Table 2. Chemical biology and computational technologies to study metabolite activity.
This table delinates recently developed technologies that can be used to identify the primary target(s) and activities of a metabolite.
| Approach | Description | Advantage | Limitations | Example |
|---|---|---|---|---|
| Purification and isolation from a complex mixture | Fractionation of complex biological mixtures by chromatography (e.g. HPLC) and subsequent activity testing using biological assays | Universal, flexible regarding the biological assay | Tedious, signal overlap, minor components might be missed | Identification of PGE2 as immune modulator derived from Trichuris suis worm eggs 143, identification of bioactive drug metabolites 144,145 |
| Affinity selection mass spectrometry | Incubation of metabolite mixture and target enzymes/proteins, size exclusion separation of bound and unbound components, MS based characterization of bound fraction | Universal approach, no protein immobilization necessary | High grade of non-specific binding may be obtained. Ligand binding and not activity is assessed. | Identification of protein-metabolite and protein-protein interactions in Arabidopsis thaliana 146,147 |
| Affinity purification (chromatography) – mass spectrometry | Affinity based protein purification and MS based characterization of components. Pulldown is done from complex cellular or metabolite mixtures | Universal approach which under certain circumstances can be used in vivo (e.g. yeast cells) | Antibody-dependent. Ligand binding and not activity is assessed. | Identification of several small molecule interaction partners within ergosterol biosynthesis. 60 |
| Thermal proteome profiling | Binding of a ligand to a protein in vivo or in vitro results in increased thermal stability | Universal approach, physical stabilization of proteins | Low throughput, and potentially significant amount of non-specific binding. Requires absolute metabolite concentrations, and binding is not necessarily bioactivity. | Identification of the protein metabolite interaction between STING and 2’3’-cGAMP 148 |
| Metabolite profiling combined with orthogonal molecular biology approaches | Integration of metabolomics data with the data obtained from orthogonal molecular biology experiments (i.e. gene silencing, enzyme inhibition, etc.) | Identification of mechanism of action of a (bio)active signaling metabolite | Long-term, fastidious | Various examples 26 |
| Integrated network analysis (GAM) | Combination of transcriptional and metabolomics data for the identification of active metabolic sub-networks | Comprehensive network analysis allowing for a systems wide comparison of two biological states, e.g. control and experiment conditions | Transcriptional data mandatory, preassembled metabolic networks (species dependent) | Integration of metabolic and transcriptional data to understand macrophage immune metabolism. 24,149 |
| Flux balance analysis | Mathematical approach for the calculation of the metabolic flux through a network, in silico approach. | Easily computable. No kinetic parameters needed. |
In silico approach based on genome scale metabolic network reconstructions. Only predicts steady state. Does not predict metabolite concentrations. |
Determination of Metabolite balance to determine behaviour and composition of engineered microbial communities 150,151 |
| Metabolite set enrichment and network analysis | Computational approach based on overrepresentation and probability analysis of metabolomics data to identify the active biochemical pathways or locally enriched parts of the metabolic network associated with phenotype | Rapid, allows for direct association with biochemically relevant information | In silico approach, significant amount of false positive metabolite IDs due to high levels of redundancy and noise in metabolomics data | Multi-omics discovery of RCC6-encoded protein CSB to potentailly alter defects in DNA-repair mechanisms in Huntington’s disease -98,152,153 |
| Bioactive Natural Products Prioritization using massive multi-informational Molecular Networks | Molecular networks embedding known bioactivity and taxonomical data to highlight potentially bioactive scaffolds in crude extracts | Computational approach that facilitates the identification of potentially bioactive compounds using databases and molecular/fragmentation similarity in mass | Complete structural elucidation of active compounds remains a challenge | Isolation of new cytotoxic prenylated stilbenes of the schweinfurthin series from Macaranga tanarius 154,155 |