Biomarkers in amyotrophic lateral sclerosis: combining metabolomic and clinical parameters to define disease progression

Mitochondria: A Promising Convergent Target for the Treatment of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease characterized by the progressive loss of motor neurons, for which current treatment options are limited. Recent studies have shed light on the role of mitochondria in ALS pathogenesis, making them an attractive therapeutic intervention target. This review contains a very comprehensive critical description of the involvement of mitochondria and mitochondria-mediated mechanisms in ALS. The review covers several key areas related to mitochondria in ALS, including impaired mitochondrial function, mitochondrial bioenergetics, reactive oxygen species, metabolic processes and energy metabolism, mitochondrial dynamics, turnover, autophagy and mitophagy, impaired mitochondrial transport, and apoptosis. This review also highlights preclinical and clinical studies that have investigated various mitochondria-targeted therapies for ALS treatment. These include strategies to improve mitochondrial function, such as the use of dichloroacetate, ketogenic and high-fat diets, acetyl-carnitine, and mitochondria-targeted antioxidants. Additionally, antiapoptotic agents, like the mPTP-targeting agents minocycline and rasagiline, are discussed. The paper aims to contribute to the identification of effective mitochondria-targeted therapies for ALS treatment by synthesizing the current understanding of the role of mitochondria in ALS pathogenesis and reviewing potential convergent therapeutic interventions. The complex interplay between mitochondria and the pathogenic mechanisms of ALS holds promise for the development of novel treatment strategies to combat this devastating disease.

Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected using the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than the controls (64.0 versus 62.0 years, P = 0.009). Cohort 2 controls were over-represented in females (68%, P < 0.001). The most concordant cohort 1 and 2 pathways centred heavily on lipid sub-pathways, including complex and signalling lipid species and metabolic intermediates. There were differences in sub-pathways that were enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA of the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centred on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.

The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology

Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.

Neurodegenerative Disorders of Alzheimer, Parkinsonism, Amyotrophic Lateral Sclerosis and Multiple Sclerosis: An Early Diagnostic Approach for Precision Treatment

Neurodegenerative diseases (NDs) are characterised by progressive dysfunction of synapses, neurons, glial cells and their networks. Neurodegenerative diseases can be classified according to primary clinical features (e.g., dementia, parkinsonism, or motor neuron disease), anatomic distribution of neurodegeneration (e.g., frontotemporal degenerations, extrapyramidal disorders, or spinocerebellar degenerations), or principal molecular abnormalities. The most common neurodegenerative disorders are amyloidosis, tauopathies, a-synucleinopathy, and TAR DNA-binding protein 43 (TDP-43) proteopathy. The protein abnormalities in these disorders have abnormal conformational properties along with altered cellular mechanisms, and they exhibit motor deficit, mitochondrial malfunction, dysfunctions in autophagic-lysosomal pathways, synaptic toxicity, and more emerging mechanisms such as the roles of stress granule pathways and liquid-phase transitions. Finally, for each ND, microglial cells have been reported to be implicated in neurodegeneration, in particular, because the microglial responses can shift from neuroprotective to a deleterious role. Growing experimental evidence suggests that abnormal protein conformers act as seed material for oligomerization, spreading from cell to cell through anatomically connected neuronal pathways, which may in part explain the specific anatomical patterns observed in brain autopsy sample. In this review, we mention the human pathology of select neurodegenerative disorders, focusing on how neurodegenerative disorders (i.e., Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis) represent a great healthcare problem worldwide and are becoming prevalent because of the increasing aged population. Despite many studies have focused on their etiopathology, the exact cause of these diseases is still largely unknown and until now with the only available option of symptomatic treatments. In this review, we aim to report the systematic and clinically correlated potential biomarker candidates. Although future studies are necessary for their use in early detection and progression in humans affected by NDs, the promising results obtained by several groups leads us to this idea that biomarkers could be used to design a potential therapeutic approach and preclinical clinical trials for the treatments of NDs.

