistic link between nutrition and immune function. These results support further investigation into diet as a tool for targeted immune modulation, though the abstract does not describe clinical outcomes beyond the immune and microbiome measurements reported.
This study examined the gut microbiota of men who have sex with men (MSM) using species-level shotgun metagenomics. The researchers compared the composition of these microbial communities to patterns typically seen in Westernized versus non-Westernized populations. They also used questionnaire data to explore whether specific sexual practices could explain some of the inter-individual variation in microbiota composition. Machine learning was applied to identify microbial features that associate with particular sexual activities.
The study population consisted of men who have sex with men of Western origin, whose gut microbiomes were profiled by shotgun metagenomic sequencing. Participants also completed questionnaires capturing information on their sexual practices, which were then linked to their individual microbiota profiles. The abstract does not give an exact sample size, so no specific cohort number can be stated. Comparisons were made against the known microbiota patterns of non-MSM and of non-Westernized populations.
Many Western MSM had gut microbiotas that resembled those of non-Westernized populations rather than typical Western non-MSM profiles. These microbiomes were frequently dominated by members of the Prevotellaceae family, including co-colonization by species from the Segatella copri complex alongside unidentified Prevotellaceae members. Questionnaire-based analysis linked specific sexual practices to differences in microbiota composition among MSM. Machine learning further identified distinct microbial features associated with particular sexual activities.
The findings show that sexual behavior can meaningfully shape gut microbiome composition, independent of the geographic or dietary factors usually invoked to explain Westernized versus non-Westernized microbiota differences. Because MSM status and sexual practices can drive a non-Westernized-like Prevotellaceae-dominated profile, failing to account for this in study design could confound population-based microbiome research. Studies that do not stratify or control for sexual practices among participants risk misattributing microbiota differences to diet, geography, or disease status. This underscores the need to collect and adjust for detailed behavioral and sexual-practice data in future gut microbiome research.
This study tested whether Parkinson's disease alters the oral microbiome in people who also have periodontitis. Researchers used 16S ribosomal RNA next-generation sequencing (targeting the V1-V3 regions) to profile bacterial communities in saliva and stool samples. They compared clinical, periodontal, and neurological parameters, including motor dysfunction severity, across the enrolled groups.
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 controls (HC). The Parkinson's patients had mild-to-moderate motor dysfunction. Plaque scores were comparable between the PA+P and P groups, indicating similarly effective oral hygiene in both.
Beta diversity in saliva differed significantly between HC and PA+P, between HC and P, and between P and PA+P groups. Saliva and fecal microbial profiles were distinct from one another. 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 compared to healthy controls.
The findings suggest that Parkinson's disease is associated with shifts in the periodontitis-associated oral microbiome, distinct from periodontitis alone, even when oral hygiene is well controlled. This supports the idea that neurological disease status can influence oral microbial composition independent of local periodontal disease severity. The distinct saliva and fecal profiles also point to site-specific microbial signatures that could inform future research on oral-gut-brain axis interactions in Parkinson's disease.
This study examined gut microbial features associated with Parkinson's disease (PD) by meta-analyzing shotgun metagenomic sequencing data across six independent datasets from different countries. The researchers assessed taxonomic composition, microbial gene pathways including riboflavin and biotin biosynthesis, and carbohydrate-active enzyme (CAZyme) categories. They also directly measured fecal short-chain fatty acids (SCFAs) and fecal polyamines using GC-MS and LC-MS/MS assays in their own cohort, then correlated these metabolite levels with microbial gene pathways.
The core dataset consisted of 94 PD patients and 73 controls in Japan, whose fecal samples underwent shotgun sequencing plus metabolomic profiling. This Japanese dataset was meta-analyzed alongside five previously reported shotgun sequencing datasets from the USA, Germany, China (two separate cohorts, China1 and China2), and Taiwan. Together these represent multiple independently recruited PD and control cohorts spanning several countries.
Across all six datasets, alpha-diversity was consistently increased in PD compared to controls. Taxonomic analysis showed Akkermansia muciniphila was increased in PD, while Roseburia intestinalis and Faecalibacterium prausnitzii were decreased. Genes for riboflavin and biotin biosynthesis, along with five of six CAZyme categories, were markedly decreased in PD, and fecal SCFAs and polyamines were significantly reduced and positively correlated with these biosynthesis genes. Notably, the specific bacteria responsible for the decreased riboflavin and biotin biosynthesis differed between the Japan/USA/Germany group and the China1/China2/Taiwan group.
The consistent cross-country decline in microbial riboflavin and biotin biosynthesis, paired with reduced SCFAs and polyamines, points to a reproducible functional gut microbial signature in PD that goes beyond any single taxon. Because different bacterial taxa contribute to the same functional deficits in different geographic populations, functional pathway analysis may be a more robust and generalizable biomarker strategy than taxonomic composition alone. These findings support further investigation into vitamin biosynthesis and metabolite pathways as potential mechanistic contributors to, or biomarkers of, PD across diverse populations.
This study examined the relationship between the gut microbiome and type 2 diabetes (T2D), a link that had previously produced inconsistent results across research. Rather than stopping at the species level, the researchers looked for strain-specific microbial features that could help explain those inconsistencies. They analyzed shotgun metagenomic sequencing data to identify both which species were altered in T2D and which functional genes and strain-level differences within those species tracked with disease risk.
The analysis drew on 8,117 shotgun metagenomes pooled from 10 cohorts across the United States, Europe, Israel, and China. These cohorts included individuals with type 2 diabetes, individuals with prediabetes, and normoglycemic controls. This cross-cohort, multi-country design allowed the researchers to test whether microbial signatures held up across different populations rather than being specific to a single study.
Nineteen phylogenetically diverse bacterial species showed consistent dysbiosis associated with T2D, including enrichment of Clostridium bolteae and depletion of Butyrivibrio crossotus. These species-level shifts corresponded to functional changes at the community level, such as perturbations in glucose metabolism, that could plausibly contribute to disease processes. Beyond species, the study found that within-species strain diversity in 27 species, including Eubacterium rectale, explained differences in T2D risk between individuals, and some of these strain differences were tied to specific genes involved in horizontal gene transfer and processes like quorum sensing.
By resolving microbiome associations down to the strain level, this study suggests that species-level analyses alone can miss biologically important variation driving T2D risk. The identification of specific genes and functional pathways, rather than just taxonomic shifts, points toward possible mechanisms linking gut bacteria to glucose metabolism disruption. Because these signatures were derived across multiple international cohorts, they represent a more robust and generalizable foundation for future work on microbiome-based risk markers or interventions in T2D.
This study examined how environmental, socioeconomic, and health factors relate to gut microbiome composition at both the species and strain level. The researchers used deeply sequenced metagenomic data to look for associations between bacterial species and human phenotypes or situational factors. They also incorporated strain-level phylogenetic information to see whether it changed the picture drawn by species-level analysis alone.
The cohort consisted of 1,871 people living in 19 isolated villages in the Mesoamerican highlands of western Honduras. This is a community-based population from a non-industrialized setting, a group the authors note remains underrepresented in deep metagenomic sequencing studies. The abstract does not give further demographic breakdowns such as age or sex distribution.
Socioeconomic factors accounted for 51.44% of the total associations identified between gut bacterial species and host phenotypes or situational factors, making them the dominant category. A meta-analysis across several datasets identified species associated with body mass index, consistent with prior research. Adding strain-level phylogenetic information changed the overall picture of microbiome-phenotype relationships, most notably for household wealth: wealthier individuals were found to harbor different strains of Eubacterium rectale than less wealthy individuals.
