Biology subjects

Ghosh, T. S.

Publications and source records attributed to Ghosh, T. S..

10 recordsLinked to original sources

bioRxiv2026

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

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 indole3propionic acid, a gut barrier-protective microbial tryptophan metabolite, and 3phenylpropionate. 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.

bioinformatics↗

bioRxiv2026

Multi-cohort analysis of 37,739 oral microbiomes reveals ecologically influential health-associated microbial sub-communities across major oral subsites

The oral cavity contains multiple microbial sub-niches, but which taxa consistently play an ecologically important, health-associated role within each niche, and how conserved they are across populations, remains poorly understood, partly due to the lack of a standardised identification framework. We developed a multi-cohort framework integrating 37,739 oral microbiome profiles (16S rRNA and shotgun sequencing) from 142 cohorts (41 countries) ranking 542 taxa across four oral habitats, supragingival, subgingival, tongue-tonsil, and buccal-palate-mucosa, via a new Health-Associated-Core (HAC) score capturing consistent prevalence, ecological influence, and health-association. For saliva, with available longitudinal sampling, we extended this into a salivary-Health-Associated-Core-Keystone (sHACK) score additionally capturing stability-association, ranking 499 taxa. Using two complementary approaches for identifying ecological modules, high-sHACK salivary taxa concentrated within a single, connected sub-community of 28 members, consistently linked to prevalence, ecological influence, stability, and health. This sub-communitys abundance alone outperformed conventional dysbiosis indices in distinguishing healthy from diseased individuals and tracked stability in an independent cohort of 4,621 microbiomes. Comparable sub-communities emerged across three other subsites, with compositional differences mirroring physicochemical variation between sites. Machine learning linked taxa-specific-genome-encoded functions to their corresponding subsite-specific HAC/sHACK scores, offering a unified framework for prioritizing oral microbes diagnostically and therapeutically.

microbiology↗

bioRxiv2026

MifRix: An Integrated microbiome framework for predicting generic and disease-specific risks investigating inter-disease diagnostic cross-talks and intra-disease signature variability

The human gut microbiome is increasingly recognized as a diagnostic indicator across diverse diseases, yet unified frameworks integrating taxonomic and functional features for multi-disease risk assessment, while capturing disease-specific variability in microbiome-alteration signatures, remain limited. Here we present MifRix (Microbiome-inferred Risk-scores with explainability), a two-step ensemble machine-learning framework integrating microbial composition with composition-derived functional signatures to predict generic and disease-specific risk scores across 10 major diseases, coupled with profiling of risk-explainable microbiome features. MifRix was trained using 38,054 gut microbiomes spanning 150 cohorts and 48 nationalities, leveraging taxa abundance and taxa-inferred functional profiles derived from 57,743 functional features mapped across 4,814 species-level taxa. On unseen validation datasets (4,649 microbiomes, 32 cohorts), MifRix outperformed established microbiome health metrics, with disease-specific risk scores achieving strong discrimination (AUC: 0.92-0.99). The explainability module revealed shared microbial signatures across disease pairs, organizing all ten diseases along a continuous gastrointestinal-to-neurological risk gradient, driven not by whole-disease microbiome alteration signatures but by specific, reproducible sub-signatures within each disease. Applied across independent cohorts, MifRix scores further identified population subgroups and individuals at elevated risk for related diseases, flagged precursor/pre-disease states, and tracked therapy-associated response, establishing an interpretable framework for microbiome-based precision diagnostics and disease-risk stratification.

microbiology↗

bioRxiv2026

Scalable Microbiome Network Inference: Mitigating Sparsity and Computational Bottlenecks in Random Effects Models

Withdrawal StatementThe authors have withdrawn their manuscript because the biological validations associated with the inferred microbial interaction directions are currently incomplete and require further verification. We are actively working on validating these biological directions and ensuring the scientific correctness of the reported findings before any further dissemination. Therefore, the authors do not wish this work to be cited as a reference for the project at this stage. If you have any questions, please contact the corresponding author.

bioinformatics↗

bioRxiv2025

Identifying the non-Bifidobacterial hallmarks of a Bifidobacterium-receptive gut microbiome

