Depression Metabolite Database Construction And Metabolite Big Data Analysis

PART1 DATABASE CONSTRUCTION OF METABOLITES IN DEPRESSIONBackground:Major depressive disorder(MDD)is a seriously disabling of neuropsychiatric 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.The construction of a database based on big data is expected to provide an important source of information for molecular research of diseases.However,its application in depression has two major difficulties.First,for complex diseases represented by depression,it is still needed to develop a complex data extraction and annotation scheme.Second,there is no systematic analysis framework to guide the analysis of collected big data to reveal the relevant biological information.Objective:To provide a panoramic and systematic view of metabolic characterization in the context of depression,a database of metabolic characterization in depression and its treatment was developed.Methods:1.Studies that investigated the metabolic characterization associated with depression and its treatment,and that used nuclear magnetic resonance,mass spectrometry,or magnetic resonance spectroscopy technologies,were collected.2.Available data for depressed patients and animal models,as well as metabolic changes resulting from treatments,were manually integrated.The standardized data extraction process was used for data collection,a multi-faceted annotation scheme was developed.3.A web database was built to store the collected big data.A user-friendly search engine and web interface were integrated for database access.4.To facilitate data analysis and interpretation,a systematic analytical framework was proposed,which was applied in the case studies.Results:1.A total of 464 studies were collected from five electronic literature databases and five metabolomics databases.2.A total of 5,675 metabolite entries were manually curated from these studies,including human,rat,mouse,and non-human primate.3.Based on the collected data,a new metabolite-disease association database,called MENDA(http://menda.cqmu.edu.cn:8080/index.php),was developed.4.A systematic analytical framework,which was consist of quantitative analysis and biological function analysis,was proposed based on the curated big data.The framework was applied based on data from brain and peripheral tissues of both depressed patients and animal models,respectively.Conclusions:In this study,MENDA database was constructed,which could provide a comprehensive curation of metabolic characterization in depression.PART2 AN INTEGRATED BIOLOGICAL FUNCTION ANALYSIS OF PERIPHERAL BLOOD METABOLITES IN MAJOR DEPRESSIVE DISORDERBackground:Major depressive disorder(MDD)is a seriously disabling neuropsychiatric disorder,characterized by high morbidity,which has increased in recent decades.However,the molecular mechanisms underlying MDD remain unclear.Previous studies have identified altered metabolic profiles in peripheral tissues associated with MDD.Despite advances in metabolomics research examining MDD,the majority of these non-targeted or targeted metabolomic studies have had small sample sizes and have reported inconsistent findings,limiting their clinical applicability.No systematic studies have been performed examining the metabolomic profiles of MDD to screen for underlying biomarkers.A comprehensive bioinformatics analysis,based on the differential metabolites identified at the metabolomics level,may provide important insights into the pathological molecular mechanisms underlying MDD.Objective:To identify metabolic changes and the biological themes in the peripheral blood of MDD patients based on the aforementioned database.Methods:1.A meta-analysis was performed to identify differential metabolites in the peripheral blood of patients with MDD,based on eligible studies included in the database.2.Subgroup analysis,sensitivity analysis,and regression analysis were performed to test the sources of potential heterogeneity.3.Comprehensive pathway and network analyses were also performed to examine the biological themes associated with these metabolic changes.Results:1.A total of 23 differentially expressed metabolites between MDD patients and controls were identified from 46 studies.MDD patients were characterized by higher levels of asymmetric dimethylarginine,tyramine,2-hydroxybutyric acid,phosphatidylcholine(32:1),and taurochenodesoxycholic acid and lower levels of L-acetylcarnitine,creatinine,L-asparagine,L-glutamine,linoleic acid,pyruvic acid,palmitoleic acid,L-serine,oleic acid,myo-inositol,dodecanoic acid,L-methionine,hypoxanthine,palmitic acid,L-tryptophan,kynurenic acid,taurine,and 25-hydroxyvitamin D compared with controls.2.L-Tryptophan and kynurenic acid were consistently downregulated in MDD patients,regardless of antidepressant exposure.Depression rating scores were negatively associated with decreased levels of L-tryptophan.3.Pathway and network analyses revealed altered amino acid metabolism and lipid metabolism,especially for the tryptophan–kynurenine pathway and fatty acid metabolism,in the peripheral system of MDD patients.Conclusions:The integrated analysis revealed that metabolic changes in the peripheral blood were associated with MDD,particularly decreased tryptophan and kynurenic acid levels,and alterations in the tryptophan–kynurenine and fatty acid metabolism pathways.The findings may facilitate biomarker development and the elucidation of the molecular mechanisms that underly MDD.PART3 SEX DIFFERENCES OF PERIPHERAL BLOOD METABOLOMICS IN MAJOR DEPRESSIVE DISORDER:A COEXPRESSION NETWORK ANALYSISBackground:Major depressive disorder(MDD)is a highly prevalent mental disorder with sex differences in morbidity.In recent years,sex differences in depression have aroused widespread research interest,and increasing studies have found that men and women with MDD have diverse molecular signatures.Despite these data,little is known about the potential biological mechanisms contributing to these sex differences.In conventional metabolomics studies,researchers only focus on differential metabolites,resulting in a large amount of information loss.Weighted gene co-expression network analysis(WGCNA),the most widely used methods for constructing statistical inference networks,offers a new perspective for evaluating high-throughput metabolome data by determining interconnected modules of correlated metabolites,regardless these metabolites were differential expressed or not.Objective:To investigate the sex differences of plasma metabolome signatures in patients with MDD using bioinformatics methods.Methods:The plasma metabolomes of patients with MDD(29 males and 30females;all patients were first-episode and drug-free)and healthy controls(30 males and 29 females)from a liquid chromatography mass spectrometry-based metabolomics approach were compared using bioinformatics analysis,as follows.1.Multivariate statistical analysis was used to compare the metabolome characteristics of each group.2.Based on the correlation between pairs of metabolites,the male and female blood metabolomes were divided into different metabolic modules using WGCNA,and the correlation between metabolic modules and phenotypes were calculated.3.Fisher exact test was used to evaluate the overlap of depression related metabolic modules between men and women,and the rank-rank hypergeometric overlap test was used to analyze whether there were similar expression patterns of these overlapping metabolite peaks.4.The potential metabolic pathways involved in gender specific and shared metabolic modules were identified by pathway analysis.Results:1.The partial least square-discriminant analysis score chart shows that the four groups of samples are clearly separated in positive ion mode and negative ion mode,indicating that disease(MDD and controls)and gender(male and female)are important factors affecting the metabolome.2.WGCNA clustered 6,698 metabolite features into 37 and 30 metabolic modules in male and female patients,where 6 and 9,respectively,were significantly associated with MDD.3.Two thirds of these modules,4 for males and 6 for females,were sex-specific,while the metabolites of the shared modules showed similar overlapping trends.4.Moreover,sex-specific modules were mainly involved in bile acid biosynthesis and the carnitine shuttle for males,and glycosphingolipid metabolism and tryptophan metabolism for females.Furthermore,the porphyrin metabolism was a common pathway in both sexes.Conclusions:The findings of this study revealed sex differences in plasma metabolome signatures in patients with MDD.This may contribute to a better understanding of sex differences in the pathophysiology of and antidepressant development for MDD.