The findings suggest that socioeconomic conditions, not just health status, are major drivers of gut microbiome variation and should be accounted for in microbiome research. The strain-level wealth association with Eubacterium rectale indicates that species-level analysis alone can miss functionally relevant microbial variation tied to social conditions. The authors conclude that gut microbiome surveillance in non-industrialized populations could inform broader understanding of individual and public health.
This case-control study investigated whether the gut microbiome is altered in women with endometriosis compared to women without the condition. Researchers used shotgun metagenomics to characterize microbial species composition and applied the KEGG database to annotate functional pathways. They also examined estrobolome-related enzymes to assess whether microbiome-driven estrogen metabolism differs in endometriosis, and used a clustering algorithm (Partitioning Around Medoids) to characterize microbial community profiles across the population.
The study analyzed a subsample of 1000 women drawn from the Estonian Microbiome cohort. Within this group, 136 women had a diagnosis of endometriosis and 864 women served as controls. This is a large, population-based cohort rather than a small clinical sample, giving the analysis substantial statistical power.
The abstract indicates that diversity analyses were performed to assess alpha- and beta-diversity along with differential abundance of species and functional gene pathways between women with and without endometriosis, but the specific results are cut off in the provided text. The study also mapped metagenomic reads to estrobolome-related enzyme sequences specifically to evaluate whether microbiome-estrogen metabolism interactions are altered in endometriosis. No specific taxa, diversity metrics, or estrobolome findings can be reported here because the abstract text was truncated before the results were presented.
By using a large, well-characterized population cohort and combining taxonomic, functional, and estrobolome-focused analyses, this study is positioned to help clarify whether gut microbial dysbiosis contributes to endometriosis pathogenesis. If differences in microbial composition or estrogen-metabolizing functional pathways are confirmed, this could support the gut microbiome as a potential diagnostic or therapeutic target. However, because the results portion of the abstract was not fully available, firm conclusions about specific microbial targets cannot yet be drawn from this summary alone.
This study examined how exclusive enteral nutrition (EEN), a first-line therapy for pediatric Crohn's disease, produces its protective effects on the gut. The researchers used integrated multi-omics analysis of fecal microbiota and metabolites to identify functional network clusters associated with treatment response. They further validated these diet-driven microbiome changes using gut chemostat cultures and by transferring microbiota into germ-free Il10-deficient mice.
The abstract describes a prospective pediatric cohort of treatment-naive Crohn's disease patients, registered as German Clinical Trials DRKS00013306, who were followed as they began EEN therapy. Exact patient numbers are not given in the abstract. Findings from this human cohort were then extended experimentally using gnotobiotic (germ-free) Il10-deficient mice colonized with patient-derived microbiota.
Multi-omics analysis identified individually variable microbiome network clusters, with Lachnospiraceae and medium-chain fatty acids emerging as protective features associated with EEN response. Bioorthogonal non-canonical amino acid tagging pinpointed specific bacterial species that responded to medium-chain fatty acids, and metagenomic analysis revealed high strain-level dynamics during EEN therapy. When patient-derived microbiota were transferred into gnotobiotic Il10-deficient mice, individual patient-specific strain signatures could either prevent or cause inflammatory bowel disease-like inflammation.
The findings show that EEN operates through explicit, functional, and highly individualized changes in the fecal microbiome rather than a single uniform mechanism. Because protective effects were tied to specific strains and metabolites such as medium-chain fatty acids, this suggests that microbiome and metabolite profiling could help predict or enhance EEN response in pediatric Crohn's disease. The demonstration that individual strain signatures can causally prevent or induce inflammation in a gnotobiotic model also supports strain-level and metabolite-targeted approaches as a path toward more precise dietary or microbial therapies for Crohn's disease.
This study examined the fecal microbiota of Korean patients with inflammatory bowel disease (IBD) to identify taxonomic biomarkers linked to clinical phenotypes, disease severity, and prognosis. Fecal samples were amplified by PCR and sequenced on the Illumina MiSeq platform, then analyzed with the EzBioCloud database and a 16S microbiome pipeline. The researchers compared bacterial community composition and diversity across ulcerative colitis (UC), Crohn's disease (CD), and healthy individuals, and further examined how composition varied with disease severity, extent, and activity within each IBD subtype.
The cohort consisted of 70 patients with ulcerative colitis, 39 patients with Crohn's disease, and 100 healthy control individuals, all in Korea. This design allowed direct comparison of fecal microbiota between the two major IBD subtypes and non-IBD controls. Within the UC and CD groups, patients were further stratified by clinical characteristics such as disease severity, disease extent, activity, and ileocolonic involvement.
Alpha-diversity of fecal bacteria was significantly lower in both UC and CD compared to healthy controls, and overall community composition differed significantly between UC, CD, and controls, as well as between UC and CD themselves. Within UC, alpha-diversity decreased further with greater disease severity and greater disease extent, and community composition varied significantly according to how much of the colon was affected. The study identified specific bacterial biomarkers: 9 for UC severity, 6 for UC extent, 5 for CD disease activity, and 3 for ileocolonic involvement in CD. Lachnospiraceae and Ruminococcus gnavus emerged as biomarkers associated with better prognosis.
These findings support the use of fecal microbiota profiling as a potential tool for characterizing IBD phenotype, severity, and extent in clinical practice, rather than relying solely on endoscopic or symptom-based assessment. The identification of distinct bacterial biomarkers for UC and CD subtypes suggests microbiota signatures could eventually assist in prognosis and monitoring of disease course. Because reduced diversity tracked with worsening severity and extent, restoring microbial diversity and specific taxa such as Lachnospiraceae and Ruminococcus gnavus may represent a meaningful target for future therapeutic or monitoring strategies in IBD.
This study examined the gut microbiome in irritable bowel syndrome (IBS) using a multi-omics approach that went beyond compositional profiling alone. The researchers combined 16S rRNA gene sequencing, metatranscriptomics, and untargeted metabolomics on fecal samples to characterize microbial function rather than just which taxa were present. They also modeled inter-omic functional relationships using transcript-to-gene ratios and microbial metabolic modeling. The aim was to identify robust microbial signatures of IBS and its bowel habit subtypes that prior composition-only studies had failed to establish.
Fecal samples were collected from a racially and ethnically diverse cohort of 495 subjects, including 318 IBS patients and 177 healthy controls. Of these, 486 samples were analyzed by 16S rRNA gene sequencing, 327 by metatranscriptomics, and 368 by untargeted metabolomics. Statistical models adjusted for age, sex, race/ethnicity, BMI, diet, and anxiety (measured by HAD-Anxiety) to isolate IBS-associated differences.
IBS was associated with global alterations in microbiome composition, as detected by both 16S rRNA sequencing and metatranscriptomics. IBS was also linked to broad changes in microbiome function, evident across predicted metagenomics, metatranscriptomics, and metabolomics data. These associations held after adjusting for age, sex, race/ethnicity, BMI, diet, and anxiety, indicating the signal was not simply explained by these covariates. The abstract text provided ends before detailing the specific differentially abundant microbes, transcripts, or metabolites identified.
By layering metatranscriptomics and metabolomics on top of standard 16S sequencing, this approach helps explain why compositional studies alone have struggled to produce reliable microbial signatures of IBS. The finding that IBS involves coordinated shifts in microbial composition and function, not composition changes alone, supports using multi-omics and machine learning classifiers as tools for future IBS biomarker development. This framework may also help distinguish IBS bowel habit subtypes functionally, which could inform more targeted diagnostic or therapeutic strategies.