Altered Metabolic Profiles of the Plasma of Patients with Amyotrophic Lateral Sclerosis

Currently, there is no objective biomarker to indicate disease progression and monitor therapeutic effects for amyotrophic lateral sclerosis (ALS). This study aimed to identify plasma biomarkers for ALS using a targeted metabolomics approach. Plasma levels of 185 metabolites in 36 ALS patients and 36 age- and sex-matched normal controls (NCs) were quantified using an assay combining liquid chromatography with tandem mass spectrometry and direct flow injection. Identified candidates were correlated with the scores of the revised ALS Functional Rating Scale (ALSFRS-r). Support vector machine (SVM) learning applied to selected metabolites was used to differentiate ALS and NC subjects. Forty-four metabolites differed significantly between ALS and NC subjects. Significant correlations with ALSFRS-r score were seen in 23 metabolites. Six of them showing potential to distinguish ALS from NC-asymmetric dimethylarginine (area under the curve (AUC): 0.829), creatinine (AUC: 0.803), methionine (AUC: 0.767), PC-acyl-alkyl C34:2 (AUC: 0.808), C34:2 (AUC: 0.763), and PC-acyl-acyl C42:2 (AUC: 0.751)-were selected for machine learning. The SVM algorithm using selected metabolites achieved good performance, with an AUC of 0.945. In conclusion, our findings indicate that a panel of metabolites were correlated with disease severity of ALS, which could be potential biomarkers for monitoring ALS progression and therapeutic effects.

Clinicopathological features of neuronal intranuclear inclusion disease diagnosed by skin biopsy

STUDY OBJECTIVES

Neuronal intranuclear inclusion disease (NIID) is a rare progressive neurodegenerative disorder, with complex and diverse of clinical manifestations characterized by eosinophilic hyaline inclusions in neurons and somatic cells. Due to the improvement in diagnostic methods, NIID is being increasingly diagnosed.

METHODS

Herein, we reported three NIID cases, which were diagnosed by skin biopsy and FMR1 gene, after DWI showed the characteristic corticomedullary junction hyperintensity. Then we reviewed all the published cases of NIID in PubMed, which were diagnosed by the same method.

RESULTS

We discussed 15 NIID cases, including three cases diagnosed by us. The average age was 63.4 ± 14.0 years. The average time from onset of symptom to diagnosis was 5.4 ± 7.9 years. Nine cases had dementia or cognitive impairment. Three cases presented with encephalitis. Three cases showed bladder dysfunction and two cases only presented with dizziness and headache. Two cases showed acute neurological deficit mimicking stroke. All cases were diagnosed by skin biopsy, after DWI showed abnormal corticomedullary junction hyperintensity. Ten cases showed inclusions in sweat gland cells, and seven cases in adipocytes, sweat gland cells, and fibroblasts. EMG was performed in five cases, four of whom had abnormal results, showing simultaneous involvement of motor and sensory nerves.

CONCLUSIONS

The results indicated that inclusions were more easily detected in sweat gland cells in skin biopsy. The early stage of NIID could only characterized by autonomic nerve function involvement. Combined autonomic nerve dysfunction might be another relatively common manifestation in NIID.

Untargeted metabolomics yields insight into ALS disease mechanisms

OBJECTIVE

To identify dysregulated metabolic pathways in amyotrophic lateral sclerosis (ALS) versus control participants through untargeted metabolomics.

METHODS

Untargeted metabolomics was performed on plasma from ALS participants (n=125) around 6.8 months after diagnosis and healthy controls (n=71). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon rank-sum tests, adjusted logistic regression and partial least squares-discriminant analysis (PLS-DA), while group lasso explored sub-pathway-level differences. Adjustment parameters included sex, age and body mass index (BMI). Metabolomics pathway enrichment analysis was performed on metabolites selected by the above methods. Finally, machine learning classification algorithms applied to group lasso-selected metabolites were evaluated for classifying case status.

RESULTS

There were no group differences in sex, age and BMI. Significant metabolites selected were 303 by Wilcoxon, 300 by logistic regression, 295 by PLS-DA and 259 by group lasso, corresponding to 11, 13, 12 and 22 enriched sub-pathways, respectively. 'Benzoate metabolism', 'ceramides', 'creatine metabolism', 'fatty acid metabolism (acyl carnitine, polyunsaturated)' and 'hexosylceramides' sub-pathways were enriched by all methods, and 'sphingomyelins' by all but Wilcoxon, indicating these pathways significantly associate with ALS. Finally, machine learning prediction of ALS cases using group lasso-selected metabolites achieved the best performance by regularised logistic regression with elastic net regularisation, with an area under the curve of 0.98 and specificity of 83%.

CONCLUSION

In our analysis, ALS led to significant metabolic pathway alterations, which had correlations to known ALS pathomechanisms in the basic and clinical literature, and may represent important targets for future ALS therapeutics.

