Acute lung injury (ALI) induced by sepsis is characterized by disruption of the epithelial barrier and activation of alveolar macrophages (AMs), which leads to uncontrolled pulmonary inflammation. However, effective treatments for ALI are unavailable. The exact mechanism by which the initial mediator of alveolar epithelial cells (AECs) induces inflammation remains elusive. Here we investigated the roles of AEC-derived exosomes in AM activation and sepsis-induced ALI in vivo and in vitro. Cecal ligation and puncture (CLP) was utilized to establish septic lung injury model in rats. The effect of exosomal inhibition by intratracheal GW4869 administration on lung injury was investigated. To assess the effects of AEC-derived exosomes on ALI, we treated the rat alveolar epithelial cell line RLE-6TN with LPS to induce cell damage. Exosomes from conditioned medium of LPS-treated AECs (LPS-Exos) were isolated by ultracentrifugation. The miRNAs in LPS-Exos were screened by miRNA expression profile analysis. The effects of miR-92a-3p on the function of AMs were studied. We found that intratracheal GW4869 administration ameliorated lung injury following CLP-induced ALI. LPS-Exos were taken up by AMs and activated these cells. Consistently, administration of LPS-Exos in rats significantly aggravated pulmonary inflammation and alveolar permeability. Moreover, miR-92a-3p was enriched in LPS-Exos and could be delivered to AMs. Inhibition of miR-92a-3p in AECs diminished the proinflammatory effects of LPS-Exos in vivo and in vitro. Mechanistically, miR-92a-3p activates AMs along with pulmonary inflammation. This process results in activation of the NF-κB pathway and downregulation of PTEN expression, which was confirmed by a luciferase reporter assay. In conclusion, AEC-derived exosomes activate AMs and induce pulmonary inflammation mediated by miR-92a-3p in ALI. The present findings revealed a previously unidentified role of exosomal miR-92a-3p in mediating the crosstalk between injured AEC and AMs. miR-92a-3p in AEC exosomes might represent a novel diagnostic biomarker for ALI, which may lead to a new therapeutic approach.

Objectives: Sepsis is a clinical disease that is typically treated in the intensive care unit, and the complex pathophysiology under this disease has not been thoroughly understood. While ferroptosis is involved in inflammation and infection, its effect in sepsis is still unknown. The study aimed to identify ferroptosis-related genes in sepsis, providing translational potential therapeutic targets.Methods: The dataset GSE65682 was used to download the sample source from the Gene Expression Omnibus (GEO) database. Consensus weighted gene co-expression network analysis (WGCNA) was performed to find suspected modules of sepsis. The differentially expressed genes (DEGs) most significantly associated with mortality were intersected with those altered by lipopolysaccharide (LPS) treatment and were further analyzed for the identification of main pathways of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The related pathway markers were further verified by qPCR.Results: A total of 802 blood samples with sepsis were included for WGCNA, which identified 21 modules. Intersected with ferroptosis databases and LPS treatment groups, we identified two ferroptosis-related genes: PEBP1 and LPIN1. Only LPIN1 contributes to a poor outcome. Then, 205 DEGs were further identified according to the high or low LPIN1 expression. Among them, we constructed a gene regulatory network with several transcriptional factors using the NetworkAnalyst online tool and identified that these genes mostly correlate with inflammation and immune response. The immune infiltration analysis showed that lower expression of LPIN1 was related to macrophage infiltration and could be an independent predictor factor of the survival status in sepsis patients. Meanwhile, the multivariate Cox analysis showed that LPIN1 had a significant correlation with survival that was further verified by in vitro and in vivo experiments.Conclusion: In conclusion, LPIN1 could become a reliable biomarker for patient survival in sepsis, which is associated with immune and inflammation status.

