Metabolomic analysis identifies altered metabolic pathways in Multiple Sclerosis

Transcriptomic profiling identifies ferroptosis and NF-κB signaling involved in α-dimorphecolic acid regulation of microglial inflammation

BACKGROUND

Microglia-evoked neuroinflammation contributes to neurodegenerative diseases such as multiple sclerosis (MS). Metabolic reprogramming, including changes in polyunsaturated fatty acids (PUFAs), plays a critical role in MS pathophysiology. Previous studies identified reduced plasma α-dimorphecolic acid (α-DIPA), a linoleic acid derivative, in MS patients. This study investigated the anti-inflammatory effects of α-DIPA on microglia and the underlying pathways.

METHODS

Lipopolysaccharide (LPS)-induced BV-2 microglial inflammation was used as an in vitro model. α-DIPA effects were assessed via ELISA for nitric oxide (NO) release, flow cytometry was used to examine cell proliferation, activation and polarization, and transcriptomic analysis was applied to identify key signaling pathways regulated by α-DIPA.

RESULTS

ELISA results showed that exogenous α-DIPA treatment significantly inhibited LPS-induced NO release from BV-2 cells in a concentration-dependent manner. Moreover, flow cytometry analysis suggested that 40 µM α-DIPA treatment significantly repressed LPS-induced BV-2 cell proliferation, activation, as well as M1 and M2 type polarization. Furthermore, transcriptome analysis revealed that exogenous α-DIPA extensively and drastically decreased the transcriptional level of numerous genes that are involved in the regulation of inflammatory responses, for instance, proinflammatory genes such as Tnf and Ccl3 related to IL-17 and TNF-α signaling. In addition, we also observed that the expression of multiple genes in NF-κB signaling were also inhibited greatly by α-DIPA, such as Nfkb2 and Nfkbia. Notably, α-DIPA robustly suppressed LPS-induced mRNA expression of abundant genes participating in the ferroptosis pathway, including Acsl4, Slc7a11, Me1, and Hmox1. Interestingly, the expressions of multiple ferroptosis-related genes were regulated specifically by α-DIPA but not LPS, such as Acsl5, Acsl6, Alox5, Cars, Dpp3, Dpp10, Slc2a5, and Slc7a1.

CONCLUSION

α-DIPA inhibits microglial inflammation likely through regulating the pathways of the ferroptosis and NF-κB signaling. These results provided preliminary evidence for α-DIPA as a potential therapeutic candidate for neurodegenerative diseases like MS.

Recent Advances in Metabolomics and Lipidomics Studies in Human and Animal Models of Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative and inflammatory disease of the central nervous system (CNS) that leads to a loss of myelin. There are three main types of MS: relapsing-remitting MS (RRMS) and primary and secondary progressive disease (PPMS, SPMS). The differentiation in the pathogenesis of these two latter courses is still unclear. The underlying mechanisms of MS are yet to be elucidated, and the treatment relies on immune-modifying agents. Recently, lipidomics and metabolomics studies using human biofluids, mainly plasma and cerebrospinal fluid (CSF), have suggested an important role of lipids and metabolites in the pathophysiology of MS. In this review, the results from studies on metabolomics and lipidomics analyses performed on biological samples of MS patients and MS-like animal models are presented and analyzed. Based on the collected findings, the biochemical pathways in human and animal cohorts involved were investigated and biological mechanisms and the potential role they have in MS are discussed. Limitations and challenges of metabolomics and lipidomics approaches are presented while concluding that metabolomics and lipidomics may provide a more holistic approach and provide biomarkers for early diagnosis of MS disease.

NMR-based metabolomics identification of potential serum biomarkers of disease progression in patients with multiple sclerosis

Multiple sclerosis (MS) is a chronic and progressive neurological disorder, characterized by neuroinflammation and demyelination within the central nervous system (CNS). The etiology and the pathogenesis of MS are still unknown. Till now, no satisfactory treatments, diagnostic and prognostic biomarkers are available for MS. Therefore, we aimed to investigate metabolic alterations in patients with MS compared to controls and across MS subtypes. Metabolic profiles of serum samples from patients with MS (n = 90) and healthy control (n = 30) were determined by Nuclear Magnetic Resonance (1H-NMR) Spectroscopy using cryogenic probe. This approach was also utilized to identify significant differences between the metabolite profiles of the MS groups (primary progressive, secondary progressive, and relapsing-remitting) and the healthy controls. Concentrations of nine serum metabolites (adenosine triphosphate (ATP), tryptophan, formate, succinate, glutathione, inosine, histidine, pantothenate, and nicotinamide adenine dinucleotide (NAD)) were significantly higher in patients with MS compared to control. SPMS serum exhibited increased pantothenate and tryptophan than in PPMS. In addition, lysine, myo-inositol, and glutamate exhibited the highest discriminatory power (0.93, 95% CI 0.869-0.981; 0.92, 95% CI 0.859-0.969; 0.91, 95% CI 0.843-0.968 respectively) between healthy control and MS. Using NMR- based metabolomics, we identified a set of metabolites capable of classifying MS patients and controls. These findings confirmed untargeted metabolomics as a useful approach for the discovery of possible novel biomarkers that could aid in the diagnosis of the disease.

Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

Metabolomics of Cerebrospinal Fluid Amino and Fatty Acids in Early Stages of Multiple Sclerosis

Multiple sclerosis (MS) is a demyelinating and neurodegenerative autoimmune disease of the central nervous system (CNS) damaging myelin and axons. Diagnosis is based on the combination of clinical findings, magnetic resonance imaging (MRI) and analysis of cerebrospinal fluid (CSF). Metabolomics is a systematic study that allows us to track amounts of different metabolites in a chosen medium. The aim of this study was to establish metabolomic differences between the cerebrospinal fluid of patients in the early stages of multiple sclerosis and healthy controls, which could potentially serve as markers for predicting disease activity. We collected CSF from 40 patients after the first attack of clinical symptoms who fulfilled revised McDonald criteria of MS, and the CSF of 33 controls. Analyses of CSF samples were performed by using the high-performance liquid chromatography system coupled with a mass spectrometer with a high-resolution detector. Significant changes in concentrations of arginine, histidine, spermidine, glutamate, choline, tyrosine, serine, oleic acid, stearic acid and linoleic acid were observed. More prominently, Expanded Disability Status Scale values significantly correlated with lower concentrations of histidine. We conclude that these metabolites could potentially play a role as a biomarker of disease activity and predict presumable inflammatory changes.

