A metabonomics investigation of multiple sclerosis by nuclear magnetic resonance

Guidelines for the role of autophagy in drug delivery vectors uptake pathways

The process of autophagy refers to the intracellular absorption of cytoplasm (such as proteins, nucleic acids, tiny molecules, complete organelles, and so on) into the lysosome, followed by the breakdown of that cytoplasm. The majority of cellular proteins are degraded by a process called autophagy, which is both a naturally occurring activity and one that may be induced by cellular stress. Autophagy is a system that can save cells' integrity in stressful situations by restoring metabolic basics and getting rid of subcellular junk. This happens as a component of an endurance response. This mechanism may have an effect on disease, in addition to its contribution to the homeostasis of individual cells and tissues as well as the control of development in higher species. The main aim of this study is to discuss the guidelines for the role of autophagy in drug delivery vector uptake pathways. In this paper, we discuss the meaning and concept of autophagy, the mechanism of autophagy, the role of autophagy in drug delivery vectors, autophagy-modulating drugs, nanostructures for delivery systems of autophagy modulators, etc. Later in this paper, we talk about how to deliver chemotherapeutics, siRNA, and autophagy inducers and inhibitors. We also talk about how hard it is to make a drug delivery system that takes nanocarriers' roles as autophagy modulators into account.

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.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

Association of Selenium Levels with Neurodegenerative Disease: A Systemic Review and Meta-Analysis

BACKGROUND

Neurodegenerative diseases (NDs) have posed significant challenges to public health, and it is crucial to understand their mechanisms in order to develop effective therapeutic strategies. Recent studies have highlighted the potential role of selenium in ND pathogenesis, as it plays a vital role in maintaining cellular homeostasis and preventing oxidative damage. However, a comprehensive analysis of the association between selenium and NDs is still lacking.

METHOD

Five public databases, namely PubMed, Web of Science, EMBASE, Cochrane and Clinical Trials, were searched in our research. Random model effects were chosen, and Higgins inconsistency analyses (I2), Cochrane's Q test and Tau2 were calculated to evaluate the heterogeneity.

RESULT

The association of selenium in ND patients with Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD) was studied. A statistically significant relationship was only found for AD patients (SMD = -0.41, 95% CI (-0.64, -0.17), p < 0.001), especially for erythrocytes. However, no significant relationship was observed in the analysis of the other four diseases.

CONCLUSION

Generally, this meta-analysis indicated that AD patients are strongly associated with lower selenium concentrations compared with healthy people, which may provide a clinical reference in the future. However, more studies are urgently needed for further study and treatment of neurodegenerative diseases.

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.

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.

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.

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.

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.

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.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

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.

12-months prospective Pentraxin-3 and metabolomic evaluation in multiple sclerosis patients treated with glatiramer acetate

BACKGROUND

Pentraxin-3 (PTX-3) is involved in acute immunological responses and it is a pro-inflammatory protein and a novel biomarker of inflammatory diseases. It is demonstrated that PTX-3 is higher in cerebrospinal fluid (CSF) of aggressive Multiple Sclerosis (MS). Metabolomics, the identification of small endogenous molecules, offers a molecular profile of MS. Glatiramer acetate (GA) is a widely used treatment for (MS) but its mechanism of action is not completely defined. The aim of our study is to analyze PTX-3 and metabolomic profile in MS patients compared to controls and to investigate the effect of GA on PXT-3 and metabolic molecules during treatment in responder and not responder MS patients.

METHODS

28 unrelated MS patients and 27 age-and sex-matched controls were recruited. In serum, PTX-3 levels were measured by ELISA and Metabolomic panel was evaluated trough Nuclear Magnetic Resonance (NMR). According to clinical practice patients started GA treatment; PTX-3 and metabolomic identification were performed before and during treatment. Responders to treatment were identified if no evidence of instrumental, clinical relapses and disability progression (NEDA) occurred during follow up.

