The molecular mechanism of Reduning (RDN) in the treatment of sepsis was analyzed based on network pharmacology. The system pharmacology method was administered to search the active ingredients and targets of RDN, identify the sepsis-related genes, and determine the targets of RDN in the treatment of sepsis. Cytoscape was used to build a “drug component-target” network to screen key compounds. A protein-protein interaction (PPI) network was constructed using STRING, and core targets were revealed through topological analysis. 404 shared targets of RDN and sepsis were introduced into DAVID Bioinformatics Resources 6.8 for GO and KEGG enrichment analysis to predict their possible signaling pathways and explore their molecular mechanisms. GO enrichment analysis highlighted that they were largely related to protein phosphorylation, inflammatory reaction, and positive regulation of mitogen-activated protein kinase (MAPK) cascade. KEGG enrichment analysis outlined that they were enriched in PI3K-AKT signaling pathway, calcium signaling pathway, rhoptry-associated protein 1 (Rap1) signaling pathway, and advanced glycation end products and receptors for advanced glycation end products (AGE-RAGE) signaling pathway. Molecular biological validation results exposed that RDN could significantly improve the protein expression of p-AKT and p-PI3K, alleviate apoptosis-related proteins expression level and decrease apoptosis rate in LPS-induced HUVECs. In conclusion, it was illustrated that RDN could considerably constrain LPS-induced apoptosis by activating the PI3K-AKT signaling pathway, which advocated a basis for fundamental mechanism research and clinical application of RDN in the treatment of sepsis.

BackgroundSepsis is an organ dysfunction syndrome caused by the body’s dysregulated response to infection. Yet, due to the heterogeneity of this disease process, the diagnosis and definition of sepsis is a critical issue in clinical work. Existing methods for early diagnosis of sepsis have low specificity.AimsThis study evaluated the diagnostic and predictive values of pyroptosis-related genes in normal and sepsis patients and their role in the immune microenvironment using multiple bioinformatics analyses and machine-learning methods.MethodsPediatric sepsis microarray datasets were screened from the GEO database and the differentially expressed genes (DEGs) associated with pyroptosis were analyzed. DEGs were then subjected to multiple bioinformatics analyses. The differential immune landscape between sepsis and healthy controls was explored by screening diagnostic genes using various machine-learning models. Also, the diagnostic value of these diagnosis-related genes in sepsis (miRNAs that have regulatory relationships with genes and related drugs that have regulatory relationships) were analyzed in the internal test set and external test.ResultsEight genes (CLEC5A, MALT1, NAIP, NLRC4, SERPINB1, SIRT1, STAT3, and TLR2) related to sepsis diagnosis were screened by multiple machine learning algorithms. The CIBERSORT algorithm confirmed that these genes were significantly correlated with the infiltration abundance of some immune cells and immune checkpoint sites (all P<0.05). SIRT1, STAT3, and TLR2 were identified by the DGIdb database as potentially regulated by multiple drugs. Finally, 7 genes were verified to have significantly different expressions between the sepsis group and the control group (P<0.05).ConclusionThe pyroptosis-related genes identified and verified in this study may provide a useful reference for the prediction and assessment of sepsis.

In 2016, the SOFA score was proposed as the main evaluation system for diagnosis in the definition of sepsis 3.0, and the SOFA score has become a new research focus in sepsis. Some people are skeptical about diagnosing sepsis using the SOFA score. Experts and scholars from different regions have proposed different, modified versions of SOFA score to make up for the related problems with the use of the SOFA score in the diagnosis of sepsis. While synthesizing the different improved versions of SOFA proposed by experts and scholars in various regions, this paper also summarizes the relevant definitions of sepsis put forward in recent years to build a clear, improved application framework of SOFA score. In addition, the comparison between machine learning and SOFA scores related to sepsis is described and discussed in the article. Taken together, by summarizing the application of the improved SOFA score proposed in recent years in the related definition of sepsis, we believe that the SOFA score is still an effective means of diagnosing sepsis, but in the process of the continuous refinement and development of sepsis in the future, the SOFA score needs to be further refined and improved to provide more accurate coping strategies for different patient populations or application directions regarding sepsis. Against the big data background, machine learning has immeasurable value and significance, but its future applications should add more humanistic references and assistance.

PurposeAKT1 is an important target in sepsis acute lung injury (SALI). The current study was aim to construct a high-throughput screening (HTS) system based on the ChemDiv database (https://www.chemdiv.com/complete-list/) and use the system to screen for AKT1 activation agents, which may provide clues for the research and development of new drugs to treat SALI.MethodsBased on the existing X-ray structure of AKT1 and known AKT activators, a large-scale virtual HTS was performed on the ChemDiv database of small molecules by the cascade docking method and demonstrated both accuracy and screening efficiency. Molecular docking and molecular dynamics simulations were used to assess the stability and binding characteristics of the identified small-molecule compounds. The protective effect of the new highly selective compound on SALI were verified both in vitro and in vivo experiments.ResultsThe small-molecule compound 7460-0250 was screened out as a specific activator of AKT1. Molecular validation experiments confirmed that compound 7460-0250 specifically promoted the phosphorylation of AKT1 and down-regulated the LPS-induced apoptosis of human umbilical vein endothelial cells (HUVECs) by activating the AKT-mTOR pathway. Up-regulated mTOR was detected to directly interact with Bax to reduce apoptosis. In vivo, compound 7460-0250 could improved survival rate and alleviated lung injury of sepsis mice induced by cecum ligation and puncture (CLP), parallel with the activation of the AKT-mTOR pathway.ConclusionSmall-molecule compound 7460-0250 was successfully screened and confirmed as a highly selective AKT1 activator, which is a critical target in the development of new therapeutics for SALI.

RIPK1 is a global cellular sensor that can determine the survival of cells. Generally, RIPK1 can induce cell apoptosis and necroptosis through TNF, Fas and lipopolysaccharide stimulation, while its scaffold function can sense the fluctuation of cellular energy and promote cell survival. Sepsis is a nonspecific disease that seriously threatens human health. There is some dispute in the literature about the role of RIPK1 in sepsis. In this review, the authors attempt to comprehensively discuss the differential results for RIPK1 in sepsis by summarizing the underlying molecular mechanism and putting forward a tentative idea as to whether RIPK1 can serve as a biomarker for the monitoring of treatment and progression in sepsis.