Is There a Role for Vitamin D in Amyotrophic Lateral Sclerosis? A Systematic Review and Meta-Analysis

Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative condition characterized by the progressive loss of motor neurons. Patients usually die 3–5 years after diagnosis from respiratory failure. Several studies investigated the role of vitamin D as a biomarker or a therapeutic option for ALS patients. To clarify the scientific evidence, we performed a systematic review and different meta-analyses regarding the potential role of vitamin D in ALS.

Methods: We performed a systematic review of clinical trials, cohorts, and case–control studies retrieved from PubMed, EMBASE, and Cochrane databases reporting vitamin D levels as a putative biomarker for ALS diagnosis or prognosis or the effect of vitamin D supplementation in ALS patients. Whenever possible, data were pooled using a random-effects model, with an assessment of heterogeneity.

Results: Out of 2,996 articles retrieved, we finally included 13 research articles, 12 observational studies (50% prospective), and 1 clinical trial. We found that ALS patients had slightly lower levels of vitamin D than controls (mean difference −6 ng/ml, 95% CI [−10.8; −1.3]), but important confounding factors were not considered in the studies analyzed. We found no relationship between vitamin D levels and ALS functional rate score—revised (ALSFRS-R), with highly heterogeneous results. Discordant results were reported in three studies regarding survival. Finally, five studies reported the effects of vitamin D supplementation with discordant results. Two of them showed a small improvement, whereas two others showed a deleterious effect on ALSFRS-R. One very small clinical trial with important methodological limitations showed some improvement in ALSFRS-R with high doses of vitamin D compared with normal doses.

Conclusions: Our review did not find evidence to support the role of vitamin D on ALS diagnosis, prognosis, or treatment. Most studies had important limitations, mostly regarding the risk of bias for not considering confounding factors. Vitamin D supplementation should be offered to ALS patients to avoid other health issues related to vitamin D deficiency, but there is not enough evidence to support the use of vitamin D as a therapy for ALS.

Amyotrophic Lateral Sclerosis: Current Status in Diagnostic Biomarkers

Amyotrophic lateral sclerosis (ALS) is a rare, neurodegenerative disease that affects the human motor system. ALS is a highly heterogeneous disease, depending on several causative factors. The heterogeneity of the disease is also reflected in the variation of the symptoms in ALS patients. The worldwide annual incidence of ALS is about 2.08 per 100,000 with uniform rates in Caucasian populations and lower rates in African, Asian, and Hispanic populations, while the number of individuals with ALS is expected to grow significantly between 2015 and 2040 with an estimated increase of 69% (Chio et al. 2013a; Arthur et al. 2016).

Advanced Statistical Methods for NMR-Based Metabolomics

Despite the increasing popularity and applicability of metabolomics for putative biomarker identification, analysis of the data is challenged by low statistical power resulting from the small sample sizes and large numbers of metabolites and other omics information, as well as confounding demographic and clinical variables. To enhance the statistical power and improve reproducibility of the identified metabolite-based biomarkers, we advocate the use of advanced statistical methods that can simultaneously evaluate the relationship between a group of metabolites and various types of variables including other omics profiles, demographic and clinical data, as well as the complex interactions between them. Accordingly, in this chapter, we describe the method of seemingly unrelated regression that can simultaneously analyze multiple metabolites while controlling the confounding effects of demographic and clinical variables (such as gender, age, BMI, smoking status). We also introduce penalized orthogonal components regression as a screening approach that can handle millions of omics predictors in the model.

Metabolic and Stress Response Changes Precede Disease Onset in the Spinal Cord of Mutant SOD1 ALS Mice