Characterizing causal relationships of visceral fat and body shape on multiple sclerosis risk

BACKGROUND

Epidemiologic studies have established obesity as a risk factor for multiple sclerosis (MS). These studies relied on body-mass index (BMI) and body size silhouettes as the primary measures of obesity. Unfortunately, the causal mechanisms through which obesity confers MS risk are not yet known.

OBJECTIVES

To investigate the causal effects of multiple specific measures of body fat on MS risk in populations of European descent, using Mendelian randomization (MR).

METHODS

MR is a genetic instrumental variable analysis utilizing genome-wide association (GWA) summary statistics to infer causality between phenotypes. MR analyses were performed to investigate the relationships between seven measures of body fat (BMI, waist-hip ratio, visceral adipose tissue [VAT], subcutaneous adipose tissue, and arm-, leg-, and trunk-fat to total body fat ratio) and MS risk.

RESULTS

Only BMI and VAT were significantly associated with MS risk in separate MR analyses (βBMI=0.27, pBMI<0.001; βVAT=0.28, pVAT=0.006). High correlation between BMI and VAT instruments suggest that two-sample MR associations for BMI and VAT likely capture the same causal mechanisms.

CONCLUSIONS

BMI and VAT were causally associated with MS risk in European populations, though their effects do not appear independent, suggesting overlap in the role of overall body mass and visceral obesity in MS pathogenesis.

Metabolomic Changes in Patients Affected by Multiple Sclerosis and Treated with Fingolimod

Current treatment for Multiple Sclerosis (MS) consists of a multidisciplinary approach including disease-modifying therapies. The response to treatment is heterogeneous, representing a crucial challenge in the classification of patients. Metabolomics is an innovative tool that can identifies biomarkers/predictors of treatment response. We aimed to evaluate plasma metabolic changes in a group of naïve Relapsing-Remitting MS patients starting Fingolimod treatment, to find specific metabolomic features that predict the therapeutic response as well as the possible side effects. The study included 42 Relapsing-Remitting MS blood samples, of which 30 were classified as responders after two years of FINGO treatment, whereas 12 patients were Not-Responders. For fifteen patients, samples were collected at four time points: before starting the therapy; at six months after the initiation; at twelve months after; and at twenty-four months after initiation. The serum was analysed through Nuclear Magnetic Resonance and multivariate and univariate statistical analysis. Considering the single comparison between each time point, it was possible to identify a set of metabolites changing their concentrations based on the drug intake. FINGO influences aminoacidic and energy metabolisms and reduces oxidative stress and the activity of the immune system, both typical features of MS.

Metabolomics as a promising tool for improving understanding of multiple sclerosis: A review of recent advances

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system that usually affects young adults. The development of MS is closely related to the changes in the metabolome. Metabolomics studies have been performed using biofluids or tissue samples from rodent models and human patients to reveal metabolic alterations associated with MS progression. This review aims to provide an overview of the applications of metabolomics that for the investigations of the perturbed metabolic pathways in MS and to reveal the potential of metabolomics in personalizing treatments. In conclusion, informative variations of metabolites can be potential biomarkers in advancing our understanding of MS pathogenesis for MS diagnosis, predicting the progression of the disease, and estimating drug effects. Metabolomics will be a promising technique for improving clinical care in MS.

Metabolomics of Cerebrospinal Fluid in Multiple Sclerosis Compared With Healthy Controls: A Pilot Study

Background

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) leading to the loss of myelin and axons. Diagnosis is based on clinical findings, MRI, and analysis of cerebrospinal fluid (CSF). CSF is an ultrafiltrate of plasma and reflects inflammatory processes in the CNS. The aim of this study was to perform metabolomics analysis of CSF in patients after the first attack of MS and healthy controls and try to find new specific analytes for MS including those potentially predicting disease activities at the onset.

Methods

We collected CSF from 19 patients (16 females, aged 19–55 years) after the first attack of clinical symptoms who fulfilled revised McDonald criteria of MS and CSF of 19 controls (16 females, aged 19–50 years). Analyses of CSF samples were provided using the high-performance liquid chromatography system coupled with a mass spectrometer with a high-resolution detector (TripleTOF 5600, AB Sciex, Canada).

Results

Approximately 130 selected analytes were identified, and 30 of them were verified. During the targeted analysis, a significant decrease in arginine and histidine and a less significant decrease in the levels of asparagine, leucine/isoleucine, and tryptophan, together with a significant increase of palmitic acid in the patient group, were found.

Conclusion

We observed significant differences in amino and fatty acids in the CSF of newly diagnosed patients with MS in comparison with controls. The most significant changes were observed in levels of arginine, histidine, and palmitic acid that may predict inflammatory disease activity. Further studies are necessary to support these findings as potential biomarkers of MS.

Impaired metabolism of oligodendrocyte progenitor cells and axons in demyelinated lesion and in the aged CNS

The key pathology of multiple sclerosis (MS) comprises demyelination, axonal damage, and neuronal loss, and when MS develops into the progressive phase it is essentially untreatable. Identifying new targets in both axons and oligodendrocyte progenitor cells (OPCs) and rejuvenating the aged OPCs holds promise for this unmet medical need. We summarize here the recent evidence showing that mitochondria in both axons and OPCs are impaired, and lipid metabolism of OPCs within demyelinated lesion and in the aged CNS is disturbed. Given that emerging evidence shows that rewiring cellular metabolism regulates stem cell aging, to protect axons from degeneration and promote differentiation of OPCs, we propose that restoring the impaired metabolism of both OPCs and axons in the aged CNS in a synergistic way could be a promising strategy to enhance remyelination in the aged CNS, leading to novel drug-based approaches to treat the progressive phase of MS.

