Search

Pain is frequent in multiple sclerosis (MS) and includes different types, with neuropathic pain (NP) being most closely related to MS pathology. However, prevalence estimates vary largely, and causal relationships between pain and biopsychosocial factors in MS are largely unknown. Longitudinal studies might help to clarify the prevalence and determinants of pain in MS. To this end, we analyzed data from 410 patients with newly diagnosed clinically isolated syndrome or relapsing-remitting MS participating in the prospective multicenter German National MS Cohort Study (NationMS) at baseline and after 4 years. Pain was assessed by self-report using the PainDETECT Questionnaire. Neuropsychiatric assessment included tests for fatigue, depression, and cognition. In addition, sociodemographic and clinical data were obtained. Prevalence of pain of any type was 40% and 36% at baseline and after 4 years, respectively, whereas prevalence of NP was 2% and 5%. Pain of any type and NP were both strongly linked to fatigue, depression, and disability. This link was even stronger after 4 years than at baseline. Moreover, changes in pain, depression, and fatigue were highly correlated without any of these symptoms preceding the others. Taken together, pain of any type seems to be much more frequent than NP in early nonprogressive MS. Moreover, the close relationship between pain, fatigue, and depression in MS should be considered for treatment decisions and future research on a possible common pathophysiology.

Multiple sclerosis is a disease of the central nervous system characterized by recurrent inflammatory demyelinating lesions in the early disease stage. Lesion formation and mechanisms leading to lesion remyelination are not fully understood. Matrix Assisted Laser Desorption Ionisation Mass Spectrometry imaging (MALDI-IMS) is a technology which analyses proteins and peptides in tissue, preserves their spatial localization, and generates molecular maps within the tissue section. In a pilot study we employed MALDI imaging mass spectrometry to profile and identify peptides and proteins expressed in normal-appearing white matter, grey matter and multiple sclerosis brain lesions with different extents of remyelination. The unsupervised clustering analysis of the mass spectra generated images which reflected the tissue section morphology in luxol fast blue stain and in myelin basic protein immunohistochemistry. Lesions with low remyelination extent were defined by compounds with molecular weight smaller than 5300Da, while more completely remyelinated lesions showed compounds with molecular weights greater than 15,200Da. An in-depth analysis of the mass spectra enabled the detection of cortical lesions which were not seen by routine luxol fast blue histology. An ion mass, mainly distributed at the rim of multiple sclerosis lesions, was identified by liquid chromatography and tandem mass spectrometry as thymosin beta-4, a protein known to be involved in cell migration and in restorative processes. The ion mass of thymosin beta-4 was profiled by MALDI imaging mass spectrometry in brain slides of 12 multiple sclerosis patients and validated by immunohistochemical analysis. In summary, our results demonstrate the ability of the MALDI-IMS technology to map proteins within the brain parenchyma and multiple sclerosis lesions and to identify potential markers involved in multiple sclerosis pathogenesis and/or remyelination.

Objectives There is convergent evidence for an important role of interleukin-16 (IL-16) in the pathogenesis of multiple sclerosis (MS). IL-16 serves as a chemoattractant for different immune cells that are involved in developing lesions. Here, we compared IL-16 levels of MS patients and controls and addressed the long-term effect of IFN-β, the most common immunomodulatory MS therapy, on IL-16 serum levels in MS patients over 2 years. Beyond this, we analysed the expression of IL-16 in two CD4+ T-cell subsets, Th1 and Th17 cells, which are important autoimmune mediators and affected by IFN-β treatment, derived from myelin-specific T-cell transgenic mice. Materials and methods IL-16 serum levels of 17 controls and of 16 MS patients before therapy and at months 1, 2, 3, 6, 9, 12 and 24 during IFN-β1a therapy were determined by ELISA. MRI was performed before therapy, at months 12 and 24. IL-16 expression of in vitro differentiated murine myelin oligodendrocyte glycoprotein (MOG)-specific Th1 and Th17 cells was quantified by real-time PCR. Results Before therapy, MS patients showed significantly elevated IL-16 levels compared with controls irrespective of disease activity determined by MRI. Therapy with IFN-β1a led to a significant linear decrease in IL-16 serum levels beginning after 2 months. MOG-specific Th17 cells expressed more IL-16 than Th1 cells. Conclusions Reduction in increased IL-16 levels may be of relevance for the therapeutic effect of IFN-β1a in MS. Easily accessible IL-16 serum levels hold a potential as biomarker of treatment efficacy in MS.

Genome-wide study in Germans identifies four novel multiple sclerosis risk genes and confirms already known gene loci., We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis.

