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PubMed2026

Genome-resolved analysis of colonization factor repertoires reveals ecological stratification in cervid gut microbiomes.

INTRODUCTION: Colonization factors (CFs) are important microbial traits associated with persistence and host adaptation in the gut, yet their large-scale organization in cervid gut microbiomes remains unclear. METHODS: A total of 3,311 non-redundant high-quality metagenome-assembled genomes (MAGs), derived from 688 cervid gut metagenomic samples across 15 publicly available projects and one in-house dataset, were analyzed. CF-associated genes were identified by comparison against the GHA CF database, and CF repertoires were characterized at genome, host-species, and gastrointestinal-segment levels. RESULTS: A total of 138,729 CF-associated genes spanning 71 CF families were identified. MAGs from Cervinae contained richer CF repertoires than those from Caprinae, and CF47 (Peptidase_C69), CF24_29 (QueH), and CF18 (Glycos_transf_2) were among the most prevalent families. CF repertoires were strongly structured by taxonomy, showed a moderate association with bacterial phylogenetic distance, and formed two recurrent genome-level configurations with distinct KEGG functional profiles. Integration of sample metadata further revealed differentiation of CF repertoires across host species and gastrointestinal segments, representing the major ecological dimensions examined in this study. Segment-associated CF variation was accompanied by redistribution of broader functional profiles, including enrichment of carbohydrate and lipid metabolism in the jejunum, membrane transport in the ileum, xenobiotics biodegradation in the cecum, and environmental adaptation in the rumen. DISCUSSION: These findings provide a genome-resolved view of CF repertoire organization in cervid gut microbiomes and demonstrate that colonization-associated functions are structured across microbial lineages and ecological contexts. This study highlights the importance of considering microbial taxonomy and host-associated environments when interpreting the distribution of CF repertoires in mammalian gut ecosystems.

Cervidae↗

PubMed2026

Integrative multi-omics analyses suggest a candidate microbial metabolite-associated host gene network in ulcerative colitis.

Ulcerative colitis (UC) is associated with gut microbial dysbiosis, but the host molecular alterations potentially linked to microbially derived metabolites remain incompletely understood. We integrated Mendelian randomization (MR), microbial metabolite annotation, computational target prediction, colonic transcriptomics, network analysis, and machine learning. MiBioGen microbiome GWAS data were used as exposures and FinnGen Release 12 ULCERENTER as the outcome. Metabolites linked to MR-prioritized taxa were retrieved from GutMGene, and human targets were predicted using SwissTargetPrediction and SEA. UC-related genes were defined by integrating differential expression analysis and WGCNA and then intersected with predicted metabolite targets. MR prioritized one family and eight genera showing nominal genetically supported associations with UC, but none remained significant after Benjamini-Hochberg FDR correction. Three prioritized genera were linked to 15 microbe-metabolite records, corresponding to 13 unique metabolites; nine were retained for target prediction, yielding 277 unique predicted human targets. Transcriptomic analysis identified 1,530 DEGs and a 312-gene MEgrey60 module, with 273 overlapping genes, producing 1,569 unique UC-related genes. Their intersection with the 277 predicted targets yielded 47 candidate genes. Enrichment analyses highlighted mainly metabolic and lipid-related processes. Random Forest showed the highest mean AUC across the two independent external benchmarking cohorts, and SHAP prioritized EPHX1, HSD17B2, IGFBP5, and MMP10. IBDome analysis showed inflammation-associated expression differences in these genes. This study provides a genomics-informed, hypothesis-generating framework that prioritizes candidate microbe-metabolite-host relationships in UC for future experimental validation.

Humans↗

PubMed2026

Potential survival strategies of novel comammox and nitrite-oxidizing Nitrospira synthesizing osmoprotectants in a wastewater microbiome treating high-ammonia brackish landfill leachate.

