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Therapeutic Strategies and Future Research
Published in Mark A. Mentzer, Mild Traumatic Brain Injury, 2020
The Human Metabolome Database (HMDB) database, supported by the University of Alberta and Genome Canada (Human metabolome database version 2.5) contains links to chemical, clinical, and biochemical/molecular biological data, with links to protein and DNA sequences. This systems approach to biological studies promises rapid streamlining of previously tedious processes.
The Volatilome in Metabolomics
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
Raquel Cumeras, Xavier Correig
The human metabolome database is a freely available electronic database containing detailed information about small molecule metabolites found in the human body. The database contains 42,003 metabolite entries including water-soluble and lipid soluble metabolites as well as metabolites that would be regarded as either abundant (> 1 uM) or relatively rare (<1 nM). Additionally, 5,701 protein sequences are linked to these metabolite entries. Even though HMBD integrates databases of almost all the human tissues or biofluids, it doesn’t have a specific entry for breath or volatilome.
Metabolomics and Proteomics
Published in Crystal D. Karakochuk, Kyly C. Whitfield, Tim J. Green, Klaus Kraemer, The Biology of the First 1,000 Days, 2017
Richard D. Semba, Marta Gonzalez-Freire
In 2003, the Human Genome Project, which had the goal of mapping all the genes of the human genome, was declared complete [4]. In the postgenomic era, two major challenges in the life sciences include the elucidation of all the proteins and metabolites in the human body. The proteome and metabolome have a level of complexity that far exceeds the genome. In humans, ~20,000 protein-coding genes give rise to ~100,000 proteins and an estimated 1 million different protein-modified forms [5,6]. The many forms of proteins arise from mutations, RNA editing, RNA splicing, posttranslational modifications, and protein degradation; the proteome does not strictly reflect the genome. Proteins function as enzymes, hormones, receptors, immune mediators, structure, transporters, and modulators of cell communication and signaling. The metabolome consists of amino acids, amines, peptides, sugars, oligonucleotides, ketones, aldehydes, lipids, steroids, vitamins, and other molecules. These metabolites reflect intrinsic chemical processes in cells, as well as environmental exposures such as diet and gut microbial flora. The current Human Metabolome Database contains more than 40,000 entries [7]—a number that is expected to grow quickly in the future.
Exhaled metabolic markers and relevant dysregulated pathways of lung cancer: a pilot study
Published in Annals of Medicine, 2022
Yingchang Zou, Yanjie Hu, Zaile Jiang, Ying Chen, Yuan Zhou, Zhiyou Wang, Yu Wang, Guobao Jiang, Zhiguang Tan, Fangrong Hu
Metabolomics works as a multidiscipline crossed diagnostic tool for exploring differences and dynamic changes in endogenous micromolecular metabolites, combining analytical chemistry and bioinformatics to systematically detect and analyze changes in metabolites in the body. It has been widely used in diverse metabolic samples including urine [9,10], serum/plasma [11,12] and tissue [13,14]. Based on those high-throughput data and online metabolic database, e.g. the human metabolome database (HMDB) and kyoto encyclopaedia of genes and genomes (KEGG), pathway enrichment analysis can be done, which may explain the relationship between metabolic data and pathophysiological state. As a mature technique, metabolomics provides a scientific and systematic data mining process for differential metabolites analysis, ensuring validity, interpretability, and reproducibility of their results. However, few studies analyzed breath data with metabolic database, even though there are various software and tools designed for guiding and performing metabolomics data analysis.
Metabolomics in antimicrobial drug discovery
Published in Expert Opinion on Drug Discovery, 2022
Given the extraordinary diversity of instrumentation and software used, with wide experimental variables, databases for metabolomic data currently do not have standardized reporting formats. These standards are in the process of development, with the inclusion of various components such as the spectral and chromatographic data, the chemical structure associated, biological roles, locations, and concentrations, as well as metadata describing assays and the study as a whole. One of the metabolics repositories for a number of metabolomics journals is MetaboLights [https://www.ebi.ac.uk/metabolights, accessed on 13 July 2022] within EMBL. Other databases and repositories are MetabolomeExpress for GC-MS data [https://www.metabolome-express.org, accessed on 30 April 2022], Metabolomics Workbench [https://www.metabolomicsworkbench.org, accessed on 13 July 2022], Global Natural Product Social Molecular Networking (GNPS) [https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp, accessed on 13 July 2022], and NMR-based Human Metabolome Database [https://hmdb.ca, accessed on 13 July 2022].
Metabonomics analysis of serum from rats given long-term and low-level cadmium by ultra-performance liquid chromatography–mass spectrometry
Published in Xenobiotica, 2018
Liyan Hu, Lu Bo, Meiyan Zhang, Siqi Li, Xiujuan Zhao, Changhao Sun
The selected metabolites which found in EZinfo were re-imported into Progenesis QI software for metabolite identification. The Human Metabolome Database (HMDB) (the mass tolerance was set at 10 ppm or 5 mDa) was used for the identification of the initial metabolites. Detailed metabolites identification information included compound ID, description, adducts, formula, score, fragmentation score, mass error (ppm), isotope similarity, web link, description tR (min), and m/z values. The confirmation of metabolites identified based on the “Score”, “Fragmentation score”, and “Isotope similarity” given by Progenesis QI, and then the MS/MS fragmentation data from Progenesis QI software were further provided. MS/MS spectra of metabolites were matched with the structure information from the Progenesis QI software. The identification results combined with the intensity data were exported as .xls files for subsequent compound confirmation and multivariate statistical analysis. To reduce the false positive identifications, structural confirmation was conducted by comparison with the standards (MS/MS spectrum). To support the pathways of biomarkers, the following databases have been used: HMDB (http://www.hmdb.ca), METLIN (http://metlin.scripps.edu).