Chapter 6—Academic Applications of Metabolomics
In the previous chapter of this guide, we took a deeper dive into the clinical applications of metabolomics, including routine screening, diagnosis, and precision medicine. But all clinical applications begin in the scientific research laboratory: academic research is vital to gaining new insights and advancing our understanding of biological systems to improve the diagnosis, prevention, and treatment of disease. In this chapter, we’ll explore the academic applications of metabolomics, including routine research and method development, and how these applications eventually lead to clinical translation.
The Human Metabolome
As with the human genome and the human microbiome, research efforts to fully understand the human metabolome require a comprehensive map, complete with compound concentrations, biofluid/tissue locations, subcellular locations, physical properties, known disease associations, nomenclature, descriptions, enzyme data, mutation data, and characteristic mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectra, to guide hypothesis generation and testing.
That map, the Human Metabolome Database (HMDB), was first introduced by Canadian researchers in 2007 and comprised nearly 2,200 endogenous metabolites, 1,200 drugs, and 3,500 food components.1 The inclusion of drugs and food components in addition to host metabolites was critical because, as we’ve already discussed in previous chapters, metabolomics is the phenotypic expression of host-environment interactions.
Additionally, as recognition of the role the human microbiome plays in human health and disease increases, human metabolomics research increasingly includes metabolomic profiles of the human microbiome.2 In 2019, the Virtual Metabolic Human database (VMH), comprising 5,180 unique metabolites, 17,730 unique reactions, 3,695 human genes, 255 Mendelian diseases, 818 microbes, 632,685 microbial genes, and 8,790 food items, was released to the scientific community as “a novel, interdisciplinary database for data interpretation and hypothesis generation.”3
Databases like the HMDB and VMH, in addition to other human and bacterial metabolic resources and collections, have facilitated an amazing breadth of human metabolomics research, advancing our understanding of health and disease and the role metabolites play. Metabolomics is now a standard scientific practice in many academic research labs, both for routine research applications and for technique development.
Metabolomics in Routine Research
Studies of the relationship between genes and disease have improved our understanding of several diseases, including cancer. However, DNA sequences alone can’t explain the intricate biological mechanisms that contribute to many “genetic” diseases. RNA sequencing (RNA-Seq) efforts to identify the impact of different types of RNA on gene and protein expression have shed some light on the molecular mechanisms behind disease, but a knowledge gap remains.
Combining metabolomics datasets with genetic and/or transcriptomics datasets can complete the puzzle. For example, in prostate cancer, combining transcriptomics and metabolomics data has revealed that impaired sphingosine-1-phosphate receptor 2 signaling is the mechanism behind the specificity and sensitivity of sphingosine for distinguishing between prostate cancer and benign hyperplasia.4 Research such as this lays the foundation for biomarker discovery and application in the clinic for a range of diseases, not just cancer.
Histone modification is also a rather large slice of the academic research pie, because it plays such an integral role in life, from basic biological processes like DNA repair to complex physiologies like disease etiology.5 Several metabolomics studies have reported crucial relationships between metabolites and histone modifications, and how fluctuations in these relationships can impact diseases such as cancer.6 Basic research like this that provides mechanistic understanding is often the driver for drug discovery work, which can eventually make it to the clinic in the form of effective therapeutics.
No discussion of academic metabolomics studies would be complete without mentioning the human microbiome and its role in human health and disease. While the potential for leveraging the human microbiome to optimize human health has been recognized for several years, its therapeutic potential has been limited by knowledge gaps in precisely how the human microbiome interacts with its host. As we briefly touched on in Chapter 4 of this guide, by adding metabolomics components to their (traditionally DNA-based) studies, researchers are beginning to understand how the microbiome impacts our immune systems,7 digestive systems,8 metabolism,9 skin,10 and brain11 function.
Metabolomics studies on the microbiome are laying the basic research foundation necessary for pre-clinical and, eventually, clinical studies that could make testing and addressing the microbiome in the clinic as common as measuring fasting plasma glucose. For example, a collaboration of academic investigators used Metabolon’s services to link microbiome sequencing and metabolomics data in human twins to characterize microbiome-mediated differences in the incidence of food allergy.12 Their results suggest a critical protective role against food allergy provided by the microbiome which lasts beyond the infant stage. Follow-up studies will determine the precise molecular mechanisms behind this relationship, which could identify biomarkers for diagnosis and novel targets for therapeutics.
As an increasing number of researchers view multi-omics research, particularly the combination of transcriptomics and metabolomics, as a new standard for human disease research, the demand for multi-omics tools has also increased. These tools address the challenges faced by researchers integrating omics datasets.
