Identification of bioactive metabolites using activity metabolomics

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Abstract

<p class="first" id="P1">The metabolome, the small molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolomics has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. In this review, we describe the molecular mechanisms by which the active cell metabolome affects cellular physiology through modulation of other ‘omic’ levels, including the genome, epi-genome, transcriptome and proteome. This concept of activity screening guided by metabolomics to identify biologically active metabolites, or “activity metabolomics”, is having broad impact on biology. </p>

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MS-DIAL: Data Independent MS/MS Deconvolution for Comprehensive Metabolome Analysis

Hiroshi Tsugawa, Tomas Cajka, Tobias Kind … (2015)

Data-independent acquisition (DIA) in liquid chromatography tandem mass spectrometry (LC-MS/MS) provides more comprehensive untargeted acquisition of molecular data. Here we provide an open-source software pipeline, MS-DIAL, to demonstrate how DIA improves simultaneous identification and quantification of small molecules by mass spectral deconvolution. For reversed phase LC-MS/MS, our program with an enriched LipidBlast library identified total 1,023 lipid compounds from nine algal strains to highlight their chemotaxonomic relationships.

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MassBank: a public repository for sharing mass spectral data for life sciences.

Hisayuki Horai, Masanori Arita, Shigehiko Kanaya … (2010)

MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron-ionization mass spectrometry (EI-MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)-MS(n) data of 2337 authentic compounds of metabolites, 11 545 EI-MS and 834 other-MS data of 10,286 volatile natural and synthetic compounds, and 3045 ESI-MS(2) data of 679 synthetic drugs contributed by 16 research groups (January 2010). ESI-MS(2) data were analyzed under nonstandardized, independent experimental conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass-to-charge ratio are optimized to the ESI-MS(2) data. MassBank also provides a merged spectrum for each compound prepared by merging the analyzed ESI-MS(2) data on an identical compound under different collision-induced dissociation conditions. Data merging has significantly improved the precision of the identification of a chemical compound by 21-23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chemical compounds and the publication of experimental data. 2010 John Wiley & Sons, Ltd.