RESULTS

Serum PTX-3 levels were higher in MS patients compared to matched controls (7,85 ± 2,19 vs 6,20 ± 1,63 ng/ml) (p = 0,03); metabolomic evaluation shows higher levels of lactate and lower levels of valine, tyrosine and tryptophan in MS patients compared to controls. During therapy, PTX-3 levels have been reduced statistically significant (p = 0,001) at six months and one year of treatment. After one year, of the twenty patients that completed the study, 55% were considered fully responders to treatment; in these patients the mean reduction of PTX-3 at one year was higher respect to not responders (-3,82 ± 1,24 ng/ml vs -2,32 ± 1,03 ng/ml p = 0,02) and we observed a higher reduction of lactate, tyrosine and hypoxanthine and an increase of hydroxyproline and ADP as well as of three oxidative phosphorylation markers, citrulline, ornithine and tryptophan approaching the metabolic profile of healthy subjects.

DISCUSSION AND CONCLUSIONS

We demonstrated a metabolomic imbalance with mitochondrial dysfunction detected by higher levels of lactate and lower levels of tryptophan, tyrosine and valine in MS patients compared to healthy controls. The reduction of PTX-3 levels and the restoring of mitochondrial function, reducing oxidative stress by GA, allows to identify responder patients. Further and larger studies are needed to understand the predictive role of PTX-3 and metabolomic pattern in the identification of responder patients to GA.

The particle size of drug nanocarriers dictates the fate of neurons; critical points in neurological therapeutics

Neurological disorders and diseases are on the rise in the world, while pharmacists are being encouraged to encapsulate drugs into the nanocarriers. The critical key question is which size of nanocarrier has a promising neurotherapeutic effect. In the present study, FTY-720, an FDA approved drug, was encapsulated into O/W nanocarriers. SEM and DLS data indicated in ultrasonication and stirring methods resulted in spherical nanocarriers with a particle size of 60 and 195 nm (nF60 and nF195), respectively. Further to investigate the effect of particle size on neuronal cells, MTT assay, PI flow-cytometry, LDH release, and NO production examinations were performed. Results showed that small nanocarriers increased cell viability along with the decline of dead cells, while both nanocarriers decreased LDH release and NO production as compared to the conventional drug. Notably, qRT-PCR and western blotting data related to apoptotic markers indicated in the increase of cell mortality in cells treated by nF190 was not due to the increase of apoptosis and Bax/Bcl2 ratio. It is worth mentioning that integrin α5 as a cell surface receptor involves in neuritogenesis was over-expressed in neuronal cells treated by small nanocarriers. However, nF60 increased PTK2 over-expression along with neurite outgrowth, as well. In other words, nanocarriers at the size of 60 nm are preferred to 195 nm as a drug carrier in neurotherapy due to profound impacts on neural cells. Thanks to small nanocarrier broad positive action on neural viability and neurite outgrowth. The present study discloses a pharmaceutical strategy to design drugs based on their particle size efficiency.

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.

Metabolomics in multiple sclerosis disease course and progression

Multiple sclerosis (MS) is associated with changes in the metabolome. Numerous studies employing varying metabolomics platforms have examined a range of biological material ranging from brain tissue to urine and demonstrated consistently alterations in multiple metabolic pathways in MS. We review not only the studies that describe the ability of metabolomics to differentiate MS patients from healthy controls and other neurological disease but also discuss the potential of metabolomics-based methods to build predictive models that are able to stage disease, monitor progression, and select the most appropriate therapy. The increasing number of impressive claims for the capacity of metabolomics to distinguish between different types of demyelinating disease suggests that the provision of such tests may be close at hand. Besides the ability to provide potential diagnostic and prognostic biomarkers, metabolomics also provides us with unique insights into the pathophysiology of the disease and helps identify metabolic pathways that may be potential therapeutic targets. Future studies will integrate metabolomics data with other omics techniques to provide further insight into the source of these metabolic abnormalities and help with identification of the most promising targets for therapeutic intervention.

Systems Biology of Selenium and Complex Disease

Selenium is an essential trace element for maintenance of overall health, whose deficiency and dyshomeostasis have been linked to a variety of diseases and disorders. The majority of previous researches focused on characterization of genes encoding selenoproteins or proteins involved in selenium metabolism as well as their functions. Many studies in humans also investigated the relationship between selenium and complex diseases, but their results have been inconsistent. In recent years, systems biology and "-omics" approaches have been widely used to study complex and global variations of selenium metabolism and function in physiological and different pathological conditions. The present paper reviews recent progress in large-scale and systematic analyses of the relationship between selenium status or selenoproteins and several complex diseases, mainly including population-based cohort studies and meta-analyses, genetic association studies, and some other omics-based studies. Advances in ionomics and its application in studying the interaction between selenium and other trace elements in human health and diseases are also discussed.