BACKGROUND: In the late stages of landfill operation, leachate becomes brackish and contains high concentrations of ammonia with limited organic carbon. At leachate treatment facilities, it is typically subjected to nitrification followed by denitrification, with methanol supplied as an external electron donor. This unique environment may harbor novel microorganisms, including nitrifiers. Although a variety of microorganisms are involved in nitrification, their substrate specificity and salinity tolerance remain insufficiently understood. In this study, a genome-centric metagenome analysis was conducted on the microbiome from a leachate treatment facility at a closed landfill. RESULTS: A total of 68 metagenome-assembled genomes (MAGs) were reconstructed, including 64 putative novel species. Among these, two Nitrospira MAGs were recovered: a novel complete ammonia-oxidizing bacterium (comammox), Nitrospira LAS72 (88.72% completeness, 2.10% contamination), and canonical nitrite-oxidizing Nitrospira LAS18 (99.98% completeness, 2.29% contamination). Comparative genomic analysis with 260 publicly available Nitrospira genomes revealed that LAS18 represents a new sub-lineage within lineage VII of the Nitrospira genus. Two ammonia-oxidizing archaea (AOA), Candidatus Nitrosocosmicus LAS21 and Nitrosarchaeum LAS73, were also identified, while canonical ammonia-oxidizing bacteria were not detected. Given the brackish conditions (1.23% salinity) and the methanol-fed operation of the treatment facility, the genomic potential for osmotic stress adaptation and methanol metabolism was investigated. Comammox Nitrospira LAS72 harbors biosynthetic pathways for several compatible solutes (osmoprotectants), including glycine betaine, proline, trehalose, and L-glutamate. Moreover, comammox Nitrospira LAS72 possesses genetic potential for oxidizing formaldehyde, suggesting that it may exploit these methanol-derived intermediates as energy sources. These features indicate that LAS72 may withstand osmotic fluctuations through the production of various osmoprotectants and thrive under the unique conditions of a methanol-fed environment. CONCLUSIONS: The discovery of novel comammox Nitrospira and canonical Nitrospira forming a new sub-lineage within lineage VII of the Nitrospira genus in an ammonia-rich brackish environment provides the first genomic evidence for evolutionary adaptation among nitrifiers to saline, methanol-fed environments. These findings enhance our understanding of the ecological and evolutionary dynamics shaping nitrifier communities in complex treatment ecosystems. Video Abstract.

Ammonia↗

PubMed2026

Healthy subjects gut microbiome modulation by Bacillus coagulans BCP92: A randomized, double-blind, placebo-controlled clinical trial.

BACKGROUND: Probiotics are recognized for their ability to restore balance in the gut microbiome during dysbiosis. However, their effects on the gut microbiota of healthy individuals have rarely been investigated. This study aimed to evaluate the safety and efficacy of Bacillus coagulans (Heyndrickxia coagulans) BCP92 and its influence on microbiota composition in healthy subjects. METHODS: In the present investigation, healthy participants (n = 48) were allocated into 2 groups and administered either Bacillus coagulans BCP92 capsules (1 billion CFU/capsule) or a placebo containing maltodextrin for 42 days. Microbiome composition and short-chain fatty acid analyses were subsequently conducted. RESULTS: Analysis of metagenomes showed no major alterations in gut microbiome composition among participants who received B. coagulans BCP92 supplementation. However, subtle beneficial changes were observed in the treatment group, suggesting that probiotic administration may increase advantageous phyla, classes, orders, families, and some genera, while decreasing potentially harmful groups. A slight increase in short-chain fatty acids (SCFA) was also observed in the fecal samples. CONCLUSIONS: This study implies that extended supplementation with the probiotic B. coagulans BCP92 may lead to substantial improvements in gut microbiome composition and SCFA levels.

Humans↗

PubMed2006

Improved serial analysis of V1 ribosomal sequence tags (SARST-V1) provides a rapid, comprehensive, sequence-based characterization of bacterial diversity and community composition.

Serial analysis of ribosomal sequence tags (SARST) is a recently developed technology that can generate large 16S rRNA gene (rrs) sequence data sets from microbiomes, but there are numerous enzymatic and purification steps required to construct the ribosomal sequence tag (RST) clone libraries. We report here an improved SARST method, which still targets the V1 hypervariable region of rrs genes, but reduces the number of enzymes, oligonucleotides, reagents, and technical steps needed to produce the RST clone libraries. The new method, hereafter referred to as SARST-V1, was used to examine the eubacterial diversity present in community DNA recovered from the microbiome resident in the ovine rumen. The 190 sequenced clones contained 1055 RSTs and no less than 236 unique phylotypes (based on > or = 95% sequence identity) that were assigned to eight different eubacterial phyla. Rarefaction and monomolecular curve analyses predicted that the complete RST clone library contains 99% of the 353 unique phylotypes predicted to exist in this microbiome. When compared with ribosomal intergenic spacer analysis (RISA) of the same community DNA sample, as well as a compilation of nine previously published conventional rrs clone libraries prepared from the same type of samples, the RST clone library provided a more comprehensive characterization of the eubacterial diversity present in rumen microbiomes. As such, SARST-V1 should be a useful tool applicable to comprehensive examination of diversity and composition in microbiomes and offers an affordable, sequence-based method for diversity analysis.

