PLoS One. 2016 Nov 17;11(11):e0166277. doi: 10.1371/journal.pone.0166277. eCollection 2016.
Disease Type- and Status-Specific Alteration of CSF Metabolome Coordinated with Clinical Parameters in Inflammatory Demyelinating Diseases of CNS.
Central nervous system (CNS) inflammatory demyelinating diseases (IDDs) are a group of disorders with different aetiologies, characterized by inflammatory lesions. These disorders include multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and idiopathic transverse myelitis (ITM). Differential diagnosis of the CNS IDDs still remains challenging due to frequent overlap of clinical and radiological manifestation, leading to increased demands for new biomarker discovery. Since cerebrospinal fluid (CSF) metabolites may reflect the status of CNS tissues and provide an interfacial linkage between blood and CNS tissues, we explored multi-component biomarker for different IDDs from CSF samples using gas chromatography mass spectrometry-based metabolite profiling coupled to multiplex bioinformatics approach. We successfully constructed the single model with multiple metabolite variables in coordinated regression with clinical characteristics, expanded disability status scale, oligoclonal bands, and protein levels. The multi-composite biomarker simultaneously discriminated four different immune statuses (a total of 145 samples; 54 MS, 49 NMOSD, 30 ITM, and 12 normal controls). Furthermore, systematic characterization of transitional metabolic modulation identified relapse-associated metabolites and proposed insights into the disease network underlying type-specific metabolic dysfunctionality. The comparative analysis revealed the lipids, 1-monopalmitin and 1-monostearin were common indicative for MS, NMOSD, and ITM whereas fatty acids were specific for the relapse identified in all types of IDDs.
Figure: A – schematic representation of chemical categories of metabolites from the analysis of B) MS, C) neuromyelitis optica spectrum disorders, D) idiopathic transverse myelitis. Node colour represents up- (red) and down- (blue) regulation compared to the ones in controls, while the node size represents the fold changes among the metabolites.
In our eagerness to look at the brain we often overlook the biofluid which surrounds it, protects it from trauma, and provides sustenance when needed. As a biomarker aficionado, I’ve spent most of my graduate and postgraduate life studying the CSF; an average adult produces 500-600ml of CSF per day (at a rate of 0.3ml/min) with a total volume of 100-150ml, containing approximately 0.3 g/l of protein, as well as bicarbonate, carbon dioxide, glucose, sodium, potassium and chloride. It closely mirrors activities in the brain. And despite not being readily accessible, for this reason alone it is an important source of information about the brain during disease. To date, CSF biomarkers have contributed much to our understanding of the pathophysiology of MS.
In this work, Park et al. study the biochemical signature (metabolome) of the CSF in MS and other autoimmune/inflammatory disorders affecting the brain. They looked at 145 patients and control CSF and say that CSF metabolome mirrors the different metabolic dysfunction that occurs in the different disorders (see figure below). In MS, they found alteration in the amino acids (tyrosine, phenylalanine, leucine, isoleucine, valine, methionine, and proline) that were significantly decreased compared to normal controls. L-phenylalanine by the way is part of ‘Carrie Lodger’ regime (lofepramine, l-phenylalanine and vitamin B12) and thought to relieve the symptoms of MS, such as chronic pain and fatigue (Carrie Lodger passed away in 2009). When looking at differences between MS relapse and remission phases they found an increase in fatty acids during relapses; specifically saturated fatty acids. Attempts at modulating the ratio of body’s saturated and polyunsaturated fatty acids with omega-3 and -6 supplements is also felt to moderate immune cell activation.
Henceforth, CSF metabolic profiling is clearly an interesting area to explore further in MS. Some of what is reported in this study are not isolated discoveries but also reported by other researchers working in this area, which adds weight to their findings. I suspect however, owing to the stability of some of the chemicals studied, the usefulness of performing analysis on stored samples might be questionable and may not reflect real-time changes.