Interferon regulatory factor 1 (IRF1) inhibits lung endothelial regeneration following inflammation-induced acute lung injury.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a respiratory condition caused by severe endothelial barrier dysfunction within the lung. In ARDS, excessive inflammation, tissue edema, and immune cell influx prevents endothelial cell regeneration that is crucial in repairing the endothelial barrier. However, little is known about the molecular mechanism that underpin endothelial cell regeneration in ARDS. METHODS: R-based bioinformatics tools were used to analyze microarray-derived transcription profiles in human lung microvascular endothelial cells (HLMVECs) subjected to non-treatment or lipopolysaccharide (LPS) exposure. We generated endothelial cell-specific interferon regulatory factor 1 (Irf1) knockout (Irf1EC-/-) and Irf1fl/fl control mice for use in an endotoxemic murine model of acute lung injury (ALI). In vitro studies (qPCR, immunoblotting, and ChIP-qPCR) were conducted in mouse lung endothelial cells (MLECs) and HLMVECs. Dual-luciferase promoter reporter assays were performed in HLMVECs. RESULTS: Bioinformatics analyses identified IRF1 as a key up-regulated gene in HLMVECs post-LPS exposure. Endothelial-specific knockout of Irf1 in ALI mice resulted in enhanced regeneration of lung endothelium, while liposomal delivery of endothelial-specific Irf1 to wild-type ALI mice inhibited lung endothelial regeneration in a leukemia inhibitory factor (Lif)-dependent manner. Mechanistically, we demonstrated that LPS-induced Stat1Ser727 phosphorylation promotes Irf1 transactivation, resulting in downstream up-regulation of Lif that inhibits endothelial cell proliferation. CONCLUSIONS: These results demonstrate the existence of a p-Stat1Ser727-Irf1-Lif axis that inhibits lung endothelial cell regeneration post-LPS injury. Thus, direct inhibition of IRF1 or LIF may be a promising strategy for enhancing endothelial cell regeneration and improving clinical outcomes in ARDS patients.

Identification of biomarkers associated with diagnosis of acute lung injury based on bioinformatics and machine learning.

BACKGROUND: Acute lung injury (ALI) is an acute inflammatory disease characterized by excess production of inflammatory factors in lung tissue and has a high mortality. This research was designed for the identification of novel diagnostic biomarkers for ALI and analyzing the possible association between critical genes and infiltrated immune cells. METHODS: The study used 2 datasets (GSE2411 and GSE18341) to identify differentially expressed genes (DEGs) between 2 groups. Then we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to identify the functions of these DEGs. The study also used SVM-recursive feature elimination analysis and least absolute shrinkage and selection operator regression model to screen possible markers. The study further analyzed immune cell infiltration via CIBERSORT. Gene Set Enrichment Analysis was used to explore the molecular mechanism of the critical genes. RESULTS: DEGs were identified between 2 groups. In total, 690 DEGs were obtained: 527 genes were upregulated and 163 genes were downregulated. We identified PDZK1IP1, CCKAR, and CXCL2 as critical genes. And we then found that these critical genes correlated with Mast Cells, Neutrophil Cells, M1 Macrophage, dendritic cell Actived, Eosinophil Cells, B Cells Naive, Mast Cells, and dendritic cell Immature. Furthermore, we investigated the specific signaling pathways involved in key genes and derived some potential molecular mechanisms by which key genes affect disease progression by use of Gene Set Enrichment Analysis. Moreover, we predict transcription factors. Also, we obtained critical gene-related microRNAs through the targetscan database, and visualized the microRNA network of the genes. CONCLUSION: Our findings might provide some novel clue for the exploration of novel markers for ALI diagnosis. The critical genes and their associations with immune infiltration may offer new insight into understanding ALI developments.