Exploring Disease through Metabolomics

Abstract

Metabolomics approaches provide an analysis of changing metabolite levels in biological samples. In the past decade, technical advances have spurred the application of metabolomics in a variety of diverse research areas spanning basic, biomedical, and clinical sciences. In particular, improvements in instrumentation, data analysis software, and the development of metabolite databases have accelerated the measurement and identification of metabolites. Metabolomics approaches have been applied to a number of important problems, which include the discovery of biomarkers as well as mechanistic studies aimed at discovering metabolites or metabolic pathways that regulate cellular and physiological processes. By providing access to a portion of biomolecular space not covered by other profiling approaches (e.g., proteomics and genomics), metabolomics offers unique insights into small molecule regulation and signaling in biology. In the following review, we look at the integration of metabolomics approaches in different areas of basic and biomedical research, and try to point out the areas in which these approaches have enriched our understanding of cellular and physiological biology, especially within the context of pathways linked to disease.

Onchocerca volvulus -neurotransmitter tyramine is a biomarker for river blindness

“…Still, humoral response-based diagnostics carry liabilities with the potential of antibody persistence even after treatments, hence confounding active/past infections. A potential alternative would be the identification of specific metabolites in blood or urine samples because, in theory, this could provide both infection status and a quantitative measure of infection based on metabolite concentration (9,10). Using liquid chromatography (LC)-MS to analyze O. volvulus-infected serum samples, we demonstrated the promise of metabolomics as an onchocerciasis diagnostic (11).…”

Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra

“…This technique has been used to understand the pathology of disease [2] and/or to diagnose disease [3][4][5].…”

“…Thereby, it is assumed that the components are nonnegative and, as experimentally validated for the pure component mass spectra [18], sparse in support and amplitude. 2 Given that linear [17] and nonlinear [18] underdetermined BSS problems are difficult, the single-mixture nonlinear BSS problem is exceptionally difficult. Even linear BSS from a single mixture is a highly illposed problem, and hard constraints must be imposed on the source signals to enable their separation [25][26][27][28][29][30][31][32][33][34][35].…”

“…As in [18], the influence of higher order monomials, which represent error terms, is reduced by preprocessing the mixture spectra using robust principal component analysis (RPCA) [36,37] as well as hard-(HT), soft-(ST) [38] and trimmed thresholding (TT) [39]. 2 Even though nonlinear mixing system in assumption A4 is required to be nonnegative it is not strictly required by proposed method. That is, in equivalent linear blind source separation problem (4) and explicit feature mapped problem (9)/(10) the mixing coefficients can be even negative.…”

Expanding the Reach of Cancer Metabolomics

“…As a result, our tissues harbor a complex profile of metabolites that is representative of biochemical network dynamics, and this metabolome fluctuates as a function of cellular phenotypes and tissue pathology. With the advent of new technologies that enable broad-spectrum quantitation of small molecules in cells and tissues, we are beginning to understand how changes in the metabolome correlate with specific diseases (1). In turn, this information may facilitate the discovery of potential biomarkers that can enable earlier prognoses in cancer or other diseases (2).…”