Liyuan Guo1, Yang Du, Suhua Chang, Weina Zhang, Jing Wang. 1. From the Key Laboratory of Mental Health (L.G., Y.D., S.C., W.Z., J.W.), Institute of Psychology, Chinese Academy of Sciences, Beijing, China; and University of Chinese Academy of Sciences (Y.D., W.Z.), Beijing, China.
Abstract
OBJECTIVE: To systematically collect differentially expressed genes (DEGs) from rodent models of chronic stress (CS) and apply them to research of stress-related disease. CS is an important environmental factor that may affect numerous complex diseases. Its relevant DEGs identified from rodent models provide valuable information for understanding the mechanisms underlying stress-related diseases. Currently, no suitable data tool have been developed to use such data. METHODS: We systematically searched and reviewed publications in PubMed. CS-DEGs were collected from original studies that reported gene expression statuses in rodent models of CS. CS disease overlapping genes, CS pathways and CS pathway clusters, and CS regulatory elements were analyzed on the basis of CS-DEGs. An online database was developed to store and manage curated CS-DEGs and analyzed data. RESULTS: A total of 2956 CS-DEGs were collected from 195 articles, among which 815 genes are shared among CS and seven stress-related diseases. Nine hundred twenty-seven CS pathway clusters were identified. Three types of CS regulatory elements are predicted for all CS genes. An online database (CS-DEGs), freely available at http://cs.psych.ac.cn, includes and presents CS-DEGs and all analyzed data. CONCLUSIONS: CS-DEGs is the first gene database on CS research. It enables researchers to apply rodent expression data in candidate gene and pathway identification for stress-related disease study.
OBJECTIVE: To systematically collect differentially expressed genes (DEGs) from rodent models of chronic stress (CS) and apply them to research of stress-related disease. CS is an important environmental factor that may affect numerous complex diseases. Its relevant DEGs identified from rodent models provide valuable information for understanding the mechanisms underlying stress-related diseases. Currently, no suitable data tool have been developed to use such data. METHODS: We systematically searched and reviewed publications in PubMed. CS-DEGs were collected from original studies that reported gene expression statuses in rodent models of CS. CS disease overlapping genes, CS pathways and CS pathway clusters, and CS regulatory elements were analyzed on the basis of CS-DEGs. An online database was developed to store and manage curated CS-DEGs and analyzed data. RESULTS: A total of 2956 CS-DEGs were collected from 195 articles, among which 815 genes are shared among CS and seven stress-related diseases. Nine hundred twenty-seven CS pathway clusters were identified. Three types of CS regulatory elements are predicted for all CS genes. An online database (CS-DEGs), freely available at http://cs.psych.ac.cn, includes and presents CS-DEGs and all analyzed data. CONCLUSIONS:CS-DEGs is the first gene database on CS research. It enables researchers to apply rodent expression data in candidate gene and pathway identification for stress-related disease study.