Comprehensive characterization of multi-omics landscapes between gut microbial metabolites and the druggable genome in sepsis

Abstract

Background: Sepsis is a life-threatening condition with limited therapeutic options. Emerging evidence implicates gut microbial metabolites in modulating host immunity, but the specific interactions between these metabolites and host druggable targets remain poorly understood.

Methods: We utilized a systems biology framework integrating genetic analyses, multi-omics profiling, and structure-based virtual screening to systematically map the interaction landscape between human gut microbial metabolites and druggable G-protein-coupled receptors (GPCRs), ion channels (ICs), and kinases (termed the "GIKome") in sepsis. Key findings were validated by molecular dynamics (MD) simulation, microscale thermophoresis (MST), and functional assays in a murine cecal ligation and puncture (CLP) model of sepsis.

Results: We evaluated 190,950 metabolite-protein interactions, linking 114 sepsis-related GIK targets to 335 gut microbial metabolites, and prioritized indole-3-lactic acid (ILA), a metabolite enriched in Akkermansia muciniphila, as a promising therapeutic candidate. MD simulation and MST further revealed that ILA binds stably to PFKFB2, a pivotal kinase in regulating glycolytic flux and immune activation during sepsis. In vivo, ILA administration improved survival, attenuated cytokine storm, and mitigated multi-organ injury in CLP-induced septic mice.

Conclusions: This systems-level investigation unveils previously unrecognized therapeutic targets, offering a blueprint for microbiota-based precision interventions in critical care medicine.

Keywords: GPCRs; Mendelian randomization; ion channels; kinases; microbial metabolites; sepsis.

Figure 1

Overview of druggable GIKs and gut microbial metabolites in this study. (A) Classification of druggable GIKs into GPCRs, ICs, and kinases. (B) Categorization of druggable GIKs into four developmental stages: Tclin, Tchem, Tbio, and Tdark. (C) Distributions of human gut microbial metabolites by chemical classes.

Figure 2

Prioritization of druggable GIKs through MR analysis. In total, five sepsis GWAS datasets from the UK Biobank and FinnGen were used. The eQTL data for druggable GIKs were obtained from the eQTLGen Consortium. β > 0 indicates that increased expression of a GIK is associated with a higher likelihood of sepsis.

Figure 3

Prioritization of druggable GIKs through multi-omics analysis. A comprehensive analysis of 37 sepsis transcriptomic and proteomic datasets identified druggable GIKs that are differentially expressed between sepsis and control states, as well as between survivors and non-survivors. The number of datasets providing multi-omics evidence for each GIK is represented by the stacked bars.

Figure 4

Molecular docking-based discovery of human gut microbial metabolite-sepGIK interactome. (A) An integrated network illustrates the gut microbial metabolite-sepGIK interactome. A docking score (edge) connecting the 1^st metabolite (shown in red) of 114 sepGIKs or the 1^st sepGIK (shown in purple) of 335 metabolites. Metabolite and sepGIK are depicted as circle and rectangle nodes, respectively. Protein family class of sepGIKs and chemical class of metabolites are indicated with different colors. The size of the sepGIK node is proportional to the number of multi-omics or genetic evidence, while the size of the metabolite node is proportional to the number of bacteria strains with higher metabolite abundance (|log₂FC| ≥ 2). Tier 1 sepGIKs and their paired metabolites are highlighted. (B) Summary of Genetics-driven and multi-omics evidence for Tier 1 sepGIKs.

Figure 5

Identification of sepsis-relevant gut microbial metabolites using MR analysis. (A) Gut microbial origins of 18 MR-prioritized metabolites. Only the bacteria genera that have the most abundance of metabolites are shown. The color of shape and lines are based on bacteria genus types. The line width indicates the abundance of metabolite in the bacteria. HMB, 2-Hydroxy-4-(methylthio)butanoic acid; SAH, S-Adenosylhomocysteine; 4-HPPA, 4-Hydroxyphenylpyruvate. (B) Associations between MR-prioritized metabolites and sepGIKs. In total, 18 gut microbial metabolites supported by MR analysis (p_IVW < 0.05) on five sepsis GWAS datasets interact with 28 sepGIKs.

Figure 6

scRNA-seq-based discovery of immunosuppression-related gut microbial metabolite-sepGIK pairs. (A) t-distributed stochastic neighbor embedding (tSNE) plot for each cell type based on SCP548 scRNA-seq dataset. (B) Heatmap depicting the differential expression of Tier 1 sepGIKs across high- and low-activity immune cells, including T cell, B cell, and monocyte. (C) Network diagram illustrating the immunosuppression-related metabolite-sepGIK pairs based on T cell, B cell, and monocyte.

Figure 7

Prioritization of human gut microbial metabolites from sepsis-protective microbiota. (A) Abundance of gut metabolites from L. rhamnosus, L. plantarum, B. pseudocatenulatum, B. infantis, and A. muciniphila. (B) Visualization of ILA interaction with PFKFB2. (C) MST analysis for ILA binding to PFKFB2 (n = 3). (D) RMSD analysis over time, reflecting structural stability. (E) MM/PBSA energy decomposition. VDWAALS, Van der Waals interactions; EEL, electrostatic energy; EPB, polar solvation energy; ENPOLAR, nonpolar solvation energy; GGAS, gas-phase energy; GSOLV, solvation free energy; TOTAL, total binding free energy. (F) The KM survival curves assessed for up to 72 h. Each line represents the survival of mice in a group; 12 mice were in each group. (G) Representative images of HE staining exhibited the pathological alterations in multiple organs of mice, including lung, liver, kidney, and heart (Scale bar = 50 μm). (H) ELISA for detecting the serum levels of TNF-α, IL-1β, and IL-6. *p < 0.05, **p < 0.01, ***p < 0.001.