Application of metabolomics in inflammatory demyelinating diseases of the central nervous system
Keywords: neuromyelitis optica spectrum disroder
secondary progressive
serum
cerebrospinal fluid
multiple sclerosis
delayed-type hypersensitivity
relapsing-remitting
animal model
relapse
metabolites
metabolomics
inflammatory demyelination

The prototypical central nervous system (CNS) inflammatory demyelinating disease (IDD) is multiple sclerosis (MS), which is a complex, immune-mediated disease involving genetic and environmental factors, both contributing and interacting, to produce the underlying immunopathogenesis. The inherent pathogenic heterogeneity within MS makes it challenging for a single candidate biofluid marker to reflect all the underlying disease processes, both neuroinflammatory and neurodegenerative, at the same time. Thus, the combination of biofluid markers into a composite biomarker, i.e. a biomarker signature, is a feasible approach to provide a summative representation of various disease processes.

Metabolomics is well-placed for this type of summative approach as a single metabolomics experiment provides hundreds (if not thousands) of metabolites at one go, i.e. a metabolic snapshot, which represents the downstream products of all neuroinflammatory and degenerative processes. Against this background, this thesis explores whether the combination of 1H nuclear magnetic resonance (NMR) spectroscopy-based metabolomics with multivariate dimensionality reduction modelling can be used to construct discriminatory models to inform on the diagnosis, disease activity and progression of CNS IDD, in particular MS, with inference to the underlying pathophysiological processes.

The results in this thesis demonstrated that it is possible to use blood metabolomics to provide molecular validation to phenotypic clusters, derived from pattern recognition modelling, within antibody-negative CNS IDD patients, and in so doing, identified individuals who likely have MS and those with antibody-mediated CNS pathology. In a focal rat model of MS, both the blood and CSF metabolome were significantly perturbed during acute neuroinflammation and these changes evolved over time. As the model does not produce overt clinical signs, these metabolic signatures, and indeed individual metabolites, provide pathogenic insights that are solely attributable to MS-like lesion formation. Drawing from these observations, the same methodology was applied to MS patients who are in relapse. Using serum metabolomics, it is possible to differentiate patients in relapse from patients who have no evidence of clinical inflammatory activity. Similar to that observed in rodents, the metabolic differences decreased with time away from relapse and specific metabolite biomarkers could be identified. These were then found to be applicable in an individualised manner, differentiating between relapse and remission status. The results reported in this thesis also illustrate that serum metabolomics was able to distinguish between relapsing-remitting MS (RRMS) from its secondary progressive (SPMS) phase, validating the previous work done by the Oxford group. In addition to the patient-based studies, this thesis also describes studies performed to validate the methodologies used in relation to sample-handling variations that are commonly encountered in the clinic. It was found that the accuracy of this RRMS vs. SPMS metabolomics test was maintained despite these variations, supporting its clinical applicability.

In summary, these results show that metabolomics has substantial potential in precision medicine in CNS IDD by: (1) aiding in CNS IDD diagnosis, (2) providing early detection of active neuroinflammation, and (3) allowing for the objective definition of SPMS.