MENDA: a comprehensive curated resource of metabolic characterization in depression

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.

[1]  Lei Zhang,et al.  Analyzing the genes related to Alzheimer’s disease via a network and pathway-based approach , 2017, Alzheimer's Research & Therapy.

[2]  Meng Zhou,et al.  MetSigDis: a manually curated resource for the metabolic signatures of diseases , 2019, Briefings Bioinform..

[3]  D. Schaid,et al.  Glycine and a Glycine Dehydrogenase (GLDC) SNP as Citalopram/Escitalopram Response Biomarkers in Depression: Pharmacometabolomics‐Informed Pharmacogenomics , 2011, Clinical pharmacology and therapeutics.

[4]  Zhao Li,et al.  BDgene: A Genetic Database for Bipolar Disorder and Its Overlap With Schizophrenia and Major Depressive Disorder , 2013, Biological Psychiatry.

[5]  Robert Petryszak,et al.  Discovering and linking public omics data sets using the Omics Discovery Index , 2017, Nature Biotechnology.

[6]  Peng Xie,et al.  Discovery and validation of plasma biomarkers for major depressive disorder classification based on liquid chromatography-mass spectrometry. , 2015, Journal of proteome research.

[7]  Guang Chen,et al.  Cellular Mechanisms Underlying the Antidepressant Effects of Ketamine: Role of α-Amino-3-Hydroxy-5-Methylisoxazole-4-Propionic Acid Receptors , 2008, Biological Psychiatry.

[8]  T. Vos,et al.  Global, regional, and national incidence and prevalence, and years lived with disability for 328 diseases and injuries in 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016 , 2017 .

[9]  D. Goldberg,et al.  The heterogeneity of “major depression” , 2011, World psychiatry : official journal of the World Psychiatric Association.

[10]  Núria Queralt-Rosinach,et al.  DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants , 2016, Nucleic Acids Res..

[11]  Marian Joëls,et al.  Brain GABA levels across psychiatric disorders: A systematic literature review and meta‐analysis of 1H‐MRS studies , 2016, Human brain mapping.

[12]  Zhao Li,et al.  ADHDgene: a genetic database for attention deficit hyperactivity disorder , 2011, Nucleic Acids Res..

[13]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[14]  Liang Fang,et al.  Metabolite identification in fecal microbiota transplantation mouse livers and combined proteomics with chronic unpredictive mild stress mouse livers , 2018, Translational Psychiatry.

[15]  Ronald S Duman,et al.  How do antidepressants work? New perspectives for refining future treatment approaches. , 2017, The lancet. Psychiatry.

[16]  Peng Xie,et al.  Identification and Validation of Urinary Metabolite Biomarkers for Major Depressive Disorder* , 2012, Molecular & Cellular Proteomics.

[17]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..

[18]  Shyam Visweswaran,et al.  Translational bioinformatics in mental health: open access data sources and computational biomarker discovery , 2017, Briefings Bioinform..

[19]  K. Kemper,et al.  Medical management of depression. , 2006, The New England journal of medicine.

[20]  Frank B Hu,et al.  Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis , 2016, Diabetes Care.

[21]  Jeong-Eun Park,et al.  Metabolite changes in risk of type 2 diabetes mellitus in cohort studies: A systematic review and meta-analysis. , 2018, Diabetes research and clinical practice.

[22]  Carlos A. Zarate,et al.  A Randomized Trial of a Low-Trapping Nonselective N-Methyl-D-Aspartate Channel Blocker in Major Depression , 2013, Biological Psychiatry.

[23]  P. Brown,et al.  Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[24]  B. Grant,et al.  Epidemiology of Adult DSM-5 Major Depressive Disorder and Its Specifiers in the United States , 2018, JAMA psychiatry.

[25]  Lei Zhang,et al.  Network and Pathway-Based Analyses of Genes Associated with Parkinson’s Disease , 2016, Molecular Neurobiology.

[26]  P. Carmeliet,et al.  Meta‐analysis of clinical metabolic profiling studies in cancer: challenges and opportunities , 2016, EMBO molecular medicine.

[27]  Laura Inés Furlong,et al.  PsyGeNET: a knowledge platform on psychiatric disorders and their genes , 2015, Bioinform..

[28]  Florian Schubert,et al.  Abnormal Cingulate and Prefrontal Cortical Neurochemistry in Major Depression After Electroconvulsive Therapy , 2011, Biological Psychiatry.

[29]  Adriano Chiò,et al.  Network Analysis Identifies Disease-Specific Pathways for Parkinson’s Disease , 2016, Molecular Neurobiology.

[30]  John P. Overington,et al.  An atlas of genetic influences on human blood metabolites , 2014, Nature Genetics.

[31]  Matthew J. Taylor,et al.  Could glutamate spectroscopy differentiate bipolar depression from unipolar? , 2014, Journal of affective disorders.

[32]  Pim Cuijpers,et al.  Comprehensive meta-analysis of excess mortality in depression in the general community versus patients with specific illnesses. , 2014, The American journal of psychiatry.

[33]  Peng Xie,et al.  Integrated Metabolomics and Proteomics Analysis of Hippocampus in a Rat Model of Depression , 2018, Neuroscience.

[34]  Jiwon Choi,et al.  mGluR5 in the nucleus accumbens is critical for promoting resilience to chronic stress , 2015, Nature Neuroscience.

[35]  S. Janelidze,et al.  Connecting inflammation with glutamate agonism in suicidality , 2012, Neuropsychopharmacology.

[36]  M. Bachtiar,et al.  Comprehensive review of Hepatitis B Virus‐associated hepatocellular carcinoma research through text mining and big data analytics , 2018, Biological reviews of the Cambridge Philosophical Society.

[37]  G. Siuzdak,et al.  Innovation: Metabolomics: the apogee of the omics trilogy , 2012, Nature Reviews Molecular Cell Biology.

[38]  David S. Wishart,et al.  MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis , 2018, Nucleic Acids Res..

[39]  Yanli Wang,et al.  PubChem: a public information system for analyzing bioactivities of small molecules , 2009, Nucleic Acids Res..

[40]  C W Turck,et al.  Metabolite profiling of antidepressant drug action reveals novel drug targets beyond monoamine elevation , 2011, Translational Psychiatry.

[41]  Oded Gonen,et al.  Lateralized caudate metabolic abnormalities in adolescent major depressive disorder: a proton MR spectroscopy study. , 2007, The American journal of psychiatry.

[42]  David S. Wishart,et al.  HMDB 4.0: the human metabolome database for 2018 , 2017, Nucleic Acids Res..

[43]  Marco Masseroli,et al.  Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration , 2016, Briefings Bioinform..

[44]  V. Mootha,et al.  Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer , 2014, Nature Communications.

[45]  Christoph Steinbeck,et al.  MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data , 2012, Nucleic Acids Res..

[46]  T. Ideker,et al.  Integrative approaches for finding modular structure in biological networks , 2013, Nature Reviews Genetics.

[47]  Eoin Fahy,et al.  Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools , 2015, Nucleic Acids Res..