Bacteria↗

PubMed2026

Delayed maturation of the milk microbiome in women with type 1 diabetes.

AIMS/HYPOTHESIS: The breastmilk microbiome plays a crucial role in gut microbial colonisation and immune development, but little is known about how it is influenced by type 1 diabetes. METHODS: We conducted a longitudinal 16S rRNA gene sequencing study of milk from women with type 1 diabetes (n=69 pregnancies; 174 samples) and women who did not have type 1 diabetes (n=49 pregnancies; 123 samples), collected at seven timepoints from birth to 15 months postpartum. Alpha diversity (richness, inverse Simpson evenness) was analysed by generalised linear mixed models, beta diversity was analysed by Bray-Curtis dissimilarities and PERMANOVA, and differential abundance was analysed by limma. Additionally, we examined associations with maternal genetic risk score (GRS), maternal HLA type, glycaemic management (HbA1c) and breastmilk secretory IgA (sIgA), and performed a parallel analysis for the infant stool microbiome. RESULTS: A significant interaction between type 1 diabetes status and timepoint was observed for alpha diversity, both richness (p=0.01) and inverse Simpson diversity (p=0.003), indicating distinct temporal trajectories between women with and without type 1 diabetes. In those without type 1 diabetes, richness increased significantly between birth and 1 week postpartum, but this early increase was delayed in women with type 1 diabetes to between 1 week and 3 months postpartum (p=0.002). Beta diversity analysis revealed earlier and more extensive compositional shifts in women without type 1 diabetes compared to those with type 1 diabetes. These differences persisted after adjusting for Caesarean delivery, BMI, parity and infant sex, and were not attributable to a delay in initiating breastfeeding. Taxa with delayed enrichment in women with type 1 diabetes included Streptococcus spp. and Rothia mucilaginosa, which metabolise human milk oligosaccharides to short-chain fatty acids to promote development of the infant's gut barrier and immune system. Maternal GRS, HLA, HbA1c or sIgA were not associated with milk microbiota diversity trajectories. In infant stool samples, alpha diversity did not differ between exposure groups, and showed no evidence of delayed maturation. Beta diversity revealed an early compositional shift between birth and 1 week postpartum only in infants born to women without type 1 diabetes. Similarly, significant taxonomic changes between birth and 1 week postpartum were detected only in infants born to women without type 1 diabetes, but with some taxa differing between exposure groups at 1 week. CONCLUSIONS/INTERPRETATION: Maternal type 1 diabetes is associated with delayed early maturation of the breastmilk microbiome. Early compositional differences in microbiota restructuring were also observed in the infant gut, partially mirroring the pattern in the milk microbiome; however, sustained differences in infant gut microbiota diversity were not detected. Further investigation could determine whether these changes affect development of the infant's gut and immune system.

Humans↗

PubMed2025

Cardiovascular Complications Are Increased in Inflammatory Bowel Disease: A Path Toward Achievement of a Personalized Risk Estimation.

Background/Objectives: The global burden of inflammatory bowel diseases (IBDs) continues to rise, with up to 50% of patients experiencing extraintestinal manifestations. Cardiovascular diseases (CVDs) are of particular concern, ranking as the second leading cause of mortality in this population. Despite a comparatively lower prevalence of traditional cardiovascular (CV) risk factors, the persistent inflammatory milieu and immune dysregulation inherent to IBD may contribute to heightened CVD risk. In this study, following a review of the current literature, an ongoing prospective trial designed to clarify CV risk profiles in IBD patients is detailed. Methods: A cohort of patients with IBD is being enrolled for comprehensive baseline evaluation of CV risk factors, lifestyle metrics, and disease characteristics. The incidence of major adverse cardiovascular events (MACEs) will be tracked and contrasted with a gender- and age-matched non-IBD cohort over a 2-year follow-up period. In cases of MACE occurrence, a multi-omics analysis-including genomic, proteomic, transcriptomic, and microbiome profiling-will be performed, along with a parallel evaluation in matched IBD controls without MACE. An artificial intelligence (AI) framework will support the analysis of this complex dataset. Results: To date, over 150 patients with IBD have been enrolled, and detailed phenotypic data and biological samples have been collected. Conclusions: We aim to introduce an IBD-specific correction factor for existing CV risk scores upon study completion. This is particularly relevant for individuals under 40 years of age, who are often inadequately assessed by current risk stratification models.