Many Amyotrophic Lateral Sclerosis (ALS) patients experience hypermetabolism, or an increase in measured vs. calculated metabolic rate. The cause of hypermetabolism and the effects on neuronal metabolism in ALS are currently unknown, but the efficacy of dietary interventions shows promise for metabolism as an ALS therapeutic target. The goal of this study is to measure changes in metabolic pathways as a function of disease progression in spinal cords of the SOD1^G93A mouse model of ALS. We conducted a comprehensive assessment of protein expression for metabolic pathways, antioxidants, chaperones, and proteases in lumbar spinal cord from male SOD1^G93A mice at pre-onset, onset, and end-stages of the disease using targeted proteomic analysis. These results reveal that protein content of metabolic proteins including proteins involved in glycolysis, β-oxidation, and mitochondrial metabolism is altered in SOD1^G93A mouse spinal cord well before disease onset. The changes in mitochondrial metabolism proteins are associated with decreased maximal respiration and glycolytic flux in SOD1^G93A dermal fibroblasts and increased hydrogen peroxide and lipid hydroperoxide production in mitochondria from sciatic nerve and gastrocnemius muscle fibers at end stage of disease. Consistent with redox dysregulation, expression of the glutathione antioxidant system is decreased, and peroxiredoxins and catalase expression are increased. In addition, stress response proteases and chaperones, including those involved in the mitochondrial unfolded protein response (UPR^mt), are induced before disease onset. In summary, we report that metabolic and stress response changes occur in SOD1^G93A lumbar spinal cord before motor symptom onset, and are primarily caused by SOD1^G93A expression and do not vary greatly as a function of disease course.

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics

BACKGROUND

Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding.

RESULTS

The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate (p-value = 5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways.

CONCLUSION

Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases.

Metabolomics Biomarkers: A Strategy Toward Therapeutics Improvement in ALS

Biomarkers research in amyotrophic lateral sclerosis (ALS) holds the promise of improving ALS diagnosis, follow-up of patients, and clinical trials outcomes. Metabolomics have a big impact on biomarkers identification. In this mini-review, we provide the main findings of metabolomics studies in ALS and discuss the most relevant therapeutics attempts that targeted some prominent alterations found in ALS, like glutamate excitotoxicity, oxidative stress, alterations in energetic metabolism, and creatinine levels. Metabolomics studies have reported putative diagnosis or prognosis biomarkers, but discrepancies among these studies did not allow validation of metabolic biomarkers for clinical use in ALS. In this context, we wonder whether metabolomics knowledge could improve ALS therapeutics. As metabolomics identify specific metabolic pathways modified by disease progression and/or treatment, we support that adjuvant or combined treatment should be used to rescue these pathways, creating a new perspective for ALS treatment. Some ongoing clinical trials are already trying to target these pathways. As clinical trials in ALS have been disappointing and considering the heterogeneity of the disease presentation, we support the application of a pharmacometabolomic approach to evaluate the individual response to drug treatments and their side effects, enabling the development of personalized treatments for ALS. We suggest that the best strategy to apply metabolomics for ALS therapeutics progress is to establish a metabolic signature for ALS patients in order to improve the knowledge of patient metabotypes, to choose the most adequate pharmacological treatment, and to follow the drug response and side effects, based on metabolomics biomarkers.

A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression

There is an urgent and unmet need for accurate biomarkers in Amyotrophic Lateral Sclerosis. A pharmaco-metabolomics study was conducted using plasma samples from the TRO19622 (olesoxime) trial to assess the link between early metabolomic profiles and clinical outcomes. Patients included in this trial were randomized into either Group O receiving olesoxime (n = 38) or Group P receiving placebo (n = 36). The metabolomic profile was assessed at time-point one (V1) and 12 months (V12) after the initiation of the treatment. High performance liquid chromatography coupled with tandem mass spectrometry was used to quantify 188 metabolites (Biocrates® commercial kit). Multivariate analysis based on machine learning approaches (i.e. Biosigner algorithm) was performed. Metabolomic profiles at V1 and V12 and changes in metabolomic profiles between V1 and V12 accurately discriminated between Groups O and P (p<5×10–6), and identified glycine, kynurenine and citrulline/arginine as the best predictors of group membership. Changes in metabolomic profiles were closely linked to clinical progression, and correlated with glutamine levels in Group P and amino acids, lipids and spermidine levels in Group O. Multivariate models accurately predicted disease progression and highlighted the discriminant role of sphingomyelins (SM C22:3, SM C24:1, SM OH C22:2, SM C16:1). To predict SVC from SM C24:1 in group O and SVC from SM OH C22:2 and SM C16:1 in group P+O, we noted a median sensitivity between 67% and 100%, a specificity between 66.7 and 71.4%, a positive predictive value between 66 and 75% and a negative predictive value between 70% and 100% in the test sets. This proof-of-concept study demonstrates that the metabolomics has a role in evaluating the biological effect of an investigational drug and may be a candidate biomarker as a secondary outcome measure in clinical trials.