In Vivo Assessment of the Ameliorative Impact of Some Medicinal Plant Extracts on Lipopolysaccharide-Induced Multiple Sclerosis in Wistar Rats

Multiple sclerosis is a chronic autoimmune disorder that leads to the demyelination of nerve fibers, which is the major cause of non-traumatic disability all around the world. Herbal plants Nepeta hindustana L., Vitex negundo L., and Argemone albiflora L., in addition to anti-inflammatory and anti-oxidative effects, have shown great potential as neuroprotective agents. The study was aimed to develop a neuroprotective model to study the effectiveness of herbal plants (N. hindustana, V. negundo, and A. albiflora) against multiple sclerosis. The in vivo neuroprotective effects of ethanolic extracts isolated from N. hindustana, V. negundo, and A. albiflora were evaluated in lipopolysaccharides (LPS) induced multiple sclerosis Wistar rat model. The rat models were categorized into seven groups including group A as normal, B as LPS induced diseased group, while C, D, E, F, and G were designed as treatment groups. Histopathological evaluation and biochemical markers including stress and inflammatory (MMP-6, MDA, TNF-α, AOPPs, AGEs, NO, IL-17 and IL-2), antioxidant (SOD, GSH, CAT, GPx), DNA damage (Isop-2α, 8OHdG) as well as molecular biomarkers (RAGE, Caspase-8, p38) along with glutamate, homocysteine, acetylcholinesterase, and myelin binding protein (MBP) were investigated. The obtained data were analyzed using SPSS version 21 and GraphPad Prism 8.0. The different extract treated groups (C, D, E, F, G) displayed a substantial neuroprotective effect regarding remyelination of axonal terminals and oligodendrocytes migration, reduced lymphocytic infiltrations, and reduced necrosis of Purkinje cells. The levels of stress, inflammatory, and DNA damage markers were observed high in the diseased group B, which were reduced after treatments with plant extracts. The antioxidant activity was significantly reduced in diseased induced group B, however, their levels were raised after treatment with plant extract. Group F (a mélange of all the extracts) showed the most significant change among all other treatment groups (C, D, E, G). The communal dose of selected plant extracts regulates neurodegeneration at the cellular level resulting in restoration and remyelination of axonal neurons. Moreover, 400 mg/kg dose of three plants in conjugation (Group F) were found to be more effective in restoring the normal activities of all measured parameters than independent doses (Group C, D, E) and is comparable with standard drug nimodipine (Group G) clinically used for the treatment of multiple sclerosis. The present study, for the first time, reported the clinical evidence of N. hindustana, V. negundo, and A. albiflora against multiple sclerosis and concludes that all three plants showed remyelination as well neuroprotective effects which may be used as a potential natural neurotherapeutic agent against multiple sclerosis.

Altered Plasma Metabolic Profiles in Chinese Patients With Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune disease that leads to the demyelination of nerve axons. An increasing number of studies suggest that patients with MS exhibit altered metabolic profiles, which might contribute to the course of MS. However, the alteration of metabolic profiles in Chinese patients with MS and their potential roles in regulating the immune system remain elusive. In this study, we performed a global untargeted metabolomics approach in plasma samples from 22 MS-affected Chinese patients and 21 healthy subjects. A total of 42 differentially abundant metabolites (DAMs) belonging to amino acids, lipids, and carbohydrates were identified in the plasma of MS patients and compared with those in healthy controls. We observed an evident reduction in the levels of amino acids, such as L-tyrosine, L-isoleucine, and L-tryptophan, whereas there was a great increase in the levels of L-glutamic acid and L-valine in MS-affected patients. The levels of lipid and carbohydrate metabolites, such as sphingosine 1-phosphate and myo-inositol, were also reduced in patients with MS. In addition, the concentrations of proinflammatory cytokines, such as IL-17 and TNF-α, were significantly increased, whereas those of several anti-inflammatory cytokines and chemokines, such as IL-1ra, IL-7, and MIP-1α, were distinctly reduced in the plasma of MS patients compared with those in healthy subjects. Interestingly, some DAMs, such as L-tryptophan and sphingosine 1-phosphate, showed an evident negative correlation with changes in the level of TNF-α and IL-17, while tightly positively correlating with altered concentrations of anti-inflammatory cytokines and chemokines, such as MIP-1α and RANTES. Our results revealed that altered metabolomic profiles might contribute to the pathogenesis and course of MS disease by modulating immuno-inflammatory responses in the peripheral system, which is essential for eliciting autoimmune responses in the central nervous system, thus resulting in the progression of MS. This study provides potential clues for developing therapeutic strategies for MS in the near future.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

Objective biomarkers for clinical relapse in multiple sclerosis: a metabolomics approach

Accurate determination of relapses in multiple sclerosis is important for diagnosis, classification of clinical course and therapeutic decision making. The identification of biofluid markers for multiple sclerosis relapses would add to our current diagnostic armamentarium and increase our understanding of the biology underlying the clinical expression of inflammation in multiple sclerosis. However, there is presently no biofluid marker capable of objectively determining multiple sclerosis relapses although some, in particular neurofilament-light chain, have shown promise. In this study, we sought to determine if metabolic perturbations are present during multiple sclerosis relapses, and, if so, identify candidate metabolite biomarkers and evaluate their discriminatory abilities at both group and individual levels, in comparison with neurofilament-light chain. High-resolution global and targeted ¹H nuclear magnetic resonance metabolomics as well as neurofilament-light chain measurements were performed on the serum in four groups of relapsing-remitting multiple sclerosis patients, stratified by time since relapse onset: (i) in relapse (R); (ii) last relapse (LR) ≥ 1 month (M) to < 6 M ago; (iii) LR ≥ 6 M to < 24 M ago; and (iv) LR ≥ 24 M ago. Two hundred and one relapsing-remitting multiple sclerosis patients were recruited: R (n = 38), LR 1–6 M (n = 28), LR 6–24 M (n = 34), LR ≥ 24 M (n = 101). Using supervised multivariate analysis, we found that the global metabolomics profile of R patients was significantly perturbed compared to LR ≥ 24 M patients. Identified discriminatory metabolites were then quantified using targeted metabolomics. Lysine and asparagine (higher in R), as well as, isoleucine and leucine (lower in R), were shortlisted as potential metabolite biomarkers. ANOVA of these metabolites revealed significant differences across the four patient groups, with a clear trend with time since relapse onset. Multivariable receiver operating characteristics analysis of these four metabolites in discriminating R versus LR ≥ 24 M showed an area under the curve of 0.758, while the area under the curve for serum neurofilament-light chain was 0.575. Within individual patients with paired relapse–remission samples, all four metabolites were significantly different in relapse versus remission, with the direction of change consistent with that observed at group level, while neurofilament-light chain was not discriminatory. The perturbations in the identified metabolites point towards energy deficiency and immune activation in multiple sclerosis relapses, and the measurement of these metabolites, either singly or in combination, are useful as biomarkers to differentiate relapse from remission at both group and individual levels.