Crohn’s disease↗

PubMed2025

Novel insights into the genetic architecture and mechanisms of host/microbiome interactions from a multi-cohort analysis of outbred laboratory rats.

The intestinal microbiome influences health and disease. Its composition is affected by host genetics and environmental exposures. Understanding host genetic effects is critical but challenging in humans, due to the difficulty of detecting, mapping and interpreting them. To address this, we analysed host genetic effects in four cohorts of outbred laboratory rats exposed to distinct but controlled environments. We found that polygenic host genetic effects were consistent across environments. We identified three replicated microbiome-associated loci, one of which involved a sialyltransferase gene and Paraprevotella. We found a similar association in a human cohort, between ST6GAL1 and Paraprevotella, both of which have been linked with immune and infectious diseases. Moreover, we found evidence of indirect genetic effects on microbiome phenotypes, which substantially increased their total genetic variance. Finally, we identified a novel mechanism whereby indirect genetic effects can contribute to "missing heritability".

Journal Article↗

PubMed2026

Multimodal risk assessment for oral potentially malignant disorders: Integrating patient-centered and specimen-derived data.

BACKGROUND: Oral potentially malignant disorders exhibit heterogeneous malignant transformation risk that clinical approaches fail to adequately predict. Histopathologic dysplasia grading, the reference standard of risk assessment, is associated with poor interobserver reliability and limited prognostic discrimination. It is necessary to define other potential patient- and tissue-associated risk modifiers to improve patient-specific disease prediction. TYPES OF STUDIES REVIEWED: PubMed was queried for patient- and specimen-derived factors as they relate to oral cancer and oral potentially malignant disorders, with preference for systematic review and meta-analysis articles published within the past 5 years. When not available, guidelines from the American Cancer Society, National Cancer Institute, or other national organizations or the most recent best articles were referenced to support the data presented. RESULTS: Within patient-associated factors, validated measures of tobacco and alcohol exposure, clinical lesion characteristics, systemic health factors including metabolic syndrome components, comorbidity risk, and dental health indexes were found. Within specimen-derived data, tissue-based analyses encompassing histopathology and advanced molecular profiling (genomic, epigenomic, transcriptomic, spatial approaches), blood-based germline and somatic mutation analysis, and saliva-based microbiome characterization and inflammatory biomarker assessment were addressed. PRACTICAL IMPLICATIONS: Malignant transformation reflects intersecting patient and specimen risk pathways that affect each patient differently; no single modality captures this complexity. Realizing precision prognostication in oral precancer will require coordinated expansion and standardization of data collection across research groups. This review is intended to guide covariate selection for prospective study design, improve reproducibility, and ultimately enable the development of validated multimodal risk prediction tools for clinical deployment.

Humans↗

PubMed2024

Stool Protein Mass Spectrometry Identifies Biomarkers for Early Detection of Diffuse-type Gastric Cancer.

There is a high unmet need for early detection approaches for diffuse gastric cancer (DGC). We examined whether the stool proteome of mouse models of gastric cancer (GC) and individuals with hereditary diffuse gastric cancer (HDGC) have utility as biomarkers for early detection. Proteomic mass spectrometry of the stool of a genetically engineered mouse model driven by oncogenic KrasG12D and loss of p53 and Cdh1 in gastric parietal cells [known as Triple Conditional (TCON) mice] identified differentially abundant proteins compared with littermate controls. Immunoblot assays validated a panel of proteins, including actinin alpha 4 (ACTN4), N-acylsphingosine amidohydrolase 2 (ASAH2), dipeptidyl peptidase 4 (DPP4), and valosin-containing protein (VCP), as enriched in TCON stool compared with littermate control stool. Immunofluorescence analysis of these proteins in TCON stomach sections revealed increased protein expression compared with littermate controls. Proteomic mass spectrometry of stool obtained from patients with HDGC with CDH1 mutations identified increased expression of ASAH2, DPP4, VCP, lactotransferrin (LTF), and tropomyosin-2 relative to stool from healthy sex- and age-matched donors. Chemical inhibition of ASAH2 using C6 urea ceramide was toxic to GC cell lines and GC patient-derived organoids. This toxicity was reversed by adding downstream products of the S1P synthesis pathway, which suggested a dependency on ASAH2 activity in GC. An exploratory analysis of the HDGC stool microbiome identified features that correlated with patient tumors. Herein, we provide evidence supporting the potential of analyzing stool biomarkers for the early detection of DGC. Prevention Relevance: This study highlights a novel panel of stool protein biomarkers that correlate with the presence of DGC and has potential use as early detection to improve clinical outcomes.