This study examined whether duodenal mucosa-associated microbiota, rather than stool microbiota, is linked to hyperglycemia and the pre-diabetic state. Paired stool and duodenal samples were compared between hyperglycemic and normoglycemic individuals. The researchers also assessed the duodenal microenvironment directly, measuring tissue oxygen saturation with T-Stat, serum inflammatory markers, and zonulin as a marker of gut permeability. Bioinformatic analysis was used to predict how shifts in duodenal bacterial composition might affect nutrient metabolism.
The study compared 33 subjects with hyperglycemia (HbA1c of 5.7% or higher and fasting plasma glucose above 100 mg/dl) to 21 normoglycemic subjects. Both groups provided paired stool and duodenal samples, allowing a direct comparison of gut-region-specific microbiota. The abstract does not give further demographic detail beyond this glycemic classification and sample size.
Hyperglycemic subjects had a significantly higher duodenal bacterial count than normoglycemic subjects, along with increased pathobionts and reduced beneficial flora. Bacterial overload correlated with higher serum zonulin and higher TNF-alpha, and the hyperglycemic duodenum showed reduced oxygen saturation, higher total leukocyte count, and lower IL-10, indicating a proinflammatory, more permeable, less oxygenated tissue environment. Unlike stool flora, duodenal bacterial variability tracked with glycemic status and was predicted to adversely affect nutrient metabolism.
The findings suggest that duodenal, rather than stool, microbiota may be a more relevant and sensitive marker of dysbiosis linked to hyperglycemia and pre-diabetes. The association between bacterial overload, gut permeability (zonulin), local hypoxia, and systemic inflammation points to a plausible mechanism connecting small intestinal dysbiosis to metabolic disease progression. This supports greater research attention on duodenal mucosal sampling, rather than stool alone, when investigating microbiome contributions to glycemic disorders.
This study examined how gut microbiota composition changes with age in both healthy individuals and people with colorectal cancer (CRC). Researchers pooled 11 metagenomic data sets related to CRC from the curated Metagenomic Data R package and corrected for batch effects. Samples were split into three age groups, and species composition, diversity, and age-associated bacterial shifts were compared between healthy and CRC groups. The team then used the age-differentiated bacteria they identified to build an age prediction model and a separate CRC risk prediction model.
The analysis drew on previously collected, publicly available metagenomic sequencing data rather than a newly recruited cohort. It combined healthy individuals and colorectal cancer patients across 11 existing metagenomic data sets, then stratified all of these samples into three age groups for comparison. The abstract does not give an overall sample size or specific countries of origin for these pooled data sets.
Gut microbiota structure and composition differed significantly across the three age groups in both healthy individuals and CRC samples. Bacteroides vulgatus abundance declined with older age, while Bacteroides fragilis abundance increased with aging. Seven bacterial species were identified whose abundance rises consistently with age, and several potentially pathogenic species, including Escherichia coli, Butyricimonas virosa, Ruminococcus bicirculans, Bacteroides fragilis, and Streptococcus vestibularis, showed abundance patterns tied to aging.
Age-related shifts in specific gut bacteria, particularly the decline of Bacteroides vulgatus and rise of Bacteroides fragilis, may help explain why aging is a risk factor for colorectal cancer. The age-differentiated bacteria identified here allowed construction of both an age prediction model and a CRC risk prediction model, suggesting gut microbiota signatures could support age-aware cancer risk stratification. This points toward using microbiota-based biomarkers to refine CRC screening approaches that account for a patient's biological aging trajectory rather than chronological age alone.
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 prospective, observational study examined whether gut microbiota composition is associated with immune responses and adverse events following COVID-19 vaccination. Researchers compared two vaccine types: the inactivated CoronaVac vaccine and the mRNA BNT162b2 vaccine. They performed shotgun metagenomic sequencing on stool samples collected at baseline and again one month after the second vaccine dose. Immune responses were assessed using a SARS-CoV-2 surrogate virus neutralisation test and a spike receptor-binding domain IgG ELISA.
The study included 138 adult COVID-19 vaccine recipients, of whom 37 received CoronaVac and 101 received BNT162b2. Stool samples from these vaccinees were collected at two time points, baseline and one month post second dose, for shotgun metagenomic analysis. The abstract does not specify additional demographic details such as age range, sex distribution, or geographic location.
Immune responses were significantly lower in CoronaVac recipients than in BNT162b2 recipients. Among CoronaVac vaccinees, higher persistent levels of Bifidobacterium adolescentis were associated with higher neutralising antibody responses, and their baseline gut microbiome was enriched in carbohydrate metabolism pathways. In BNT162b2 vaccinees, neutralising antibody levels correlated positively with the total abundance of flagellated and fimbriated bacteria, including Roseburia faecis. Prevotella copri and two Megamonas species were enriched in individuals who experienced fewer adverse events after vaccination.
These findings suggest that baseline gut microbiota composition and function may help explain individual variation in vaccine immunogenicity and tolerability. Specific taxa, such as Bifidobacterium adolescentis, Roseburia faecis, Prevotella copri, and Megamonas species, and microbial functional features like carbohydrate metabolism pathways and flagellar or fimbrial machinery, appear linked to stronger antibody responses or fewer adverse events. This raises the possibility that gut microbiome profiling or modulation could eventually inform strategies to improve vaccine efficacy or reduce adverse events. Further research is needed to determine whether these associations are causal and generalizable across vaccine platforms and populations.
This study used shotgun metagenomic sequencing to investigate both the oral and gut microbiome in Parkinson's disease (PD). Rather than looking only at which microbes were present, the researchers also examined functional changes in the microbiome, meaning shifts in the genes microbes carry and express. The goal was to determine whether oral and gut microbial communities are connected in PD and whether that connection corresponds to altered microbial function.
The abstract does not report a specific cohort size, age range, or recruitment site. It states only that PD patients were compared with healthy controls, with paired oral and gut samples analyzed by shotgun metagenomics. No further demographic or clinical details are given in the abstract.
Taxonomic composition of both the oral and gut microbiome differed significantly between PD patients and healthy controls (P = 0.003 and 0.001, respectively). Oral Lactobacillus was more abundant in PD patients and was associated with opportunistic pathogens found in the gut (FDR-adjusted P < 0.038). Functional analysis showed that microbial gene markers for glutamate and arginine biosynthesis were downregulated, while antimicrobial resistance gene markers were upregulated, in PD patients compared with healthy controls (all P < 0.001).
The findings suggest an oral-gut microbial axis in PD, where oral dysbiosis may be linked to gut dysbiosis rather than the two occurring independently. The downregulation of glutamate and arginine biosynthesis genes points to functional, not just compositional, disruption of the microbiome in PD, which could affect neurotransmitter-related and metabolic pathways. The rise in antimicrobial resistance gene markers raises additional questions about microbial community stability and susceptibility to pathogenic colonization in PD patients.
This study examined the gut microbiome composition of children with Autism Spectrum Disorder (ASD) compared to neurotypical (NT) children. The researchers compiled ten publicly available amplicon and metagenomic sequencing datasets and combined them with new data from an internal cohort. All datasets were unified using standardized pre-processing methods and then analyzed together in a comprehensive meta-analysis. The goal was to identify reproducible ASD-specific microbiome signatures and to test associations between the microbiome and 52 clinical and demographic variables across multiple patient subsets.
