Aug 2026· Cell Reports· Vol 45 9, pp.
117913
· 0 citations· 91 references
Medicine
TL;DR
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.
Abstract
Metabolic interactions govern gut microbiome assembly, yet their functional rules remain obscured by genomic incompleteness and fragmentation. Here, we leverage 1,150 complete genomes to construct genome-scale metabolic models, demonstrating that draft assemblies introduce systematic artifacts and omit critical transport functions. We observe that genomic traits and niche specialization, rather than random association, shape microbial metabolic competition and complementarity. Interaction asymmetry stratifies strains into four ecological groups, including active players, resource predators, resource utilizers, and resource contributors, with distinct signatures of metabolite exchange, competition, and secondary metabolism. In inflammatory bowel disease, these groups show subtype-specific temporal instability, and group-specific dysbiosis predicts clinical phenotypes better than the whole-community profiles. Keystone features derived from integrated metabolic interaction and co-occurrence networks also improve cross-validated disease classification. Together, these findings connect genome completeness with microbial ecological organization and provide a framework for linking metabolic interactions to microbiome-associated disease.
A fundamental challenge in microbiome research lies in elucidating the functional capacity of microbial communities through community membership and genomic data. As community structuring and emergent functional traits are determined by bacterial community metabolic networks, it is important to gain insights into the principles that govern bacteria-bacteria interactions. Here, we applied an integrative framework linking individual strain-level traits to community structuring in a simplified synthetic bacterial community (SSC8) that promotes the growth of ungrafted watermelon. By combining mono- and coculture assays with genome-scale metabolic modeling and metabolomic profiling of spent media, we characterized directional interactions and resource dependencies among community members. Our findings show that positive interactions dominated the community network, accounting for 55% of all pairwise combinations, indicating a high prevalence of growth-promoting effects among strains. Genome-scale metabolic modeling showed that functional divergence among strains enhanced the potential for metabolic complementarity as phylogenetic distance increased. Integrating metabolic modeling with metabolomics further suggested that Pseudomonas azotifigens Q6 not only benefited from all other community members, but also exhibited mutualistic interactions with the other three strains, with metabolite exchange involving compounds such as L-lysine and L-cysteine. Pseudomonas azotifigens Q6 acted as an important driver of community composition by affecting the abundance of several other consortium members in vitro. These findings highlight the role of metabolic complementarity in driving community structuring by promoting selective persistence of specific strains. Our work provides mechanistic insights into microbial interaction networks in vitro and offers a conceptual foundation for the rational design of functionally robust and plant-beneficial microbiomes.
Yi-Zhu Qiao, Ting-Ting Wang, He Zhang et al.· Ecology· 0 citations
Microbial communities are dynamic, adaptive ecosystems whose collective behavior emerges from metabolic interactions such as cross-feeding, competition, and cooperation, rather than taxonomic diversity or individual metabolic potential alone. This distinction is clinically significant in the postmenopausal urinary tract, where recurrent urinary tract infections (rUTIs) are associated with complex, persistent infection dynamics including multiple contributing bacterial species. The ability of resident microbial communities to prevent pathogen establishment, known as colonization resistance, is increasingly attributed to the metabolic interactions within the urobiome itself rather than any single resident species. However, current approaches, such as taxonomic profiling and classical differential abundance analysis, can only partially describe the presence or maintenance of such interactions. Consequently, the community-level metabolic architecture determining pathogen resistance remains incompletely understood. To address this gap, we developed PhenoRewire, a network-based framework that quantifies how metabolite co-variation is rewired between biological states using untargeted metabolomics data. We applied this framework to an induced pluripotent stem cell (iPSC) urothelial organoid-derived barrier co-cultured with synthetic urobiome communities as a model of urobiome-pathogen dynamics relevant to rUTIs in two approaches. In an infection model, clinically isolated uropathogens Escherichia coli and Enterococcus faecalis, were co-cultured with a three-member urobiome community consisting of Lactobacillus gasseri, Lactobacillus crispatus, and Gardnerella vaginalis. Here we show how E. coli drove the metabolic reorganization, while E. faecalis amplified it disproportionately. PhenoRewire disentangled the 6-fold metabolic network amplification mediated by E. faecalis as a metabolic facilitator, revealing an emergent urobiome-pathogen co-variation architecture (1,781 vs 227 edges) not recapitulated by either community alone. Moreover, in a six-member urobiome single-strain dropout experiment, we revealed that removal of the sole Actinomycete Winkia anitrata caused significant network collapse (Louvain modularity falls from 0.707 to 0.038), identifying it as the single non-redundant keystone of the community. More broadly, these results demonstrate how untargeted metabolomics co-variation network analysis can be applied to defined synthetic urobiomes in combination with a urothelial host model to elucidate community dynamics. This framework provides a template that can be extended beyond the urobiome to investigate any complex microbial community where ecological behavior remains an open question.
L. della Vedova, Adam J. Bindas, Mariana Teixeira Dias et al.· bioRxiv· 0 citations
Abstract The human gut microbiome is increasingly viewed as an active regulator of host physiology, extending beyond earlier taxonomy‐centered descriptions of a complex microbial community. Accumulating evidence supports an organ‐like conceptual framework in which the gut microbiome exhibits spatially structured organization, extensive metabolic capacity, and continuous bidirectional communication with host systems. Through the production of bioactive metabolites with endocrine‐like, immunomodulatory, and neuromodulatory properties, the microbiome contributes to metabolic, immune, and neuroendocrine regulation, thereby influencing systemic homeostasis and disease susceptibility. Recent advances in multi‐omics, spatial biology, and computational modeling are moving the field from taxonomic association toward functional interpretation, mechanistic insight, and causal inference. These approaches are beginning to reveal microbiome‐derived functional modules and host–microbe signaling networks that are shaped by host genetics, diet, medications, feeding patterns, circadian rhythms, and environmental exposures. In this review, we synthesize current mechanistic and translational evidence to conceptualize the gut microbiome as an organ‐like functional system, delineate its structural and functional organization, and propose a framework for mapping, modeling, and therapeutically targeting microbiome‐derived circuits to support precision medicine in metabolic, inflammatory, and selected gut–brain axis‐related disorders.
