“…For example, carnitine, which has anti-inflammatory and antioxidative properties, showed significantly lower levels in individuals with MS ( Figure S1 ; Mann-Whitney U test, p < 0.007 after 5% FDR correction), as shown previously by Kasakin et al. 36 Figure 3 Global differences between serum metabolites of individuals with MS and healthy controls (A) Volcano plot of serum metabolites. The data are plotted as log (base 2) fold change versus the −log (base 10) of the adjusted p value (computed with Mann-Whitney U test).…”
Section: Resultssupporting
confidence: 75%
“…Some of the metabolites we detected here to be significantly different in individuals with MS replicated previous results, supporting the validity of our results. These include carnitine, which we found to be lower in individuals with MS 36 and which is an essential component in mitochondrial energy production and involved in transporting lipids into mitochondria for β-oxidation. People with a mutation in carnitine palmitoyl transferase 1A (CPT1A) are at lower risk of developing MS, possibly because of downregulation of lipid metabolism.…”
Section: Discussionmentioning
confidence: 66%
“… 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 These studies vary in the number of individuals with MS who were followed from 7 22 to 60. 27 Other studies used targeted or untargeted serum metabolomics to identify metabolites that are altered in individuals with MS compared with healthy controls (their main findings are shown in Table S1 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ) or to distinguish MS from other neurological diseases. 39 , 40 , 41 …”
Summary
Multiple sclerosis (MS) is an immune-mediated disease whose precise etiology is unknown. Several studies found alterations in the microbiome of individuals with MS, but the mechanism by which it may affect MS is poorly understood. Here we analyze the microbiome of 129 individuals with MS and find that they harbor distinct microbial patterns compared with controls. To study the functional consequences of these differences, we measure levels of 1,251 serum metabolites in a subgroup of subjects and unravel a distinct metabolite signature that separates affected individuals from controls nearly perfectly (AUC = 0.97). Individuals with MS are found to be depleted in butyrate-producing bacteria and in bacteria that produce indolelactate, an intermediate in generation of the potent neuroprotective antioxidant indolepropionate, which we found to be lower in their serum. We identify microbial and metabolite candidates that may contribute to MS and should be explored further for their causal role and therapeutic potential.
“…For example, carnitine, which has anti-inflammatory and antioxidative properties, showed significantly lower levels in individuals with MS ( Figure S1 ; Mann-Whitney U test, p < 0.007 after 5% FDR correction), as shown previously by Kasakin et al. 36 Figure 3 Global differences between serum metabolites of individuals with MS and healthy controls (A) Volcano plot of serum metabolites. The data are plotted as log (base 2) fold change versus the −log (base 10) of the adjusted p value (computed with Mann-Whitney U test).…”
Section: Resultssupporting
confidence: 75%
“…Some of the metabolites we detected here to be significantly different in individuals with MS replicated previous results, supporting the validity of our results. These include carnitine, which we found to be lower in individuals with MS 36 and which is an essential component in mitochondrial energy production and involved in transporting lipids into mitochondria for β-oxidation. People with a mutation in carnitine palmitoyl transferase 1A (CPT1A) are at lower risk of developing MS, possibly because of downregulation of lipid metabolism.…”
Section: Discussionmentioning
confidence: 66%
“… 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 These studies vary in the number of individuals with MS who were followed from 7 22 to 60. 27 Other studies used targeted or untargeted serum metabolomics to identify metabolites that are altered in individuals with MS compared with healthy controls (their main findings are shown in Table S1 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ) or to distinguish MS from other neurological diseases. 39 , 40 , 41 …”
Summary
Multiple sclerosis (MS) is an immune-mediated disease whose precise etiology is unknown. Several studies found alterations in the microbiome of individuals with MS, but the mechanism by which it may affect MS is poorly understood. Here we analyze the microbiome of 129 individuals with MS and find that they harbor distinct microbial patterns compared with controls. To study the functional consequences of these differences, we measure levels of 1,251 serum metabolites in a subgroup of subjects and unravel a distinct metabolite signature that separates affected individuals from controls nearly perfectly (AUC = 0.97). Individuals with MS are found to be depleted in butyrate-producing bacteria and in bacteria that produce indolelactate, an intermediate in generation of the potent neuroprotective antioxidant indolepropionate, which we found to be lower in their serum. We identify microbial and metabolite candidates that may contribute to MS and should be explored further for their causal role and therapeutic potential.
