Daily Sepsis Research Analysis
Three papers advance sepsis science across mechanisms and informatics: an AI-screened peptide that blocks gasdermin D pores delays pyroptosis and improves survival in murine sepsis; endothelial IL-6 signaling is shown to activate a noncanonical STAT1–cGAS–STING pathway driving IFN-like responses in endotoxemia; and NLP applied to ED triage free text markedly improves early sepsis prediction in a 134,266-patient cohort.
Summary
Three papers advance sepsis science across mechanisms and informatics: an AI-screened peptide that blocks gasdermin D pores delays pyroptosis and improves survival in murine sepsis; endothelial IL-6 signaling is shown to activate a noncanonical STAT1–cGAS–STING pathway driving IFN-like responses in endotoxemia; and NLP applied to ED triage free text markedly improves early sepsis prediction in a 134,266-patient cohort.
Research Themes
- Targeting pyroptosis via gasdermin D pore blockade as a sepsis therapy
- Endothelial IL-6 signaling through STAT1–cGAS–STING in shock pathophysiology
- NLP-enhanced early sepsis prediction from unstructured ED text
Selected Articles
1. Delaying pyroptosis with an AI-screened gasdermin D pore blocker mitigates inflammatory response.
An AI-screened peptide, SK56, directly blocks gasdermin D pores, delaying pyroptosis and curbing cytokine efflux. In murine LPS and CLP sepsis models, SK56 improved survival without altering IL-1β or GSDMD cleavage, and reduced mitochondrial damage and bystander activation in organoid and dendritic cell assays.
Impact: This work identifies a tractable, first-in-class pore-blocking strategy against pyroptosis with survival benefit in sepsis models, linking AI-guided peptide discovery to actionable immunotherapy.
Clinical Implications: Although preclinical, GSDMD pore blockade could inaugurate a therapeutic class targeting pyroptosis in sepsis and other hyperinflammatory states; next steps include safety, PK/PD, and translational studies.
Key Findings
- SK56 blocks GSDMD-NT pores and inhibits pyroptosis and cytokine release in macrophages.
- SK56 improves survival in LPS-induced endotoxemia and CLP polymicrobial sepsis in mice.
- SK56 does not affect IL-1β or GSDMD cleavage, indicating pore-level action.
- In organoid and immune co-cultures, SK56 reduces dendritic cell activation and prevents widespread death of human alveolar organoids; it also mitigates mitochondrial damage.
Methodological Strengths
- Multiple complementary systems: primary macrophages, dendritic cells, human alveolar organoids, and two in vivo sepsis models (LPS, CLP).
- Mechanistic specificity demonstrated by unchanged IL-1β and GSDMD cleavage with functional pore blockade.
Limitations
- Preclinical mouse models may not fully recapitulate human sepsis heterogeneity.
- Peptide delivery, stability, and immunogenicity require evaluation for clinical translation.
Future Directions: Define SK56 PK/PD, toxicity, and delivery; test efficacy in additional infection models; and explore combinations with antibiotics or immunomodulators.
2. Endothelial STING and STAT1 mediate IFN-independent effects of IL-6 in an endotoxemia-induced model of shock.
IL-6 elicits a transient, IFN-independent IFN-like gene program in endothelial cells via STAT1–cGAS–STING and IRFs, uncoupled from STAT3-mediated barrier regulation. Endothelial STING or global STAT1 deficiency attenuates endotoxemia responses and suppresses IFN-like signatures in vivo, revealing parallel IL-6 downstream pathways.
Impact: This study uncovers a noncanonical IL-6–STAT1–cGAS–STING axis in endothelium that drives IFN-like transcriptional responses in shock, redefining IL-6 signaling complexity and suggesting new intervention points.
Clinical Implications: Targeting endothelial DNA-sensing components (e.g., STING) or STAT1 may modulate IL-6–driven vascular inflammation in shock and sepsis, potentially complementing IL-6 or JAK pathway inhibitors.
Key Findings
- IL-6 induces a transient IFN-like gene program in human endothelial cells via a noncanonical, IFN-independent mechanism.
- This program requires STAT1, cGAS, STING, and IRF1/3/4 but is independent of STAT3.
- Endothelial STING knockout or global STAT1 knockout mice show attenuated responses to endotoxemia and lack the endotoxin-induced IFN-like signature.
- SOCS3 loss in endothelium enhances IFN-like responses in kidneys and brains during endotoxemia.
Methodological Strengths
- Integrated in vivo genetic models (endothelial STING-KO, global STAT1-KO) with primary human endothelial cell mechanistic assays.
- Clear dissection of pathway specificity distinguishing STAT1–cGAS–STING from STAT3-mediated functions.
Limitations
- Endotoxemia models may not capture the complexity of human polymicrobial sepsis.
- Therapeutic targeting feasibility and safety of endothelial STING/STAT1 modulation remain to be tested.
Future Directions: Evaluate endothelial-targeted STING/STAT1 modulators in polymicrobial sepsis models and assess synergy with IL-6/JAK pathway inhibitors.
3. Automated sepsis prediction from unstructured electronic health records using natural language processing: a retrospective cohort study.
In a 134,266-patient ED cohort, adding triage free text to ML models significantly improved sepsis prediction: random forest achieved AUPRC 0.789 and AUC 0.80, while BERT using raw text reached AUPRC 0.7542 and AUC 0.7735. Key operational and demographic variables further contributed to performance.
Impact: Demonstrates scalable, real-world NLP of triage text to enhance early sepsis detection at the point of ED intake, a critical window for outcome improvement.
Clinical Implications: Integrating free-text NLP into ED triage decision support could flag high-risk patients earlier, enabling timely antibiotics and resuscitation while reducing missed sepsis cases.
Key Findings
- Random forest using free-text triage features achieved AUPRC 0.789 (95% CI 0.7668–0.8018) and AUC 0.80 (95% CI 0.7842–0.8173).
- BERT on raw text achieved AUPRC 0.7542 (95% CI 0.7418–0.7741) and AUC 0.7735 (95% CI 0.7628–0.8017).
- Key predictors included ED treatment time, age, arrival-to-treatment time, Australasian Triage Scale, and visit type; incorporating free text improved detection and identified missed cases.
Methodological Strengths
- Very large retrospective cohort (N=134,266) with both structured variables and unstructured free-text triage comments.
- Comparative evaluation across multiple algorithms including BERT, with performance assessed by AUPRC and AUC.
Limitations
- Retrospective single-system data may limit generalizability; prospective external validation is lacking.
- Sepsis labeling and documentation quality in EHRs may introduce bias despite model performance.
Future Directions: Prospective deployment with clinician-in-the-loop evaluation, cross-site external validation, and assessment of time-to-antibiotics and outcome impact.