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

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

Application to a human sample from a patient with type 2 Diabetes Mellitus recovered a dysbiotic signature consistent with the literature, including reduced Firmicutes abundance, elevated Bacteroidetes and Proteobacteria, and a predominance of clinical associations within metabolic and gastrointestinal categories.

Complete genome-derived metabolic interactions reveal the impact of gut ecology on human health.

This work leverage 1,150 complete genomes to construct genome-scale metabolic models, demonstrating that draft assemblies introduce systematic artifacts and omit critical transport functions, and connects genome completeness with microbial ecological organization and provides a framework for linking metabolic interactions to microbiome-associated disease.

Serum-Cecal Metabolome Integration Predicts Gut Microbial Communities and Reveals Pathway-Level Host-Microbe Crosstalk Under Disease-Induced Dysbiosis.

The gut microbiome shapes systemic physiology through metabolites that enter circulation, yet most computational approaches focus on predicting metabolite profiles from microbial features rather than inferring microbial composition from host metabolomes. Here, we investigate whether host-derived metabolomic profiles can be leveraged to predict gut microbial community structure and to determine how disease-associated dysbiosis reshapes metabolite-microbe interactions and gut-to-systemic metabolic communication. We developed an integrative multi-omics framework combining serum and cecal metabolomics with 16S rRNA-based microbiome profiling. Supervised learning models demonstrated that cecal metabolites carry predictive signals for microbial abundances across conditions. Regularized canonical correlation analysis (rCCA) revealed cross-compartment metabolite-microbe networks. These analyses showed both conserved and condition-specific interaction patterns, indicating substantial network reorganization under disease-associated dysbiosis. Pathway-level integration further identified metabolic pathways linking the gut microbiome, the cecal environment, and the systemic circulation, representing coordinated gut-to-systemic communication axes. Together, our results establish a multi-omics strategy for predictive inference of gut microbial composition from host metabolomes and provide a framework for identifying pathway-level mechanisms underlying host-microbe metabolic crosstalk.

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

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. In this Perspective, the authors discuss recent advances in multi-omics microbiome research, outlining key obstacles in data integration and metadata harmonization, and proposing a roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence advances.

Modern approaches to gut microbiome investigation: Sequencing, culturomics, metabolomics, and beyond.

This review offers a thorough overview of both current and emerging methods for studying the gut microbiome, including sample collection techniques, culture-based approaches like culturomics and microfluidics, as well as culture-independent methods such as 16S rRNA sequencing, shotgun metagenomics, and the integration of multi-omics approaches like metabolomics, proteomics, and transcriptomics.