Abstract
Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.
Highlights
Depression belongs to the mental health disorder, which is an emotional disorder that causes persistent sadness and loss of interest, and is the leading cause of worldwide disability (Malhi and Mann, 2018)
In order to extract comprehensive pathogenic genes with confirmed evidence of depression, the DisGeNET and OMIM databases were used to collect the genes associated with depression. 1,329 genes and 3,069 documentary evidences have been reserved as pathogenic genes with confirmed evidence for further construction of the effective intervention space (Supplementary Table S1)
In order to test whether genes with more documentary support have more extensive functions, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses on all pathogenic genes and found that genes with more literature support are associated with more pathways and GO terms
Summary
Depression belongs to the mental health disorder, which is an emotional disorder that causes persistent sadness and loss of interest, and is the leading cause of worldwide disability (Malhi and Mann, 2018). Western medicine mainly adopts selective serotonin reuptake inhibitor (SSRI) (Bondar et al, 2020), serotonin norepinephrine reuptake inhibitor (SNRI) (Bondar et al, 2020), norepinephrine, noradrenergic and specific serotonergic antidepressants (NaSSA), tricyclic antidepressants, and monoamine oxidase inhibitor (MAOI) for treating depression (Delgado and Moreno, 2000; Narasingam et al, 2017). Western medicine, as the mainstream drug for treating depression, has a single mechanism of action, which leads to certain side effects and drug resistance. Traditional Chinese medicine (TCM), as a new antidepressant, can make up for the deficiency of western medicine because of its multicomponent, multitarget, and multi-mechanism pharmacological mechanism, with relatively small side effects and can be used for a long time (Shi et al, 2019; Zong et al, 2019; Ren et al, 2021)
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