1. Proteomic Analysis of the Antidepressant Effects of Shen-Zhi-Ling in Depressed Patients: Identification of Proteins Associated with Platelet Activation and Lipid Metabolism.
- Author
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Chen C, Hu Y, Dong XZ, Zhou XJ, Mu LH, and Liu P
- Subjects
- Adult, Antidepressive Agents pharmacology, Computational Biology, Drugs, Chinese Herbal pharmacology, Female, Gene Ontology, Humans, Male, Reproducibility of Results, Antidepressive Agents therapeutic use, Depression drug therapy, Depression metabolism, Drugs, Chinese Herbal therapeutic use, Lipid Metabolism drug effects, Platelet Activation drug effects, Proteomics methods
- Abstract
Shen-Zhi-Ling (SZL) is a Chinese medicine formulated from a Kai-Xin-San decoction that is commonly used to treat depression caused by dual deficiencies in the heart and spleen. However, the underlying mechanisms remain unclear. We investigated biological changes in depression patients (DPs) exhibiting antidepressant responses to SZL treatment using proteomic techniques. We performed label-free quantitative proteomic analysis and liquid chromatography-tandem mass spectrometry to discover and examine altered proteins involved in depression and antidepressant treatment. Serum samples were collected from DPs, DPs who underwent 8 weeks of SZL treatment and healthy controls (HCs). The proteins that differed among the three groups were further validated by Western blot analysis. By performing multivariate analyses, we identified 12 potential serum biomarkers that were differentially expressed among the HC, DP, and SZL groups. We then confirmed the significant changes in alpha-1-antitrypsin, von Willebrand factors, apolipoprotein C-III, and alpha-2-macroglobulin among the three groups by performing Western blot analysis, which supported the proteomic results. Profiling the proteomic changes in DPs treated with SZL could improve our understanding of the pathways involved in SZL responses, such as alterations in platelet activation, inflammatory regulation, and lipid metabolism. Future studies involving larger patient cohorts are necessary to draw more definitive conclusions.
- Published
- 2018
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