Yang Bi, Wei-Bin Song, Maria Glymenaki et al.· iMeta· 0 citations
The human gut microbiome is a complex adaptive ecosystem whose functions arise from interactions among microbial populations rather than from isolated taxa. Nevertheless, many microbiome-directed interventions still rely on administering individual strains, with limited consideration of the ecological processes governing community assembly, succession, and resilience. This review integrates evidence from microbial ecology, comparative genomics, systems biology, mechanistic physiology, and clinical microbiome research to propose a testable framework for ecologically engineering the human gut microbiome. Within this framework, selected spore-forming probiotics are hypothesized to function as transient pioneer organisms that modify intestinal physicochemical and metabolic conditions, thus facilitating the establishment and activity of functionally complementary microbial populations delivered through rationally designed synbiotic consortia. The proposed process comprises five stages: pioneer activity, niche remodeling, facilitated community assembly, functional-network stabilization, and the emergence of host-associated outcomes. Available genomic, physiological, and clinical observations support the biological plausibility of individual components of this model but do not yet demonstrate directed ecological succession as a complete causal process. Accordingly, the framework distinguishes established evidence from ecological inference and generates experimentally testable predictions of temporal niche modification, metabolic cross-feeding, functional redundancy, resilience after treatment withdrawal, and host metabolic responses. This ecological perspective shifts the objective of microbiome therapeutics from transient strain supplementation toward the predictable modulation of community trajectories, providing an experimental foundation for developing more resilient, mechanism-based interventions.
Antonio Díaz, Gissel García, Raúl de Jesús Cano· Microorganisms· 0 citations
The human gut microbiome plays a very important role in the regulation of host metabolism and overall physiological homeostasis. Disruptions in microbial community function have been increasingly implicated in cardiometabolic diseases, including obesity, type 2 diabetes, cardiovascular disease, and metabolic dysfunction-associated liver disease. Advances in metagenomic sequencing have identified functional genetic signatures within the gut microbiome for short-chain fatty acid biosynthesis, bile acid metabolism, lipopolysaccharide (LPS) production, amino acid metabolism, trimethylamine N-oxide (TMAO) generation, and carbohydrate-active enzymes (CAZymes). Across cardiometabolic conditions, a consistent pattern emerges of depletion of beneficial metabolic functions and enrichment of pro-inflammatory and metabolically disruptive pathways. These findings point to the importance of microbial functional capacity, rather than taxonomic composition alone, in shaping disease risk and progression. This review explores the functional genetic signatures for cardiometabolic diseases and translational potential of these signatures including their potential roles as diagnostic biomarkers, therapeutic targets, and tools for precision therapy. This understanding of microbiome-derived functional pathways may inform the development of targeted strategies aimed at restoring metabolic balance and improving cardiometabolic health.
M. N. Muigano· Frontiers in microbiomes· 0 citations
Deciphering gut-microbiome–to–host-metabolome interaction is critical for understanding how microbial communities generate bioactive signals that shape host physiology and disease. Progress, however, has been hindered by inconsistent metabolite annotations, poor interoperability across studies, and the absence of integrated resources placing microbiome-derived metabolites within their functional, microbial, physiological, and clinical context. Here we present HuMMANet (Human Microbiome–Metabolome Annotation Network), a harmonized resource integrating 46 paired gut microbiome–metabolome studies (59 study-units; 14,405 samples; 13 disease categories plus a healthy/control reference category) with a scalable metabolite-harmonization framework. HuMMANet resolves heterogeneous annotations through a multi-stage workflow spanning RefMet, HMDB, PubChem, Metabolomics Workbench, SMPDB, MiMeDB-2.0, GNPS/microbeMASST, DrugBank, and DrugCentral, yielding a reference atlas of 54,914 unique metabolites, annotated with standardized chemical identifiers, biochemical pathways, microbial producer associations, physiological distributions, disease links, and structural relationships to approved therapeutics — a unified reference framework for microbiome– metabolome research. Applying HuMMANet to a multi-cohort integration of adult serum and fecal metabolomes, we identified 519 serum and 322 fecal metabolites reproducibly associated with gut microbial community composition (PERMANOVA, P < 0.05 in at least 50% of studies in which detected), enriched for specific biomolecular classes and pathways. Cross-referencing these against Health-Associated-Core-Keystone (HACK) taxa revealed 58 serum and 25 fecal metabolites (HACK-positive) whose taxon-level associations tracked positively with the taxon-specific-HACK indices. These reproducible metabolomic signatures of microbiome health included indole-3-propionic acid, a gut barrier-protective microbial tryptophan metabolite, and 3-phenylpropionate. Drug-similarity annotation within HuMMANet linked 16 of this serum and 13 fecal HACK-positive metabolites to therapeutics used in neurological, inflammatory, and vascular disease. Conversely, 38 serum and 65 fecal metabolites, including imidazole propionate and long-chain acylcarnitines such as ACar 18:0, showed HACK-negative signatures previously associated with dysbiosis-linked disease. GNPS/microbeMASST and MiMeDB-2.0 annotations further traced subsets of these metabolites to putative bacterial producers. HuMMANet thus provides a standardized framework for reproducible microbiome–metabolome integration, enabling cross-study discovery and translational prioritization of conserved microbiome-derived metabolic signatures across human populations and disease states.
