Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

Abstract

Metabolomics-based technologies map in vivo biochemical changes that may be used as early indicators of pathological abnormalities prior to the development of clinical symptoms in neurological conditions. Metabolomics may also reveal biochemical pathways implicated in tissue dysfunction and damage and thus assist in the development of novel targeted therapeutics for neuroinflammation and neurodegeneration. Metabolomics holds promise as a non-invasive, high-throughput and cost-effective tool for early diagnosis, follow-up and monitoring of treatment response in multiple sclerosis (MS), in combination with clinical and imaging measures. In this review, we offer evidence in support of the potential of metabolomics as a biomarker and drug discovery tool in MS. We also use pathway analysis of metabolites that are described as potential biomarkers in the literature of MS biofluids to identify the most promising molecules and upstream regulators, and show novel, still unexplored metabolic pathways, whose investigation may open novel avenues of research.

Keywords: MRI; biofluids; biomarkers; disease modifying treatment; metabolomics; multiple sclerosis.

Figure 1

Orange boxes indicate pathophysiological triggers of tissue damage. Light cyan boxes describe metabolic pathways, in which metabolites from the dataset are involved. These metabolic pathways are involved in MS pathophysiology through alteration of biological functions such as small molecule biochemistry, controls of cell death, metabolic disease and cell to cell signalling. Dark orange boxes represent the endpoint of the metabolic cascades that lead to tissue damage and death. Upstream regulators, i.e., molecular regulator of metabolite expression, as identified by our pathway analysis, can promote tissue damage and death directly or through an activation of the immune system. These regulators and their effects are indicated in green (for huntingtin, an endogenous product) and in yellow (for curcumin, an exogenous product).

Figure 2

Merged networks based on inter-metabolite connections from the total matrix. Red and green shapes indicate genes significantly increased or decreased in expression in MS patients, whereas the number below represents the fold change log. The relationship between genes may lead to direct (solid lines) or indirect interaction (dashed lines). Yellow shapes indicate proteins and metabolites from the total matrix.

Figure 3

Merged networks based on inter-metabolite connections from the cerebrospinal fluid (CSF) matrix. Red and green shapes indicate genes significantly increased and decreased in expression in MS patients, whereas the number below represents the fold change log. The relationship between genes may lead to direct (solid lines) or indirect interaction (dashed lines). Light blue shapes indicate proteins and metabolites from the CSF matrix.

Figure 4

Merged networks based on inter-metabolite connections from the blood matrix. Red shapes indicate genes significantly decreased in expression in MS, whereas the number below represents the fold change log. The relationship between genes may lead to direct (solid lines) or indirect interaction (dashed lines). Light orange shapes indicate proteins and metabolites from the blood matrix.