Background: The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and development. Nevertheless, the role of PANoptosis in sepsis has not been fully elucidated.Methods: We obtained Sepsis samples and scRNA-seq data from the GEO database. PANoptosis-related genes were subjected to consensus clustering and functional enrichment analysis, followed by identification of differentially expressed genes and calculation of the PANoptosis score. A PANoptosis-based prognostic model was developed. In vitro experiments were performed to verify distinct PANoptosis-related genes. An external scRNA-seq dataset was used to verify cellular localization.Results: Unsupervised clustering analysis using 16 PANoptosis-related genes identified three subtypes of sepsis. Kaplan-Meier analysis showed significant differences in patient survival among the subtypes, with different immune infiltration levels. Differential analysis of the subtypes identified 48 DEGs. Boruta algorithm PCA analysis identified 16 DEGs as PANoptosis-related signature genes. We developed PANscore based on these signature genes, which can distinguish different PANoptosis and clinical characteristics and may serve as a potential biomarker. Single-cell sequencing analysis identified six cell types, with high PANscore clustering relatively in B cells, and low PANscore in CD16+ and CD14+ monocytes and Megakaryocyte progenitors. ZBP1, XAF1, IFI44L, SOCS1, and PARP14 were relatively higher in cells with high PANscore.Conclusion: We developed a machine learning based Boruta algorithm for profiling PANoptosis related subgroups with in predicting survival and clinical features in the sepsis.

Acute lung injury (ALI) is a common lung pathology that is accompanied by alveolar macrophage (AM) activation and inflammatory response. This study investigated the role of the long non-coding RNA NONRATT004344 (hereafter named lncRNA NLRP3) in regulating the Nod-like receptor protein 3 (NLRP3)-triggered inflammatory response in early ALI and the underlying mechanism as well. We established LPS-induced ALI models to explore their interactive mechanisms in vitro and in vivo. Luciferase reporter assays were performed to determine that miR-138-5p could bind to lncRNA NLRP3 and NLRP3. We observed increased lncRNA NLRP3 expression, decreased miR-138-5p expression, NLRP3 inflammasome activation, and upregulated caspase-1, IL-1β, and IL-18 expression in the LPS-induced ALI model. Furthermore, lncRNA NLRP3 overexpression activated the NLRP3 inflammasome and promoted IL-1β and IL-18 secretion; the miR-138-5p mimic abolished these effects in vivo and in vitro. Consistently, miR-138-5p inhibition reversed the effects of lncRNA NLRP3 silencing on the expression of NLRP3-related molecules and inhibition of the NLRP3/caspase-1/IL-1β signalling pathway. Mechanistically, lncRNA NLRP3 sponging miR-138-5p facilitated NLRP3 activation through a competitive endogenous RNA (ceRNA) mechanism. In summary, our results suggested that lncRNA NLRP3 binding miR-138-5p promotes NLRP3-triggered inflammatory response via lncRNA NLRP3/miR-138-5p/NLRP3 ceRNA network (ceRNET) and provides insights into the treatment of early ALI.

Purpose: To explore the molecular mechanism and search for candidate lncRNA and mRNA associated with pyroptosis in the gene expression profile of LPS-induced acute lung injury (ALI). Methods: We investigated lncRNA and mRNA expression in lipopolysaccharide (LPS)-induced ALI at an early stage. RNA sequencing (RNA-Seq) was carried out to analyze lncRNA and mRNA expression profiles between the LPS-induced and control groups. We used bioinformatics analysis to predict target genes of early differential lncRNAs among obtained the differential mRNAs. Results: A total of 78 lncRNAs and 248 mRNAs were upregulated at 2 hours and downregulated at 9 hours, and 21 lncRNAs and 107 mRNAs were downregulated at 2 and upregulated at 9 hours in early ALI models. We predicted 7 cis-and trans-regulated target genes of the top 20 lncRNAs. Gene Ontology (GO) analysis indicated that the target genes for the screened lncRNAs were most enriched in three-terms: regulation of protein serine/threonine kinase activity, pertussis, and cellular response to LPS. Additionally, target genes of lncRNAs were the top three enriched in pertussis, osteoclast differentiation, and cAMP signaling pathways with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. We also identified vital mRNAs and lncRNAs. Protein-protein interaction (PPI) network analysis suggested that Tnf, Jun, and Atf3 were the top three key genes. Hub lncRNA4344 (NONRATT004344.2) and cis-regulated target mRNA (NLRP3) were validated in vitro. Finally, luciferase assay results confirmed that lncRNA4344 sponged miR‐138-5p to promote pyroptosis in inflammatory responses to LPS‐induced acute lung injury by targeting NLRP3. Conclusion: Based on analysis of lncRNA and mRNA expression profiles by RNA-Seq and experimental verification, this study is the first to reveal that lncRNA4344 sponged miR‐138-5p to promote pyroptosis in inflammatory responses of LPS‐induced acute lung injury by targeting NLRP3. These newly identified lncRNA, miRNA, and mRNA might be novel potential targets for early treatment and prevention in early ALI.