Metabolic Biomarkers and Neurodegeneration: A Pathway Enrichment Analysis of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis

Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) lack robust diagnostics and prognostic biomarkers. Metabolomics is a postgenomics field that offers fresh insights for biomarkers of common complex as well as rare diseases. Using data on metabolite-disease associations published in the previous decade (2006-2016) in PubMed, ScienceDirect, Scopus, and Web of Science, we identified 101 metabolites as putative biomarkers for these three neurodegenerative diseases. Notably, uric acid, choline, creatine, L-glutamine, alanine, creatinine, and N-acetyl-L-aspartate were the shared metabolite signatures among the three diseases. The disease-metabolite-pathway associations pointed out the importance of membrane transport (through ATP binding cassette transporters), particularly of arginine and proline amino acids in all three neurodegenerative diseases. When disease-specific and common metabolic pathways were queried by using the pathway enrichment analyses, we found that alanine, aspartate, glutamate, and purine metabolism might act as alternative pathways to overcome inadequate glucose supply and energy crisis in neurodegeneration. These observations underscore the importance of metabolite-based biomarker research in deciphering the elusive pathophysiology of neurodegenerative diseases. Future research investments in metabolomics of complex diseases might provide new insights on AD, PD, and ALS that continue to place a significant burden on global health.

Immununochemical Markers of the Amyloid Cascade in the Hippocampus in Motor Neuron Diseases

BACKGROUND

Several findings suggest that the amyloid precursor protein (APP) and the amyloid cascade may play a role in motor neuron disease (MND).

OBJECTIVE

Considering that dementia is one of the most frequent non-motor symptoms in amyotrophic lateral sclerosis (ALS) and that hippocampus is one of the brain areas with greater presence of amyloid-related changes in neurodegenerative diseases, our aim was to analyze the molecular markers of the amyloid cascade of APP in pathology studies of the hippocampus of autopsied patients with ALS and ALS-frontotemporal dementia (FTD).

METHODS

We included nine patients with MND and four controls. Immunohistochemical studies and confocal microscopy were used to analyze the expression of APP, TDP-43, pho-TDP-43, Aβ, APP intracellular cytoplasmatic domain (AICD) peptide, Fe65 protein, and pho-TAU in the hippocampus of seven patients with ALS, two patients with ALS-FTD, and four controls. These findings were correlated with clinical data.

RESULTS

Patients displayed increased expression of APP and Aβ peptide. The latter was correlated with cytoplasmic pho-TDP-43 expression. We also found decreased Fe65 expression. A parallel increase in AICD expression was not found. Patients showed increased expression of pho-TAU in the hippocampus. Findings were similar in patients with ALS and those with ALS-FTD, though more marked in the latter group.

CONCLUSION

Post-mortem analyses showed that the amyloid cascade is activated in the hippocampus of patients with MND and correlated with cytoplasmic pho-TDP-43 expression. The number of intracellular or extracellular aggregates of Aβ peptides was not significant.

Multiple system atrophy: pathogenic mechanisms and biomarkers

Multiple system atrophy (MSA) is a unique proteinopathy that differs from other α-synucleinopathies since the pathological process resulting from accumulation of aberrant α-synuclein (αSyn) involves the oligodendroglia rather than neurons, although both pathologies affect multiple parts of the brain, spinal cord, autonomic and peripheral nervous system. Both the etiology and pathogenesis of MSA are unknown, although animal models have provided insight into the basic molecular changes of this disorder. Accumulation of aberrant αSyn in oligodendroglial cells and preceded by relocation of p25α protein from myelin to oligodendroglia results in the formation of insoluble glial cytoplasmic inclusions that cause cell dysfunction and demise. These changes are associated with proteasomal, mitochondrial and lipid transport dysfunction, oxidative stress, reduced trophic transport, neuroinflammation and other noxious factors. Their complex interaction induces dysfunction of the oligodendroglial-myelin-axon-neuron complex, resulting in the system-specific pattern of neurodegeneration characterizing MSA as a synucleinopathy with oligodendroglio-neuronopathy. Propagation of modified toxic αSyn species from neurons to oligodendroglia by "prion-like" transfer and its spreading associated with neuronal pathways result in a multi-system involvement. No reliable biomarkers are currently available for the clinical diagnosis and prognosis of MSA. Multidisciplinary research to elucidate the genetic and molecular background of the deleterious cycle of noxious processes, to develop reliable diagnostic biomarkers and to deliver targets for effective treatment of this hitherto incurable disorder is urgently needed.