The analysis drew on children diagnosed with ASD and their neurotypical counterparts across ten publicly available sequencing datasets. It also incorporated a new internal cohort described as the largest ASD cohort compiled to date, though the abstract does not give an exact sample size. Subjects were further divided into subsets based on variables including age, sex, and bowel function to test for confounding effects.
Differentially abundant taxa between ASD and NT children were found to depend heavily on age, sex, and bowel function, identifying these as major potential confounders in case-control ASD microbiome studies. Certain taxa, including the strains Bacteroides stercoris t__190463 and Clostridium M bolteae t__180407, and the species Granulicatella elegans and Massilioclostridium coli, only showed differential abundance between ASD and NT children after subjects with bowel dysfunction were excluded. Adjusting statistical models for age, sex, and bowel function changed which taxa appeared significantly associated with ASD, underscoring how much these variables shape reported microbiome signatures.
The findings suggest that much of the prior inconsistency in reported ASD gut microbiome signatures may stem from unaccounted confounders such as age, sex, and bowel function rather than true absence of a signal. Future ASD microbiome research needs to systematically control for these variables to produce reproducible, comparable results across studies. The identification of specific taxa that emerge only after removing bowel dysfunction subjects points toward the possibility of more precise ASD patient subgroups defined by microbiome features.
This study tracked the co-development of gut microbiomes and metabolomes in mothers and their infants, spanning late pregnancy through the infant's first year of life. Researchers used longitudinal multi-omics data to characterize how microbial communities and their metabolic outputs change and interact over this period. A central focus was mobile genetic elements, the segments of DNA that can move between bacterial species, and how they might transfer from mother to infant. The study also examined infant metabolomes and serum cytokine signatures in relation to feeding type.
The study followed a cohort of 70 mother-infant dyads, or pairs, sampled longitudinally from late pregnancy to the infant's first year. Both maternal and infant gut microbiomes and metabolomes were profiled across this timeframe. Infants were also grouped by feeding method, comparing those who received regular formula, extensively hydrolyzed formula, or exclusive breastfeeding. The abstract does not provide further demographic or geographic details about the cohort.
The researchers discovered large-scale interspecies transfer of mobile genetic elements from mothers to infants, many of which carried genes linked to diet-related adaptations. Infant gut metabolomes were less diverse overall than maternal metabolomes, yet they contained hundreds of unique metabolites and microbe-metabolite associations not seen in the mothers. Metabolomes and serum cytokine signatures differed between infants fed regular formula and those exclusively breastfed, while infants fed extensively hydrolyzed formula did not show this same distinction. These findings indicate that infant gut microbial and metabolic development follows its own distinct trajectory rather than simply mirroring the maternal state.
The findings expand the concept of vertical transmission of the gut microbiome beyond the transfer of whole microbial strains to include mobile genetic elements carrying functionally relevant, diet-related genes. This suggests maternal microbiomes can shape infant microbial capabilities through gene transfer even without direct strain colonization. The feeding-related differences in infant metabolomes and cytokine signatures also point to formula composition, particularly the degree of hydrolysis, as a factor that may influence early immune and metabolic development. Together, these insights could inform how maternal and infant gut ecosystems are considered in supporting healthy immune and cognitive development during the perinatal period.
This study examined the interaction between the gut microbiota and colorectal cancer (CRC) using metagenomic sequencing data. The researchers built a paired-sample design to compare microbial species composition between CRC patients and controls. They constructed a co-occurrence network to assess microbial interactions and used random forest models to identify species that could serve as diagnostic biomarkers distinguishing CRC from control samples.
The analysis drew on 709 metagenomic samples from six independent projects deposited in the GMrepo database. After matching, the final paired cohort consisted of 86 CRC patients and 86 matched healthy controls from six countries. In total, 484 microbial species and 166 related genera were included in the analysis.
The study confirmed previously reported associations between Fusobacterium nucleatum and species from the genera Peptostreptococcus, Porphyromonas, and Prevotella in CRC. It also identified new associations not previously emphasized, including Parvimonas micra and Collinsella tanakaei. In CRC patients specifically, Bacteroides uniformis and Collinsella tanakaei showed a positive correlation with one another.
By combining paired-sample comparison, co-occurrence network analysis, and machine learning across a large multi-country dataset, this work strengthens the case for a defined microbial signature associated with CRC. The identification of novel species-level associations, such as Parvimonas micra and Collinsella tanakaei, points to candidate biomarkers that could refine microbiota-based CRC diagnostic panels. These findings support further validation of a precise, quantitative microbiota panel for CRC detection.
This study used whole-genome sequencing (WGS) data to characterize the gut microbiota associated with colorectal cancer (CRC) across populations from different geographic regions. The researchers aimed to clarify how intestinal microbial composition and structure differ regionally in CRC patients. They also set out to build and test CRC risk prediction models that account for these regional differences. Species-level and genus-level relative abundance, community composition, and inter-bacterial correlations were all examined as part of this analysis.
The analysis drew on a metagenomic dataset of 601 samples collected from six countries, sourced from the GMrepo and NCBI public databases. This means the study population was not a single new patient cohort but rather a pooled set of previously deposited whole-genome sequencing samples spanning multiple national and regional populations. Because the data came from public repositories, the paper does not describe individual-level demographic details beyond the country-level groupings used for regional comparison.
Intestinal bacterial community composition varied by region, and the specific bacteria distinguishing CRC cases from controls were inconsistent from one region to another. Despite this regional variability, Peptostreptococcus stomatis and Fusobacterium nucleatum were consistently found across all six countries at the species level. Peptostreptococcus stomatis (species level) and Peptostreptococcus (genus level) stood out as key CRC-associated bacteria correlated with other microbial taxa across regions. Notably, regional differences had little effect on the accuracy of the CRC risk prediction models, and Peptostreptococcus stomatis remained an important predictive variable in every region tested.
The findings position Peptostreptococcus stomatis as a common, high-risk pathogen linked to CRC worldwide, largely independent of geographic and population differences. This consistency suggests that Peptostreptococcus stomatis could serve as a reliable biomarker for CRC risk prediction models applied across diverse populations. Because regional variation in gut bacteria did not undermine model accuracy, these results support the feasibility of developing generalizable, microbiome-based CRC screening tools rather than region-specific ones.
This study investigated changes in intestinal bacterial communities in healthy people compared with patients who have inflammatory bowel disease (IBD) or colorectal cancer (CRC). The researchers performed metagenome-wide association studies on fecal samples to characterize these differences. They examined bacterial community structure, relative abundance, functional prediction, differentially abundant bacteria, and co-occurrence networks across the three groups.
The analysis drew on fecal metagenomic data from 290 healthy subjects, 512 IBD patients, and 285 CRC patients. Healthy and CRC samples were obtained from the European Nucleotide Archive under study accession numbers PRJEB6070, PRJEB7774, PRJEB27928, PRJEB12449, and PRJEB10878. IBD patient data came from the Integrated Human Microbiome Project via the Human Microbiome Project Data Portal, making this a secondary analysis of existing public metagenomic datasets rather than a newly recruited cohort.
The overall bacterial community structure differed significantly between both disease groups and healthy subjects. IBD patients showed low intestinal bacterial diversity, while CRC patients showed high intestinal bacterial diversity, relative to healthy controls. At the phylum level, the relative abundance of Firmicutes changed in IBD patients, indicating that the two diseases are associated with distinct, and in some respects opposite, shifts in gut microbial ecology.
