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. 2018 Apr 5;36(4):316-320.
doi: 10.1038/nbt.4101.

Metabolomics Activity Screening for Identifying Metabolites That Modulate Phenotype

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Free PMC article

Metabolomics Activity Screening for Identifying Metabolites That Modulate Phenotype

Carlos Guijas et al. Nat Biotechnol. .
Free PMC article

Abstract

Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype. Although appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell's or an organism's phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.

Conflict of interest statement

COMPETING INTERESTS

The authors declare no competing interests.

Figures

Figure 1
Figure 1
MAS for the identification of endogenous metabolites that modulate phenotype. Metabolomics data analysis and identification of candidates for screening are carried out by XCMS Online or other data-processing approaches. Initial candidates are generated using statistical and fold-change cut-offs and can then be further investigated using high-throughput screening to identify biologically active metabolites. Pathway analysis can provide additional metabolite candidates, while a third level of screening would identify candidates following perturbations with known pathway inhibitors.
Figure 2
Figure 2
MAS demonstrated in stem-cell differentiation, a mouse model of type 2 diabetes, T-cell function and activity, macrophage response to a fungal stimulus, and a remyelination model for multiple sclerosis. (a) Experiments with embryonic stem cells identified the metabolites involved in their differentiation. Among them, protectin D1, a lipid, was found to enhance differentiation to neurons by a factor of 15 (ref. 14). (b) 9-PAHSA was discovered in adipose tissue and plasma of glucose-tolerant mice. This metabolite was identified as a key molecule that maintains correct glucose homeostasis in a model of type 2 diabetes induced by a high-fat diet. (c) l-arginine levels decreased in activated naive T-cells. When l-arginine levels were raised externally, this amino acid actively increased survival and anti-tumor activity of T cells by modulating the activity of several transcriptional factors. (d) Minor phospholipid species PI(20:4/20:4) is actively synthesized by activated macrophages. When exogenously added, this lipid amplified microbicidal capacity of macrophages in response to the fungal stimulus zymosan. (e) Taurine, that was observed to be highly upregulated during OPC differentiation, enhanced the effect of a novel drug treatment (miconazole) to induce OPC differentiation into mature oligodendrocytes, a promising cell target for multiple sclerosis treatment.

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