Feces↗

PubMed2025

Host clustering of Campylobacter species and enteric pathogens in a longitudinal cohort of infants, family members and livestock in rural Eastern Ethiopia.

BACKGROUND: Livestock are recognized as major reservoirs for Campylobacter species and other enteric pathogens, posing infection risks to humans. High prevalence of Campylobacter during early childhood has been linked to environmental enteric dysfunction and stunting, particularly in low-resource settings. METHODS: A total of 280 samples from Campylobacter positive households with complete metadata were analyzed by shotgun metagenomic sequencing followed by bioinformatic analysis via the CZ-ID metagenomic pipeline (Illumina mNGS Pipeline v7.1). Further statistical analyses in JMP PRO 16 explored the microbiome, emphasizing Campylobacter and other enteric pathogens. Two-way hierarchical clustering and split k-mer analysis examined host structuring, patterns of co-infections and genetic relationships. Principal component analysis was used to characterize microbiome composition across the seven sample types. RESULTS: The study identified that microbiome composition was strongly host-driven, with more than 3844 genera detected, and two principal components explaining 62% of the total variation. Twenty-one dominant (based on relative abundance) Campylobacter species showed distinct clustering patterns for humans, ruminants, and broad hosts. The broad-host cluster included the most prevalent species, C. jejuni, C. concisus, and C. coli, present across sample types and a sub-cluster within C. jejuni involving humans, chickens, and ruminants. Campylobacter species from chickens showed strong positive correlations with mothers (r = 0.76), siblings (r = 0.61) and infants (r = 0.54), while co-occurrence analysis found a higher likelihood (Pr > 0.5) of pairs such as C. jejuni with C. coli, C. concisus, and C. showae. Analysis of the top 50 most abundant microbial taxa showed a distinct cluster uniquely present in human stool and absent in all livestock. The study also found frequent co-occurrence of C. jejuni with other enteric pathogens such as Salmonella, and Shigella, particularly in human and chicken. Additionally, instances of Candidatus Campylobacter infans (C. infans) were identified co-occurring with Salmonella and Shigella species in stool samples from infants, mothers, and siblings. CONCLUSIONS: A comprehensive analysis of Campylobacter diversity in humans and livestock in a low-resource setting revealed that infants can be exposed to multiple Campylobacter species early in life. C. jejuni is the dominant species with a propensity for co-occurrence with other notable enteric bacterial pathogens, including Salmonella, and Shigella, especially among infants. Video Abstract.

Animals↗

PubMed2026

Causal relationships between oral-gut microbiome and bone neoplasm-related phenotypes: Insights from bidirectional Mendelian randomization.

The human oral and gut microbiota are the 4 largest microbial communities in the body and play crucial roles in maintaining homeostasis and influencing disease. Observational studies have suggested links between these microbiota and bone neoplasm-related phenotypes, but establishing causality has been challenging due to confounding factors and reverse causality. We conducted a bidirectional, 2-sample Mendelian randomization (MR) study to investigate evidence consistent with a potential causal association between the saliva and gut microbiota and various bone neoplasm-related phenotypes. Genetic instruments for saliva and gut microbiota were sourced from large genome-wide association studies. Inverse variance weighted was the primary MR method, supplemented by 4 other MR techniques. Sensitivity analyses, including MR-Egger regression, were performed to assess pleiotropy and heterogeneity. In the forward MR analysis, Veillonella parvula from the saliva microbiota was associated with a decreased risk of bone and connective tissue neoplasms (β: -0.236, 95% CI: [-0.275, -0.197], P = 8.20E-33). MR analyses identified genetically predicted associations between several microbial taxa and bone neoplasm-related phenotypes. Reverse MR analyses showed that genetic liability to bone neoplasm-related phenotypes was associated with variation in the composition of the oral (e.g., Order Bacteroidales, Rothia mucilaginosa) and gut microbiota (e.g., Class Methanobacteria, Genus Eubacterium oxidoreducens group). Sensitivity analyses confirmed the robustness of these findings, as no statistical evidence of substantial heterogeneity or directional horizontal pleiotropy was detected. This study provides genetic evidence supporting a bidirectional causal relationship between specific saliva and gut microbiota and bone neoplasm-related phenotypes. Our findings identify several microbial taxa as potential candidates for future biomarker development and therapeutic investigation in bone neoplasm-related phenotypes. However, these genetically informed associations require further mechanistic, experimental, and prospective clinical validation before clinical application.

