MULTI-OMICS ANALYSES OF FECAL EXTRACTS TO EXPLORE THE MICROBIOME OF RATS FOLLOWING ADMINISTRATION OF AN AGONIST FOR THE GPR40 RECEPTOR

Multi-omics analysis of rat fecal extracts following administration of a GPR40 (FFAR1) agonist (fasiglifam)

Significance of the topic

Understanding how orally administered drugs interact with the gut microbiome and host biology is critical for drug safety, efficacy, and metabolic fate. Fasiglifam (TAK-875) is a GPR40 agonist developed for type 2 diabetes that exhibited desirable glucose-dependent insulinotropic effects but raised liver-safety concerns in late-stage clinical trials. Most metabolites of fasiglifam are excreted in feces, making fecal multi-omics a valuable approach to link drug biotransformation, microbiome shifts, and host responses that may contribute to adverse outcomes.

Objectives and study overview

The study aimed to characterize compositional and functional changes in the rat gut ecosystem after fasiglifam dosing using integrated metaproteomics and metabolomics/lipidomics. Key goals were to: identify microbial and host proteins in fecal extracts, detect small-molecule perturbations (including lipids and stress-related metabolites), map affected biological pathways, and explore correlations between metabolite markers (e.g., cortisol) and proteomic signatures that could indicate dysbiosis or host stress responses.

Methodology

Design and sampling:

• Animals: Sprague Dawley rats (15 males, 9 females). Animals were acclimatized then housed individually for a 96-hour study.
• Dosing: Fasiglifam administered IV (5 mg/kg) or orally (PO; 10 and 50 mg/kg); vehicle controls included for each route.
• Fecal sampling: Collected at four timepoints from pre-dose to 96 hours post-dose.

Sample preparation and analytical workflows:

• Polar metabolites: Extracted using a Folch-based protocol optimized for fecal material.
• Proteins: Extracted from <10 mg feces, reduced, alkylated and digested via filter-aided sample preparation (FASP) to generate tryptic peptides.
• LC-MS acquisition: Proteomics employed data-independent acquisition (DIA) using the SONAR Pulse stepped quadrupole strategy. Metabolomics/lipidomics used ion-mobility-enabled LC-MS (cyclic IMS HDMSE) to add an orthogonal separation dimension.
• Data processing: Peak picking, normalization, and stringent database searching for confident metabolite and protein identifications, followed by multivariate statistics and pathway mapping.

Used instrumentation

• Waters ACQUITY UPLC for chromatographic separations.
• Xevo MRT P10 mass spectrometer (high sensitivity and mass accuracy) with SONAR Pulse DIA for proteomics.
• Cyclic ion mobility separation (Cyclic IMS / HDMSE) incorporated into metabolomics workflows.
• Standard informatic tools for mzML conversion, protein/peptide searching, and metabolite annotation (progenesis-type pipelines and Metacore pathway mapping reported).

Main results and discussion

Proteomics and microbiome composition:

• High-depth metaproteomics generated a curated list of >1,500 unique protein identifications from <10 mg fecal material, enabling confident taxonomic and functional profiling.
• Phylum-level shifts were observed after fasiglifam administration: a marked decrease in Firmicutes, a smaller decrease in Bacteroidetes, relatively stable Actinobacteria, and a time- and dose-related increase in Proteobacteria. The rise in Proteobacteria is consistent with dysbiosis and potential inflammatory states.

Functional and pathway insights:

• Pathway mapping highlighted mitochondrial dysfunction and perturbations in metabolic and stress-response processes. Increased intestinal oxygenation associated with mitochondrial dysfunction can favor facultative Proteobacteria, linking host metabolic perturbation to compositional microbiome changes.
• Gene ontology enrichment among identified proteins emphasized metabolic processes and stress responses, aligning with inferred dysbiosis.

Metabolomics and biomarker correlations:

• Complex metabolite and lipid perturbations were detected across compound classes. Multivariate analysis showed strong technical reproducibility and clear separation of dosed animals over time, with distinct time-course trajectories.
• Cortisol emerged as a top, high-confidence feature elevated in dosed groups and temporally variable (diurnal signature). Cortisol correlated with multiple proteomic markers involved in metabolic and stress pathways, supporting a link between host stress responses and microbiome alterations.

Statistical observations:

• Principal component and hierarchical clustering analyses revealed time-dependent shifts, especially notable at 24–48 hours post-dose, and evidence of diurnal variation affecting metabolite profiles.

Overall interpretation:

• The combined multi-omics data suggest that fasiglifam administration perturbs host metabolic and stress pathways, coincident with a shift toward Proteobacteria-dominated dysbiosis. These interactions could contribute to altered drug metabolism and potential downstream toxicity.

Benefits and practical applications of the method

• High-sensitivity, integrated metaproteomics plus ion-mobility metabolomics allows simultaneous taxonomic, functional, and small-molecule readouts from minimal fecal input, suitable for longitudinal toxicology studies.
• The approach can identify candidate biomarkers (e.g., cortisol and protein markers) that link host response to microbiome changes, informing mechanistic hypotheses and safety assessments.
• Such workflows support drug development decisions by revealing off-target effects on the gut ecosystem and host metabolism that may influence efficacy or adverse events.

Future trends and potential uses

• Broader integration of metagenomics (shotgun sequencing) with metaproteomics and metabolomics to resolve taxonomic identity to strain level and confirm functional gene expression.
• Longitudinal and higher-resolution temporal sampling to disentangle acute versus sustained drug-induced microbiome changes and to control for diurnal variation.
• Development of targeted assays for identified biomarker panels (proteins, cortisol, lipid species) to enable translational monitoring in preclinical and clinical studies.
• Use of in vitro gut models and gnotobiotic animals to establish causality between specific microbial shifts and host metabolic/toxic outcomes.
• Improved annotation workflows and spectral libraries to reduce 'dark matter' in host-microbiome metabolomics and enhance compound-level interpretation.

Conclusion

This multi-omics investigation demonstrates that oral administration of fasiglifam in rats leads to coordinated shifts in the fecal proteome and metabolome consistent with dysbiosis, increased Proteobacteria, mitochondrial-related metabolic perturbation, and elevated cortisol-linked stress responses. The combined metaproteomic and metabolomic strategy provided sensitive, high-confidence molecular signatures from small fecal samples and highlighted pathways for further mechanistic and translational study relevant to drug safety assessment.

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