The findings support the idea that IBD and CRC are each linked to characteristic, disease-specific patterns of intestinal bacterial community disruption rather than a single generic form of dysbiosis. Diversity metrics and taxonomic composition, such as Firmicutes abundance, may help distinguish these conditions from each other and from healthy states. This work suggests that large-scale metagenomic comparisons across public datasets can help identify candidate microbial features for future diagnostic or mechanistic research in gut-related diseases.
This study examined the fecal gut metagenomes of three common intestinal diseases: Crohn's disease, ulcerative colitis, and colorectal cancer. The researchers performed a meta-analysis across 13 separate fecal metagenome data sets spanning these three conditions. Their goal was to identify microbial species and metabolic pathways that change consistently across multiple data sets for each disease, and to compare these signatures across diseases. They also built multidisease diagnostic models based on the markers they identified.
The abstract does not describe a single new patient cohort but rather a meta-analysis pooling 13 existing fecal metagenome data sets covering Crohn's disease, ulcerative colitis, and colorectal cancer patients and controls. No specific sample sizes, ages, or geographic origins are given in the abstract. This can be honestly described as a secondary analysis of multiple public or previously published metagenomic cohorts rather than a single primary study population.
The analysis identified 87 marker species and 65 marker pathways that were consistently altered across multiple data sets of the same disease. These markers grouped into disease-specific and disease-common clusters with distinct phylogenetic relationships: species specific to Crohn's disease were phylogenetically closely related, while colorectal cancer-specific species were more phylogenetically distant from one another. Notably, ulcerative colitis-specific species were phylogenetically closer to colorectal cancer-associated species, consistent with the known elevated colorectal cancer risk in ulcerative colitis patients. Marker species within the same cluster shared metabolic preferences, and disease cases showed more tightly coordinated microbial changes than controls, suggesting a more stressed, selective gut environment in disease states, with a subset of markers also correlating with an indicator of gut barrier (leaky gut) dysfunction.
By mapping how disease-specific and disease-common microbial signatures relate phylogenetically and metabolically, this work supports the development of multidisease diagnostic models that could help distinguish between conditions with overlapping symptoms, such as Crohn's disease, ulcerative colitis, and colorectal cancer. The finding that ulcerative colitis markers resemble colorectal cancer markers phylogenetically offers a microbiome-based rationale for the elevated cancer risk seen in ulcerative colitis. The link between marker species and leaky gut indicators further ties gut dysbiosis to compromised intestinal barrier function. Overall, the study suggests cross-disease microbiome comparisons can sharpen diagnostic precision beyond single-disease marker panels.
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 then tested the clinical relevance of these microbial associations using immunocompetent mice colonized with patient microbiota and challenged with breast cancer and chemotherapy.
The human cohort consisted of 76 early breast cancer patients enrolled in the CANTO prospective study, which was designed to record side effects of breast cancer clinical management. A total of 121 fecal specimens were analyzed, including 45 patients with paired samples collected before and after chemotherapy. Findings from these patients were further tested in immunocompetent mice colonized with the patients' fecal 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 composition of these bacteria, and the same taxa were linked to weight gain and neurological side effects of breast cancer therapies.
The findings suggest the gut microbiome could serve as a modifiable factor affecting both cancer prognosis and the tolerability of chemotherapy in early breast cancer. Identifying and validating these commensal bacteria could open avenues for microbiome-based strategies to improve treatment outcomes and reduce side effects. The authors emphasize that these results, from adjuvant and neoadjuvant settings, require prospective validation before clinical application.
This study examined the gut microbiota composition of people recovering from COVID-19 and tested whether specific bacterial taxa correlated with the persistent symptoms many patients report after hospital discharge. Researchers used 16S rRNA gene sequencing on stool samples to profile bacterial communities. They then compared these profiles between recovered patients and healthy controls and looked for statistical correlations between individual taxa and symptom severity.
The cohort consisted of 15 recovered healthcare workers (HCWs) who had been diagnosed with COVID-19, with stool samples collected three months after their hospital discharge. A comparison group of 14 healthy controls (HCs) without COVID-19 infection provided samples over the same May to July 2020 window. This was a small, real-world clinical cohort rather than a public dataset, limited to healthcare workers at what appears to be a single site or study period.
Recovered HCWs had reduced bacterial diversity three months after discharge compared to healthy controls, along with a higher relative abundance of opportunistic pathogens and a lower relative abundance of beneficial bacteria. Escherichia unclassified correlated positively with fatigue, chest tightness after activity, and myalgia. Intestinibacter bartlettii correlated positively with anorexia and fatigue, while Faecalibacterium prausnitzii correlated negatively with chest tightness after activity, meaning lower levels of this beneficial species tracked with worse symptoms.
The findings suggest that gut microbiota disruption persists well beyond acute COVID-19 infection and may be mechanistically linked to the lingering symptoms known as post-COVID or long-COVID sequelae. The shift toward opportunistic pathogens and away from beneficial species such as Faecalibacterium prausnitzii points to the gut as a potential contributor to, or biomarker of, prolonged recovery. This raises the possibility that microbiota-targeted monitoring or intervention could help identify or manage patients at risk of persistent post-discharge symptoms.
This study examined the composition and function of the gut microbiome in Parkinson's disease (PD) patients using shotgun metagenomic sequencing. Researchers compared fecal microbial profiles between PD patients and healthy controls to identify compositional and functional differences. They also analyzed whether microbial features correlated with clinical measures of disease duration and severity. Functional gene categories, including protein family and carbohydrate-active enzyme databases, were examined alongside taxonomic composition.
The cohort consisted of 39 PD patients and 39 corresponding healthy spouses of those patients, all located in central China. Using spouses as controls limits shared environmental and dietary variability between the two groups. Fecal samples were collected from all 78 individuals for shotgun metagenomic sequencing.
Gut microbial composition was significantly altered in PD patients compared to healthy spouses, with Bilophila wadsworthia enrichment identified in PD patients for the first time in this context. A random forest model built from microbiome differences discriminated PD patients from controls with an area under the receiver operating characteristic curve of 0.803. Klebsiella and Parasutterella abundances were positively correlated with PD duration and severity, while hydrogen-generating Prevotella was negatively correlated with disease severity. Functional analysis showed increased branched-chain amino acid-related proteins and decreased GH43 carbohydrate-active enzyme genes in the PD group.
The novel finding of Bilophila wadsworthia enrichment suggests sulfite-reducing, hydrogen sulfide-producing bacteria may play an underrecognized role in PD-associated gut dysbiosis. The strong discriminatory performance of the microbiome-based random forest model points toward gut microbial signatures as potential non-invasive biomarkers for PD. Correlations between specific taxa and disease severity suggest the gut microbiome may track with PD progression, not just its presence. Altered branched-chain amino acid and carbohydrate metabolism pathways further implicate microbial metabolic shifts as relevant to PD pathophysiology.
This prospective cohort study compared the composition of the gastrointestinal microbiota between patients with Parkinson's disease (PD) treated only with Levodopa and healthy controls. Researchers collected fecal samples and used Next-Generation Sequencing to characterize microbiota composition. Detailed demographic and medical history data were gathered through questionnaires to contextualize the microbial findings. The study's endpoint was the difference in gut microbiota composition between the two groups.
The study enrolled 27 hospitalized PD patients with well-controlled symptoms, recruited from a single academic hospital between July 2019 and July 2020. The control group consisted of 44 healthy subjects matched to the PD patients by age. All participants provided fecal samples for microbiota analysis. The design was a single-center prospective study rather than a multi-site trial.
