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Multiple sclerosis is a leading cause of neurological disability in adults. Heterogeneity in multiple sclerosis clinical presentation has posed a major challenge for identifying genetic variants associated with disease outcomes. To overcome this challenge, we used prospectively ascertained clinical outcomes data from the largest international multiple sclerosis registry, MSBase. We assembled a cohort of deeply phenotyped individuals of European ancestry with relapse-onset multiple sclerosis. We used unbiased genome-wide association study and machine learning approaches to assess the genetic contribution to longitudinally defined multiple sclerosis severity phenotypes in 1813 individuals. Our primary analyses did not identify any genetic variants of moderate to large effect sizes that met genome-wide significance thresholds. The strongest signal was associated with rs7289446 (β = −0.4882, P = 2.73 × 10−7), intronic to SEZ6L on chromosome 22. However, we demonstrate that clinical outcomes in relapse-onset multiple sclerosis are associated with multiple genetic loci of small effect sizes. Using a machine learning approach incorporating over 62 000 variants together with clinical and demographic variables available at multiple sclerosis disease onset, we could predict severity with an area under the receiver operator curve of 0.84 (95% CI 0.79–0.88). Our machine learning algorithm achieved positive predictive value for outcome assignation of 80% and negative predictive value of 88%. This outperformed our machine learning algorithm that contained clinical and demographic variables alone (area under the receiver operator curve 0.54, 95% CI 0.48–0.60). Secondary, sex-stratified analyses identified two genetic loci that met genome-wide significance thresholds. One in females (rs10967273; βfemale = 0.8289, P = 3.52 × 10−8), the other in males (rs698805; βmale = −1.5395, P = 4.35 × 10−8), providing some evidence for sex dimorphism in multiple sclerosis severity. Tissue enrichment and pathway analyses identified an overrepresentation of genes expressed in CNS compartments generally, and specifically in the cerebellum (P = 0.023). These involved mitochondrial function, synaptic plasticity, oligodendroglial biology, cellular senescence, calcium and G-protein receptor signalling pathways. We further identified six variants with strong evidence for regulating clinical outcomes, the strongest signal again intronic to SEZ6L (adjusted hazard ratio 0.72, P = 4.85 × 10−4). Here we report a milestone in our progress towards understanding the clinical heterogeneity of multiple sclerosis outcomes, implicating functionally distinct mechanisms to multiple sclerosis risk. Importantly, we demonstrate that machine learning using common single nucleotide variant clusters, together with clinical variables readily available at diagnosis can improve prognostic capabilities at diagnosis, and with further validation has the potential to translate to meaningful clinical practice change.

Multiple sclerosis is a complex autoimmune disease that causes neuronal demyelination and debilitating physical and cognitive symptoms. Epigenetic factors can mediate genetic and environmental effects on disease risk. Here we profiled blood-based DNA methylation in 583 MS cases and 643 healthy controls representing 3 independent study groups. An epigenome-wide association study was performed, incorporating statistical deconvolution to identify immune cell-specific signals, which were validated in purified cell subsets. First, we elucidated the mediation effects of methylation at the HLA risk locus. Second, we identified methylation differences in MS that occur independently of known genetic risk loci and show that these differences more strongly differentiate disease than HLA genotype and the polygenic risk score. Finally, we show that methylation differences in MS occur predominantly in B cells and monocytes and indicate the involvement of cell-specific biological pathways. Overall, this study comprehensively characterizes the immune cell specific epigenetic architecture of MS.

Background The variation in multiple sclerosis (MS) disease severity is incompletely explained by genetics, suggesting genetic and environmental interactions are involved. Moreover, the lack of prognostic biomarkers makes it difficult for clinicians to optimise care. DNA methylation is one epigenetic mechanism by which gene–environment interactions can be assessed. Here, we aimed to identify DNA methylation patterns associated with mild and severe relapse-onset MS (RMS) and to test the utility of methylation as a predictive biomarker. Methods We conducted an epigenome-wide association study between 235 females with mild (n = 119) or severe (n = 116) with RMS. Methylation was measured with the Illumina methylationEPIC array and analysed using logistic regression. To generate hypotheses about the functional consequence of differential methylation, we conducted gene set enrichment analysis using ToppGene. We compared the accuracy of three machine learning models in classifying disease severity: (1) clinical data available at baseline (age at onset and first symptoms) built using elastic net (EN) regression, (2) methylation data using EN regression and (3) a weighted methylation risk score of differentially methylated positions (DMPs) from the main analysis using logistic regression. We used a conservative 70:30 test:train split for classification modelling. A false discovery rate threshold of 0.05 was used to assess statistical significance. Results Females with mild or severe RMS had 1472 DMPs in whole blood (839 hypermethylated, 633 hypomethylated in the severe group). Differential methylation was enriched in genes related to neuronal cellular compartments and processes, and B-cell receptor signalling. Whole-blood methylation levels at 1708 correlated CpG sites classified disease severity more accurately (machine learning model 2, AUC = 0.91) than clinical data (model 1, AUC = 0.74) or the wMRS (model 3, AUC = 0.77). Of the 1708 selected CpGs, 100 overlapped with DMPs from the main analysis at the gene level. These overlapping genes were enriched in neuron projection and dendrite extension, lending support to our finding that neuronal processes, rather than immune processes, are implicated in disease severity. Conclusion RMS disease severity is associated with whole-blood methylation at genes related to neuronal structure and function. Moreover, correlated whole-blood methylation patterns can assign disease severity in females with RMS more accurately than clinical data available at diagnosis.