Multi-omic evaluation of metabolic alterations in multiple sclerosis identifies shifts in aromatic amino acid metabolism

The circulating metabolome provides unique insights into multiple sclerosis (MS) pathophysiology, but existing studies are relatively small or characterized limited metabolites. We test for differences in the metabolome between people with MS (PwMS; n = 637 samples) and healthy controls (HC; n = 317 samples) and assess the association between metabolomic profiles and disability in PwMS. We then assess whether metabolic differences correlate with changes in cellular gene expression using publicly available scRNA-seq data and whether identified metabolites affect human immune cell function. In PwMS, we identify striking abnormalities in aromatic amino acid (AAA) metabolites (p = 2.77E-18) that are also strongly associated with disability (p = 1.01E-4). Analysis of scRNA-seq data demonstrates altered AAA metabolism in CSF and blood-derived monocyte cell populations in PwMS. Treatment with AAA-derived metabolites in vitro alters monocytic endocytosis and pro-inflammatory cytokine production. We identify shifts in AAA metabolism resulting in the reduced production of immunomodulatory metabolites and increased production of metabotoxins in PwMS.

Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

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.

Metabolomics Analysis of Amniotic Fluid in Euploid Foetuses with Thickened Nuchal Translucency by Gas Chromatography-Mass Spectrometry

Persistence of a fetal thickened nuchal translucency (NT), one of the most sensitive and specific individual markers of fetal disorders, is strongly correlated with the possibility of a genetic syndrome, congenital infections, or other malformations. Thickened NT can also be found in normal pregnancies. Several of its pathophysiological aspects still remain unexplained. Metabolomics could offer a fresh opportunity to explore maternal-foetal metabolism in an effort to explain its physiological and pathological mechanisms. For this prospective case-control pilot study, thirty-nine samples of amniotic fluids were collected, divisible into 12 euploid foetuses with an enlarged nuchal translucency (>NT) and 27 controls (C). Samples were analyzed using gas chromatography mass spectrometry. Multivariate and univariate statistical analyses were performed to find a specific metabolic pattern of >NT class. The correlation between the metabolic profile and clinical parameters was evaluated (NT showed an R2 = 0.75, foetal crown-rump length showed R2 = 0.65, pregnancy associated plasma protein-A showed R2 = 0.60). Nine metabolites significantly differing between >NT foetuses and C were detected: 2-hydroxybutyric acid, 3-hydroxybutyric, 1,5 Anydro-Sorbitol, cholesterol, erythronic acid, fructose, malic acid, threitol, and threonine, which were linked to altered pathways involved in altered energetic pathways. Through the metabolomics approach, it was possible to identify a specific metabolic fingerprint of the fetuses with >NT.

Potential role of indolelactate and butyrate in multiple sclerosis revealed by integrated microbiome-metabolome analysis

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.

Metabolomics Analysis of Viral Therapeutics

Virotherapy, enabled by recent advances in the transdisciplinary field of biotechnology, has emerged as a powerful tool for use in anticancer treatment, gene therapy, immunotherapy, etc. Examining the effects of viruses and virus-derived immune-modulating therapeutics is of great fundamental and clinical interest. Here we describe a sample preparation protocol for metabolite extraction from virus-infected tissue, in addition to liquid chromatography-mass spectrometry conditions essential for subsequent analysis. This metabolomics approach delivers highly sensitive and specific metabolite information on various biospecimens. Such an approach may be adopted to monitor biological changes in over 30 relevant metabolic pathways in response to viral infection and also viral therapeutics.

Correlation of Metabolic Profiles of Plasma and Cerebrospinal Fluid of High-Grade Glioma Patients

This work compares the metabolic profiles of plasma and the cerebrospinal fluid (CSF) of the patients with high-grade (III and IV) gliomas and the conditionally healthy controls using the wide-range targeted screening of low molecular metabolites by HPLC-MS/MS. The obtained data were analyzed using robust linear regression with Huber’s M-estimates, and a number of metabolites with correlated content in plasma and CSF was identified. The statistical analysis shows a significant correlation of metabolite content in plasma and CSF samples for the majority of metabolites. Several metabolites were shown to have high correlation in the control samples, but not in the glioma patients. This can be due to the specific metabolic processes in the glioma patients or to the damaged integrity of blood-brain barrier. The results of our study may be useful for the understanding of molecular mechanisms underlying the development of gliomas, as well as for the search of potential biomarkers for the minimally invasive diagnostic procedures of gliomas.

