Daily Sepsis Research Analysis

Sepsis-associated acute kidney injury is a critical condition driven by immune dysregulation, particularly involving neutrophils, yet their heterogeneity and molecular contributions remain underexplored. This study employed a multi-omics approach, integrating single-cell and bulk RNA sequencing from 21 sepsis samples and Escherichia coli-induced sepsis datasets, alongside bioinformatics, machine learning, and experimental validation in a rat model and human peripheral blood. We identified four neutrophil subtypes-pro-inflammatory, anti-inflammatory, mature, and immature-revealing a significant increase in pro-inflammatory neutrophils in sepsis (40.53% versus 4.19% in controls) and a decrease in anti-inflammatory neutrophils (18.43% versus 27.04%). Four hub genes, peptidyl arginine deiminase 4, caspase 4, complement receptor 1, and mitogen-activated protein kinase 14, were pinpointed as key drivers, with peptidyl arginine deiminase 4 mediating neutrophil extracellular trap formation and exacerbating renal damage. In a rat model, peptidyl arginine deiminase 4 knockdown reduced trap formation and alleviated kidney injury (p-value less than 0.01). Human samples confirmed elevated gene expression in sepsis (p-value less than 0.05). These findings highlight neutrophil heterogeneity and molecular mechanisms in sepsis, with potential implications for sepsis-associated acute kidney injury (SAKI), proposing novel biomarkers and therapeutic targets for precision medicine.

BACKGROUND: The effectiveness of immunomodulatory therapies in sepsis is often hampered by profound and patient-specific immune heterogeneity. Classical monocytes play a central role in the progression toward sepsis-induced immunoparalysis, with their apoptotic rate serving as a sensitive marker of immune dysfunction. Traditional bulk transcriptomic approaches fail to resolve this complexity. Here, we harness single-cell RNA sequencing to delineate the apoptotic landscape of classical monocytes and identify robust molecular biomarkers for immunological stratification and targeted intervention. METHODS: We integrated single-cell and bulk transcriptomic data from four independent cohorts. A machine learning pipeline incorporating SVM, RF, XGB, and GLM algorithms was used to identify hub genes associated with monocyte apoptosis. A diagnostic nomogram was constructed based on the selected gene signature and validated across external datasets. Clinical relevance was confirmed through Western blot analysis of purified monocytes from sepsis patients and healthy controls. RESULTS: A four-gene signature (G0S2, GZMA, ITM2A, PAG1) emerged as a specific apoptotic fingerprint of classical monocytes. The diagnostic model based on these signature genes demonstrated excellent discriminatory performance, effectively stratifying patients into high-risk and low-risk groups (AUC >0.8 across multiple validation cohorts), with each risk group exhibiting distinctly different immune states. High-risk patients exhibited a pro-inflammatory transcriptomic profile with elevated apoptotic pathway activity (e.g., neutrophil degranulation), whereas the low-risk group showed enrichment in adaptive immunity and T cell receptor signaling. Protein-level validation in clinical samples corroborated the transcriptomic findings. CONCLUSION: This study elucidates a critical facet of immune heterogeneity in sepsis through the identification of a validated, four-gene apoptotic signature in classical monocytes. Beyond its diagnostic utility, this signature serves as a molecular indicator of immune state, enabling refined patient stratification. These findings lay the groundwork for precision immunopharmacology, where apoptosis-targeted or anti-inflammatory therapies can be tailored to individual immune profiles.

BACKGROUND: Sepsis is a life-threatening condition characterized by systemic inflammation and dysfunction of multiple organs. Recently, regulatory cell death (RCD) has emerged as a distinct pathological feature and serve as a potential source of biomarkers or therapeutic targets in sepsis. METHODS: Comprehensive transcriptomic datasets of sepsis were accessed from the Gene Expression Omnibus (GEO) database. Genes involved in 18 RCD pathways were compiled from databases and published literature. The limma package was utilized to identify differentially expressed genes (DEGs). The Gene Set Variation Analysis (GSVA), CIBERSORT, Weighted Gene Co-expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses were combined to identify key RCDs pathways. Core RCD-related DEGs (RRDs) were selected using Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) machine learning methods. The expression patterns and diagnostic performance of the core RRDs were validated across multiple datasets and further confirmed through meta-analysis. Immune localization of RRDs was examined using single-cell transcriptomic data. Prognostic significance was evaluated using multivariate Cox analysis. Finally, the mRNA expression level was validated using quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: Zinc Finger DHHC-Type Containing 3 (ZDHHC3), Chloride Intracellular Channel 1 (CLIC1), Glutathione S-Transferase Omega 1 (GSTO1), Biogenesis of Lysosomal Organelles Complex 1 Subunit 1 (BLOC1S1), and Toll-Like Receptor 5 (TLR5) were considered as core RRDs, with monocytes and neutrophils serving as the principal cell types responsible for their overexpression and likely contributing critically to their downstream biological effects. Among them, ZDHHC3 and TLR5 were identified as independent risk factors for sepsis. Their significantly elevated mRNA expression in septic mice was confirmed by qRT-PCR. CONCLUSION: Findings from this study underscored the crucial role of RCD pathways in the development of sepsis. Notably, ZDHHC3 and TLR5 were identified as novel and robust biomarkers for sepsis.