Metabolomics: beyond biomarkers and towards mechanisms

Abstract

Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.

Figure 1. From metabolites to pathways and mechanisms

The workflow outlines a holistic approach that begins with high-throughput untargeted metabolite profiling. Analysis of biofluids, cells or tissues reveals quantitative metabolite changes (as a result of a stimulus) that can be validated further. Metabolites can be mapped and analysed within metabolic pathways to relate the metabolites to each other, and within interconnected biological pathways, providing potential targets for further mechanistic studies. The combination of metabolomic, orthogonal biological analysis and isotope–assisted deciphering of pathways allows the mechanism of the aberrant phenotype to be ascertained.

Figure 2. Controlling and influencing metabolism: perspectives from metabolomics

Using various orthogonal techniques, targets identified with metabolomics can be further verified and investigated in more detail. For instance, other ‘omics’ approaches, including (epi)genomics, transcriptomics and proteomics, can reveal further mechanistic insights into phenotypical changes associated with the metabolite. Various orthogonal techniques also allow targeting of metabolic pathways and can be used to influence metabolite levels and to interfere with metabolic pathways. These approaches can be directed at the gene level and aimed at silencing gene expression, with techniques like CRISPR–Cas-mediated knock outs or RNA interference (RNAi). Alternatively, metabolic pathways can be influenced at at the protein level with the use of antimetabolites. Manipulating sources of exposure to different stimuli can also influence the metabolome, providing further mechanistic insights. For instance, using antibiotics or germ-free models with species-specific inoculation reveals the direct effect of the microbiome on metabolite production. Similarly, immunomodulators can be used to change the efficacy of the host immune system to respond to both the resident microbiota and pathogens, and their metabolic products. This collectively opens up possibilities for better understanding and, eventually, controlling metabolism.

Figure 3. Novel biological insights

Diacetylspermine (DAS) has a role in biofilm-associated colon cancer. Various metabolomic and orthogonal biological techniques contributed to the association of DAS with bacterial biofilms and their role in the pathology of cancer. Fluorescence in situ hybridization (FISH) analysis and 16S rRNA sequencing identified the presence of bacterial species and biofilms on colon tissues. Untargeted and targeted metabolomics identified and validated the association of polyamine metabolites with colon cancer tissues. Stratification by biofilm status showed that DAS was upregulated primarily in biofilm-associated tissues, which was confirmed by mass spectrometry imaging. Network modelling using the KEGG and BioCyc databases, and pathway analysis using untargeted stable-isotope assisted metabolomics, showed that DAS is an end-product of polyamine metabolism. For further analysis, orthogonal techniques were used. Immunohistochemistry and immunofluorescence revealed increased cellular proliferation and pro-inflammatory cytokines in biofilm-associated tissues. The combination of these techniques led to the conclusion that bacterial biofilms induce a pro-carcinogenic state in the colon epithelium.