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.
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 CONCLUSIONS: This systems-level investigation unveils previously unrecognized therapeutic targets, offering a blueprint for microbiota-based precision interventions in critical care medicine.
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.
OBJECTIVES: Gram-negative bacteria cause a significant proportion of neonatal late-onset sepsis (LOS) and are associated with high mortality. Emerging evidence implicates the gut as a reservoir for invasive pathogens; however, the mechanisms of gut-to-blood translocation and the role of virulence factors remain unclear. METHODS: We conducted a secondary analysis of microbiological samples from the NeoMero-1 trial, a multicentre study of neonatal LOS. Whole-genome sequencing was performed on paired blood and faecal Enterobacterales isolates from 22 neonates with gram-negative bacteria bloodstream infection and concurrent gut samples. Genetic relatedness was assessed using multilocus sequence typing and species-specific single-nucleotide polymorphism thresholds. Virulence gene profiles were characterized using the virulence factor database. RESULTS: In 18 of 22 cases (82%), blood and gut isolates were genetically highly related, supporting gut-to-blood translocation. All invasive Escherichia coli (7 over 7) strains consistently harboured haemolysin genes (hlyA-D), absent in all the noninvasive strains (2/2 p 0.028). Extremely preterm and low birth weight neonates were overrepresented among those with translocation. CONCLUSIONS: Our findings support the role of gut-derived Enterobacterales in the pathogenesis of neonatal LOS. These insights may inform infection control and targeted preventive strategies. Further prospective studies are needed to validate these findings and guide interventions for high-risk neonates.
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.
This study aims to predict the mortality rate among septic patients with early-onset hypoalbuminemia in the intensive care unit (ICU) using machine learning algorithms. Utilizing patient data from the MIMIC-IV and eICU databases, we divided MIMIC-IV samples into training and internal validation sets, with eICU samples serving as an external validation set. We developed the predictive model using various feature selection techniques and machine learning algorithms, and evaluated its performance using metrics such as AUC, accuracy, precision, recall, and F1 score. The SHAP method was used for model interpretability. The CatBoost model, developed using recursive feature elimination, outperformed other algorithms, demonstrating robust generalization with AUC values of 0.845, 0.746, and 0.827 across the respective datasets. This pioneering study presents a machine learning model with high accuracy and robust extrapolation capabilities for predicting mortality rates in septic patients with early-onset hypoalbuminemia in the ICU, providing valuable decision support for clinicians.