Identification and Validation of Autophagy-related Genes in Sepsis-related Acute Kidney Injury (S-AKI) via Bioinformatics Analysis

Background: Acute kidney injury is one of the most common complications of sepsis, while autophagy has been reported to exert protective effects against kidney diseases. background: Acute kidney injury is one of the most common complications of sepsis. Autophagy has been reported to make protective effects in kidney diseases, therefore in this study, we identified the key autophagy genes in sepsis-related acute kidney injury(S-AKI) through bioinformatics analysis of sequencing data, and activated autophagy to verify the key genes through cell experiments. Aims & Objectives: In this study, the key autophagy genes in sepsis-related acute kidney injury (SAKI) were identified by bioinformatics analysis of sequencing data. In addition, autophagy was activated in cell experiments to verify the key genes. objective: The objective of this study is to find the autophagy genes that play a key role in the process of septic kidney injury. Methods: The GSE73939, GSE30576, and GSE120879 datasets were downloaded from Gene Expression Omnibus (GEO), and the Autophagy-related Genes (ATGs) were downloaded from Kyoto Encyclopedia of Genes and Genomes (KEGG). GO enrichment analysis, KEGG pathway enrichment analysis, and protein–protein interactions (PPI) analysis were performed on the differentially expressed genes (DEGs) and ATGs. The online STRING tool and Cytoscape software were used to further identify the key genes. RNA expression of key ATGs was validated by qRT-PCR through an LPS-induced HK-2 injury cell model. method: GSE73939, GSE30576, GSE120879 were downloaded from Gene Expression Omnibus (GEO) and the Autophagy-related Genes (ATGs) were downloaded from Kyoto Encyclopedia of Genes and Genomes (KEGG), GO enrichment analysis, KEGG pathway enrichment analysis and protein–protein interactions (PPI) analysis were applied to the differentially expressed genes (DEGs) and ATGs. The online STRING tool and Cytoscape software were used to further identification the key genes. RNA expression of key ATGs was validated by qRT-PCR through LPS-induce HK-2 injury cell model. Results: A total of 2376 DEGs (1012 upregulated genes and 1364 downregulated genes) and 26 key ATGs were identified. The GO and KEGG enrichment analysis indicated several enriched terms related to the autophagy process. The PPI results revealed an interaction among these autophagy-related genes. Six hub genes with the highest scores were obtained by the intersection of different algorithms, which were further confirmed as 4 hub genes (Bcl2l1, Map1lc3b, Bnip3, Map2k1) by real-time qPCR. result: A total of 2376 DEGs (1012 upregulated genes and 1364 downregulated genes) and 26 key ATGs were identified. The GO and KEGG enrichment analysis indicated several enriched terms related to autophagy process. Results of PPI showed an interaction between these autophagy-related genes. The highest scores 6 hub genes were obtained by intersection of different algorithms, which were further confirmed as 4 hub genes (Bcl2l1, Map1lc3b, Bnip3, Map2k1) through real-time qPCR. Conclusion: Our data identified Bcl2l1, Map1lc3b, Bnip3, and Map2k1 as the key autophagyregulating genes in the development of sepsis and provided a foundation to detect biomarkers and therapeutic targets for S-AKI. other: none