Autophagy Modulators: Mechanistic Aspects and Drug Delivery Systems

Autophagy modulation is considered to be a promising programmed cell death mechanism to prevent and cure a great number of disorders and diseases. The crucial step in designing an effective therapeutic approach is to understand the correct and accurate causes of diseases and to understand whether autophagy plays a cytoprotective or cytotoxic/cytostatic role in the progression and prevention of disease. This knowledge will help scientists find approaches to manipulate tumor and pathologic cells in order to enhance cellular sensitivity to therapeutics and treat them. Although some conventional therapeutics suffer from poor solubility, bioavailability and controlled release mechanisms, it appears that novel nanoplatforms overcome these obstacles and have led to the design of a theranostic-controlled drug release system with high solubility and active targeting and stimuli-responsive potentials. In this review, we discuss autophagy modulators-related signaling pathways and some of the drug delivery strategies that have been applied to the field of therapeutic application of autophagy modulators. Moreover, we describe how therapeutics will target various steps of the autophagic machinery. Furthermore, nano drug delivery platforms for autophagy targeting and co-delivery of autophagy modulators with chemotherapeutics/siRNA, are also discussed.

Berberine as a potential autophagy modulator

Today, pharmacognosy is considered a valuable science in the prevention and treatment of diseases. Among herbals, Berberine is an isoquinoline alkaloid found in the Berberis species. Surprisingly, it shows antimicrobial, antiviral, antidiarrheal, antipyretic, and anti-inflammatory potential. Furthermore, it diminishes drug resistance in cancer therapy and enhances tumor suppression in part through autophagy and cell cycle arrest mechanisms. In the present review, we discuss the effect of berberine on diverse cellular pathways and describe how berberine acts as an autophagy modulator to adjust physiologic and pathologic conditions and diminishes drug resistance in cancer therapy.

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.

Metabolomic profiling of CSF in multiple sclerosis and neuromyelitis optica spectrum disorder by nuclear magnetic resonance

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are inflammatory diseases of the central nervous system. Although several studies have characterized the metabolome in the cerebrospinal fluid (CSF) from MS and NMOSD patients, comparative analyses between them and between the relapse and the remission of each disease have not been performed. Both univariate and multivariate analyses were used to compare 1H-NMR spectra of CSF from MS, NMOSD, and healthy controls (HCs). The statistical analysis showed alterations of eight metabolites that were dependent on the disease. Levels of 2-hydroxybutyrate, acetone, formate, and pyroglutamate were higher and levels of acetate and glucose were lower in both MS and NMOSD. Citrate was lower in MS patients, whereas lactate was higher in only NMOSD specifically. The shared feature of metabolic changes between MS and NMOSD may be related to altered energy metabolism and fatty acid biosynthesis in the brain. Another analysis to characterize relapse and remission status showed that isoleucine and valine were down-regulated in MS relapse compared to MS remission. The other metabolites identified in the disease comparison showed the same alterations regardless of disease activity. These findings would be helpful in understanding the biological background of these diseases, and distinguishing between MS and NMOSD, as well as determining the disease activity.

Metabolomic analysis identifies altered metabolic pathways in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic, demyelinating disease that affects the central nervous system and is characterized by a complex pathogenesis and difficult management. The identification of new biomarkers would be clinically useful for more accurate diagnoses and disease monitoring. Metabolomics, the identification of small endogenous molecules, offers an instantaneous molecular snapshot of the MS phenotype. Here the metabolomic profiles (utilizing plasma from patients with MS) were characterized with a Gas cromatography-mass spectrometry-based platform followed by a multivariate statistical analysis and comparison with a healthy control (HC) population. The obtained partial least square discriminant analysis (PLS-DA) model identified and validated significant metabolic differences between individuals with MS and HC (R2X=0.223, R2Y=0.82, Q2=0.562; p<0.001). Among discriminant metabolites phosphate, fructose, myo-inositol, pyroglutamate, threonate, l-leucine, l-asparagine, l-ornithine, l-glutamine, and l-glutamate were correctly identified, and some resulted as unknown. A receiver operating characteristic (ROC) curve with AUC 0.84 (p=0.01; CI: 0.75-1) generated with the concentrations of the discriminant metabolites, supported the strength of the model. Pathway analysis indicated asparagine and citrulline biosynthesis as the main canonical pathways involved in MS. Changes in the citrulline biosynthesis pathway suggests the involvement of oxidative stress during neuronal damage. The results confirmed metabolomics as a useful approach to better understand the pathogenesis of MS and to provide new biomarkers for the disease to be used together with clinical data.