Sexual dimorphism in immunometabolism and autoimmunity: Impact on personalized medicine

Immune cells play essential roles in metabolic homeostasis and thus, undergo analogous changes in normal physiology (e.g., puberty and pregnancy) and in various metabolic and immune diseases. An essential component of this close relationship between the two is sex differences. Many autoimmune diseases, such as systemic lupus erythematous and multiple sclerosis, feature strikingly increased prevalence in females, whereas in contrast, infectious diseases, such as Ebola and Middle East Respiratory Syndrome, affect more men than women. Therefore, there are fundamental aspects of metabolic homeostasis and immune functions that are regulated differently in males and females. This can be observed in sex hormone-immune interaction where androgens, such as testosterone, have shown immunosuppressive effects whilst estrogen is on the opposite side of the spectrum with immunoenhancing facilitation of mechanisms. In addition, the two sexes exhibit significant differences in metabolic regulation, with estrous cycles in females known to induce variability in traits and more pronounced metabolic disease phenotype exhibited by males. It is likely that these differences underlie both the development of metabolic and autoimmune diseases and the response to current treatment options. Sexual dimorphism in immunometabolism has emerged to become an area of intense research, aiming to uncover sex-biased effector molecules in the various metabolic tissues and immune cell types, identify sex-biased cell-type-specific functions of common effector molecules, and understand whether the sex differences in metabolic and immune functions influence each other during autoimmune pathogenesis. In this review, we will summarize recent findings that address these critical questions of sexual dimorphism in immunometabolism as well as their translational implications for the clinical management of autoimmune diseases.

An emerging potential of metabolomics in multiple sclerosis: a comprehensive overview

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.

Metabolomic Biomarkers of Multiple Sclerosis: A Systematic Review

Background

Magnetic resonance imaging (MRI), cerebrospinal fluid (CSF) analysis, and the McDonald’s clinical criteria are currently utilized tools in diagnosing multiple sclerosis. However, a more conclusive, consistent, and efficient way of diagnosing multiple sclerosis (MS) is yet to be discovered. A potential biomarker, discovered using advances in high-throughput sequencing such as nuclear magnetic resonance (NMR) spectroscopy and other “Omics”-based techniques, may make diagnosis and prognosis more reliable resulting in a more personalized and targeted treatment regime and improved outcomes. The aim of this review was to systematically search the literature for potential biomarkers from any bodily fluid that could consistently and accurately diagnose MS and/or indicate disease progression.

Methods

A systematic literature review of EMBASE, PubMed (MEDLINE), The Cochrane Library, and CINAHL databases produced over a thousand potential studies. Inclusion criteria stated studies with potential biomarker outcomes for people with MS were to be included in the review. Studies were limited to those with human participants who had a clinically defined diagnosis of MS and published in English, with no limit placed on date of publication or the type of bodily fluid sampled.

Results

A total of 1,805 studies were recorded from the literature search. A total of 1,760 studies were removed based on their abstract, with a further 18 removed after considering the full text. A total of 30 studies were considered relevant and had their data retrieved and analyzed. Due to the heterogeneity of focus and results from the refined studies, a narrative synthesis was favored.

Conclusion

Several promising candidate biomarkers suitable for clinical application in MS have been studied. It is recommended follow-up studies with larger sample sizes be completed on several potential biomarkers.

Brain energy metabolism and multiple sclerosis: progress and prospects

Multiple sclerosis (MS) is an autoimmune disease accompanied with nerve pain and paralysis. Although various pathogenic causes of MS have been suggested, including genetic and environmental factors, how MS occurs remains unclear. Moreover, MS should be diagnosed based on clinical experiences because of no disease-specific biomarker and currently available treatments for MS just can reduce relapsing frequency or severity with little effects on disease disability. Therefore, more efforts are required to identify pathophysiology of MS and diagnosis markers. Recent evidence indicates another aspect of MS pathogenesis, energy failure in the central nervous system (CNS). For instance, inflammation that is a characteristic MS symptom and occurs frequently in the CNS of MS patients can result into energy failure in mitochondria and cytosol. Indeed, metabolomics studies for MS have reported energy failure in oxidative phosphorylation and alteration of aerobic glycolysis. Therefore, studies on the metabolism in the CNS may provide another insight for understanding complexity of MS and pathogenesis, which would facilitate the discovery of promising strategies for developing therapeutics to treat MS. This review will provide an overview on recent progress of metabolomic studies for MS, with a focus on the fluctuation of energy metabolism in MS.

Metabolomic Profiling in Neuromyelitis Optica Spectrum Disorder Biomarker Discovery

There is no specific test for diagnosing neuromyelitis optica spectrum disorder (NMOSD), a disabling autoimmune disease of the central nervous system. Instead, diagnosis relies on ruling out other related disorders with overlapping clinical symptoms. An urgency for NMOSD biomarker discovery is underscored by adverse responses to treatment following misdiagnosis and poor prognosis following the delayed onset of treatment. Pathogenic autoantibiotics that target the water channel aquaporin-4 (AQP4) and myelin oligodendrocyte glycoprotein (MOG) contribute to NMOSD pathology. The importance of early diagnosis between AQP4-Ab⁺ NMOSD, MOG-Ab⁺ NMOSD, AQP4-Ab⁻ MOG-Ab⁻ NMOSD, and related disorders cannot be overemphasized. Here, we provide a comprehensive data collection and analysis of the currently known metabolomic perturbations and related proteomic outcomes of NMOSD. We highlight short chain fatty acids, lipoproteins, amino acids, and lactate as candidate diagnostic biomarkers. Although the application of metabolomic profiling to individual NMOSD patient care shows promise, more research is needed.

Changes in Amino Acid and Acylcarnitine Plasma Profiles for Distinguishing Patients with Multiple Sclerosis from Healthy Controls

McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS (n = 16) and control (n = 12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.

A blood-based metabolomics test to distinguish relapsing-remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application

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.

