MENDA: a comprehensive curated resource of metabolic characterization in depression. (3rd June 2019)

• Record Type: Journal Article
• Authors: Pu, Juncai; Yu, Yue; Liu, Yiyun; Tian, Lu; Gui, Siwen; Zhong, Xiaogang; Fan, Chu; Xu, Shaohua; Song, Xuemian; Liu, Lanxiang; Yang, Lining; Zheng, Peng; Chen, Jianjun; Cheng, Ke; Zhou, Chanjuan; Wang, Haiyang; Xie, Peng
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 4(2020)
• Page Start: 1455
• Page End: 1464
• Publication Date: 2019-06-03
• Subjects: depression -- metabolite -- database -- pathway analysis -- network analysis; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz055
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Depression is a seriously disabling psychiatric disorder with a significant burden of disease. Metabolic abnormalities have been widely reported in depressed patients and animal models. However, there are few systematic efforts that integrate meaningful biological insights from these studies. Herein, available metabolic knowledge in the context of depression was integrated to provide a systematic and panoramic view of metabolic characterization. After screening more than 10 000 citations from five electronic literature databases and five metabolomics databases, we manually curated 5675 metabolite entries from 464 studies, including human, rat, mouse and non-human primate, to develop a new metabolite-disease association database, called MENDA (http://menda.cqmu.edu.cn:8080/index.php ). The standardized data extraction process was used for data collection, a multi-faceted annotation scheme was developed, and a user-friendly search engine and web interface were integrated for database access. To facilitate data analysis and interpretation based on MENDA, we also proposed a systematic analytical framework, including data integration and biological function analysis. Case studies were provided that identified the consistently altered metabolites using the vote-counting method, and that captured the underlying molecular mechanism using pathway and network analyses. Collectively, we provided a comprehensive curation of metabolic characterization in depression. Our model of a specific psychiatry disorder may be replicated to study other complex diseases.