Metabolomics is an exciting area of study that is redefining our understanding of diseases such as multiple sclerosis (MS). It offers important clues on the metabolic pathways that contribute to the pathophysiology of the disease.
What is metabolomics? First, let’s examine a closely related word to define metabolomics in its fullest sense. A metabolome is a small molecule chemical entity involved in metabolic processes. Metabolomes have traditionally been studied to identify biomarkers involved in driving pathological biological processes.
Liu and colleagues in the Biomedical Journal provided an excellent definition of metabolomics: “Metabolomics is a promising technique that explores small molecules in various biological matrices including cells, biofluids such as serum, plasma, cerebrospinal fluid, urine, excrement, tissue, and exhaled gas.”
And its uses? “It targets small molecules and can provide information not readily obtained from genomics, transcriptomics, or proteomics,” they wrote. ”It can also offer new insights into disease mechanisms by identifying metabolic pathways that are perturbed.”
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The rise in metabolomics has allowed scientists to more quickly discover active metabolites that are implicated in disease processes, potentially leading to paradigm shifts in treatment development. In Nature Reviews Molecular Cell Biology, Rinschen and colleagues wrote, “With the advent and evolution of metabolomics’ technologies, the discovery of active metabolites that have the capability to change cell physiology has grown rapidly.”
Applications in Clinical Study
To better help us understand how metabolomics work in real-world clinical studies, we can refer to flow charts provided by Liu et al in their review article. The first step is to design an experiment in such a way that the data obtained can be interpreted in a clear, straightforward manner. This would, for example, involve identifying any metabolites of interest at the very start of the clinical study.
The next process would be to analyze the biological material collected. Liu and colleagues wrote, “Almost all biological materials, including biofluids, cells, tissue, and feces, can be analyzed via metabolomics.” The collection and storage of these samples must be done according to the highest standards.
“Most studies examine the metabolites present in the biological matrix at a particular time point, thus providing a snapshot of the metabolome,” wrote the authors of this study. However, they continued, “It is now possible to introduce a metabolite with an isotope (13C) into a biological system and then measure the concentrations of metabolites containing the isotope, thus enabling a more dynamic assessment of specific metabolic pathways.”
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We will take one real-world example to elucidate how this is carried out. Lazzarino and colleagues conducted a study involving the collection of blood samples from patients with MS to identify the compounds related to mitochondrial energy metabolism.
In their study, they found that 9 of the 15 compounds assayed (hypoxanthine, xanthine, uric acid, inosine, uracil, β-pseudouridine, uridine, creatinine, and lactate) differed significantly between patients with MS and the control group. Lazzarino and colleagues then used these 9 compounds to create a unified Biomarker Score. They noted that a higher score was associated with an increase in disability.
The significance of this study was the extent to which metabolomics was involved in helping them arrive at their conclusion. In the study, they actively examined energy metabolism, xanthine metabolism, and nucleotide metabolism.
Insights Into Disease Pathogenesis
The study of metabolites can provide us with important insights into what drives disease progression. “Metabolites drive pivotal covalent chemical modifications of DNA and RNA (such as methylation) and of proteins (post-translational modifications),“ Rinschen and colleagues wrote. “The dynamic shape of these chemical modifications has been shown to significantly affect cellular function.”
This makes the work of identifying metabolites involved in the pathogenesis of a disease that much more important. Historically, this meant using biochemical approaches to detect, identify, and quantify metabolites. However, newer technologies offer scientists the chance to do so more quickly and on a larger scale.
“A metabolome-based strategy for identifying candidates with biological activity would use a list of metabolites generated from statistical analysis of metabolomic datasets. Once metabolite abundance is quantified based on peak intensity, statistical filters can be adjusted depending on the experiment and data,” Rinschen et al wrote.
Taking a step further would entail metabolic activity screening strategies. This may not always be feasible, given the high costs attached and the specialized laboratories needed. However, technological advancements offer us a buffet of choices on how this can be carried out.
Rinschen and colleagues wrote, “Identifying active metabolites that modulate phenotype can be achieved through multiple strategies. There is a host of examples where metabolomics in combination with orthogonal molecular biology and computational approaches has been successfully used to identify active metabolites.”
Returning to the application of metabolomics in multiple sclerosis, studies have increasingly demonstrated the promising role that it can play in the diagnosis and the prediction of the prognosis of a patient with multiple sclerosis. Given that currently available tests for multiple sclerosis have a long list of limitations, this is welcome news. Liu and colleagues wrote that “metabolite variations are sensible to both genetic and environmental factors contributing to the occurrence of multiple sclerosis” and that “metabolomics is known to provide evidence for exhibiting individual differences in patients, which makes the precise treatment possible.”
Liu Z, Jeffrey W, Rui B. Metabolomics as a promising tool for improving understanding of multiple sclerosis: a review of recent advances. Biomed J. Published online January 15, 2022. doi:10.1016/j.bj.2022.01.004
Rinschen MM, Ivanisevic J, Giera M, Siuzdak G. Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol. 2019;20(6):353-367. doi:10.1038/s41580-019-0108-4
Lazzarino G, Amorini AM, Petzold A, et al. Serum compounds of energy metabolism impairment are related to disability, disease course and neuroimaging in multiple sclerosis. Mol Neurobiol. 2017;54(9):7520-7533. doi:10.1007/s12035-016-0257-9