PubMedGoogle Scholar. The displayed network diagram builds out from the known metabolites, exposures and health effects within MiMeDB that have links to the entered metabolite and to the entered microbe(s) and the network constraints. 2, 750766 (2016). Bioinforma. Christensenellales (Firmicutes_A phylum), Veill. Meehan, C. J. Bioinformatics 34, i884i890 (2018). Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Influence of gastrectomy for gastric cancer treatment on faecal microbiome and metabolome profiles. Provided by the Springer Nature SharedIt content-sharing initiative, npj Biofilms and Microbiomes (npj Biofilms Microbiomes) the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in We also thank former and current Borenstein lab members for their helpful inputs, and Uri Gophna for his valuable advice. Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton T6G 2B7, AB, Canada. Main data processing scripts, and the R notebook containing the meta-analysis described herein, are available at https://github.com/borenstein-lab/microbiome-metabolome-curated-data. Huang T.T., Lai J.B., Du Y.L., Xu Y., Ruan L.M., Hu S.H.. Current understanding of gut microbiota in mood disorders: an update of human studies, Role of the microbiota in immunity and inflammation. Rev. Figure 1b illustrates the final data scheme per study. However, acquiring, processing, and unifying such datasets from multiple sources is a daunting and challenging task. Google Scholar. J. Syst. The Download tab allows users to download most of the MiMeDB data including metabolite structures, chemical metadata, spectral data, DNA and protein sequences, reactions, microbe lists and microbe taxonomy. A diagram illustrating the relational structure of MiMeDBs backend data is provided in Figure Figure11. Immunol. This work was supported in part by National Institutes of Health [grant U19AG057377], and Israel Science Foundation [Grant 2435/19 to E.B.]. Each bar represents the number of unique genera that appear in at least the specified number of datasets; e Metabolite prevalence across datasets, interpretation equivalent to (d). If users click on the MiMeDB compound button or the compound name, they will be taken to the MiMeDB MetaboCard for that compound or metabolite (Figure (Figure3A).3A). The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease A multitude of factors contribute to complex diseases and can be measured with 'omics' methods. Wishart D.S., Feunang Y.D., Guo A.C., Lo E.J., Marcu A., Grant J.R., Sajed T., Johnson D., Li C., Sayeeda Z.et al DrugBank 5.0: a major update to the drugbank database for 2018. Indeed, we recently demonstrated the utility of a similar dataset collection in a large-scale meta-analysis of the relationship between gut microbes and metabolites26. Overall, 132,391 linear models were fitted, of which, 18,075 (13.6%) resulted in a significant genus-metabolite association (i.e. Gut microbiome structure and metabolic activity in inflammatory bowel disease. The Virtual Metabolic Human database: integrating human and gut Multi-omic studies not only require multi-omic technologies, they also require multi-omic databases and multi-omic informatic (bioinformatic and cheminformatic) tools. Neveu V., Nicolas G., Salek R.M., Wishart D.S., Scalbert A.. Exposome-Explorer 2.0: an update incorporating candidate dietary biomarkers and dietary associations with cancer risk. Below the navigation bar is a set of three coloured hyperlink bars that allow users to instantly access the most popular browsing tools in MiMeDB, namely: (i) Browse MiMeDB Metabolites, (ii) Browse MiMeDB Microbes and (iii) Learn More. The FAIR guiding principles for scientific data management and stewardship. Our own work in human microbiome studies (14) revealed that there was a shortage of these kinds of multi-omic databases, especially for integrated microbiome studies. Overall, 2900 samples from 1849 individuals are currently included in the resource (Fig. Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton T6G 2B7, AB, Canada. Other members of the curation group routinely performed additional spot checks on each entry. After choosing the filter options, users must click the blue Apply Filter button on the lower right side of the filter menu. An extended reconstruction of human gut microbiota metabolism of Here we introduce the gutSMASH algorithm for identification of primary metabolic gene clusters, and we used it to systematically profile gut microbiome metabolism, identifying 19,890 gene. A more detailed listing of MiMeDBs other content and statistics is available on the MiMeDB website via the About menu tab under the Statistics section. The gut microbiome-metabolome dataset collection: a curated - Nature E.M. conducted the literature search, obtained and processed the data, organized the final data resource and performed the meta-analysis. Evol. The Structure Class refers to the chemical class to which the compound belongs using the ClassyFire taxonomy (28). A complete listing of all major data sources is provided on the top navigation bar via the About menu tab under the Data Sources section. AMB Express 8, 19 (2018). The Microbe Table displays nine columns describing each microbe in MiMeDB. Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: Estimating species abundance in metagenomics data. To limit the size of certain exposure categories (esp. This observation may be explained by at least two potential hypotheses: (i) that these bacteria are highly metabolically active in the gut, and/or (ii) that they possess central ecological roles in the gut microbial ecosystem. The Human Microbial Metabolome Database (MiMeDB) (https://mimedb.org) is a comprehensive, multi-omic, microbiome resource that connects: (i) microbes to microbial genomes; (ii) microbial genomes to microbial metabolites; (iii) microbial metabolites to the human exposome and (iv) all of these omes to human health. Google Scholar. Finding associations across multiple datasets, as facilitated by our resource, potentially increases the likelihood that such associations are microbially driven and represent ubiquitous microbial metabolism, rather than specific host or diet-related associations. Lachnospirales (Firmicutes_A phylum), Oscil. gutSMASH predicts specialized primary metabolic pathways from - Nature Article Nucleic Acids Res. Second, this resource can be used to benchmark methods related to the joint analysis of microbiome and metabolome data. Researchers can use this resource to easily obtain multiple, curated, and unified microbiome-metabolome datasets in order to compare statistical associations between datasets, benchmark various microbiome-metabolome integration tools, and compare findings from their own dataset to similar datasets all in much greater convenience and efficiency than before. Lastly, metabolite identification in untargeted metabolomic platforms may vary in its confidence level, which could in turn imply lower confidence of downstream analyses. To address these challenges and to facilitate the reuse of published microbiome-metabolome data for convenient multi-study meta-analysis exploration of microbe-metabolite patterns, we present here a curated dataset collection of paired and processed microbiome-metabolome data from human fecal samples. One major limitation is the substantial difference between various metabolomics platforms and the impact of the used platform on the set of chemical classes that can be detected. The Metabolite category functions as a central hub category which is logically and literally connected to the five other MiMeDB categories (Microbial Sources, Exposure Sources, Biospecimen & Location, Health Effect & Bioactivity as well as Metabolic Reactions). a A highlight of data resources and main processing steps of the curated microbiome-metabolome data resource (see Methods); b A database scheme of the final data products per dataset. Similar browsing options, similar layouts and similar data tables are also available for each of the other Browse menu options including Biospecimens & Locations, Health Effects, Exposure Sources and Metabolic Reactions. Exometabolomics experiments now provide assertions of the metabolites present within specific environments and how the production and depletion of metabolites is linked to specific microbes. A total of 79% of the compounds in MiMeDB also contain reaction data showing the substrates, products and enzymes responsible for the reactions. To ensure re-usability, all data in MiMeDB is extensively sourced with clear provenance. Each MiMeDB MetaboCard contains 16 data fields. The Human Microbiome Project Data Analysis and Coordinating Center (DACC) Data Portal provides access to all publicly available HMP data sets from both phases of the program. For instance, in 2021 alone there were >13 000 papers appearing in PubMed with the terms microbiome and human. A metabolite with 0 for the Number of Microbes is a compound that is either exogenous (food, drug or cosmetic) or a host-microbe cometabolite. Nat. 83, 297302 (2020). MiMeDB was established to consolidate the growing body of data connecting the human microbiome and the chemicals it produces to both health and disease. Schorn, M. A. et al. Vasuk Gautam, However, all changes are tracked internally and external users can see from the last update date when any changes occurred. The Human Microbial Metabolome Database (MiMeDB) (https://mimedb.org) is a comprehensive, multi-omic, microbiome resource that connects: (i) microbes to microbial genomes; (ii) microbial genomes to microbial metabolites; (iii) microbial metabolites to the human exposome and (iv) all of these 'omes' to human health. MiMeDB contains detailed taxonomic, microbiological and body-site location data on most known human microbes (bacteria and fungi). Furthermore, all microbes are linked to NCBI Taxonomy entries and GenBank GI numbers, all sequences are linked to GenBank or UniProt entries and all molecular or microbial physiological data have clear references to other established reference, meta-data or data resources. official website and that any information you provide is encrypted Sukanta Saha, Therefore, changes in the microbiome after treatment with microorganisms could be deeply related to changes in SCFAs. Sumner, L. W. et al. MiMeDB also has a Download tab, an About tab and a Contact Us tab. Gut 0, 110 (2021). MiMeDB is FAIR compliant (32) and details regarding its FAIRness are provided under the About menu tab. Wishart D.S., Tian S., Allen D., Oler E., Peters H., Lui V.W., Gautam V., Djoumbou-Feunang Y., Greiner R., Metz T.O.. BioTransformer 3.0a web server for accurately predicting metabolic transformation products. Cell Metab. The data in MiMeDB are released under the Creative Commons (CC) 4.0 License Suite according to the Attribution BY and Non-Commercial NC licensing conditions. These include the Metabolite ID (or MiMeDB compound identifier), the Name, the Molecular Formula, the Mass (Average and Isotopic), a thumbnail image of the Structure, the Structure Class, the Host and Biospecimen, the Number of Microbes, the Metabolite Type, the Exposure Sources, the Health Effects and the Detection Status. Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada. Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. To monitor the data entry process, all of MiMeDBs data is entered into a centralized, password-controlled database. Either query will cause the viewer to display or highlight the entered or matching gene on the chromosome map. For studies with shotgun metagenomics we also provided species-level abundance tables. The About tab has a submenu that provides an overview on MiMeDB, database statistics, citation information, a brief tutorial on how to use MiMeDB, data sources, tables on MiMeDB data structures and information on its FAIR compliance. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. & Meng, F. GLay: Community structure analysis of biological networks. We wish to thank Mr. Fei Wang and Ms. Afia Anjum for their help in computing the MS/MS, RI and EI-MS data. Article To further assure comparable taxonomic profiles, we also collapsed taxonomy abundance tables into the genus level (species-level tables are available as well for WGSS datasets). Scott Han, Additional recommendations for how to best utilize the resource are available in the Wiki page. Details about the network nodes and edges are available in Supplementary Table 4. Moreover, to determine which genus-metabolite pairs are consistently associated in a more statistically rigorous manner, we conducted a random-effects meta-analysis using semi-partial correlations derived from the linear regression results (as suggested by Aloe and Becker, 201227). The Host and Biospecimen identifies in which mammal(s) the metabolite has been formally identified and in which type of biological specimen it has been detected. A more detailed description of MiMeDBs content, its layout and design, as well as additional details about how it was constructed and how it is maintained, are provided in the following pages. Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. Sinha, R. et al. Notably, genus-metabolite correlations can clearly stem from a direct involvement of the genus in the production, consumption, or degradation of the metabolite, but also from indirect associations related, for example, to interactions between different gut bacteria, or co-abundant metabolites present in specific diets. Lloyd-Price, J. et al. Rosero, J. Mallick, H. et al. To ensure findability, all metabolite, microbe, reaction and health effect entries in MiMeDB have a unique and permanent seven-digit MMDB identifier along with a single letter code to distinguish the type of entry. 1c). Methods 9, 357359 (2012). Screenshots showing the (A) MiMeDB MetaboCard for p-Cresol sulfate (MMDBc0000002) and (B) the MiMeDB MicrobeCard for Staphylococcus aureus (MMDBm0000423). The Metabolite Table displays 11 columns (Figure (Figure2B)2B) describing each metabolite or chemical in MiMeDB. Cell. The study also indicates that examining microbiome metabolites in human stool samples could be used as a diagnostic tool. PCycDB: a comprehensive and accurate database for fast - Microbiome Eng. Metagenomics reveals the habitat specificity of biosynthetic potential Microbiome and its Metabolites Promote Endometriosis in Animal Model [ 51 ] noted that the profile of antibiotic-resistance genes in the intestinal microbiome of deep-sea fish was related with the novelty of antibiotic-resistance genes and . None of these resources are truly connected or integrated and none of them are explicitly focused on the human microbiome. Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada. Enterobacterales (Proteobacteria phylum), b A bipartite network of consistent genus-metabolite associations, identified by a meta-analysis of 11 different microbiome-metabolome datasets from the curated microbiome-metabolome data resource. performed the processing of the WGSS data. Additional, extensively referenced data about the known or presumptive health effects, measured biosample concentrations and human protein targets for these compounds is provided. We further provide detailed documentation and a usage example in a dedicated Wiki page and via script examples also available in the repository. Each filter option provides a pulldown menu with which users may choose among several named options. Erawijantari, P. P. et al. Stothard P., Grant J.R., VanDomselaar G.. Visualizing and comparing circular genomes using the CGView family of tools. Initial challenges include downloading the data associated with each study, which are often missing or incomplete, and linking microbiome, metabolome, and metadata sample identifiers in each study. The Human Microbial Metabolome Database (MiMeDB) ( https://mimedb.org) is a comprehensive, multi-omic, microbiome resource that connects: (i) microbes to microbial genomes; (ii) microbial genomes to microbial metabolites; (iii) microbial metabolites to the human exposome and (iv) all of these 'omes' to human health. Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. Nat. The Genus Alistipes: Gut Bacteria With Emerging Implications to Inflammation, Cancer, and Mental Health. With that in mind, it is important to note that the number of datasets in which a metabolite appears should not be used as an indication of its prevalence. While the general content of MiMeDB can be grouped into three broad classes (microbes, metabolites and effects), MiMeDB is actually divided into six smaller categories for more facile browsing, searching and viewing. Using a combination of random forest regressor models (for predicting metabolites) and random-effects models (for quantifying robustness), we were able to identify 97 metabolites that were robustly well-predicted by the microbiotas composition. Per linear model, we report the adjusted R square, the coefficient of the Genus variable, its associated p-value, and for the subsequent meta-analysis we also report the semi-partial genus-metabolite correlation27. ENVIM introduces an extra step to ENM to consider variable importance (VI) scores, and thus, achieves better prediction power. volume8, Articlenumber:79 (2022) 2a). Unfortunately, however, obtaining, processing, and comparing microbiome-metabolome datasets from multiple studies is typically a cumbersome, extremely challenging, and time-consuming process. The species table is only available for studies with shotgun metagenomic data; c Data resource summary statistics; d Genera prevalence across datasets.