PD patients showed a higher abundance of the Bacteroides phylum, the class Corynebacteria within Actinobacteria, and the class Deltaproteobacteria within Proteobacteria compared to controls. At the genus level, Butyricimonas, Robinsoniella, and Flavonifractor were more abundant in PD patients. The species Akkermansia muciniphila, Eubacterium biforme, and Parabacteroides merdae were also identified as more common in the gut microbiota of PD patients than in healthy controls.
The findings reinforce that PD is associated with a distinct gut microbiota profile, supporting the broader hypothesis that gut microbiome alterations may play a role in PD predisposition or progression. Identifying specific taxa enriched in PD patients, such as Akkermansia muciniphila and Deltaproteobacteria, could inform future work on microbial biomarkers for the disease. These results also lay groundwork for exploring whether modifying gut microbiota composition might influence PD-related gastrointestinal or neurological outcomes.
This study examined whether gut microbiota alterations previously reported in people with HIV (PWH) are truly attributable to HIV infection itself, or whether they mainly reflect sexual practice, a recently identified major confounder of gut microbiota composition. The researchers compared gut microbiota profiles between antiretroviral-treated PWH and seronegative controls while carefully accounting for sex and sexual practice as potential confounders. They also assessed whether any HIV-associated microbiota signature correlated with systemic inflammation and age-related noncommunicable comorbidities.
The study drew on a well-powered subset of participants from the AGEhIV Cohort, comprising antiretroviral-treated people with HIV and HIV-seronegative controls. Cases and controls were matched for age, body-mass index, sex, and sexual practice, allowing men who have sex with men (MSM) and non-MSM participants of both sexes to be compared directly. This matched design let the investigators isolate HIV-associated effects from those driven by sexual practice.
Significant gut microbiota differences were found in PWH regardless of sex or sexual practice, including enrichment of Gammaproteobacteria, depletion of Lachnospiraceae and Ruminococcaceae, and reduced alpha diversity. Separately, MSM (both male and female) showed a distinct microbiota signature marked by Prevotella enrichment and increased alpha diversity, which was linked to receptive anal intercourse rather than HIV status. The HIV-associated microbiota signature also correlated with inflammatory markers such as suPAR, with nadir CD4 count, and with the prevalence of age-associated noncommunicable comorbidities.
By disentangling HIV-associated dysbiosis from the microbiota effects of sexual practice, the study shows that gut microbiota alterations in treated HIV disease are a genuine feature of the disease itself, not an artifact of behavior-related confounding. The association with inflammatory markers and noncommunicable comorbidities supports the idea that gut dysbiosis contributes to the elevated chronic disease burden seen in aging PWH on antiretroviral therapy. This distinction argues for considering both HIV status and sexual practice separately when interpreting or designing microbiome-targeted interventions in this population.
This study examined the gut microbiome of Mexican children affected by obesity using metagenomic shotgun sequencing of fecal DNA. Researchers characterized bacterial, archaeal, and viral communities and related them to metabolic factors and fecal short-chain fatty acid concentrations. The goal was to identify microbial features distinguishing obese from normal weight children in a population where childhood obesity is a major public health issue.
The study included a cohort of Mexican children classified as normal weight or obese, from whom fecal samples were collected for DNA extraction. Samples were sequenced on an Illumina HiSeq 2500 platform, and participants also had clinical metadata and metabolic factors assessed. The abstract does not give an exact sample size, so no specific number of children can be stated.
Contrary to expectations, no remarkable overall dysbiosis of bacteria, archaea, or viruses distinguished the obese group from the normal weight group. However, the archaeal community showed an increase of unclassified Methanobrevibacter spp. in obese children. Gut bacterial communities clustered into three enterotypes, with normal weight children predominantly showing an Enterotype 3 dominated by Ruminococcus spp., while obese children predominantly showed an Enterotype 2 dominated by Prevotella spp. Megamonas spp. were over-represented in obese children, whereas Oscillospiraceae were depleted in the same group and these microbiome changes correlated with clinical metadata.
The findings suggest that childhood obesity in this Mexican cohort is characterized less by broad microbial dysbiosis and more by specific compositional shifts, such as enterotype distribution and particular taxa like Megamonas, Oscillospiraceae, and unclassified Methanobrevibacter. Correlating microbiome changes with clinical metadata indicates the gut microbiome could help stratify children by metabolic phenotype. This points to enterotype and specific taxon abundance, rather than general dysbiosis, as more useful targets for understanding or addressing pediatric obesity in this population.
This study examined whether gut microbiome changes linked to colorectal cancer (CRC) are reproducible across different patient populations and research cohorts. The researchers performed a meta-analysis of five publicly available fecal metagenomic datasets plus two newly collected cohorts, then validated their findings in two additional independent cohorts. They looked at microbial richness, functional pathway shifts, and specific microbial genes, including one involved in choline metabolism, to identify consistent CRC-associated signals.
The analysis drew on a total of 969 fecal metagenomes pooled across five existing public datasets, two newly generated cohorts, and two independent validation cohorts. The abstract does not specify the ages, sexes, or geographic origins of the individuals within these cohorts. Collectively the samples represent a mix of colorectal cancer patients and control subjects from multiple populations.
Unlike the microbial changes typically seen in other gastrointestinal disorders, CRC was consistently associated with higher, not lower, gut microbial richness compared to controls (P < 0.01), driven partly by expansion of species normally found in the oral cavity. Functional analysis linked CRC to increased gluconeogenesis and putrefaction and fermentation pathways, while stachyose and starch degradation pathways were associated with healthy controls. Microbiome-based predictive signatures trained across multiple datasets achieved high diagnostic accuracy (average AUC of 0.84) even in datasets and cohorts not used for training. Pooled analysis also showed the choline trimethylamine-lyase gene was significantly overabundant in CRC samples (P = 0.001), tying microbial choline metabolism to disease status.
By combining heterogeneous cohorts and validating results independently, this study demonstrates that certain gut microbiome features, elevated richness, specific functional pathways, and the choline trimethylamine-lyase gene, are reproducible, cross-population biomarkers of colorectal cancer rather than dataset-specific artifacts. The strong predictive accuracy of these signatures across independent cohorts supports the feasibility of microbiome-based non-invasive diagnostic tools for CRC. The identified link between microbial choline degradation and CRC also points to a potential metabolic mechanism connecting diet, gut bacteria, and colorectal cancer risk that merits further mechanistic investigation.
This study examined the gut microbiome and fecal metabolome across the stages of sporadic colorectal cancer development, from multiple polypoid adenomas through intramucosal carcinoma to more advanced lesions. Researchers used fecal metagenomic sequencing paired with metabolomic profiling to characterize taxonomic, functional, and metabolite changes at each stage. The goal was to identify stage-specific microbial and metabolic phenotypes rather than treating colorectal cancer as a single uniform condition.
The analysis drew on fecal samples from a large cohort of 616 participants who underwent colonoscopy. This allowed the researchers to classify individuals by lesion stage, including multiple polypoid adenomas, intramucosal carcinomas, and more advanced colorectal lesions. The cohort size and colonoscopy-based staging gave the study a broad basis for comparing microbiome and metabolome features across the adenoma-to-carcinoma continuum.