Fatty Acid and Carnitine Metabolism Are Dysregulated in Systemic Sclerosis Patients

Systemic sclerosis (SSc) is a rare chronic disease of unknown pathogenesis characterized by fibrosis of the skin and internal organs, vascular alteration, and dysregulation of the immune system. In order to better understand the immune system and its perturbations leading to diseases, the study of the mechanisms regulating cellular metabolism has gained a widespread interest. Here, we have assessed the metabolic status of plasma and dendritic cells (DCs) in patients with SSc. We identified a dysregulated metabolomic signature in carnitine in circulation (plasma) and intracellularly in DCs of SSc patients. In addition, we confirmed carnitine alteration in the circulation of SSc patients in three independent plasma measurements from two different cohorts and identified dysregulation of fatty acids. We hypothesized that fatty acid and carnitine alterations contribute to potentiation of inflammation in SSc. Incubation of healthy and SSc dendritic cells with etoposide, a carnitine transporter inhibitor, inhibited the production of pro-inflammatory cytokines such as IL-6 through inhibition of fatty acid oxidation. These findings shed light on the altered metabolic status of the immune system in SSc patients and opens up for potential novel avenues to reduce inflammation.

Multi-Platform Characterization of Cerebrospinal Fluid and Serum Metabolome of Patients Affected by Relapsing-Remitting and Primary Progressive Multiple Sclerosis

Background: Multiple sclerosis (MS) is a chronic immunemediated disease of the central nervous system with a highly variable clinical presentation and disease progression. In this study, we investigate the metabolomics profile of patients affected by relapsing–remitting MS (RRMS)and primary progressive MS (PPMS), in order to find potential biomarkers to distinguish between the two forms. Methods: Cerebrospinal Fluid CSF and blood samples of 34 patients (RRMS n = 22, PPMS n = 12) were collected. Nuclear magnetic resonance (¹H-NMR) and mass spectrometry (coupled with a gas chromatography and liquid chromatography) were used as analytical techniques. Subsequently, a multivariate statistical analysis was performed; the resulting significant variables underwent U-Mann–Whitney test and correction for multiple comparisons. Receiver Operating Characteristic ROC curves were built and the pathways analysis was conducted. Results: The analysis of the serum and the CSF of the two classes, allowed the identification of several altered metabolites (lipids, biogenic amines, and amino acids). The pathways analysis indicated the following pathways were affected: Glutathione metabolism, nitrogen metabolism, glutamine–glutamate metabolism, arginine–ornithine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis etc. Conclusion: The analysis allowed the identification of a set of metabolites able to classify RRMS and PPMS patients, each of whom express different patterns of metabolites in the two biofluids.

Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis

Background

Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients.

Methods

An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein–protein interaction (PPI) networks. Then, functional clusters in PPI networks were determined. Enrichr, a gene list enrichment analysis tool, was utilized for the functional enrichment of clusters.

Results

A total of 12, 2, and 4 functional clusters from 619, 52, and 119 DEGs were determined in the lung, peripheral blood mononuclear cell (PBMC), and skin tissues, respectively. Analysis revealed that the tumor necrosis factor (TNF) signaling pathway was enriched significantly in the three investigated tissues as a common pathway. In addition, clusters associated with inflammation and immunity were common in the three investigated tissues. However, clusters related to the fibrosis process were common in lung and skin tissues.

Conclusions

Analysis indicated that there were common pathological clusters that contributed to the pathogenesis of SSc in different tissues. Moreover, it seems that the common pathways in distinct tissues stem from a diverse set of genes.

Multiple sclerosis has a distinct lipid signature in plasma and cerebrospinal fluid

ABSTRACT The diagnosis of multiple sclerosis (MS) has changed over the last decade, but remains a composite of clinical assessment and magnetic resonance imaging to prove dissemination of lesions in time and space. The intrathecal synthesis of immunoglobulin may be a nonspecific marker and there are no plasma biomarkers that are useful in the diagnosis of MS, presenting additional challenges to their early detection. Methods We performed a preliminary untargeted qualitative lipidomics mass spectrometry analysis, comparing cerebrospinal fluid (CSF) and plasma samples from patients with MS, other inflammatory neurological diseases and idiopathic intracranial hypertension. Results Lipid identification revealed that fatty acids and sphingolipids were the most abundant classes of lipids in the CSF and that glycerolipids and fatty acids were the main class of lipids in the plasma of patients with MS. The area under the curve was 0.995 (0.912–1) and 0.78 (0.583–0.917), respectively. The permutation test indicated that this ion combination was useful for distinguishing MS from other inflammatory diseases (p < 0.001 and 0.055, respectively). Conclusion This study concluded that the CSF and plasma from patients with MS bear a unique lipid signature that can be useful as a diagnostic biomarker.

Targeted metabolomics approach for identification of relapsing-remitting multiple sclerosis markers and evaluation of diagnostic models

Multiple sclerosis (MS) is an inflammatory autoimmune disease that causes demyelination of nerve cell axons. This paper is devoted to the study of relapsing-remitting multiple sclerosis (RRMS) biomarkers using an LC-MS/MS-based targeted metabolomics approach and the assessment of changes in the profile of 13 amino acids and 29 acylcarnitines in plasma during the relapse of the disease. A significant increase (p < 0.05) in the concentration of glutamate in plasma in patients with RRMS was detected, while the sum of leucine and isoleucine was reduced. A decrease in the concentration of decenoylcarnitine (C10:1, p < 0.05) was observed among acylcarnitines, and this metabolite was detected as a biomarker for the disease for the first time. Several models based on a single marker or multiple pre-selected markers and multivariate analysis with a dimension reduction technique were compared in their effectiveness for the classification of RRMS and healthy controls. The best results for cross-validation showed models of general linear regression (GLM, AUC = 0.783) and random forest model (RF, AUC = 0.769) based on pre-selected biomarkers. Validation of the models on the test set showed that the RF model based on selected metabolites was the most effective (AUC = 0.72). The results obtained are promising for further development of the system of clinical decision support for the diagnosis of RRMS based on metabolic data.

