Daily Sepsis Research Analysis

BACKGROUND: Sepsis-induced intestinal injury is a severe complication associated with dysfunction affecting multiple organ systems and a significantly elevated risk of death. Intestinal barrier dysfunction plays a central role, but the underlying molecular pathways remain incompletely understood. The present study sought to explore how the Gzma/GEF-H1/RhoA signalling axis contributes to the disruption of the intestinal epithelial barrier in sepsis. METHODS: Transcriptomic data, clinical samples, and a murine caecal ligation and puncture (CLP) model was used to assess Gzma expression and its correlation with disease severity. We investigated how Gzma-released by activated immune cells-affects epithelial structure and function using in vitro co-culture assays. These experiments assessed key tight junction proteins (occludin, claudin-1, ZO-1, E-cadherin), transepithelial electrical resistance (TEER), and paracellular permeability. GEF-H1 knockout mice and the GEF-H1 activator plinabulin were employed to evaluate the physiological roles of GEF-H1. Mutagenesis revealed how Gzma activates GEF-H1. High-throughput screening identified a GEF-H1 modulator, and its efficacy was validated in septic mice. Gzma expression was significantly elevated during sepsis and correlated with disease severity. Gzma secretion from immune cells impaired the epithelial barrier by downregulating tight junction proteins, increasing permeability, and reducing TEER. Gzma activates GEF-H1 by dephosphorylating Ser886, triggering the RhoA/ROCK pathway and subsequent phosphorylation of MLC2, LIMK, and cofilin-driving cytoskeletal remodelling. GEF-H1 knockout mice showed reduced intestinal injury, higher survival rates, and intact barrier function; conversely, GEF-H1 activation worsened intestinal damage. High-throughput screening identified Epothilone A as a potent GEF-H1 modulator that restores intestinal barrier integrity and improves survival in murine sepsis by suppressing the GEF-H1/librariesRhoA pathway. CONCLUSION: This research uncovers the Gzma/GEF-H1/RhoA signalling axis as a pivotal contributor to intestinal barrier dysfunction during sepsis. GEF-H1 represents a promising therapeutic target, and its inhibition by agents such as Epothilone A may offer a novel strategy for treating sepsis. KEY POINTS: Gzma induces the dephosphorylation of Ser886 on GEF-H1, activating the RhoA/ROCK pathway and disrupting the intestinal epithelial barrier. Knocking out GEF-H1 can alleviate intestinal damage, protect multiple organs, and increase the survival rate of septic mice. Epothilone A inhibits the activation of GEF-H1, thereby restoring the barrier function and reducing the mortality rate of sepsis.

PURPOSE: To understand how protein biomarkers in blood and urine that are aligned with host resistance to infection, disease tolerance, and damage are associated with clinical outcomes and sepsis subtypes in community-onset sepsis. METHODS: Adults meeting Sepsis-3 criteria were prospectively enrolled within 6 h of emergency department arrival and assigned clinical subtypes (α, β, γ, δ), using the Sepsis ENdotyping in Emergency CAre (SENECA) approach. Using structured expert ranking with consensus adjudication, 16 plasma and urinary biomarkers obtained from remnant biospecimens were grouped into three mechanistic axes contributing to sepsis pathophysiology: host resistance to infection, disease tolerance, and damage to the host. Biomarkers for each concept were analyzed with principal component analysis as a signature, and multivariable logistic regression tested associations of each signature with 90-day mortality and sepsis subtype membership. RESULTS: Among 444 adults, the mean age was 60 years [SD: 16], the mean SOFA score was 4.3 [SD: 2.3], and 90-day mortality was 17%. After adjustment for age, sex, and race, greater damage to the host was associated with increased 90-day mortality (adjusted odds ratio (aOR) = 1.70; 95% CI 1.38-2.11; p < 0.001), while greater host resistance (aOR = 0.83; 95% CI 0.54-1.10; p = 0.4) and greater disease tolerance (aOR = 0.83; 95% CI 0.68-1.01; p = 0.06) were not. Differences across sepsis subtypes were most pronounced for disease tolerance and damage signatures, where the δ‑type patients exhibited higher damage and lower disease tolerance and the α‑type patients had lower damage and higher disease tolerance. CONCLUSION: Biomarker signatures aligned with host resistance to infection, disease tolerance, and damage to the host, informed by expert consensus, were associated with clinical outcomes and sepsis subtype membership.

IMPORTANCE: Early detection of sepsis improves clinical outcomes, but the Early Detection of Sepsis Model, version 1 (Epic Systems Corp) has shown poor performance. Comparing sepsis models is complicated by varying outcome definitions and limited generalizability outside the development site. OBJECTIVE: To evaluate the Early Detection of Sepsis Model, version 2 using multiple standard sepsis definitions. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study included all adult (aged ≥18 years) encounters from emergency departments, inpatient units, intensive care units, and perioperative areas across 9 acute care hospitals between March 17 and August 31, 2024. The locally trained gradient-boosted tree ensemble sepsis model, incorporating patient demographics, vital signs, laboratory results, medication administration, and other clinical features, generated predictions every 15 minutes. Model performance was evaluated against 3 electronically computable sepsis definitions: the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3); Centers for Medicare & Medicaid Services Severe Sepsis and Septic Shock: Management Bundle (SEP-1); and the Centers for Disease Control and Prevention Adult Sepsis Event (ASE). EXPOSURE: Silent deployment of the sepsis model. MAIN OUTCOMES AND MEASURES: Model performance metrics included discrimination, as measured using the area under the receiver operating characteristic curve (AUROC), and precision, as measured by the area under the precision-recall curve (AUPRC). Test characteristics and potential lead-time warning were evaluated over a range of model score thresholds.