ProMENDA


38816410	ProMENDA: an updated resource for proteomic and metabolomic characterization in depression. [PMID: 38816410] Pu J, Yu Y, Liu Y, Wang D, Gui S, Zhong X, Chen W, Chen X, Chen Y, Chen X, Qiao R, Jiang Y, Zhang H, Fan L, Ren Y, Chen X, Wang H, Xie P. Abstract Depression is a prevalent mental disorder with a complex biological mechanism. Following the rapid development of systems biology technology, a growing number of studies have applied proteomics and metabolomics to explore the molecular profiles of depression. However, a standardized resource facilitating the identification and annotation of the available knowledge from these scattered studies associated with depression is currently lacking. This study presents ProMENDA, an upgraded resource that provides a platform for manual annotation of candidate proteins and metabolites linked to depression. Following the establishment of the protein dataset and the update of the metabolite dataset, the ProMENDA database was developed as a major extension of its initial release. A multi-faceted annotation scheme was employed to provide comprehensive knowledge of the molecules and studies. A new web interface was also developed to improve the user experience. The ProMENDA database now contains 43,366 molecular entries, comprising 20,847 protein entries and 22,519 metabolite entries, which were manually curated from 1370 human, rat, mouse, and non-human primate studies. This represents a significant increase (more than 7-fold) in molecular entries compared to the initial release. To demonstrate the usage of ProMENDA, a case study identifying consistently reported proteins and metabolites in the brains of animal models of depression was presented. Overall, ProMENDA is a comprehensive resource that offers a panoramic view of proteomic and metabolomic knowledge in depression. ProMENDA is freely available at https://menda.cqmu.edu.cn . Transl Psychiatry. 2024:14(1) \| 0 Citations (from Europe PMC, 2025-03-15)
31157825	MENDA: a comprehensive curated resource of metabolic characterization in depression. [PMID: 31157825] Pu J, Yu Y, Liu Y, Tian L, Gui S, Zhong X, Fan C, Xu S, Song X, Liu L, Yang L, Zheng P, Chen J, Cheng K, Zhou C, Wang H, Xie P. 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. Brief Bioinform. 2020:21(4) \| 19 Citations (from Europe PMC, 2025-03-15)

ProMENDA: an updated resource for proteomic and metabolomic characterization in depression. [PMID: 38816410]

Pu J, Yu Y, Liu Y, Wang D, Gui S, Zhong X, Chen W, Chen X, Chen Y, Chen X, Qiao R, Jiang Y, Zhang H, Fan L, Ren Y, Chen X, Wang H, Xie P.

Abstract

Depression is a prevalent mental disorder with a complex biological mechanism. Following the rapid development of systems biology technology, a growing number of studies have applied proteomics and metabolomics to explore the molecular profiles of depression. However, a standardized resource facilitating the identification and annotation of the available knowledge from these scattered studies associated with depression is currently lacking. This study presents ProMENDA, an upgraded resource that provides a platform for manual annotation of candidate proteins and metabolites linked to depression. Following the establishment of the protein dataset and the update of the metabolite dataset, the ProMENDA database was developed as a major extension of its initial release. A multi-faceted annotation scheme was employed to provide comprehensive knowledge of the molecules and studies. A new web interface was also developed to improve the user experience. The ProMENDA database now contains 43,366 molecular entries, comprising 20,847 protein entries and 22,519 metabolite entries, which were manually curated from 1370 human, rat, mouse, and non-human primate studies. This represents a significant increase (more than 7-fold) in molecular entries compared to the initial release. To demonstrate the usage of ProMENDA, a case study identifying consistently reported proteins and metabolites in the brains of animal models of depression was presented. Overall, ProMENDA is a comprehensive resource that offers a panoramic view of proteomic and metabolomic knowledge in depression. ProMENDA is freely available at https://menda.cqmu.edu.cn .

Transl Psychiatry. 2024:14(1) | 0 Citations (from Europe PMC, 2025-03-15)

MENDA: a comprehensive curated resource of metabolic characterization in depression. [PMID: 31157825]

Pu J, Yu Y, Liu Y, Tian L, Gui S, Zhong X, Fan C, Xu S, Song X, Liu L, Yang L, Zheng P, Chen J, Cheng K, Zhou C, Wang H, Xie P.

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.

Brief Bioinform. 2020:21(4) | 19 Citations (from Europe PMC, 2025-03-15)

URL:	https://menda.cqmu.edu.cn/
Full name:	Protein and Metabolite Network of Depression Database
Description:	ProMENDA (Protein and Metabolite Network of Depression Database) is a comprehensive database containing all available metabolomic and proteomic knowledge of depression. This database is the updated version of MENDA database. MENDA is developed by manually extraction of information, followed by homogeneously annotated with controlled vocabularies of interest.
Year founded:	2018
Last update:	2023-03-12
Version:
Accessibility:	Accessible
Country/Region:	China

Data type:	Other Protein
Data object:	Animal
Database category:	Health and medicine Pathway
Major species:	Homo sapiens Mus musculus Pan troglodytes
Keywords:	metabolite pathway analysis network analysis

University/Institution:	The First Affiliated Hospital of Chongqing Medical University
Address:	Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
City:	Chongqing
Province/State:	Chongqing
Country/Region:	China
Contact name (PI/Team):	Peng Xie
Contact email (PI/Helpdesk):	xiepeng@cqmu.edu.cn

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