Daily Sepsis Research Analysis
A Cell study reveals that extracellular vesicles can transplant gasdermin D pores to bystander cells, propagating pyroptosis and amplifying inflammation. Preclinical research identifies kynurenic acid as a tryptophan-derived metabolite that shifts macrophages toward an anti-inflammatory phenotype via PPARγ/NF-κB to mitigate septic colonic injury. A real-world machine learning model modestly improves empirical beta-lactam activity in Enterobacterales bloodstream infections, supporting antimicrobi
Summary
A Cell study reveals that extracellular vesicles can transplant gasdermin D pores to bystander cells, propagating pyroptosis and amplifying inflammation. Preclinical research identifies kynurenic acid as a tryptophan-derived metabolite that shifts macrophages toward an anti-inflammatory phenotype via PPARγ/NF-κB to mitigate septic colonic injury. A real-world machine learning model modestly improves empirical beta-lactam activity in Enterobacterales bloodstream infections, supporting antimicrobial stewardship.
Research Themes
- Pyroptosis propagation via extracellular vesicles
- Immunometabolism (kynurenine pathway) and macrophage polarization
- Machine learning for antimicrobial stewardship in bloodstream infections
Selected Articles
1. Transplantation of gasdermin pores by extracellular vesicles propagates pyroptosis to bystander cells.
Pyroptotic cells release extracellular vesicles carrying GSDMD pores that insert into bystander cell membranes and induce lytic death, propagating inflammation. Using DNA-PAINT and immuno-EM, the study structurally visualizes GSDMD pores on EVs and demonstrates intercellular propagation of pyroptosis in vitro and in vivo.
Impact: This work uncovers a previously unrecognized mechanism of inflammatory cell death spread via EV-mediated transplantation of gasdermin pores. It reframes how pyroptosis escalates tissue damage and identifies potential points for therapeutic intervention.
Clinical Implications: Targeting EV release, cargo loading, or GSDMD pore assembly could mitigate bystander tissue injury in infections and sepsis. The findings also motivate biomarker development based on EV-associated GSDMD.
Key Findings
- Extracellular vesicles from pyroptotic cells carry GSDMD pore structures visualized by DNA-PAINT and immunoelectron microscopy.
- Pyroptotic EVs transplant GSDMD pores onto bystander cell membranes, inducing lytic death and amplifying inflammation.
- Intercellular propagation of pyroptosis was demonstrated both in vitro and in vivo, revealing a domino-like spread mechanism.
Methodological Strengths
- Use of super-resolution DNA-PAINT and immunoelectron microscopy to structurally verify GSDMD pores on EVs
- Functional validation of pyroptosis propagation across cells in both in vitro and in vivo systems
Limitations
- Preclinical models may not capture the full complexity of human sepsis and tissue microenvironments
- Therapeutic blockade of EV-mediated pore transfer was not evaluated for efficacy
Future Directions: Test pharmacologic or genetic inhibition of EV biogenesis/cargo loading or GSDMD pore assembly in infection models, and explore EV-associated GSDMD as a circulating biomarker.
2. The tryptophan metabolite kynurenic acid ameliorates septic colonic injury through activation of the PPARγ signaling pathway.
Multiomics identified kynurenic acid as enriched in M2 macrophage supernatant and decreased in sepsis serum, inversely correlating with inflammatory mediators. KYNA administration mitigated septic colonic injury in mice by promoting M2 polarization through PPARγ activation and NF-κB inhibition.
Impact: It links a specific tryptophan metabolite to macrophage polarization and demonstrates organ-protective effects in sepsis models, suggesting a tractable immunometabolic therapy.
Clinical Implications: KYNA levels may serve as a biomarker for inflammatory status, and PPARγ-targeted strategies or KYNA analogs could be explored to restore macrophage balance and reduce gastrointestinal injury in sepsis.
Key Findings
- Non-targeted metabolomics revealed KYNA is significantly enriched in M2 macrophage supernatants.
- Serum KYNA levels are reduced in sepsis in both humans and mice and inversely correlate with inflammatory cytokines.
- Exogenous KYNA alleviates septic colonic injury in mice via PPARγ activation and NF-κB inhibition, promoting M1-to-M2 polarization.
Methodological Strengths
- Integrated multiomics with human and mouse serum data and in vivo functional validation
- Mechanistic dissection of the PPARγ/NF-κB axis linking metabolite signaling to macrophage polarization
Limitations
- Findings are primarily preclinical; no human interventional data on KYNA supplementation
- Organ-specific focus on colon; systemic sepsis outcomes, dosing, and safety profiles remain undefined
Future Directions: Validate KYNA as a biomarker in clinical sepsis cohorts, test PPARγ-targeted or KYNA-based therapies in larger animal models, and define pharmacokinetics and safety.
3. Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.
Using 4,709 Enterobacterales bloodstream infection episodes, XGBoost models achieved AUCs of 0.68–0.74 for resistance prediction, improving to 0.72–0.83 with species identification. Compared with clinician prescribing, model-guided choices could raise active beta-lactam coverage from 70% to 79% while reducing undertreatment.
Impact: This real-world analysis shows that even modest ML performance can improve empirical antibiotic coverage and stewardship compared with current practice.
Clinical Implications: Embedded decision support could guide initial beta-lactam selection before culture results, reducing undertreatment without excessive broad-spectrum use. Prospective validation is needed prior to deployment.
Key Findings
- Trained and evaluated on 4,709 Enterobacterales bloodstream infection episodes with resistance rates 7–67%.
- Held-out AUCs were 0.680–0.737 without species data and improved to 0.723–0.827 when species identification was included.
- Simulation suggested active beta-lactam coverage could increase from 70% (clinicians) to 79% (model-guided), reducing undertreatment from 30% to 21%.
Methodological Strengths
- Large, contemporary cohort with temporal external validation on held-out 2022–2023 data
- Direct comparison to clinician prescribing decisions, quantifying potential stewardship impact
Limitations
- Model performance was modest (AUCs ~0.68–0.83) and derived from a single region, limiting generalizability
- Retrospective simulation without prospective clinical outcome validation
Future Directions: Prospective, multicenter trials to assess clinical outcomes and safety of ML-guided empiric therapy; EHR integration, model calibration, and fairness assessment.