MiMeDB: the Human Microbial Metabolome Database

Abstract

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 was established to consolidate the growing body of data connecting the human microbiome and the chemicals it produces to both health and disease. MiMeDB contains detailed taxonomic, microbiological and body-site location data on most known human microbes (bacteria and fungi). This microbial data is linked to extensive genomic and proteomic sequence data that is closely coupled to colourful interactive chromosomal maps. The database also houses detailed information about all the known metabolites generated by these microbes, their structural, chemical and spectral properties, the reactions and enzymes responsible for these metabolites and the primary exposome sources (food, drug, cosmetic, pollutant, etc.) that ultimately lead to the observed microbial metabolites in humans. Additional, extensively referenced data about the known or presumptive health effects, measured biosample concentrations and human protein targets for these compounds is provided. All of this information is housed in richly annotated, highly interactive, visually pleasing database that has been designed to be easy to search, easy to browse and easy to navigate. Currently MiMeDB contains data on 626 health effects or bioactivities, 1904 microbes, 3112 references, 22 054 reactions, 24 254 metabolites or exposure chemicals, 648 861 MS and NMR spectra, 6.4 million genes and 7.6 billion DNA bases. We believe that MiMeDB represents the kind of integrated, multi-omic or systems biology database that is needed to enable comprehensive multi-omic integration.

Beyond cannabinoids: Application of NMR-based metabolomics for the assessment of Cannabis sativa L. crop health

“…When the loading factor plot of the OLPS-DA between aqueous extracts of the two chemovars was analyzed (Figure 4), positive signals at 4.10, 4.03-3.97, 3.88, 3.82-3.77, 3.70-3.63, and 3.59-3.52 ppm, and at 3.99, 2.93, and 2.84 ppm, which can be respectively assigned to fructose and aspartate by comparison to literature data (Wishart et al, 2009;Barclay et al, 2012;de Falco et al, 2016), show that the levels of these two metabolites are higher in chemovar B plants. In addition, a positive signal at 3.23 ppm with an HSQC correlation to a 13 C resonance at 53.4 ppm likely corresponds to the N-methyl groups of betaine (de Falco et al, 2016;Wishart et al, 2022), indicating that the levels of this trimethylammonium-bearing metabolite are also higher in the high-CBDA chemovar specimens (Figure S3). This initial assignment was confirmed by spiking one of the aqueous extract samples with a betaine standard solution (see Figure S4).…”

Visualizing the distribution of flavonoids in litchi (Litchi chinenis) seeds through matrix-assisted laser desorption/ionization mass spectrometry imaging

“…For the MS profiling and MS/MS data analysis, Bruker FlexAnalysis 3.4 (Bruker Daltonics) was used for the preliminary viewing and processing of the mass spectra. Once the monoisotopic peak list was generated and exported, two metabolome databases (METLIN and HMDB) (Tautenhahn et al, 2012;Wishart et al, 2022) were used for the search of the detected m/z values of precursor ions and CID fragment ions against potential metabolite identities within an acceptable mass error of ±5 ppm. Three ion adduct forms (i.e., [M + H] + , [M + Na] + , and [M + K] + ) were considered for the database search.…”

Metabolic fingerprinting of chemotherapy-resistant prostate cancer stem cells. An untargeted metabolomic approach by liquid chromatography-mass spectrometry

“…Statistically significant metabolites were tentatively annotated by matching the obtained accurate m/z to those published in the selected databases, namely, KEGG (Hashimoto et al, 2006), HMDB (Wishart et al, 2022) and Metlin (Smith et al, 2005) within a mass accuracy window of 5 ppm. Finally, MS/MS analysis performed in the QC samples using MzCloud and MassBank as databases confirmed the identity of these compounds.…”