Purpose Sepsis, which is deemed as a systemic inflammation reaction syndrome in the face of infectious stimuli, is the primary cause of death in ICUs. Sepsis-induced cardiomyopathy (SIC) may derive from systemic inflammation reaction and oxidative stress. Retinoic acid (RA) is recognized by its beneficial roles in terms of the immunoresponse to infections and antioxygen actions. However, the treatment efficacy and potential causal links of RA in SIC are still elusive. Methods By virtue of the STITCH database, we identified the targets of RA. Differentially expressed genes in SIC were acquired from the GEO database. The PPI network of intersected targets was established. GO and KEGG pathway enrichment analysis was completed. Hub genes were analyzed by cytoHubba plug-in. In the process of experimental validation, a mouse sepsis model was established by lipopolysaccharide (LPS), and the treated mice were intraperitoneally injected with RA or Dexamethasone (DEX) 60 min prior to LPS injections. Survival conditions, cardiac functions and antioxidant levels of the mice were assessed. Cardiac inflammation and injury were detected by HE and TUNEL. The levels of key genes and signal pathway expression were analyzed by RT-PCR and Western blot. Results PPARA, ITGAM, VCAM-1, IGF-1 and IL-6 were identified as key therapeutic targets of RA by network pharmacology. PI3K-Akt signaling pathway is the main regulatory pathway of RA. In vivo researches unraveled that RA can improve the survival rate and cardiac function of LPS-treated mice, inhibit inflammatory factors and myocardial injury, and regulate the expression of key therapeutic targets and key pathways, which is PI3K-Akt signaling pathway. Conclusion Network pharmacological method offers a predicative strategy to explore the treatment efficacy and causal links of RA in endotoxemic myocarditis. Through experimental verification, we discover that RA can reduce lipopolysaccharide-induced cardiac dysfunction by regulating the PI3K-Akt signaling pathway and key genes.

Background. Myopathies related to Ryanodine receptor 1 (RYR1) mutation are the most common nondystrophy muscle disorder in humans. Early detection and diagnosis of RYR1 mutation-associated myopathies may lead to more timely treatment of patients, which contributes to the management and preparation for malignant hyperthermia. However, diagnosis of RYR1 mutation-associated myopathies is delayed and challenging. The absence of diagnostic morphological features in muscle biopsy does not rule out the possibility of pathogenic variations in RYR1. Accordingly, it is helpful to seek biomarkers to diagnose RYR1 mutation-associated myopathies. Methods. Skeletal muscle tissue microarray datasets of RYR1 mutation-associated myopathies or healthy persons were built in accordance with the gene expression synthesis (GEO) database. Differentially expressed genes (DEGs) were identified on the basis of R software. Genes specific to tissue/organ were identified through BioGPS. An enrichment analysis of DEGs was conducted in accordance with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We also built protein-protein interaction (PPI) networks to explore the function and enrichment pathway of DEGs and the identification of hub genes. Lastly, the ROC curve was drawn for hub genes achieving specific expressions within skeletal muscle. Moreover, the area under the curve (AUC) was obtained to calculate the predictive value of key genes. The transcription factors of hub genes achieving specific expressions within skeletal muscle were predicted with the use of the iRegulon plugin. Results. We identified 170 DEGs among 11 healthy subject samples and 18 RYR1 mutation-associated myopathy samples in the dataset. Among the above DEGs, 31 genes achieving specific expressions within tissues/organs were found. GO and KEGG enrichment analysis of DEGs mainly focused on muscle contraction, actin-mediated cell contraction, actin filament-based movement, and muscular sliding. 12 hub genes were identified with the use of Cytoscape. Four hub genes were specifically expressed in skeletal muscle tissue, including MYH1 (AUC: 0.856), TNNT3 (AUC: 0.840), MYLPF (AUC: 0.786), and ATP2A1 (AUC: 0.765). The iRegulon predicted results suggested that the transcription factor MYF6 was found with the highest reliability. Conclusions. Four skeletal muscle tissue-specific genes were identified, including MYH1, TNNT3, MYLPF, and ATP2A1, as the potential biomarkers for diagnosing and treating RYR1 mutation-associated myopathies, which provided insights into the transcriptome-level development mechanism. The transcription factor MYF6 may be a vital upstream regulator of the above biomarkers.