This study applied a network pharmacology framework to systematically identify gut microbiota–associated metabolites implicated in sepsis and sepsis-related organ dysfunction. The authors constructed a sepsis-related microbiota–metabolite–target–pathway network to prioritize metabolites that could influence host responses in sepsis. Network analysis produced a prioritized list of candidate metabolites that were further evaluated using pharmacodynamic, ADMET, and molecular docking methods.
Network analysis predicted 19 core metabolites and 45 sepsis-associated targets. Central protein nodes in the network included IL-6, TNF-α, AKT1, TP53, IL-1β, and PPAR-γ, indicating an inflammatory and immune-related signature. Pathway enrichment analysis showed overrepresentation of inflammation and immune signaling pathways, particularly the Toll-like receptor pathway, PI3K/AKT signaling, and mitogen-activated protein kinase (MAPK) signaling. These results linked the prioritized metabolites to canonical host pathways known to mediate sepsis pathophysiology.
Following network-based prioritization, the authors performed computational ADMET assessments to evaluate absorption, distribution, metabolism, excretion, and toxicity properties of candidate metabolites. From this screening, 12 metabolites were identified with favorable predicted pharmacokinetic and safety profiles. These ADMET-filtered metabolites formed the subset recommended for further mechanistic and experimental evaluation, narrowing candidates based on predicted drug-likeness and tolerability.
Molecular docking was used to assess potential interactions between prioritized metabolites and key sepsis-associated targets. Docking analysis specifically predicted potential binding between diosmetin, a bioactive flavonoid derived from dietary flavonoid glycosides through gut microbiota-mediated biotransformation, and AKT1. This in silico result provided a mechanistic rationale for selecting diosmetin for in vivo validation in a sepsis-associated acute kidney injury (AKI) model.
The study evaluated diosmetin in a cecal ligation and puncture (CLP)-induced mouse model of sepsis-associated AKI. In treated CLP mice, diosmetin administration produced several measurable effects compared with untreated CLP animals:
These in vivo observations align with the network and docking predictions implicating the PI3K/AKT signaling axis and inflammatory mediators as relevant targets of diosmetin activity in the context of sepsis-associated AKI.
The combined systems pharmacology and experimental approach identified microbiota-related candidate metabolites associated with sepsis and offered preliminary in vivo evidence that diosmetin can mitigate inflammatory responses and renal injury in a CLP model of sepsis-associated AKI. Network analysis highlighted inflammatory cytokines and signaling pathways (Toll-like receptor, PI3K/AKT, MAPK) as central to the predicted metabolite–host interactions.
Limitations reported or implied by the study design include reliance on computational predictions (network pharmacology, ADMET, and molecular docking) to prioritize candidates and the use of a single animal disease model for in vivo validation. The abstract does not report dosing regimens, timing, sample sizes, or detailed quantitative effect sizes for the in vivo experiments; these details were not reported in the abstract and would require consultation of the full article for comprehensive assessment.
The authors conclude that network-based analysis can identify sepsis-associated microbiota metabolites and that diosmetin shows potential to attenuate inflammation and kidney injury in sepsis-associated AKI, with evidence pointing to modulation of PI3K/AKT signaling.
The study declared no competing interests. It used publicly available database data and an animal model; institutional animal care and experimental procedures were approved by the Animal Ethics Committee of Peking University Shenzhen Hospital (Approval No. 2025 − 293). Animal experiments were reported to follow ARRIVE 2.0 guidelines and applicable institutional and national regulations on laboratory animal welfare.
MeSH indexing associated with the publication includes Acute Kidney Injury (drug therapy, etiology, metabolism), Animals, Disease Models (Animal), Flavonoids (metabolism, pharmacology), Gastrointestinal Microbiome (drug effects), Mice, and Molecular Docking Simulation, reflecting the study's focus on microbiota-derived flavonoids, computational docking, and in vivo murine disease modeling.