Assessing the Metabolomic Profile of Multiple Sclerosis Patients Treated with Interferon Beta 1a by 1H-NMR Spectroscopy

Metabolomic research has emerged as a promising approach to identify potential biomarkers in multiple sclerosis (MS). The aim of the present study was to determine the effect of interferon beta (IFN ß) on the metabolome of MS patients to explore possible biomarkers of disease activity and therapeutic response. Twenty-one MS patients starting IFN ß therapy (Rebif® 44 μg; s.c. 3 times per week) were enrolled. Blood samples were obtained at baseline and after 6, 12, and 24 months of IFN ß treatment and were analyzed by high-resolution nuclear magnetic resonance spectroscopy. Changes in metabolites were analyzed. After IFN ß exposure, patients  were divided into responders and nonresponders according to the "no evidence of disease activity" (NEDA-3) definition (absence of relapses, disability progression, and magnetic resonance imaging activity), and samples obtained at baseline were analyzed to evaluate the presence of metabolic differences predictive of IFN ß response. The results of the investigation demonstrated differential distribution of baseline samples compared to those obtained during IFN ß exposure, particularly after 24 months of treatment (R2X = 0.812, R2Y = 0.797, Q2 = 0.613, p = 0.003). In addition, differences in the baseline metabolome between responder and nonresponder patients with respect to lactate, acetone, 3-OH-butyrate, tryptophan, citrate, lysine, and glucose levels were found (R2X = 0.442, R2Y = 0.768, Q2 = 0.532, p = 0.01). In conclusion, a metabolomic approach appears to be a promising, noninvasive tool that could potentially contribute to predicting the efficacy of MS therapies.

Metabolic fingerprinting of chorionic villous samples in normal pregnancy and chromosomal disorders

OBJECTIVE

Placenta-related biological samples are used in biomedical research to investigate placental development. Metabolomics represents a promising approach for studying placental metabolism in an effort to explain physiological and pathological mechanisms. The aim of this study was to investigate metabolic changes in chorionic villi during the first trimester of pregnancy in euploid and aneuploid cases.

METHODS

Samples from 21 women (13 euploid and eight aneuploid) were analyzed with ¹ H-nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC). Multivariate statistical analysis was performed, and differences in metabolites were used to identify the altered metabolic pathways.

RESULTS

A regression model to test the correlation between fetal crown-rump length (CRL) and metabolic profile of chorionic villi was performed in euploid pregnancies (R² was 0.69 for the NMR analysis and 0.94 for the GC-MS analysis). Supervised analysis was used to compare chorionic villi of euploid and aneuploid fetuses (NMR: R² X = 0.70, R² Y = 0.65, Q²  = 0.30, R² X = 0.62; GC-MS: R² Y = 0.704, Q²  = 0.444). Polyol pathways, myo-inositol, and oxidative stress seem to have a fundamental role in euploid and aneuploid pregnancies.

CONCLUSION

Polyol pathways may have a crucial role in energy production in early pregnancy. Excessive activation in aneuploid pregnancies may lead to increased oxidative stress. Metabolomics represents a promising approach to investigate placental metabolic changes.

LC-MS/MS method with chemical derivatization for quantitation of L-threonate in human plasma

An LC-MS/MS-based bioanalytical method has been developed to measure the concentration of L-threonate at its endogenous level in human plasma. Following isotope dilution and protein precipitation, the samples were acetylated and chromatographed under reversed-phase conditions for baseline separation of the derivatized L-threonate and its stereoisomer D-erythronate. The method was assessed by a fit-for-purpose validation with a calibration range from 100 to 10,000 ng/mL. The intra-run coefficients of variation (CVs) were <3.6% and the inter-run CV was 3.2% for the QC samples at endogenous level. At the lower limit of quantitation, the intra-run CV was 6.1% and the average inaccuracy was -1.4%. This method provides an efficient and reliable quantitation of L-threonate and could be useful to certain biomarker investigators.

Metabolomic Investigation of β-Thalassemia in Chorionic Villi Samples

BACKGROUND

Beta-thalassemias are blood disorders characterized by poorly understood clinical phenotypes ranging from asymptomatic to severe anemia. Metabolic composition of the human placenta could be affected by the presence of pathological states such as β-thalassemia. The aim of our study was to describe metabolic changes in chorionic villi samples of fetuses affected by β-thalassemia compared to a control group by applying a metabolomics approach.

METHODS

Chorionic villi samples were differentiated according to the genetic diagnosis of β-thalassemia: control (Group 1, n = 27); heterozygous (Group 2, n = 7); homozygous (Group 3, n = 7). Gas chromatography-mass spectrometry was used to detect the metabolic composition of the samples. Subsequently, multivariate and univariate statistical analysis was performed. The discriminant metabolites were used to identify the altered pathways.

RESULTS

Supervised multivariate models were devised to compare the groups. The model resulting from the comparison between Group 1 and Group 3 was the most significant. Discriminant metabolites were identified, and the most altered pathways were as follows: pentose phosphate pathway (PPP), arachidonic acid metabolism, glycolysis, and gluconeogenesis, suggesting the presence of an energetic shift toward the PPP and the presence of oxidative stress in β-thalassemia chorionic villi samples.

CONCLUSIONS

The metabolomics approach identified a specific metabolic fingerprint in chorionic villi of fetuses affected by β-thalassemia.

Altered Cerebrospinal Fluid Concentrations of Hydrophobic and Hydrophilic Compounds in Early Stages of Multiple Sclerosis-Metabolic Profile Analyses

The lack of a single predictive or diagnostic test in multiple sclerosis (MS) remains a major obstacle in the patient’s care. The aim of this study was to investigate metabolic profiles, especially lipids in cerebrospinal fluid (CSF) using ¹H-NMR spectroscopy and metabolomics analysis to discriminate MS patient group from the control ones. In this study, 19 MS patients and 19 controls, without neurological problems, patients were enrolled. To obtain the CSF metabolic profiles, NMR spectroscopy was used. Hydrophilic and hydrophobic compounds were analyzed using univariate and multivariate supervised analysis orthogonal partial least square discriminant analysis (OPLS-DA). Targeted OPLS-DA analysis of 32 hydrophilic and 17 hydrophobic compounds obtained 9 hydrophilic metabolites and 8 lipid functional groups which had the highest contribution to patient’s group separation. Lower concentrations of CSF hydrophilic and hydrophobic compounds were observed in MS patients as compared to control group. Acetone, choline, urea, 1,3-dimethylurate, creatinine, isoleucine, myo-inositol, leucine, and 3-OH butyrate; saturated and monounsaturated acyl groups of ω–9, ω–7, ω–6, ω–3, and fatty acid, triglycerides, 1,3-DG, 1-MG, and unassigned component signal at 3.33 ppm were the most important signal compounds in group separation. Analysis of metabolic profile of raw CSF and their lipid extract shows decreased levels of many compounds and led to the conclusion that MS patients could have a disturbance in many metabolic pathways perhaps leading to the decreased level of acetyl-CoA and/or inflammation. CSF metabolic profile analyses could be used as a fingerprint for early MS diagnosis.