Metabolomic signatures associated with disease severity in multiple sclerosis

Objective:

To identify differences in the metabolomic profile in the serum of patients with multiple sclerosis (MS) compared to controls and to identify biomarkers of disease severity.

Methods:

We studied 2 cohorts of patients with MS: a retrospective longitudinal cohort of 238 patients and 74 controls and a prospective cohort of 61 patients and 41 controls with serial serum samples. Patients were stratified into active or stable disease based on 2 years of prospective assessment accounting for presence of clinical relapses or changes in disability measured with the Expanded Disability Status Scale (EDSS). Metabolomic profiling (lipids and amino acids) was performed by ultra-high-performance liquid chromatography coupled to mass spectrometry in serum samples. Data analysis was performed using parametric methods, principal component analysis, and partial least square discriminant analysis for assessing the differences between cases and controls and for subgroups based on disease severity.

Results:

We identified metabolomics signatures with high accuracy for classifying patients vs controls as well as for classifying patients with medium to high disability (EDSS >3.0). Among them, sphingomyelin and lysophosphatidylethanolamine were the metabolites that showed a more robust pattern in the time series analysis for discriminating between patients and controls. Moreover, levels of hydrocortisone, glutamic acid, tryptophan, eicosapentaenoic acid, 13S-hydroxyoctadecadienoic acid, lysophosphatidylcholines, and lysophosphatidylethanolamines were associated with more severe disease (non-relapse-free or increase in EDSS).

Conclusions:

We identified metabolomic signatures composed of hormones, lipids, and amino acids associated with MS and with a more severe course.

1 H NMR-based metabolomics exploring urinary biomarkers correlated with proteinuria in focal segmental glomerulosclerosis: a pilot study

Focal segmental glomerulosclerosis (FSGS) is a common glomerulonephritis, and its rates of occurrence are increasing worldwide. Proteinuria is a clinical defining feature of FSGS which correlates with the severity of podocyte injury in patients with nephrotic-range protein excretion. Metabolite biomarkers corresponding with the level of proteinuria could be considered as non-invasive complementary prognostic factors to proteinuria. The urine samples of 15 patients (n = 6 women and n = 9 men) with biopsy-proven FSGS were collected and subjected to nuclear magnetic resonance (NMR) analysis for metabolite profiling. Multivariate statistical analyses, including principal component analysis and orthogonal projection to latent structure discriminant analysis, were applied to construct a predictive model based on patients with proteinuria >3000 mg/day and <3000 mg/day. In addition, random forest was performed to predict differential metabolites, and pathway analysis was performed to find the defective pathways responsible for proteinuria. Ten metabolites, significant in both statistical methods (orthogonal projection to latent structure discriminant analysis and random forest), were considered as prognostic biomarkers for FSGS: citrulline, dimethylamine, proline, acetoacetate, alpha-ketoisovaleric acid, valine, isobutyrate, D-Palmitylcarnitine, histidine, and N-methylnicotinamide. Pathway analysis revealed impairment of the branched-chain amino acid degradation pathways in patients with massive proteinuria. This study shows that metabolomics can reveal the molecular changes corresponding with disease progression in patients with FSGS and provide a new insight for pathogenic pathways. Copyright © 2016 John Wiley & Sons, Ltd.

Protective Effects of Selol Against Sodium Nitroprusside-Induced Cell Death and Oxidative Stress in PC12 Cells