Bifidobacteria are key health-associated members of the human gut and widely used as probiotics, but their colonization success varies across individuals, partly due to baseline microbiome composition. In this analysis encompassing 51,058 gut microbiomes (148 cohorts spanning 46 countries across six continents), we investigated non-Bifidobacterial features associated with the overall detection and abundance of eight major Bifidobacterial species. While non-Bifidobacterial community composition significantly correlated with Bifidobacterial detection in 74% of analyzed cohorts, the associations for the individual species showed distinct age-/lifestyle-/disease-specific patterns (greater similarity within similar cohorts). We quantified these relationships as Association-Scores, further stratified by age group, lifestyle, profiling strategy (16S/WGS) and nine diseases, and reasonably reproduced in an external validation-cohort-set of 8 Bifidobacterium intervention studies (1,063 gut microbiomes). We subsequently utilized these scores to build microbiome-level receptive-scores corresponding to the 8 major Bifidobacterium species and their overall detection. Both robust linear regression and cross-study validation showed that combining baseline abundance of the administered Bifidobacterium with the corresponding baseline receptive-scores significantly predicted post-treatment persistence/increase in 75% trial-probiotic pairs. We identify 129 species-specific-genome-level functional-groups in non-Bifidobacterial taxa correlating with Bifidobacterial Association-Scores. This global analysis highlights non-Bifidobacterial microbiome features influencing Bifidobacterial colonization and provides tools to predict personalized responses to Bifidobacterial-probiotic interventions.

microbiology↗

bioRxiv2024

Towards a Health-Associated Core Keystone (HACK) index for the human gut microbiome

Variations in the normal gut microbiome and the existence of context-dependent disease associations have confounded the identification of microbiome markers of health. A reliable indexing of taxa based on their association with host health and microbiome resilience could accelerate development of microbiome-based therapeutics including selection of live biotherapeutics and facilitate microbiome comparisons in diverse study populations. Here we first investigated 196 taxa for their association with three hallmark properties of health and microbiome-resilience, namely prevalence/community-influence in non-diseased subjects, longitudinal stability and host health, using a discovery cohort of 39,926 publicly available adult (> 18 years) gut microbiomes from 127 studies spanning 42 countries and 28 disease conditions (including 9,434 longitudinal samples). We identified 18 species-level-taxa, referred to as Health-Associated Core Keystones (HACKs), with association-strengths in the top 30 percentile for all three properties. We integrated these association-strengths into a single value, the HACK-index, that ranks taxa based on their estimated contribution to both microbiome stability and host health. We then demonstrate the reproducibility of these indices for the taxon associations with the three properties, using a validation cohort of 4,500 gut microbiomes (from 11 studies with various demographics and diseases). Specific consortia of high HACK-index taxa are also associated with positive response to Mediterranean Diet interventions and Immuno-Checkpoint-Inhibitor therapies. We finally identify the distinguishing microbial genomic functions associated with high HACK-indices/HACK-taxa that can be investigated using targeted mechanistic studies to identify microbiome effectors of improved health. The availability of HACK-indices provides a rational basis for microbiome comparisons and will facilitate the selection and design of microbiome-based therapeutics.

microbiology↗

bioRxiv2023

Artificial Intelligence uncovers Evolutionarily Conserved Intracellular Allosteric Modulators of GPCR-Gα interface.

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for unbiased cavity identification, cavity-specific ligand design, synthesis, and validation across GPCR topology. Here, we developed Gcoupler, an AI-driven generalized computational toolkit that leverages an integrative approach combining de novo ligand design, statistical methods, Graph Neural Networks, and Bioactivity-based ligand prioritization for rationally predicting high-affinity ligands. Using Gcoupler, we interrogated intracellular metabolites that target and regulate the GPCR-G interface (Ste2p-Gpa1p), affecting pheromone-induced programmed cell death in yeast. Our computational analysis, complemented by experimental validations, including genetic screening, multi-omics, site-directed mutagenesis, biochemical assays, and physiological readouts, identified endogenous hydrophobic metabolites, notably sterols, as direct intracellular allosteric modulators of Ste2p. Molecular simulations coupled with biochemical signaling assessment in site-directed Ste2p mutants further confirmed that metabolites binding to GPCR-G obstruct downstream signaling, possibly via a cohesive effect. Finally, by utilizing isoproterenol-induced, GPCR-mediated human and neonatal rat cardiac hypertrophy models, we observed that elevated metabolite levels attenuate hypertrophic response, reinforcing the evolutionary relevance of this mechanism.

bioinformatics↗

bioRxiv2021

Excessive inflammatory and metabolic responses to acute SARS-CoV-2 infection are associated with a distinct gut microbiota composition

Protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and associated clinical sequelae requires well-coordinated metabolic and immune responses that limit viral spread and promote recovery of damaged systems. In order to understand potential mechanisms and interactions that influence coronavirus disease 2019 (COVID-19) outcomes, we performed a multi-omics analysis on hospitalised COVID-19 patients and compared those with the most severe outcome (i.e. death) to those with severe non-fatal disease, or mild/moderate disease, that recovered. A distinct subset of 8 cytokines and 140 metabolites in sera identified those with a fatal outcome to infection. In addition, elevated levels of multiple pathobionts and lower levels of protective or anti-inflammatory microbes were observed in the faecal microbiome of those with the poorest clinical outcomes. Weighted gene correlation network analysis (WGCNA) identified modules that associated severity-associated cytokines with tryptophan metabolism, coagulation-linked fibrinopeptides, and bile acids with multiple pathobionts. In contrast, less severe clinical outcomes associated with clusters of anti-inflammatory microbes such as Bifidobacterium or Ruminococcus, short chain fatty acids (SCFAs) and IL-17A. Our study uncovered distinct mechanistic modules that link host and microbiome processes with fatal outcomes to SARS-CoV-2 infection. These features may be useful to identify at risk individuals, but also highlight a role for the microbiome in modifying hyperinflammatory responses to SARS-CoV-2 and other infectious agents.

immunology↗

bioRxiv2021

Fiber-associated Lachnospiraceae reduce colon tumorigenesis by modulation of the tumor-immune microenvironment

Patients with colorectal cancer (CRC) harbor gut microbiomes that differ in structure and function from those of healthy individuals, suggesting this altered microbiome could contribute to tumorigenesis. Despite increasing evidence implicating the gut microbiome in CRC, the collective role of different microbial consortia in CRC carcinogenesis is unclear. We have previously described these consortia as co-abundance groups that co-exist at different abundance levels in the same patient. Here, we report that tumor biopsy tissue from patients with a "high-risk" Pathogen-type microbiome had a different immune transcriptome and immune cell infiltrate from those with a "low-risk" Lachnospiraceae-type microbiome. Transplantation from patients of the two fecal microbiome types into mice with an orthotopic tumor differentially affected tumor growth and the systemic anti-tumor immune response. The differences in tumor volume and immunophenotype between mice receiving the high-risk and the low-risk microbiome correlated with differences in the engrafted human microbial species and predicted microbiome-encoded metabolites in the two groups. Of twelve taxa whose abundance in recipient mice led to increased tumor onset, seven corresponded with differentially abundant taxa in a global dataset of 325 CRC patients versus 310 healthy controls. These data suggest that the enrichment for a Lachnospiraceae-type configuration of the gut microbiome may influence colon cancer progression and disease outcome by modulating the local and systemic anti-tumor immune response. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/432654v2_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1de9ff5org.highwire.dtl.DTLVardef@d790d5org.highwire.dtl.DTLVardef@65be8aorg.highwire.dtl.DTLVardef@1c096da_HPS_FORMAT_FIGEXP M_FIG C_FIG Proposed model of how the high-risk Pathogen and low-risk Lachnospiraceae CAGs differentially modulate the tumor immune response.

microbiology↗

bioRxiv2019

IPCO: Inference of Pathways from Co-variance analysis

Key aspects of microbiome research are accurate identification of taxa followed by the profiling of their functionality. Amplicon profiling based on the 16S ribosomal DNA sequence is a ubiquitous technique to identify and profile the abundances of the various taxa. However, it does not provide information on their encoded functionality. Predictive tools which can accurately extrapolate the functional information of a microbiome based on taxonomic profile composition is essential. At present the applicability of these tools is however limited due to requirement of reference genomes from known species. We present IPCO (Inference of Pathways from Co-variance analysis), a new method of inferring functionality for 16S-based microbiome profiles independent of reference genomes. IPCO utilises the biological co-variance observed between paired taxonomic and functional profiles and co-varies it with the queried dataset. It outperforms other established methods both in terms of sample and feature profile prediction. Validation results confirmed that IPCO can replicate observed biological signals seen within shotgun and metabolite profiles. Comparative analysis of predicted functionality profiles with other popular 16S-based functional prediction tools indicates significantly lower performance with predicted functionality showing little to no correlation with paired shotgun features across samples. IPCO is implemented in R and available from https://github.com/IPCO-Rlibrary/IPCO.

bioinformatics↗