Multiple sclerosis (MS) is a leading cause of neurological disability in adults. Heterogeneity in MS clinical presentation has posed a major challenge for identifying genetic variants associated with disease outcomes. To overcome this challenge, we used prospectively ascertained clinical outcomes data from the largest international MS Registry, MSBase. We assembled a cohort of deeply phenotyped individuals with relapse-onset MS. We used unbiased genome-wide association study and machine learning approaches to assess the genetic contribution to longitudinally defined MS severity phenotypes in 1,813 individuals. Our results did not identify any variants of moderate to large effect sizes that met genome-wide significance thresholds. However, we demonstrate that clinical outcomes in relapse-onset MS are associated with multiple genetic loci of small effect sizes. Using a machine learning approach incorporating over 62,000 variants and demographic variables available at MS disease onset, we could predict severity with an area under the receiver operator curve (AUROC) of 0.87 (95% CI 0.83 – 0.91). This approach, if externally validated, could quickly prove useful for clinical stratification at MS onset. Further, we find evidence to support central nervous system and mitochondrial involvement in determining MS severity.

The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.

In order to further investigate the molecular mechanisms that regulate oligodendrocyte (OC) survival, we utilized microarrays to characterize changes in OC gene expression after exposure to the cytokines neurotrophin3, insulin, or leukemia inhibitory factor (LIF) in vitro. We identified and validated the induction and secretion of the neuropeptide galanin in OCs, specifically in response to LIF. We next established that galanin is an OC survival factor and showed that autocrine or paracrine galanin secretion mediates LIF-induced OC survival in vitro. We also revealed that galanin is up-regulated in OCs in the cuprizone model of central demyelination, and that oligodendroglial galanin expression is significantly regulated by endogenous LIF in this context. We also showed that knock-out of galanin reduces OC survival and exacerbates callosal demyelination in the cuprizone model. These findings suggest a potential role for the use of galanin agonists in the treatment of human demyelinating diseases.

We conducted a microarray study to identify genes that are differentially regulated in the spinal cords of mice with the inflammatory disease experimental autoimmune encephalomyelitis (EAE) relative to healthy mice. In total 181 genes with at least a two-fold increase in expression were identified, and most of these genes were associated with immune function. Unexpectedly, ceruloplasmin (Cp), a ferroxidase that converts toxic ferrous iron to its nontoxic ferric form and also promotes the efflux of iron from astrocytes in the CNS, was shown to be highly upregulated (13.2-fold increase) in EAE spinal cord. Expression of Cp protein is known to be increased in several neurological conditions, but the role of Cp regulation in CNS autoimmune disease is not known. To investigate this, we induced EAE in Cp gene knockout, heterozygous, and wild-type mice. Cp knockout mice were found to have slower disease evolution than wild-type mice (EAE days 13-17; P = 0.05). Interestingly, Cp knockout mice also exhibited a significant increase in the number of astrocytes with reactive morphology in early EAE compared with wild-type mice at the same stage of disease. CNS iron levels were not increased with EAE in these mice. Based on these observations, we propose that an increase in Cp expression could contribute to tissue damage in early EAE. In addition, endogenous CP either directly or indirectly inhibits astrocyte reactivity during early disease, which could also worsen early disease evolution.

OBJECTIVE: To analyze time-trends in age at disability milestones among MS patients who were enrolled into the MSBase International Registry during 1996-2010 period.METHODS: We used linear regression to describe the relationship between mean age at major EDSS benchmarks and calendar time. We then assessed time-trend in age at initial EDSS rating with a three level linear growth model specifying that patients were nested within each of 20 participating countries. The model estimated the average of time-trends in mean age at initial clinical assessment within each country while controlling for patients' EDSS and sex in each country. Analyses were repeated in subsamples of patients diagnosed according to Poser or McDonald criteria.RESULTS: The MSBase Registry contained data on 11,108 MS patients enrolled between 1996 and 2010 who fulfilled our inclusion criteria. During the 1996-2010 period, enrollment age for patients with EDSS 4/4.5 increased by 7.9 years, from 43 to 51 years (pCONCLUSIONS: The more recent MSBase enrollees in each of the mild-to-moderate disability strata were significantly older than earlier enrollees. Possible explanations for this phenomenon are discussed.