Selol is an organic selenitetriglyceride formulation containing selenium at +4 oxidation level that can be effectively incorporated into catalytic sites of of Se-dependent antioxidants. In the present study, the potential antioxidative and cytoprotective effects of Selol against sodium nitroprusside (SNP)-evoked oxidative/nitrosative stress were investigated in PC12 cells and the underlying mechanisms analyzed. Spectrophoto- and spectrofluorimetic methods as well as fluorescence microscopy were used in this study; mRNA expression was quantified by real-time PCR. Selol dose-dependently improved the survival and decreased the percentage of apoptosis in PC12 cells exposed to SNP. To determine the mechanism of this protective action, the effect of Selol on free radical generation and on antioxidative potential was evaluated. Selol offered significant protection against the elevation of reactive oxidative species (ROS) evoked by SNP. Moreover, this compound restored glutathione homeostasis by ameliorating the SNP-evoked disturbance of GSH/GSSG ratio. The protective effect exerted by Selol was associated with the prevention of SNP-mediated down-regulation of antioxidative enzymes: glutathione peroxidase (Se-GPx), glutathione reductase (GR), and thioredoxin reductase (TrxR). Finally, GPx inhibition significantly abolished the cytoprotective effect of Selol. In conclusion, these results suggest that Selol effectively protected PC12 cells against SNP-induced oxidative damage and death by adjusting free radical levels and antioxidant system, and suppressing apoptosis. Selol could be successfully used in the treatments of diseases that involve oxidative stress and resulting apoptosis.

NMR spectroscopy-based metabolomic study of serum in sulfur mustard exposed patients with lung disease

Sulfur mustard (SM) is a vesication chemical warfare agent for which there is currently no antidote. Despite years of research, there is no common consensus about the pathophysiological basis of chronic pulmonary disease caused by this chemical warfare agent. In this study, we combined chemometric techniques with nuclear magnetic resonance (NMR) spectroscopy to explore the metabolic profile of sera from SM-exposed patients. A total of 29 serum samples obtained from 17 SM-injured patients, and 12 healthy controls were analyzed by Random Forest. Increased concentrations of seven amino acids, glycerol, dimethylamine, ketone bodies, lactate, acetate, citrulline and creatine together with the decreased very low-density lipoproteins (VLDL) levels were observed in patients compared with control subjects. Our study reveals the metabolic profile of sera from SM-injured patients and indicates that NMR-based methods can distinguish these patients from healthy controls.

(1)H-NMR analysis provides a metabolomic profile of patients with multiple sclerosis

Objective:

To investigate the metabolomic profiles of patients with multiple sclerosis (MS) and to define the metabolic pathways potentially related to MS pathogenesis.

Methods:

Plasma samples from 73 patients with MS (therapy-free for at least 90 days) and 88 healthy controls (HC) were analyzed by ¹H-NMR spectroscopy. Data analysis was conducted with principal components analysis followed by a supervised analysis (orthogonal partial least squares discriminant analysis [OPLS-DA]). The metabolites were identified and quantified using Chenomx software, and the receiver operating characteristic (ROC) curves were calculated.

Results:

The model obtained with the OPLS-DA identified predictive metabolic differences between the patients with MS and HC (R2X = 0.615, R2Y = 0.619, Q2 = 0.476; p < 0.001). The differential metabolites included glucose, 5-OH-tryptophan, and tryptophan, which were lower in the MS group, and 3-OH-butyrate, acetoacetate, acetone, alanine, and choline, which were higher in the MS group. The suitability of the model was evaluated using an external set of samples. The values returned by the model were used to build the corresponding ROC curve (area under the curve of 0.98).

Conclusion:

NMR metabolomic analysis was able to discriminate different metabolic profiles in patients with MS compared with HC. With the exception of choline, the main metabolic changes could be connected to 2 different metabolic pathways: tryptophan metabolism and energy metabolism. Metabolomics appears to represent a promising noninvasive approach for the study of MS.

The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.

Selenium, selenoproteins and neurodegenerative diseases

A review of selenium's essential role in normal brain function and its potential involvement in neurodegenerative diseases.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

1H NMR based metabolic profiling in Crohn's disease by random forest methodology

The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment. Proton nuclear magnetic resonance spectroscopy ((1)H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for the external test set showed a 94% correct classification of CD and healthy subjects. The present study suggests Valine and Isoleucine as differentiating metabolites for CD diagnosis. These metabolites can be used for screening of risky samples at the early stages of CD diagnoses. Moreover, a robust random forest regression model with good prediction outcomes was developed for correlating serum zinc level and metabolite concentrations. The regression model showed the correlation (R(2)) and root mean square error values of 0.83 and 6.44, respectively. This model suggests valuable clues for understanding the mechanism of zinc deficiency in CD patients.