Humans↗

PubMed2024

Comprehensive Assessment of the Intrinsic Pancreatic Microbiome.

OBJECTIVE: To sought comprehensively profile tissue and cyst fluid in patients with benign, precancerous, and cancerous conditions of the pancreas to characterize the intrinsic pancreatic microbiome. BACKGROUND: Small studies in pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasm (IPMN) have suggested that intrapancreatic microbial dysbiosis may drive malignant transformation. METHODS: Pancreatic samples were collected at the time of resection from 109 patients. Samples included tumor tissue (control, n = 20; IPMN, n = 20; PDAC, n = 19) and pancreatic cyst fluid (IPMN, n = 30; serous cystadenomas, n = 10; mucinous cystic neoplasm, n = 10). Assessment of bacterial DNA by quantitative polymerase chain reaction and 16S ribosomal RNA gene sequencing was performed. Downstream analyses determined the relative abundances of individual taxa between groups and compared intergroup diversity. Whole-genome sequencing data from 140 patients with PDAC in the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium were analyzed to validate findings. RESULTS: Sequencing of pancreatic tissue yielded few microbial reads regardless of diagnosis, and analysis of pancreatic tissue showed no difference in the abundance and composition of bacterial taxa between normal pancreas, IPMN, or PDAC groups. Low-grade and high-grade dysplasia IPMN were characterized by low bacterial abundances with no difference in tissue composition and a slight increase in Pseudomonas and Sediminibacterium in high-grade dysplasia cyst fluid. Decontamination analysis using the Clinical Proteomic Tumor Analysis Consortium database confirmed a low-biomass, low-diversity intrinsic pancreatic microbiome that did not differ by pathology. CONCLUSIONS: Our analysis of the pancreatic microbiome demonstrated very low intrinsic biomass that is relatively conserved across diverse neoplastic conditions and thus unlikely to drive malignant transformation.

Humans↗

PubMed2024

Exclusive enteral nutrition initiates individual protective microbiome changes to induce remission in pediatric Crohn's disease.

Exclusive enteral nutrition (EEN) is a first-line therapy for pediatric Crohn's disease (CD), but protective mechanisms remain unknown. We established a prospective pediatric cohort to characterize the function of fecal microbiota and metabolite changes of treatment-naive CD patients in response to EEN (German Clinical Trials DRKS00013306). Integrated multi-omics analysis identified network clusters from individually variable microbiome profiles, with Lachnospiraceae and medium-chain fatty acids as protective features. Bioorthogonal non-canonical amino acid tagging selectively identified bacterial species in response to medium-chain fatty acids. Metagenomic analysis identified high strain-level dynamics in response to EEN. Functional changes in diet-exposed fecal microbiota were further validated using gut chemostat cultures and microbiota transfer into germ-free Il10-deficient mice. Dietary model conditions induced individual patient-specific strain signatures to prevent or cause inflammatory bowel disease (IBD)-like inflammation in gnotobiotic mice. Hence, we provide evidence that EEN therapy operates through explicit functional changes of temporally and individually variable microbiome profiles.

Crohn Disease↗

PubMed2025

Mendelian randomization and FinnGen analysis of the causal relationship between 473 gut microbiota species and chronic sinusitis.

