Daily Sepsis Research Analysis

Excess systemic inflammation can often be lethal in septic and trauma patients due to the onset of multiple organ dysfunction syndrome (MODS). As of right now, there are no effective immunomodulatory therapeutics that can promote survival within this patient population. Pro-regenerative extracellular matrix (ECM) biomaterials have shown success for the treatment of local inflammation but have not been fully explored for treating systemic inflammation. Here, we demonstrate the efficacy of an intravenously delivered infusible ECM (iECM) material, which promotes increased survival in a murine model of MODS by decreasing systemic mediators of inflammation. Lung and kidney failure are associated with higher mortality in MODS compared to other organ failures, and we demonstrate that iECM localizes primarily to kidney and lung tissues during systemic inflammation induced by endotoxin. iECM successfully lowered vascular permeability within lung tissue and lowered levels of inflammatory cytokine signaling, such as IL-6, verified via ELISA and gene expression analyses. We also demonstrated that immune cell infiltration into lung tissue was modulated with iECM treatment, with an increase in neutrophil retention in the lung and decreases in pro-inflammatory macrophage presence. In summation, iECM improves survival from severe systemic inflammation by decreasing the local and systemic inflammatory signaling pathways that contribute to MODS. These results provide a strong rationale for translational studies of iECM treatment in systemic inflammatory syndromes, including sepsis and trauma.

BACKGROUND: The limited genomic targeting range of current targeted next-generation sequencing (tNGS) workflows results in limited detection of pathogen-derived cell-free DNA (cfDNA), making it challenging to apply this approach to bloodstream infections (BSIs). Here, we developed an ultra-broad hybrid capture-based tNGS method to detect plasma pathogen-derived cfDNA and evaluate its suitability for the diagnosis of BSI. METHODS: This study introduced an ultra-broad hybrid capture-based tNGS method featuring an ultra-broad pathogen panel (1872 pathogens) and high-density probe coverage. To adequately evaluate its performance, we conducted retrospective tests in 208 suspected BSI patients (139 immunocompromised), comparing tNGS results with mNGS, conventional microbiological testing (CMT), and comprehensive clinical diagnoses. RESULTS: In pathogen detection, the concordance between ultra-broad hybrid capture-based tNGS and mNGS results was 93.75%. The diagnostic accuracy of tNGS in BSI was comparable to mNGS (76.44% vs. 75.00%) and significantly higher than CMT (45.67%, p < 0.0001). In immunocompromised populations, the diagnostic accuracy of tNGS was similar to mNGS (77.70% vs. 76.98%). tNGS detected 92.09% (163/177) of pathogens identified by mNGS. Two of the missed pathogens were not included in the 1872 pathogens panel, and both were from the immunocompromised group. CONCLUSIONS: Ultra-broad hybrid capture-based tNGS exhibits sensitivity and accuracy comparable to mNGS, effectively covering a relatively wide range of pathogens, and may serve as an economic screening tool for BSI in the future.

LPS-induced peritonitis is a prevalent clinical condition with incompletely understood underlying mechanisms. This study aimed to characterize the gene transcriptome and key proteins in the blood of mice with LPS-induced peritonitis. We established a mouse peritonitis model by administering LPS (10 mg/kg, i.p.) and collected blood samples (n = 6 per group) for bulk RNA sequencing. Through various bioinformatics approaches, we identified 290 differentially expressed genes (242 upregulated and 48 downregulated). Functional enrichment revealed the activation of inflammation-related pathways (e.g., NOD-like and Toll-like receptor signaling) and the suppression of adaptive immunity pathways (e.g., Th1/Th2 cell differentiation and T cell receptor signaling). From these, eight hub proteins (LDLR, FNBP1L, SNX18, FAM20C, INPP5F, PACSIN1, ZAP70, and SYNJ2) were identified, and their structural stability was confirmed via 300 ns molecular dynamics simulations. Critically, to validate our findings at the cellular level and in a clinical context, we further integrated four independent public single-cell RNA-sequencing datasets from human sepsis patients. This cross-platform analysis confirmed that the expression patterns of our hub genes are conserved in human patients with high cell-type specificity (e.g., ZAP70 downregulation in T cells and LDLR upregulation in monocytes). Moreover, a module score derived from these genes demonstrated strong clinical relevance, as it significantly distinguished sepsis patients from healthy controls and stratified clinical subtypes of sepsis. In conclusion, by integrating bulk transcriptomics, molecular dynamics, and cross-platform single-cell data, this research provides multi-scale insights into the systemic inflammatory mechanisms of peritonitis. It identifies therapeutic targets with clinical translational potential.