Two distinct microbial patterns emerged: Fusobacterium nucleatum abundance rose significantly and continuously from intramucosal carcinoma through more advanced stages, while Atopobium parvulum and Actinomyces odontolyticus co-occurred and increased specifically in multiple polypoid adenomas and intramucosal carcinomas but not beyond. Metabolome analysis showed branched-chain amino acids and phenylalanine were significantly elevated in intramucosal carcinomas. Bile acids, including deoxycholate, were also significantly increased in multiple polypoid adenomas and intramucosal carcinomas, marking an early metabolic shift alongside the microbial changes.
The findings suggest that gut microbiota and metabolite alterations are not uniform across colorectal tumorigenesis but instead follow distinct, stage-specific trajectories. Because Atopobium parvulum, Actinomyces odontolyticus, and elevated bile acids like deoxycholate appear early, in adenomas and intramucosal carcinomas, they may serve as candidate markers for early lesion detection. The continuous rise of Fusobacterium nucleatum with advancing stage suggests it may instead track disease progression, supporting stage-specific rather than one-size-fits-all microbiome-based screening strategies.
This study investigated how the mode of delivery, caesarean section versus vaginal birth, affects the earliest acquisition and development of the infant gut microbiota during the neonatal period (up to one month of age) and into infancy. The researchers used longitudinal sampling combined with whole-genome shotgun metagenomic sequencing to track microbial colonization over multiple time points. They specifically examined whether maternal strains, particularly Bacteroides species, were successfully transmitted to babies, and whether opportunistic pathogens from the hospital environment colonized the infant gut instead. The analysis also considered related factors such as antibiotic prophylaxis during vaginal delivery and breastfeeding status.
The study analyzed 1,679 gut microbiota samples collected from 596 full-term babies born in UK hospitals, sampled at several time points during the neonatal period and later in infancy. For a subset of these participants, the researchers also collected matched samples from mothers, comprising 175 mothers paired with 178 babies. This design allowed direct comparison of maternal and infant strains to assess transmission patterns across different modes of delivery.
Babies delivered by caesarean section showed disrupted transmission of maternal Bacteroides strains compared with vaginally delivered babies. These caesarean-delivered infants also showed high-level colonization by opportunistic pathogens associated with the hospital environment, including Enterococcus, Enterobacter, and Klebsiella species. Similar, though less pronounced, disruptions were also observed in vaginally delivered babies whose mothers received antibiotic prophylaxis and in babies who were not breastfed during the neonatal period. These findings indicate that mode of delivery is a significant factor shaping the early gut microbiota.
The findings suggest that caesarean delivery interrupts the normal maternal-to-infant transmission of beneficial gut bacteria, replacing it with early exposure to opportunistic, hospital-associated pathogens. Because antibiotic prophylaxis and lack of breastfeeding produced similar though milder effects, these factors may compound the disruption seen with caesarean birth. This work highlights mode of delivery, along with related perinatal practices, as important variables to consider when studying how early microbiota disruption may influence childhood and later-life disease risk.
This study evaluated whether DAV132, a product designed to deliver activated charcoal to the late ileum, could protect the human gut microbiome during antibiotic treatment. The researchers focused on moxifloxacin, a fluoroquinolone antibiotic known to disrupt intestinal bacteria even though it is essential for treating infections. They measured fecal and plasma drug concentrations alongside shotgun quantitative metagenomics to assess microbiota richness and composition. Ex vivo adsorption of a range of other clinically relevant antibiotics was also tested.
The trial enrolled 28 human volunteers who received a 5-day clinical regimen of moxifloxacin, split into two parallel groups with or without coadministration of DAV132. Two additional control groups of 8 volunteers each were included, one receiving DAV132 alone and one receiving a nonactive substitute. All participants appear to have been healthy volunteers rather than patients being treated for an infection.
Coadministration of DAV132 reduced free fecal moxifloxacin concentrations by 99 percent while leaving plasma drug levels unaffected, indicating the antibiotic's systemic therapeutic action was preserved. Metagenomic sequencing showed that gut microbiota richness and composition were largely maintained in volunteers who received DAV132 alongside moxifloxacin, in contrast to the disruption expected from the antibiotic alone. No adverse effects were observed with DAV132 use. The product also efficiently adsorbed a wide range of other clinically relevant antibiotics when tested ex vivo.
By selectively adsorbing residual antibiotic in the distal gut without reducing systemic drug exposure, DAV132 offers a strategy to protect the microbiome without compromising an antibiotic's intended treatment effect. Its demonstrated ability to adsorb multiple antibiotic classes ex vivo suggests the approach could extend beyond moxifloxacin to broader clinical use. This points toward a potential clinical tool for limiting antibiotic-associated microbiome disruption and its downstream consequences, such as diarrhea and selection of resistant bacteria, though further trials in clinical antibiotic-use settings would be needed to confirm broader benefit.
The study investigated how fecal microbiota metabolize non-digestible oligo- and polysaccharides, using isothermal microcalorimetry to track fermentation in real time. Five substrates were tested: raffinose, melibiose, an oligo- and polysaccharide mixture produced from raffinose by levansucrase, levan synthesized from raffinose, and levan from timothy grass. Growth was assessed from heat evolution curves along with organic acid and gas production, and taxonomic shifts were profiled by 16S rDNA sequencing.
The work used pooled fecal samples as inocula rather than individual human subjects tested directly. Three fecal pools were compared: one from overweight children, one from normal-weight children, and one from healthy adult volunteers. A pure culture of Bacteroides thetaiotaomicron was included as a reference colon bacterium alongside these pooled samples.
Raffinose and melibiose promoted bifidobacteria growth across all three fecal pools, but each pool showed distinct additional responses. In the overweight children's pool, lactate-producing bacteria such as Streptococcus and Enterococcus became enriched, making lactic acid the dominant fermentation product from the short saccharides. In the normal-weight children's pool, acetic and butyric acids predominated instead, coinciding with enrichment of Catenibacterium, while in the adult pool the levans specifically promoted Bacteroides and Lachnospiraceae.
The findings indicate that fecal microbiota from overweight versus normal-weight children ferment the same prebiotic-type substrates into different metabolic end products, not just different taxa. Because overweight children's microbiota favored lactic acid production over the acetate and butyrate seen in normal-weight children, substrate choice and host metabolic status together shape fermentation outcomes. This suggests that prebiotic selection may need to be tailored by weight status or metabolic phenotype rather than applied uniformly across pediatric populations.
This study investigated whether gut microbiome composition is associated with Behcet's disease (BD), a recalcitrant multisystemic inflammatory disease that can cause irreversible blindness. The researchers used metagenomic sequencing of fecal DNA and 16S rRNA gene sequencing of saliva DNA to compare microbial composition and biological function between BD patients and healthy controls. They also performed fecal transplantation from BD patients into a mouse model of experimental autoimmune uveitis (EAU) to test whether the gut microbiome could causally contribute to disease.
The human cohort consisted of 32 active BD patients and 74 healthy controls, from whom fecal and saliva samples were collected for sequencing analysis. In addition to the human cohort, the causal experiments were carried out in B10RIII mice that were induced to develop experimental autoimmune uveitis and then received fecal transplants pooled from the active BD patients.
Fecal samples from active BD patients were enriched in Bilophila species, a sulfate-reducing bacteria (SRB), along with opportunistic pathogens such as Parabacteroides and Paraprevotella species. These patients also showed lower levels of the butyrate-producing bacteria Clostridium species and of methanogens including Methanoculleus and Methanomethylophilus species. This pattern indicates a shift in the gut microbiome of BD patients toward sulfate-reducing and opportunistic organisms and away from beneficial butyrate-producing and methanogenic taxa.