Technique Development with Metabolomics
Metabolomics research also plays an important role in technique development, both by necessitating new research tools and techniques and by helping to support their development. A major area of development in the near term will be multi-omics tools capable of collecting, analyzing, and visualizing data. One of the biggest challenges facing the routine use of metabolomics datasets in many academic laboratories is the difficulty in combining metabolomics datasets with others, such as genetic and metatranscriptomic datasets.
New data analysis tools will address these challenges and fully realize the power of including metabolomics datasets in any research effort. As one group of researchers demonstrated, this could have significant implications for direct clinical translation.13 Using the Metabolon Discover Global Platform, the researchers identified several inflammatory pathways associated with childhood tuberculosis, and by combining this data with transcriptomics data, they accurately identified treatment responses and improved the interpretation of metabolic biomarkers in children with confirmed TB.
Metabolomics analyses can also support the development and use of novel animal models for furthering our understanding of disease and how to prevent or treat it. For example, researchers compared the plasma metabolomes of humans with a murine model of cardiac arrest to prove that their animal model metabolically replicates human disease and is therefore suitable for translational research on cardiac arrest.14 This, of course, is just one example.
What’s Next for Academic Applications of Metabolomics?
This chapter has more deeply explored how metabolomics contributes to academic research, which, inevitably, eventually translates to the clinic. In the next chapter, we’ll explore how metabolomics datasets are used for commercial efforts, including in drug development, personal care and cosmetics, and nutrition.
1. Wishart DS, Tzur D, Knox C, et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007;35(Database issue):D521-D526. doi:10.1093/nar/gkl923
2. Visconti A, Le Roy CI, Rosa F, et al. Interplay between the human gut microbiome and host metabolism. Nat Commun. 2019;10(1):4505. Published 2019 Oct 3. doi:10.1038/s41467-019-12476-z
3. Noronha A, Modamio J, Jarosz Y, et al. The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res. 2019;47(D1):D614-D624. doi:10.1093/nar/gky992
4. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics Data Integration, Interpretation, and Its Application. Bioinform Biol Insights. 2020;14:1177932219899051. Published 2020 Jan 31. doi:10.1177/1177932219899051
5. Greer EL, Shi Y. Histone methylation: a dynamic mark in health, disease and inheritance. Nat Rev Genet. 2012;13(5):343-357. Published 2012 Apr 3. doi:10.1038/nrg3173
6. Simithy J, Sidoli S, Garcia BA. Integrating Proteomics and Targeted Metabolomics to Understand Global Changes in Histone Modifications. Proteomics. 2018;18(18):e1700309. doi:10.1002/pmic.201700309
7. Zheng D, Liwinski T, Elinav E. Interaction between microbiota and immunity in health and disease. Cell Res. 2020;30(6):492-506. doi:10.1038/s41422-020-0332-7
8. Vernocchi P, Del Chierico F, Putignani L. Gut Microbiota Metabolism and Interaction with Food Components. Int J Mol Sci. 2020;21(10):3688. Published 2020 May 23. doi:10.3390/ijms21103688
9. Jin Q, Black A, Kales SN, Vattem D, Ruiz-Canela M, Sotos-Prieto M. Metabolomics and Microbiomes as Potential Tools to Evaluate the Effects of the Mediterranean Diet. Nutrients. 2019;11(1):207. Published 2019 Jan 21. doi:10.3390/nu11010207
10. Roux PF, Oddos T, Stamatas G. Deciphering the Role of Skin Surface Microbiome in Skin Health: An Integrative Multiomics Approach Reveals Three Distinct Metabolite‒Microbe Clusters. J Invest Dermatol. 2022;142(2):469-479.e5. doi:10.1016/j.jid.2021.07.159
11. Lai Y, Liu CW, Yang Y, Hsiao YC, Ru H, Lu K. High-coverage metabolomics uncovers microbiota-driven biochemical landscape of interorgan transport and gut-brain communication in mice. Nat Commun. 2021;12(1):6000. Published 2021 Oct 19. doi:10.1038/s41467-021-26209-8
12. Bao R, Hesser LA, He Z, Zhou X, Nadeau KC, Nagler CR. Fecal microbiome and metabolome differ in healthy and food-allergic twins. J Clin Invest. 2021;131(2):e141935. doi:10.1172/JCI141935
13. Dutta NK, Tornheim JA, Fukutani KF, et al. Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children. Sci Rep. 2020;10(1):19527. Published 2020 Nov 11. doi:10.1038/s41598-020-75513-8
14. Shoaib M, Choudhary RC, Choi J, et al. Plasma metabolomics supports the use of long-duration cardiac arrest rodent model to study human disease by demonstrating similar metabolic alterations. Sci Rep. 2020;10(1):19707. Published 2020 Nov 12. doi:10.1038/s41598-020-76401-x
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