The role of G protein-coupled receptor in neutrophil dysfunction during sepsis-induced acute respiratory distress syndrome

S100A8/A9hi neutrophils induce mitochondrial dysfunction and PANoptosis in endothelial cells via mitochondrial complex I deficiency during sepsis

S100a8/a9, largely released by polymorphonuclear neutrophils (PMNs), belongs to the S100 family of calcium-binding proteins and plays a role in a variety of inflammatory diseases. Although S100a8/a9 has been reported to trigger endothelial cell apoptosis, the mechanisms of S100a8/a9-induced endothelial dysfunction during sepsis require in-depth research. We demonstrate that high expression levels of S100a8/a9 suppress Ndufa3 expression in mitochondrial complex I via downregulation of Nrf1 expression. Mitochondrial complex I deficiency contributes to NAD+-dependent Sirt1 suppression, which induces mitochondrial disorders, including excessive fission and blocked mitophagy, and mtDNA released from damaged mitochondria ultimately activates ZBP1-mediated PANoptosis in endothelial cells. Moreover, based on comprehensive scRNA-seq and bulk RNA-seq analyses, S100A8/A9hi neutrophils are closely associated with the circulating endothelial cell count (a useful marker of endothelial damage), and S100A8 is an independent risk factor for poor prognosis in sepsis patients.

A novel anoikis-related gene signature predicts prognosis in patients with sepsis and reveals immune infiltration

Sepsis is a common acute and severe medical condition with a high mortality rate. Anoikis, an emerging form of cell death, plays a significant role in various diseases. However, the role of anoikis in sepsis remains poorly understood. Based on the datasets from Gene Expression Omnibus and anoikis-related genes from GeneCards, the differentially expressed anoikis-related genes (DEARGs) were identified. Based on hub genes of DEARGs, a novel prognostic risk model was constructed, and the pattern of immune infiltration was investigated by CIBERSORT algorithm. And small molecule compounds targeting anoikis in sepsis were analyzed using Autodock. Of 23 DEARGs, CXCL8, CFLAR, FASLG and TP53 were significantly associated with the prognosis of sepsis (P < 0.05). Based on the prognostic risk model constructed with these four genes, high-risk population of septic patients had significant lower survival probability than low-risk population (HR = 3.30, P < 0.001). And the level of CFLAR was significantly correlated with the number of neutrophils in septic patients (r = 0.54, P < 0.001). Moreover, tozasertib had low binding energy with CXCL8, CFLAR, FASLG and TP53, and would be a potential compound for sepsis. Conclusively, our results identified a new prognostic model and potential therapeutic molecular for sepsis, providing new insights on mechanism and treatment of sepsis.

Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis

The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package ( https://github.com/InfectionMedicineProteomics/BINN ).