GTX-GUT: A Standardized Metagenomic Workflow for Gut Microbiome Profiling and Clinical Associations

Robust taxonomic classification in gut and vaginal microbiomes demonstrated through benchmarking with age-specific synthetic communities

Multi-layer gut microbiome variation in type 2 diabetes despite preserved higher-order community structure

Background Type 2 diabetes (T2D) has been consistently associated with alterations in the gut microbiome, although disease, treatment, diet, and other host factors may contribute to the observed patterns. How these associations are organized across different biological levels of the microbial ecosystem remains incompletely understood. Methods We performed shotgun metagenomic sequencing of fecal samples from 82 individuals, including 41 patients with T2D and 41 age-, sex-, and body mass index-matched healthy controls. Taxonomic profiling, functional pathway analysis, enterotype characterization, ecological network inference, and interpretable machine-learning approaches were integrated to characterize microbiome variation across multiple organizational levels. Results Despite clear clinical differences between groups, particularly fasting blood glucose, the overall ecological architecture of the gut microbiome remained broadly preserved. Dominant phylum-level composition and enterotype structure were maintained, whereas variation became apparent at finer biological scales. Species-level analyses identified 42 differentially abundant taxa. Community diversity analysis showed reduced Chao1 richness (P = 0.024), increased Simpson diversity (P = 0.024), unchanged Shannon diversity (P = 0.126), and a modest shift in community composition (PERMANOVA, R2 = 0.040, P = 0.006). Functional profiling showed no pathway-level significance after multiple-testing correction but directional trends across several metabolic modules. Exploratory Spearman-based networks differed in topology between groups; because relative-abundance data are compositional, these differences cannot be interpreted as direct ecological interactions or definitive network rewiring. Machine-learning models achieved a within-cohort cross-validated AUC of up to 0.91, but lacked independent external validation. Conclusions These findings provide a multi-layer description of T2D-associated gut microbiome variation within this cohort. The data are consistent with preserved higher-order community organization accompanied by finer-scale differences in species composition, functional potential, community-state occupancy, statistical co-occurrence, and within-cohort discriminative features. Medication confounding, compositional effects, technical artifacts, and the absence of external validation limit causal, ecological, and diagnostic interpretation. Larger longitudinal, multi-site, medication-resolved, and independently validated studies are required.

Bioinformatic tools for microbiome analysis: from raw sequences to biological insights

This review presents a practical, workflow-oriented guide to microbiome data analysis, from raw DNA sequence processing to statistical interpretation and biological insight, and highlights emerging technologies, including machine learning methods that are beginning to reshape the field.

MGM2 as a Unified Foundation Model for Microbiome World Exploration

MGM2 provides a sequence-aware and interpretable representation layer for microbiome classification, paired-community prediction, forecasting and feature discovery and a 4,096-feature sparse autoencoder atlas resolves taxonomic, abundance, ecological and technical signals.

HuMMANet: A Harmonized Cross-Study Resource for Integrative Analysis of Human Gut Microbiome–Metabolome Associations

Metagenomic analysis of tongue samples from healthy subjects identifies distinct microbiome orotypes

Abstract Background The tongue dorsum harbors a complex microbiome that remains incompletely characterized. Objective This study aimed to characterize the tongue microbiome—including its phageome—in a healthy Qatari population. Design Shotgun metagenomic sequencing was performed on tongue-coating samples from 92 systemically healthy adults to comprehensively profile the bacteriome, phageome and functional potential of the tongue microbiome. Results Taxonomic profiling revealed a predominantly bacterial community (>99%) dominated by Veillonella, Streptococcus, Neisseria, Rothia, Prevotella, Haemophilus and Pauljensenia. Among low-abundance domains, the fungus Saccharomyces and the protist Entamoeba were most prevalent. Dirichlet–multinomial mixture clustering identified three distinct bacterial ‘orotypes’ (C1–C3) showing significant compositional separation (PERMANOVA, p = 0.001) and alpha diversity differences at both genus and species levels. A major compositional gradient involved enrichment of Neisseria and Haemophilus in C2, their absence in C3 and intermediate representation in C1. Functional profiling revealed a conserved core of housekeeping pathways across orotypes, wherease adaptive functionsdiffered across orotypes, particularly in the Neisseria/Haemophilus-enriched C2 orotype. The phageome was dominated by Uroviricota (class Caudoviricetes). Conclusion The findings identify distinct tongue microbiome orotypes with conserved core functions, divergent taxonomic and metabolic profiles, and provide new insights into the tongue phageome, establishing a foundation for investigating their roles in health.