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Review
. 2019 Jun;20(6):353-367.
doi: 10.1038/s41580-019-0108-4.

Identification of Bioactive Metabolites Using Activity Metabolomics

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Free PMC article
Review

Identification of Bioactive Metabolites Using Activity Metabolomics

Markus M Rinschen et al. Nat Rev Mol Cell Biol. .
Free PMC article

Abstract

The metabolome, the collection of 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 metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other 'omics' levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites - which we term activity metabolomics - is already having a broad impact on biology.

Conflict of interest statement

Competing interests.

The authors declare no competing interests.

Figures

Figure 1.
Figure 1.
Metabolites – active modulators of gene and protein activity. Metabolites actively control protein activity via allosteric regulation of transmembrane receptors and transcription factors, as enzyme co-factors and co-substrates in the catalysis of biochemical reactions, and via post-translational modifications. They also significantly influence RNA metabolism via sensing (small molecule ligands are sensed by riboswitches) and post-transcriptional modifications. Moreover, it is well known that metabolites serve as signaling molecules to control transcription factors and thus gene expression. Finally as co-factors and co-substrates of chromatin modifying enzymes they are actively involved in epigenetic regulation.
Figure 2:
Figure 2:
Examples of macromolecule modification by the active metabolome. A. Examples for macromolecule modifications by the tricarboxylic acid cycle (TCA) intermediates and related products. Acetyl-CoA, α-ketoglutarate, Succinyl-CoA, UDP-glucose and ATP are energy-rich molecules that can directly modify proteins, or nucleic acids. B. Cysteine alkylation by itaconate, a branched fatty acid. Recently, it was shown that itaconate is an anti-inflammatory metabolite that directly alkylates cysteine residues at KEAP1 to control expression of NRF2. KEAP1 is the primary negative regulator of NRF2. This results in increased expression of anti-oxidant and anti-inflammatory gene expression. C. ADP-ribosylation of proteasome unit PI31 to control proteostasis. By the TNKS enzyme, PI31 is ADP-ribosylated to promote proteasome 26S assembly (from the 20S subunit). This results in increased proteasome activity. D. S-Adenosylmethionine is a central methyl donor for DNA, RNA and histone methylation, thereby controlling gene expression at the level of the genome, transcriptome and epigenome.
Figure 3.
Figure 3.
Mechanisms for non-covalent modification of macromolecules by the active metabolome. A. Palmitic hydroxystearic acid (Pahsa) activates GPR40 to induce calcium signaling and augmentation of insulin and GLP1 release. B Phytoestrogenes activate transcription factors that control programs involved in cell metabolism and cell proliferation. C. Riboswitches are controlled by metabolites. D. Metabolites assemble higher molecular proteins, e.g. of the bacterial protein galF, a glucose-1-phosphate uridylyltransferase.
Figure 4.
Figure 4.
Metabolomics-guided identification of bioactive metabolite candidate(s) followed by activity assessment. Workflow to elucidate the metabolite activity and role at the system’s level starts with the comparative metabolome analyses - a discovery-oriented untargeted or broad-scale targeted profiling analysis is followed by data mining to select the metabolite candidate(s) based on the significance (p-value), amplitude (fold change) and direction of its change (i.e. accumulation or depletion) in the studied system (e.g. cell media, mice/human plasma, etc.). To this end, statistical approaches are applied, and the metabolite is identified using spectral libraries (i.e. MS/MS matching). Protein and gene expression data (as a result of proteome and/or transcriptome analyses) are then used to support and help filter the candidate metabolites involved in the locally enriched part of the metabolic network, i.e. the same module or biochemical pathway. For example, the depletion of one a metabolite will usually be coupled by the significantly higher increased expression of its converting enzyme(s)) responsible for metabolite consumption. To gain further insights into the metabolic fate of the “metabolite-candidates” metabolite, the next step involves stable isotope-assisted (13C or 15N) tracing experiments to identify the active pathways used for metabolite catabolism. Different conditions can be tested in a longitudinal assay to gather the information about the metabolite uptake and conversions (i.e. label-enriched metabolites based on MS-based isotopologue abundance analyses, or NMR based flux analysis). Lastly, the metabolite supplementation experiments are performed using in vitro cell or organoid models, and in vivo models. These assays provide the direct information about how the phenotype is affected or modified via endogenous metabolite supplementation (ingestion through diet, drinking water or by addition to cell culture medium).
Figure 5.
Figure 5.
A. Metabolite Activity for Phenotype Modulation. Metabolomics has already made a significant impact in a wide variety of scientific areas through discovery of active, mainly endogenous metabolites that can regulate different biological processes and thus modulate the phenotype in health and disease. B-F. Metabolic activity of α-ketoglutarate (AKG) in a variety of prokaryote to eukaryote systems. AKG accumulates in exercise, but can also be supplemented. Direct supplementation has a wide effect on various systems via different mechanisms. Common primary AKG sensors may be ATPase b subunit (as revealed by DARTs proteomics) or PII (in plants/prokaryotes). B. In C. elegans, AKG addition leads to binding of the ATPase b subunit to inhibit ATP production, oxygen consumption, and thereby stimulating mTOR dependent autophagy. All of these mechanisms result in an extended life span of the worm. C. In embryonic stem cells, an increased AKG/Succinate ratio drives activation of JMJD3 and TET1/2 to perform epigenetic changes. These include a reduced trimethylation at H3K27Me3 and also a reduced methylation of DNA (5-methylcytosine reduced). This enables increased gene expression required for pluripotency. D. In specialized mammalian organs such as the kidney, AKG is being secreted to control organ function in a paracellular manner. Urinary AKG derived from metabolic stress results in activation of Oxgr1 and subsequent activation of salt transporters to regulate electrolyte balance and, presumably, hypertension. E. In bacteria, intracellular AKG induced by nitrogen limitation inhibits Enzyme 1 to decrease glycolytic flux and to couple nitrogen consumption to glucose consumption. F. In T-cells, AKG induces activity of mTOR and the T-cell specific transcription factor TBET via unknown mechanism to promote differentiation to TH1 cells over TH2 cells.

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