“…These included hydrocortisone, glutamic acid, tryptophan, eicosapentaenoic acid, 13S-hydroxyoctadecadienoic acid, lysophosphatidylcholines, and lysophosphatidylethanolamines. Similarly, another study profiled 13 amino acids and 29 acylcarnitines in plasma acquired from RRMS patients during disease relapse using a targeted metabolomics approach with LC-MS/MS [71]. The general linear regression and random forest model generated by the data indicated a significant increase in the levels of glutamate coupled with a decrease in leucine-isoleucine and decenoylcarnitine in patient samples, suggesting acylcarnitines as novel biomarker candidates for MS.…”
Multiple sclerosis (MS) is an inflammatory demyelinating disease of the nervous system that primarily affects young adults. Although the exact etiology of the disease remains obscure, it is clear that alterations in the metabolome contribute to this process. As such, defining a reliable and disease-specific metabolome has tremendous potential as a diagnostic and therapeutic strategy for MS. Here, we provide an overview of studies aimed at identifying the role of metabolomics in MS. These offer new insights into disease pathophysiology and the contributions of metabolic pathways to this process, identify unique markers indicative of treatment responses, and demonstrate the therapeutic effects of drug-like metabolites in cellular and animal models of MS. By and large, the commonly perturbed pathways in MS and its preclinical model include lipid metabolism involving alpha-linoleic acid pathway, nucleotide metabolism, amino acid metabolism, tricarboxylic acid cycle, d-ornithine and d-arginine pathways with collective role in signaling and energy supply. The metabolomics studies suggest that metabolic profiling of MS patient samples may uncover biomarkers that will advance our understanding of disease pathogenesis and progression, reduce delays and mistakes in diagnosis, monitor the course of disease, and detect better drug targets, all of which will improve early therapeutic interventions and improve evaluation of response to these treatments.
“…As metabolites are the biological end products of upstream processes involving gene and protein expression, the metabolome closely reflects the clinical phenotype and, thus, can provide valuable insight in to underlying pathological processes and identify novel biomarkers of disease. The majority of metabolomics studies in MS focus on the identification of blood-borne metabolite perturbations in MS patients relative to healthy controls using both nuclear magnetic resonance (NMR)-based 6 – 8 and mass spectrometry-based metabolomics 9 – 12 . While this is useful in aiding our understanding of MS disease activity as a whole, the distinction between MS and controls is not a clinical diagnostic challenge.…”
The transition from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS) represents a huge clinical challenge. We previously demonstrated that serum metabolomics could distinguish RRMS from SPMS with high diagnostic accuracy. As differing sample-handling protocols can affect the blood metabolite profile, it is vital to understand which factors may influence the accuracy of this metabolomics-based test in a clinical setting. Herein, we aim to further validate the high accuracy of this metabolomics test and to determine if this is maintained in a 'real-life' clinical environment. Blood from 31 RRMS and 28 SPMS patients was subjected to different sample-handling protocols representing variations encountered in clinics. The effect of freeze-thaw cycles (0 or 1) and time to erythrocyte removal (30, 120, or 240 min) on the accuracy of the test was investigated. For test development, samples from the optimised protocol (30 min standing time, 0 freeze-thaw) were used, resulting in high diagnostic accuracy (mean ± SD, 91.0 ± 3.0%). This test remained able to discriminate RRMS and SPMS samples that had experienced additional freeze-thaw, and increased standing times of 120 and 240 min with accuracies ranging from 85.5 to 88.0%, because the top discriminatory metabolite biomarkers from the optimised protocol remained discriminatory between RRMS and SPMS despite these sample-handling variations. In conclusion, while strict sample-handling is essential for the development of metabolomics-based blood tests, the results confirmed that the RRMS vs. SPMS test is resistant to sample-handling variations and can distinguish these two MS stages in the clinics.
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