OBJECTIVE: To investigate the causal associations between Gut Microbiota (GM) and Chronic Sinusitis (CRS) using Mendelian Randomization (MR). METHODS: Genome-Wide Association Study (GWAS) summary statistics for 473 GM taxa were obtained from MiBioGen consortium. CRS data (22,099 cases vs. 371,520 controls) were sourced from the FinnGen R12 cohort. Causal effects were estimated via Inverse Variance-Weighted (IVW), MR-Egger, weighted median, and Bayesian-weighted MR methods. Sensitivity analyses (heterogeneity and horizontal pleiotropy tests) were performed to validate robustness. RESULTS: IVW analysis identified 20 GM taxa significantly associated with CRS risk (p < 0.05). Of these, 7 taxa (e.g., Francisellales, Roseibacillus, Merdibacter massiliensis) exhibited risk-increasing effects, while 13 taxa (e.g., Firmicutes I, Succinivibrionaceae) showed protective effects. Sensitivity analyses confirmed the absence of significant heterogeneity (Cochran's Q p > 0.05) or pleiotropy (MR-Egger intercept p > 0.05). Bayesian-weighted MR validated 18 causal relationships (posterior probability > 95%), except for RUG420 sp900317985 and UBA7703 (non-significant). CONCLUSIONS: This MR study provides genetic evidence supporting causal roles of specific GM taxa in CRS pathogenesis. These findings highlight the gut-sinus axis as a potential therapeutic target and underscore the utility of large-scale biobanks (e.g., FinnGen) in advancing precision medicine. LEVEL OF EVIDENCE: Level 5. Mendelian Randomized (MR) studies are second only to randomized controlled trials in terms of the level of evidence.

Humans↗

PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗

PubMed2025

Alterations of gut microbiome in chronic rhinosinusitis: insights from a mendelian randomization study.

OBJECTIVE: Gut microbiome dysbiosis is associated with various diseases. Causal association between Chronic Rhinosinusitis (CRS) and gut microbiome is yet unknown. This study aimed to investigate the potential causal relationship between CRS and gut microbiome dysbiosis. METHODS: We used Genome-Wide Association Study (GWAS) data from FinnGen database for CRS. The Dutch Microbiome Project study provided data on gut microbiota species. A total of 334,182 individuals were included. Two-sample bidirectional Mendelian Randomization (MR) analysis was used to investigate causal relationship between CRS and gut microbiome. The main methods of evaluation were Inverse Variance Weighting (IVW), weighted median, weighted mode, and MR-Egger regression. Sensitivity analyses were performed to assess heterogeneity and pleiotropy. RESULTS: Forward MR analysis indicated CRS is potentially linked to decreased risk of Haemophilus parainfluenzae (OR = 0.79, 95% CI 0.66‒0.94, p = 0.009) and increased risk of Bilophila's (OR = 1.14, 95% CI 1.02-1.27, p = 0.023) within the gut. Reduced risks in gut microbiota-related pathways like UDP-N-acetyl-d-glucosamine biosynthesis I (OR = 0.85, 95% CI 0.77‒0.94, p = 0.002) and increased risk in pathway NAD biosynthesis I from aspartate (OR = 1.14, 95% CI 1.03-1.27, p = 0.010) were also linked to CRS. Reverse MR analyses, we obtained no positive results (p > 0.05/412). CONCLUSION: This study reveals CRS exerts a causal impact on shifts within the composition of the gut microbiome and also links to the changes of gut microbiota-related metabolic pathways. The risk of changes in gut microbiota should be of greater concern in patients with CRS than in the general population. LEVEL OF EVIDENCE: Mendelian Randomized (MR) studies are second only to randomized controlled trials in terms of the level of evidence.

Humans↗

PubMed2025

Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic Profiles.

In microbiome studies, sample processing errors are frequent and difficult to detect, especially in large studies involving multiple sites, personnel, and sample types. We present two complementary approaches to identify such errors using host DNA profiled via metagenomic sequencing of microbiome samples. The first approach compares host SNPs inferred from metagenomics to independently obtained genotypes (e.g., microarray genotypes) to match samples to their donors, while the second method compares metagenomics-inferred SNPs between samples to identify samples supplied by the same donor. Furthermore, we demonstrate that combining these methods with experimental metadata provides greater confidence in the identification of errors. Analyzing a longitudinal vaginal microbiome dataset, we demonstrate the ability of our approach to identify mislabeled samples. Using subsampling, we further show that our methods are robust to low sequencing coverage. Overall, our analysis highlights the frequency of processing errors in microbiome studies. We therefore recommend applying error-detection methods in all studies with suitable data.

Journal Article↗