Daily Sepsis Research Analysis

Microglia are critical in the neuroinflammatory cascade of sepsis-associated encephalopathy (SAE), yet their functional heterogeneity and transcriptional regulators remain poorly characterized. Here, through single-cell RNA sequencing (scRNA-seq) of murine brains post-cecal ligation and puncture (CLP)-induced sepsis, we resolved six microglial clusters. Notably, a subset of inflammasome-activated microglia emerged as a driver for neuroinflammation and cognitive impairment, with marked upregulation of Nlrp3, Il1b, Tnf and enriched pathways for interleukin-1β (IL-1β) production and neuron death. Transcriptional profiling of the cluster highlighted nucleoside diphosphate kinase 2 (NME2) as a marker transcription factor, with its expression and nuclear localization dynamically upregulated post-CLP. Mechanistically, NME2 directly bound the Nlrp3 promoter and recruited enhancer of polycomb homolog 2 (EPC2), a component of the NuA4 histone acetyltransferase complex, to induce H2AK5 acetylation and chromatin remodeling, thereby enhancing Nlrp3 transcription. Conditional knockout of Nme2 in microglia or pharmacological inhibition using stauprimide significantly decreased cerebrospinal fluid IL-1β, attenuated neuronal cell death, and rescued both working memory and recognition memory in septic mice. These findings identify NME2 as a critical transcription regulator of inflammasome-activated microglial lineage dynamics through epigenetic control of NLRP3, offering a mechanistic rationale for targeting the NME2-EPC2 axis to mitigate sepsis-induced cognitive impairment.

BACKGROUND: Sepsis-induced coagulopathy (SIC) is a life-threatening complication characterized by high heterogeneity and mortality. Current prognostic models relying solely on clinical indices often fail to capture complex molecular pathophysiology, limiting precise risk stratification. This study aimed to unveil the molecular landscape of SIC via multi-omics integration and develop a robust machine learning (ML) predictive model. METHODS: We conducted a comprehensive study of 878 SIC patients. A discovery cohort of 626 patients underwent blood transcriptomic profiling (RNA-seq) and a subset of 214 patients was analyzed for proteomic validation. Weighted Gene Co-expression Network Analysis (WGCNA) and Gene Set Enrichment Analysis (GSEA) were used to identify survival-associated modules and pathways. An ensemble ML framework was developed to integrate the clinical features with transcriptomic signatures for survival prediction. RESULTS: Clinical analysis identified age, lung infection, higher SOFA scores, and lactate levels as significant independent risk factors for mortality. Transcriptomic profiling revealed that elevated expression of GABARAPL1, PHLPP1, and KLF6 was strongly associated with an increased risk of death, whereas elevated expression of genes, including TSN, NUP155, and TTC39C, was associated with better outcomes. Functionally, GSEA enrichment analysis revealed the suppression of oxidative phosphorylation and ribosome biogenesis along with the activation of hypoxia, heme metabolism, and inflammatory pathways in non-survivors. Proteomic analyses validated the mechanistic findings. The integrated ensemble machine learning survival model (Clinical + Transcriptomics) achieved a C-index of 0.735, which significantly outperformed the clinical-only model (C-index: 0.694). Stratification based on the model successfully distinguished high-risk patients with significantly lower survival rates (p = 0.00057). CONCLUSION: Our multi-omics analysis highlights metabolic reprogramming, hypoxia, and dysregulated heme metabolism as the key molecular features of SIC. The developed ensemble ML model, which integrates molecular and clinical features, offers a superior tool for early risk stratification and precision management of septic coagulopathy.

INTRODUCTION: The survival benefit of early antibiotic administration in septic shock is well established. In contrast, for sepsis without shock, the current Surviving Sepsis Campaign (SSC) guidelines recommend initiating antibiotics according to the likelihood of infection. Given the persistent challenges in accurately diagnosing sepsis, there is a need to identify patient characteristics to guide antimicrobial timing in this population. METHODS: In this cohort study, we included adult intensive care unit (ICU) patients meeting Third International Consensus Definitions for Sepsis (Sepsis-3) criteria without shock, identified from 24 hours before to 48 hours after ICU admission, who received antibiotics within 0-12 hours after sepsis diagnosis. The primary exposure was shorter time-to-antibiotics (≤ 3 hours), and the primary outcome was 28-day mortality. Heterogeneity of treatment effect (HTE) was evaluated with conventional subgroup, risk-based, and effect-based analyses. RESULTS: A total of 8400 patients were included, of whom 2891 (34.4%) received antibiotics within 3 hours and emergency admission was the most common admission type (64.2%). Baseline characteristics were well balanced after overlap weighting. Shorter time-to-antibiotics was not associated with 28-day mortality in the overall population (median value for the posterior distribution of the odds ratio (OR) 0.92; 95% credible interval (CrI) 0.79-1.06). In conventional subgroups, the impact of shorter time-to-antibiotics on 28-day mortality varied substantially between patients aged ≥ 78 years and other age subgroups (median value for the posterior distribution of the OR 0.73; 95% CrI 0.55-0.94). In risk-based analysis, patients in the third quartile of baseline mortality risk exhibited the most favorable estimated association with shorter time-to-antibiotics (median value for the posterior distribution of the OR 0.65; 95% CrI 0.46-0.87). In effect-based analysis, patients with advanced age, higher Charlson Comorbidity Index (CCI), higher Acute Physiology Score III (APS III), or acute liver dysfunction were identified as having greater benefit from shorter time-to-antibiotics. CONCLUSION: Among ICU patients with sepsis without shock, those who are older, have higher CCI or APS III, or acute liver dysfunction are more likely to benefit from shorter time-to-antibiotics.