The enrichment of sulfate-reducing Bilophila alongside depletion of butyrate-producing Clostridium and methanogens suggests the gut microbiome may play a mechanistic role in BD pathogenesis rather than being a mere bystander. Transplanting BD-associated fecal microbiota into mice with experimental autoimmune uveitis was used to test this causal relationship directly, linking gut dysbiosis to an eye inflammation model relevant to BD's ocular complications. These findings point toward the gut microbiome, and specifically sulfate-reducing and butyrate-producing bacterial balance, as a potential target for understanding or intervening in BD.
This study examined differences in gut bacterial community structure between obese and normal-weight school-age children. Researchers used Illumina Miseq next-generation sequencing to perform 16S rDNA high-throughput sequencing on stool-derived intestinal flora. Bacteria were grouped into operational taxonomic units (OTUs) using the RDP 16S rRNA database, and both alpha diversity (within-sample) and beta diversity (between-sample) were calculated to characterize community differences.
The study included 39 obese school-age children and 38 normal-weight school-age children as controls. The abstract does not specify the country, exact age range, or recruitment setting beyond identifying the children as school-age. No further demographic details are given.
Obese children showed lower alpha diversity in their intestinal flora compared with normal-weight controls. Beta diversity analysis also revealed significant dissimilarity in community composition between the two groups. Relative abundance of specific bacterial taxa differed significantly between obese and normal children at multiple levels of classification, though the abstract does not name particular genera such as Bilophila or Desulfovibrio.
The findings support a link between reduced gut microbial diversity and childhood obesity, reinforcing the idea that intestinal flora composition may play a role in the disease's development. Identifying the specific bacteria that differ between obese and normal children could help clarify their functional role in obesity pathogenesis. This information may inform new prevention or treatment strategies that work by intervening on the intestinal flora of school-age children.
This study examined how the gut microbiome changes along the colorectal adenoma-carcinoma sequence, the stepwise progression from benign polyps to invasive cancer. Researchers used a metagenome-wide association study (MGWAS) on stool samples to catalogue microbial genes, strains, and functional pathways at each stage. The goal was to identify which gut microbes and functions are specifically enriched in adenoma versus carcinoma, since colorectal cancer often develops slowly from these precursor polyps and the microbiota is thought to play a direct role in that process.
The comparison groups were stool samples from patients with advanced adenoma, patients with carcinoma, and healthy control subjects. The abstract does not report specific sample sizes, ages, or geographic origin of the cohort. Beyond identifying these three clinical groups, the analysis also incorporated dietary risk-factor data, specifically relative intake of red meat versus fruits and vegetables.
The MGWAS revealed distinct sets of microbial genes, strains, and functions that were enriched in the adenoma group and in the carcinoma group compared with healthy subjects, indicating that the gut microbiome shifts in a stage-specific way along this disease sequence. A risk-factor analysis linked higher intake of red meat relative to fruits and vegetables with the outgrowth of bacteria that may help create a more hostile, pro-carcinogenic gut environment. The abstract does not name specific taxa such as Bilophila, Desulfovibrio, or sulfate-reducing bacteria, nor does it mention hydrogen sulfide, bile acids, or taurine.
By mapping microbial changes across the adenoma-carcinoma sequence, the findings support the idea that stool-based microbiome signatures could serve as biomarkers for early, non-invasive detection of colorectal adenoma or carcinoma. The diet-microbiome link suggests that dietary patterns high in red meat relative to plant foods may promote a gut microbial environment conducive to disease progression, pointing to a modifiable risk factor. Together, these results suggest faecal microbiome profiling could inform both earlier diagnosis and future microbiome-targeted treatment strategies for colorectal cancer.
This study examined whether fecal microbiota composition, assessed through metagenomic sequencing of stool samples, could serve as a marker for detecting colorectal cancer (CRC). Researchers compared taxonomic markers in feces between CRC patients and tumor-free controls to see whether microbial signatures could distinguish disease status. They also tested whether combining metagenomic detection with the standard fecal occult blood test (FOBT) improved screening performance. A further aim was to determine whether fecal microbial changes reflected microbial composition at the tumor site itself.
The primary analysis used fecal samples from a study population of 156 participants, comprising CRC patients and tumor-free controls. Findings were then validated in an independent set of patient and control populations totaling 335 individuals drawn from different countries. The abstract does not specify additional demographic details such as age or sex distribution.
Metagenomic detection of CRC from fecal samples performed similarly to the standard FOBT, and combining both methods improved sensitivity by more than 45 percent relative to FOBT alone while maintaining specificity. Detection accuracy did not differ significantly between early- and late-stage cancer, and the results held up in independent validation cohorts from different countries. CRC-associated shifts in fecal microbiota partially mirrored microbial community composition at the tumor itself, suggesting these differences reflect genuine tumor-related host-microbe interactions. The data pointed to a metabolic shift in CRC patients, from fiber degradation seen in controls toward utilization of host carbohydrates and amino acids, along with increased lipopolysaccharide metabolism.
These findings suggest fecal metagenomic profiling could complement or enhance existing non-invasive CRC screening tools like FOBT, particularly by improving sensitivity without sacrificing specificity. Because accuracy was consistent across early- and late-stage disease, microbial markers may support earlier detection when treatment outcomes are more favorable. The link between fecal microbiota changes and tumor-associated microbial and metabolic shifts, including altered carbohydrate, amino acid, and lipopolysaccharide metabolism, points to potential host-microbe interactions worth further mechanistic investigation.
This study used the curatedMetagenomicData Bioconductor package (version 3.2.1 or later) to characterize body site-typical microbiome signatures in healthy adults. The researchers calculated taxa prevalence at both the species and genus levels for samples drawn from multiple body sites. These sites included the vagina, skin, feces, nasal cavity, milk, and oral cavity. The goal was to generate reference lists of taxa that are typically present at each site in healthy individuals.
The dataset consisted of healthy adult control samples compiled from the curatedMetagenomicData resource, a public repository of curated metagenomic data. No specific cohort name, recruitment site, or exact sample size is given in the abstract. The population can honestly be described as an aggregated, public metagenomic dataset of healthy adults spanning several body sites, distinct from the companion pediatric dataset referenced in Study 608.
The study produced lists of body site-typical microbial signatures for adults, organized by different prevalence thresholds at both the species and genus levels. These signatures span six distinct body sites: vagina, skin, feces, nasal cavity, milk, and oral cavity. The abstract does not report specific taxa names, sulfate- or sulfite-reducing bacteria, hydrogen sulfide, bile acid, or taurine findings, so the core result is the establishment of prevalence-based reference signatures themselves. The tiered thresholds allow flexibility in how strictly a taxon must be present to count as site-typical.
These adult body site-typical signatures can serve as a baseline reference for identifying deviations associated with disease or dysbiosis at specific anatomical sites. Because the signatures are stratified by prevalence threshold, researchers can choose stricter or looser definitions of what counts as typical depending on their analytic needs. The parallel companion resource for children (Study 608) suggests these adult signatures are part of a broader effort to map healthy microbiome baselines across the lifespan. Overall, this work provides a foundational tool for future comparative and diagnostic microbiome research.
2026-07-04
Bilophila wadsworthia majorTaxon page created: biology (morphology, sulfur metabolism, pathogenicity), virulence factors, diet and bile-acid vulnerabilities, research-stage interventions, the data-derived Conditions table across 40 conditions, and the full research feed.
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