Daily Sepsis Research Analysis
Three papers advanced sepsis science across pathogenesis, therapeutics discovery, and prognosis. Genomic epidemiology linked neonatal late-onset sepsis to gut-derived Enterobacterales, while a systems-level multi-omics and structure-based pipeline uncovered microbiota-derived metabolite–target pairs with preclinical validation. An externally validated machine-learning model accurately predicted mortality in ICU patients with early hypoalbuminemia.
Summary
Three papers advanced sepsis science across pathogenesis, therapeutics discovery, and prognosis. Genomic epidemiology linked neonatal late-onset sepsis to gut-derived Enterobacterales, while a systems-level multi-omics and structure-based pipeline uncovered microbiota-derived metabolite–target pairs with preclinical validation. An externally validated machine-learning model accurately predicted mortality in ICU patients with early hypoalbuminemia.
Research Themes
- Microbiota–host interface and pathogen translocation in sepsis
- Translational multi-omics target discovery from gut metabolites
- Predictive analytics and risk stratification in critical care sepsis
Selected Articles
1. Comprehensive characterization of multi-omics landscapes between gut microbial metabolites and the druggable genome in sepsis.
A systems biology pipeline mapped 190,950 interactions between gut microbial metabolites and druggable GPCRs, ion channels, and kinases relevant to sepsis, linking 114 targets to 335 metabolites. Indole-3-lactic acid emerged as a prioritized candidate, with target engagement and functional effects validated via MD, MST, and a murine CLP model, highlighting therapeutic opportunities from microbiota-derived ligands.
Impact: This work provides a scalable blueprint to translate microbiome chemistry into sepsis therapeutics, combining in silico mapping with orthogonal biophysical and in vivo validation. It identifies actionable targets and a lead metabolite (ILA), advancing precision interventions.
Clinical Implications: While preclinical, the validated metabolite–target interactions suggest new drug targets and adjuvant strategies in sepsis, motivating biomarker-guided trials of microbiota-derived ligands or pathway modulators.
Key Findings
- Mapped 190,950 metabolite–protein interactions across GPCRs, ion channels, and kinases (the GIKome) in the context of sepsis.
- Linked 114 sepsis-relevant druggable targets to 335 gut microbial metabolites and prioritized indole-3-lactic acid (ILA).
- Validated target engagement and functional effects using MD simulation, MST biophysics, and a murine CLP sepsis model.
Methodological Strengths
- Integrated multi-omics and structure-based virtual screening with orthogonal validation (MD, MST, in vivo).
- Systems-scale interaction mapping enabling target prioritization with mechanistic follow-up.
Limitations
- Preclinical findings; no human interventional validation.
- Focused on GPCRs, ion channels, and kinases; may miss other target classes and context-specific effects.
Future Directions: Translate prioritized metabolite–target pairs into early-phase human studies with pharmacodynamic biomarkers; expand to additional target classes and patient-derived samples.
2. Enterobacterales gut colonization and late-onset sepsis in neonates: a multicentre prospective study across 18 neonatal intensive care units in six countries.
Using WGS of paired blood and fecal isolates from neonates with Gram-negative LOS, 82% showed high genetic relatedness consistent with gut-to-blood translocation. Invasive E. coli uniformly carried hlyA–D hemolysin genes, absent in noninvasive strains, and extremely preterm/low-birth-weight infants were overrepresented among translocation cases.
Impact: By genetically linking gut colonization to bloodstream infections, this study elucidates neonatal LOS pathogenesis and identifies virulence (E. coli hemolysin) associated with invasiveness, informing targeted prevention and surveillance.
Clinical Implications: Supports NICU strategies focusing on gut colonization surveillance, stewardship to limit Enterobacterales overgrowth, and risk stratification of extremely preterm/low-birth-weight infants; virulence profiling may refine targeted interventions.
Key Findings
- In 18/22 neonates (82%), blood and gut Enterobacterales isolates were highly genetically related, indicating gut-to-blood translocation.
- All invasive E. coli carried hlyA–D hemolysin genes, absent in noninvasive strains (p=0.028).
- Extremely preterm and low-birth-weight neonates were overrepresented among translocation cases.
Methodological Strengths
- Whole-genome sequencing of paired isolates enabling high-resolution source attribution.
- Multicentre design leveraging samples from 18 NICUs across six countries.
Limitations
- Small number of paired cases (n=22) limits generalizability.
- Secondary analysis; cannot establish causality or quantify translocation rates across all LOS cases.
Future Directions: Prospective, larger WGS-based surveillance with integration of host factors and microbiome dynamics to inform preventive interventions and evaluate virulence-guided strategies.
3. Prediction of the mortality rate in the intensive care unit for early sepsis patients with combined hypoalbuminemia based on machine learning.
Using MIMIC-IV and eICU data, a CatBoost model with recursive feature elimination and SHAP interpretability predicted ICU mortality among septic patients with early hypoalbuminemia, achieving AUCs of 0.845 (train/internal), 0.746 (internal test), and 0.827 (external). The study demonstrates generalizable, interpretable risk stratification beyond traditional scores.
Impact: Externally validated, interpretable ML focused on a high-risk sepsis subgroup provides actionable prognostication and may guide early resource allocation and treatment intensity.
Clinical Implications: Enables early identification of high-risk patients with hypoalbuminemic sepsis, informing triage, monitoring intensity, and potential enrollment into trials of nutritional or organ-support strategies.
Key Findings
- CatBoost with recursive feature elimination achieved AUCs of 0.845 (training/internal), 0.746 (internal test), and 0.827 (external eICU).
- Use of SHAP provided model interpretability and feature impact insights.
- Model outperformed alternative algorithms and generalized across two large ICU databases.
Methodological Strengths
- External validation across independent databases (MIMIC-IV and eICU).
- Model interpretability using SHAP and rigorous feature selection (RFE).
Limitations
- Retrospective design with potential residual confounding and data quality biases.
- Focused on early hypoalbuminemia subgroup; generalizability to all sepsis patients requires further study.
Future Directions: Prospective, multi-center impact studies to assess clinical utility and workflow integration; adaptive updating and fairness audits across diverse ICUs.