Metabolome-based signature of disease pathology in MS

BACKGROUND

Diagnostic delays are common for multiple sclerosis (MS) since diagnosis typically depends on the presentation of nonspecific clinical symptoms together with radiologically-determined central nervous system (CNS) lesions. It is important to reduce diagnostic delays as earlier initiation of disease modifying therapies mitigates long-term disability. Developing a metabolomic blood-based MS biomarker is attractive, but prior efforts have largely focused on specific subsets of metabolite classes or analytical platforms. Thus, there are opportunities to interrogate metabolite profiles using more expansive and comprehensive approaches for developing MS biomarkers and for advancing our understanding of MS pathogenesis.

METHODS

To identify putative blood-based MS biomarkers, we comprehensively interrogated the metabolite profiles in 12 non-Hispanic white, non-smoking, male MS cases who were drug naïve for 3 months prior to biospecimen collection and 13 non-Hispanic white, non-smoking male controls who were frequency matched to cases by age and body mass index. We performed untargeted two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS) and targeted lipidomic and amino acid analysis on serum. 325 metabolites met quality control and supervised machine learning was used to identify metabolites most informative for MS status. The discrimination potential of these select metabolites were assessed using receiver operator characteristic curves based on logistic models; top candidate metabolites were defined as having area under the curves (AUC) >80%. The associations between whole-genome expression data and the top candidate metabolites were examined, followed by pathway enrichment analyses. Similar associations were examined for 175 putative MS risk variants and the top candidate metabolites.

RESULTS

12 metabolites were determined to be informative for MS status, of which 6 had AUCs >80%: pyroglutamate, laurate, acylcarnitine C14:1, N-methylmaleimide, and 2 phosphatidylcholines (PC ae 40:5, PC ae 42:5). These metabolites participate in glutathione metabolism, fatty acid metabolism/oxidation, cellular membrane composition, and transient receptor potential channel signaling. Pathway analyses based on the gene expression association for each metabolite suggested enrichment for pathways associated with apoptosis and mitochondrial dysfunction. Interestingly, the predominant MS genetic risk allele HLA-DRB1×15:01 was associated with one of the 6 top metabolites.

CONCLUSION

Our analysis represents the most comprehensive description of metabolic changes associated with MS in serum, to date, with the inclusion of genomic and genetic information. We identified atypical metabolic processes that differed between MS patients and controls, which may enable the development of biological targets for diagnosis and treatment.

Biochemical Differences in Cerebrospinal Fluid between Secondary Progressive and Relapsing⁻Remitting Multiple Sclerosis

To better understand the pathophysiological differences between secondary progressive multiple sclerosis (SPMS) and relapsing-remitting multiple sclerosis (RRMS), and to identify potential biomarkers of disease progression, we applied high-resolution mass spectrometry (HRMS) to investigate the metabolome of cerebrospinal fluid (CSF). The biochemical differences were determined using partial least squares discriminant analysis (PLS-DA) and connected to biochemical pathways as well as associated to clinical and radiological measures. Tryptophan metabolism was significantly altered, with perturbed levels of kynurenate, 5-hydroxytryptophan, 5-hydroxyindoleacetate, and N-acetylserotonin in SPMS patients compared with RRMS and controls. SPMS patients had altered kynurenine compared with RRMS patients, and altered indole-3-acetate compared with controls. Regarding the pyrimidine metabolism, SPMS patients had altered levels of uridine and deoxyuridine compared with RRMS and controls, and altered thymine and glutamine compared with RRMS patients. Metabolites from the pyrimidine metabolism were significantly associated with disability, disease activity and brain atrophy, making them of particular interest for understanding the disease mechanisms and as markers of disease progression. Overall, these findings are of importance for the characterization of the molecular pathogenesis of SPMS and support the hypothesis that the CSF metabolome may be used to explore changes that occur in the transition between the RRMS and SPMS pathologies.

Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis

Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS). Methods: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability. Results: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate. The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression. Conclusion: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

A classification modeling approach for determining metabolite signatures in osteoarthritis

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

Metabolomic profile of systemic sclerosis patients

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterized by vascular lesions, immunological alterations and diffuse fibrosis of the skin and internal organs. Since recent evidence suggests that there is a link between metabolomics and immune mediated disease, serum metabolic profile of SSc patients and healthy controls was investigated by ¹H-NMR and GC-MS techniques. The results indicated a lower level of aspartate, alanine, choline, glutamate, and glutarate in SSc patients compared with healthy controls. Moreover, comparing patients affected by limited SSc (lcSSc) and diffuse SSc (dcSSc), 6 discriminant metabolites were identified. The multivariate analysis performed using all the metabolites significantly different revealed glycolysis, gluconeogenesis, energetic pathways, glutamate metabolism, degradation of ketone bodies and pyruvate metabolism as the most important networks. Aspartate, alanine and citrate yielded a high area under receiver-operating characteristic (ROC) curves (AUC of 0.81; CI 0.726–0.93) for discriminating SSc patients from controls, whereas ROC curve generated with acetate, fructose, glutamate, glutamine, glycerol and glutarate (AUC of 0.84; CI 0.7–0.98) discriminated between lcSSc and dcSSc. These results indicated that serum NMR-based metabolomics profiling method is sensitive and specific enough to distinguish SSc from healthy controls and provided a feasible diagnostic tool for the diagnosis and classification of the disease.