Daily Sepsis Research Analysis

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a life-threatening and heterogeneous disorder leading to lung injury. To date, effective therapies for ARDS remain limited. Sepsis is a frequent inducer of ARDS. However, the precise mechanisms underlying sepsis-induced ARDS remain unclear. METHODS: Here RNA methylation was detected by methylated RNA immunoprecipitation (MeRIP), RNA stability was determined by RNA decay assay while RNA antisense purification (RAP) was used to identify RNA-protein interaction. Besides, co-immunoprecipitation (Co-IP) was utilized to detect protein-protein interaction. Moreover, mice were injected with lipopolysaccharide (LPS) to establish sepsis-induced ARDS model in vivo. RESULTS: This study revealed that long non-coding RNA (lncRNA) nuclear-enriched abundant transcript 1 (NEAT1) aggravated lung injury through suppressing angiotensin-converting enzyme 2 (ACE2) in sepsis-induced ARDS models in vitro and in vivo. Mechanistically, NEAT1 declined ACE2 mRNA stability through heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNPA2B1) in lipopolysaccharide (LPS)-treated alveolar type II epithelial cells (AT-II cells). Besides, NEAT1 destabilized ACE2 mRNA depending on RNA methylation by forming methylated NEAT1/hnRNPA2B1/ACE2 mRNA complex in LPS-treated AT-II cells. Moreover, lin-28 homolog A (LIN28A) improved NEAT1 stability whereas insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) augmented NEAT1 destabilization by associating with LIN28A to disrupt the combination of LIN28A and NEAT1 in LPS-treated AT-II cells. Nevertheless, hnRNPA2B1 increased NEAT1 stability by blocking the interaction between LIN28A and IGF2BP3 in LPS-treated AT-II cells. CONCLUSIONS: These findings uncover mechanisms of sepsis-triggering ARDS and provide promising therapeutic targets for sepsis-induced ARDS.

Sepsis is a life-threatening organ failure resulting from a poorly regulated infection response. Organ dysfunction includes hepatic involvement, weakening the immune system due to excretory liver failure, and metabolic dysfunction, increasing the death risk. Although experimental studies correlated excretory liver functionality with immune performance and survival rates in sepsis, the proteins and pathways involved remain unclear. This study identified protein kinase C-α (PKCα) as a novel target for managing excretory liver function during sepsis. Using a preclinical murine sepsis model, we found that both PKCα knockout and the use of a PKCα-inhibitor midostaurin successfully restored liver function without hindering the host's response or ability to clear the pathogen, highlighting PKCα's vital role in excretory liver failure. In septic animals, both approaches significantly boosted survival rates. Midostaurin is the clinically approved active pharmaceutical ingredient in Rydapt, approved for the adjuvant treatment of FTL3-mutated AML. Here, it reduced plasma bile acids and related inflammation in those patients, opening a translational avenue for therapeutics in sepsis. Conclusively, our research underscores the significance of PKCα in controlling excretory liver function during inflammation. This suggests that targeting this protein could restore liver function without compromising the immune system, thereby decreasing sepsis mortality and supporting the recent paradigm that the liver is a hub for the host response to infection that might, in the future, result in novel host-directed therapies supporting the current state-of-the-art intensive care medicine in patients with sepsis-associated liver failure.

The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive triage information beyond vital signs. This retrospective cohort study utilized data from the MIMIC-IV database. Two models were developed: Model 1 based on vital signs alone, and Model 2 incorporating vital signs, demographic characteristics, medical history, and chief complaints. Eight ML algorithms were employed, and model performance was evaluated using metrics such as AUC, F1 Score, and calibration curves. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) methods were used to enhance model interpretability. The study included 189,617 patients, with 5.95% diagnosed with sepsis. Model 2 consistently outperformed Model 1 across most algorithms. In Model 2, Gradient Boosting achieved the highest AUC of 0.83, followed by Extra Tree, Random Forest, and Support Vector Machine (all 0.82). The SHAP method provided more comprehensible explanations for the Gradient Boosting algorithm. Modeling with comprehensive triage information using sEMR and ML methods was more effective in predicting sepsis at triage compared to using vital signs alone. Interpretable ML enhanced model transparency and provided sepsis prediction probabilities, offering a feasible approach for early sepsis screening and aiding healthcare professionals in making informed decisions during the triage process.