Multiple sclerosis (MS) is characterized by inflammation, axonal and oligodendrocyte pathology and progressive neurological disability. Epidemiologic data indicate that MS may be caused by interplay of genetic and environmental factors. Large samples collected in cooperative efforts and new technologies such as high throughput single nucleotide polymorphism (SNP) genotyping allowed recently to discover non-HLA genes associated with MS susceptibility that are mostly involved in the immune response. In addition, several studies indicate an effect of genetic variations on disease onset, progression and response to therapy. However, the polymorphisms discovered so far explain the genetic variation in MS only in part and are mostly common variants that have only low impact on MS susceptibility. Functional studies are required to validate the importance of the newly identified SNPs. Taking into account the interplay of genetic and environmental factors a combination of genome wide genotyping including HLA-typing and genome wide expression profiling as well as a collection on relevant or putatively relevant environmental factors in patients well characterized clinically and by MRI is a promising way to identify new disease relevant biomarkers.

Matrix metalloproteinase 9 (MMP9) plays an important role in the pathogenesis of multiple sclerosis (MS). However, the impact of genetic variants affecting MMP9 on MS susceptibility is still in debate. We could not detect an association of MMP9 SNPs with MS on a genome-wide significance level by SNP genotyping, followed by imputation of SNPs within a region stretching 2Mbp up- and down-stream of MMP9. Rs6073751, located within WFDC2, was found associated with MS most strongly. Rs3918242, associated with MS according to previous reports, showed nominal significance only. Meta-analysis of our own and published data did not confirm this effect.

Recently, associations of several single-nucleotide polymorphisms (SNPs) within the CLEC16A gene with multiple sclerosis (MS), type-I diabetes, and primary adrenal insufficiency were reported.We performed linkage disequilibrium (LD) fine mapping with 31 SNPs from this gene, searching for the region of highest association with MS in a German sample consisting of 603 patients and 825 controls.Four SNPs located in intron 19 of the CLEC16A gene were found associated. We could replicate the finding for SNP rs725613 and were able to show for the first time the association of rs2041670, rs2080272 and rs998592 with MS.All described base polymorphisms are mapping to one LD block of approximately 50 kb within intron 19 of the CLEC16A gene, suggesting a pivotal role of this region for susceptibility of MS and possibly also for other autoimmune diseases.

In a genome wide association study consisting of 592 German multiple sclerosis (MS) patients and 825 controls we were able to replicate the association of the HLA region with MS independently of previous case control studies. No SNPs outside the HLA region reached a genome wide level of significance. Nevertheless, we found suggestive evidence for an association of MS with variants in two new genes, the VAV2 gene and the gene for ZNF433.

Protein analysing examinations were carried through in children--fit to be compared to their age--suffering from inflammable ear diseases (acute middle ear inflammation, mastoiditis) and those who were healthy as to their ears. The obtained values statistically analysed resulted in significant differences as to the acute phase and those of closely associated proteins in the sense that haptoglobin, C3c and C1-inactivators in patients with a mastoiditis and partially in those who suffered from an acute middle ear inflammation were increased but prealbumin and transferrin in the same group were decreased. Subsequently the correlative connections of the parameters with singled-out clinical findings in the mastoiditis-group were examined. A factor of the acute phase, a factor of the subacute phase and the immunoglobulin factor could have been marked off, whereby the two first-mentioned are significant for mastoiditis, a fact that will help to facilitate the decision on further therapeutical practice. Presently there are no specific proteins for diagnostics of 'mastoiditis', so that to clinical expertness and experience of the therapeutist will come up the most significant role for the ill child.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS., Author summary In the era of steadily increasing amounts of available genetic data, we still lack novel and innovative ideas on how to improve fine-mapping of regulatory variants identified by genome-wide association studies (GWAS), especially in non-coding regions. Current approaches for the identification of functional variants conduct functional annotation after the GWAS analysis either using position-based overlaps of each variant with regulatory elements or deep-learning-based methods predicting regulatory effects per variant on cell-type-specific chromatin features. We here present DeepWAS, which integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Our results provide evidence that DeepWAS results directly identify disease/trait-associated SNPs with a common effect on a specific chromatin feature in a relevant tissue. We can show for multiple sclerosis, major depressive disorder, and body height, that the SNPs identified by DeepWAS are at least nominally significant in classical univariate GWAS analysis of the same cohorts or larger published GWAS. By integrating expression and methylation quantitative trait loci (eQTL and meQTL) information of multiple resources and tissues, we can show that DeepWAS identifies disease/trait-relevant transcriptionally active genomic loci. We demonstrate that DeepWAS identifies both known variants and highlights underlying molecular mechanisms.