Background Sepsis is an uncontrolled reaction to infection that causes severe organ dysfunction and is a primary cause of ARDS. Patients suffering both sepsis and ARDS have a poor prognosis and high mortality. However, the mechanisms behind their simultaneous occurrence are unclear. Methods We acquired sepsis and ARDS datasets from GEO and Arrayexpress databases and screened hub genes by WGCNA and machine learning algorithm. For diagnosis and prognosis, ROC curve and survival analysis were used. We performed GO, KEGG, GSEA, immune cell infiltration, drug prediction, molecular docking, transcription factor prediction, and constructed PPI and ceRNA networks to explore these genes and the common mechanisms of sepsis and ARDS. Single-cell data analysis compared immune cell profiles and hub gene localization. Finally, RT-qPCR and H&E staining confirmed the reliability of hub genes using PBMCs samples and mouse models. Results We identified 242 common differentially expressed genes in sepsis and ARDS. WGCNA analysis showed that the turquoise module in GSE95233 is strongly linked to sepsis occurrence and poor prognosis, while the black module in GSE10474 is associated with ARDS. Using WGCNA and three machine learning methods (LASSO, random forest and Boruta), we identified three key genes CX3CR1, PID1 and PTGDS. Models built with them showed high AUC values in ROC curve evaluations and were validated by external datasets, accurately predicting the occurrence and mortality. We further explored the immunological landscape of these genes using immune infiltration and single-cell analysis. Then, the ceRNA, predicted drugs and molecular docking were analyzed. Ultimately, we demonstrated that these genes are expressed differently in human and mouse samples with sepsis and ARDS. Conclusion This study identified three molecular signatures (CX3CR1, PID1 and PTGDS) linked to the diagnosis and poor prognosis of sepsis and ARDS, validated by RT-qPCR and H&E staining in both patient and mouse samples. This research may be valuable for identifying shared biological mechanisms and potential treatment targets for both diseases.
Objective: To study the potential targets and molecular mechanisms of Fritiliariae Irrhosae Bulbus (FIB) in the treatment of ischemic strokes based on a network pharmacology strategy, with a combination of molecular docking and animal experiments.Methods: The active components and targets of FIB were screened by TCMSP database and TCMIP database, and the related targets of ischemic strokes were screened by GeneCards, OMIM, CTD, and DrugBank, then the intersection targets of the two were taken. The protein interaction network was constructed by STRING, the PPI network diagram was drawn by using Cytoscape software, and the key targets of FIB treatment of ischemic strokes were analyzed by MCODE. The DAVID database was used for GO and KEGG enrichment analysis, and the potential pathway of FIB against ischemic strokes was obtained. Molecular docking was performed by using AutoDock Tools 1.5.6 software. Finally, a mouse model of ischemic stroke was established, and the results of network pharmacology were verified by in vivo experiments. Realtime Polymerase Chain Reaction was used to detect the expression levels of relevant mRNAs in the mouse brain tissue. Western blot was used to detect the expression levels of related proteins in the mouse brain tissue.Results: 13 kinds of active components of FIB were screened, 31 targets were found in the intersection of FIB and ischemic strokes, 10 key targets were obtained by MCODE analysis, 236 biological processes were involved in GO enrichment analysis, and key targets of KEGG enrichment analysis were mainly concentrated in Neuroactive light receptor interaction, Calcium signaling pathway, Cholinergic synapse, Hepatitis B, Apoptosis—multiple specifications, Pathways in cancer and other significantly related pathways. There was good binding activity between the screened main active components and target proteins when molecular docking was performed. Animal experiments showed that the infarct volume of brain tissue in the FIB treatment group was considerably reduced. RT-qPCR and the results of Western Blot showed that FIB could inhibit the expression of active-Caspase3, HSP90AA1, phosphorylated C-JUN, and COX2.Conclusion: Based on network pharmacology, the effect of FIB in the treatment of ischemic strokes was discussed through the multi-component-multi-target-multi-pathway. The therapeutic effect and potential mechanisms of FIB on ischemic strokes were preliminarily explored, which provided a ground work for further researches on the pharmacodynamic material basis, mechanism of action and clinical application.
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