Metabolomic Profiling of Autoimmune Hepatitis: The Diagnostic Utility of Nuclear Magnetic Resonance Spectroscopy

Autoimmune hepatitis (AIH) is often confused with other liver diseases because of their shared nonspecific symptoms and serological and histological overlap. This study compared the plasma metabolomic profiles of patients with AIH, primary biliary cirrhosis (PBC), PBC/AIH overlap syndrome (OS), and drug-induced liver injury (DILI) with those of healthy subjects to identify potential biomarkers of AIH. Metabolomic profiling and biomarker screening were performed using proton nuclear magnetic resonance spectroscopy (¹H NMR) coupled with a partial least-squares discriminant analysis. Compared with the levels in healthy volunteers and other liver disease patients, AIH patients exhibited relatively high levels of plasma pyruvate, lactate, acetate, acetoacetate, and glucose. Such metabolites are typically related to energy metabolism alterations and may be a sign of metabolic conversion to the aerobic glycolysis phenotype of excessive immune activation. Increased aromatic amino acids and decreased branched-chain amino acids were found in the plasma of AIH patients. The whole NMR profiles were stepwise-reduced, and nine metabolomic biomarkers having the greatest significance in the discriminant analysis were obtained. The diagnostic utility of the selected metabolites was assessed, and these biomarkers achieved good sensitivity, specificity, and accuracy (all above 93%) in distinguishing AIH from PBC, DILI, and OS. This report is the first to present the metabolic phenotype of AIH and the potential utility of ¹H NMR metabolomics in the diagnosis of AIH.

Serum analysis by 1H nuclear magnetic resonance spectroscopy: a new tool for distinguishing neuromyelitis optica from multiple sclerosis

BACKGROUND

Neuromyelitis optica (NMO) and multiple sclerosis (MS), two inflammatory demyelinating diseases, are characterized by different therapeutic strategies. Currently, the only biological diagnostic tool available to distinguish NMO from MS is the specific serum autoantibody that targets aquaporin 4, but its sensitivity is low.

OBJECTIVE

To assess the diagnostic accuracy of metabolomic biomarker profiles in these two neurological conditions, compared to control patients.

METHODS

We acquired serum spectra (47 MS, 44 NMO and 42 controls) using proton nuclear magnetic resonance ((1)H-NMR) spectroscopy. We used multivariate pattern recognition analysis to identify disease-specific metabolic profiles.

RESULTS

The (1)H-NMR spectroscopic analysis evidenced two metabolites, originating probably from astrocytes, scyllo-inositol and acetate, as promising serum biomarkers of MS and NMO, respectively. In 87.8% of MS patients, scyllo-inositol increased 0.15 to 3-fold, compared to controls and in 74.3% of NMO patients, acetate increased 0.4 to 7-fold, compared to controls. Using these two metabolites simultaneously, we can discriminate MS versus NMO patients (sensitivity, 94.3%; specificity, 90.2%).

CONCLUSION

This study demonstrates the potential of (1)H-NMR spectroscopy of serum as a novel, promising analytical tool to discriminate populations of patients affected by NMO or MS.

Is a hypothetical melanoma-like neuromelanin the underlying factor essential for the aetiopathogenesis and clinical manifestations of multiple sclerosis?

Background

Multiple sclerosis (MS) has undergone a significant increase in incidence in the industrialised nations over the last 130 years. Changing environmental factors, possibly infections or a lack of or altered timing of them, determine the prevalence of the disease. Although a plethora of aetiological factors, clearly evident in a group of children with MS, appear relevant, there may nevertheless be a single factor essential for the aetiopathogenesis and clinical manifestation of MS.

Description and discussion

This hitherto unknown factor is postulated to be a ‘melanoma-like neuromelanin’ (MLN) dependent on the activation of a gene for syncytin-1. An involvement of MLN could explain the diverse findings in the epidemiology, immunology and pathology of MS, requiring a consideration of a complex infectious background, the human leucocyte antigens, as well as cosmic radiation causing geomagnetic disturbances, vitamin D deficiency, smoking, and lower levels of uric acid.

Summary

In principle, the MLN-based concept is a unifying one, capable of explaining a number of characteristics of the disease. To date, MLN has not been addressed in studies on MS and future work will need to be done on human patients, as there is little or no neuromelanin (the precursor of MLN) in the animals used as experimental models in the study of MS.