Locus for severity implicates CNS resilience in progression of multiple sclerosis

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that results in significant neurodegeneration in the majority of those affected and is a common cause of chronic neurological disability in young adults(1,2). Here, to provide insight into the potential mechanisms involved in progression, we conducted a genome-wide association study of the age-related MS severity score in 12,584 cases and replicated our findings in a further 9,805 cases. We identified a significant association with rs10191329 in the DYSF-ZNF638 locus, the risk allele of which is associated with a shortening in the median time to requiring a walking aid of a median of 3.7 years in homozygous carriers and with increased brainstem and cortical pathology in brain tissue. We also identified suggestive association with rs149097173 in the DNM3-PIGC locus and significant heritability enrichment in CNS tissues. Mendelian randomization analyses suggested a potential protective role for higher educational attainment. In contrast to immune-driven susceptibility(3), these findings suggest a key role for CNS resilience and potentially neurocognitive reserve in determining outcome in MS

Smoking and multiple sclerosis risk: a Mendelian randomization study

BACKGROUND: Striking changes in the demographic pattern of multiple sclerosis (MS) strongly indicate an influence of modifiable exposures, which lend themselves well to intervention. It is important to pinpoint which of the many environmental, lifestyle, and sociodemographic changes that have occurred over the past decades, such as higher smoking and obesity rates, are responsible. Mendelian randomization (MR) is an elegant tool to overcome limitations inherent to observational studies and leverage human genetics to inform prevention strategies in MS. METHODS: We use genetic variants from the largest genome-wide association study for smoking phenotypes (initiation: N = 378, heaviness: N = 55, lifetime smoking: N = 126) and body mass index (BMI, N = 656) and apply these as instrumental variables in a two-sample MR analysis to the most recent meta-analysis for MS. We adjust for the genetic correlation between smoking and BMI in a multivariable MR. RESULTS: In univariable and multivariable MR, smoking does not have an effect on MS risk nor explains part of the association between BMI and MS risk. In contrast, in both analyses each standard deviation increase in BMI, corresponding to roughly 5 kg/m2 units, confers a 30% increase in MS risk. CONCLUSION: Despite observational studies repeatedly reporting an association between smoking and increased risk for MS, MR analyses on smoking phenotypes and MS risk could not confirm a causal relationship. This is in contrast with BMI, where observational studies and MR agree on a causal contribution. The reasons for the discrepancy between observational studies and our MR study concerning smoking and MS require further investigation.status: Published onlin

Genetics of multiple sclerosis: lessons from polygenicity

Large-scale mapping studies have identified 236 independent genetic variants associated with an increased risk of multiple sclerosis. However, none of these variants are found exclusively in patients with multiple sclerosis. They are located throughout the genome, including 32 independent variants in the MHC and one on the X chromosome. Most variants are non-coding and seem to act through cell-specific effects on gene expression and splicing. The likely functions of these variants implicate both adaptive and innate immune cells in the pathogenesis of multiple sclerosis, provide pivotal biological insight into the causes and mechanisms of multiple sclerosis, and some of the variants implicated in multiple sclerosis also mediate risk of other autoimmune and inflammatory diseases. Genetics offers an approach to showing causality for environmental factors, through Mendelian randomisation. No single variant is necessary or sufficient to cause multiple sclerosis; instead, each increases total risk in an additive manner. This combined contribution from many genetic factors to disease risk, or polygenicity, has important consequences for how we interpret the epidemiology of multiple sclerosis and how we counsel patients on risk and prognosis. Ongoing efforts are focused on increasing cohort sizes, increasing diversity and detailed characterisation of study populations, and translating these associations into an understanding of the biology of multiple sclerosis

CHIT1 at Diagnosis Reflects Long-Term Multiple Sclerosis Disease Activity

OBJECTIVE: Evidence for a role of microglia in the pathogenesis of multiple sclerosis (MS) is growing. We investigated association of microglial markers at time of diagnostic lumbar puncture (LP) with different aspects of disease activity (relapses, disability, magnetic resonance imaging parameters) up to 6 years later in a cohort of 143 patients. METHODS: In cerebrospinal fluid (CSF), we measured 3 macrophage and microglia-related proteins, chitotriosidase (CHIT1), chitinase-3-like protein 1 (CHI3L1 or YKL-40), and soluble triggering receptor expressed on myeloid cells 2 (sTREM2), as well as a marker of neuronal damage, neurofilament light chain (NfL), using enzyme-linked immunosorbent assay and electrochemiluminescence. We investigated the same microglia-related markers in publicly available RNA expression data from postmortem brain tissue. RESULTS: CHIT1 levels at diagnostic LP correlated with 2 aspects of long-term disease activity after correction for multiple testing. First, CHIT1 increased with reduced tissue integrity in lesions at a median 3 years later (p = 9.6E-04). Second, CHIT1 reflected disease severity at a median 5 years later (p = 1.2E-04). Together with known clinical covariates, CHIT1 levels explained 12% and 27% of variance in these 2 measures, respectively, and were able to distinguish slow and fast disability progression (area under the curve = 85%). CHIT1 was the best discriminator of chronic active versus chronic inactive lesions and the only marker correlated with NfL (r = 0.3, p = 0.0019). Associations with disease activity were, however, independent of NfL. INTERPRETATION: CHIT1 CSF levels measured during the diagnostic LP reflect microglial activation early on in MS and can be considered a valuable prognostic biomarker for future disease activity. ANN NEUROL 2020;87:633-645.status: publishe

Genetic Variation in WNT9B Increases Relapse Hazard in Multiple Sclerosis

Objective Many multiple sclerosis (MS) genetic susceptibility variants have been identified, but understanding disease heterogeneity remains a key challenge. Relapses are a core feature of MS and a common primary outcome of clinical trials, with prevention of relapses benefiting patients immediately and potentially limiting long-term disability accrual. We aim to identify genetic variation associated with relapse hazard in MS by analyzing the largest study population to date. Methods We performed a genomewide association study (GWAS) in a discovery cohort and investigated the genomewide significant variants in a replication cohort. Combining both cohorts, we captured a total of 2,231 relapses occurring before the start of any immunomodulatory treatment in 991 patients. For assessing time to relapse, we applied a survival analysis utilizing Cox proportional hazards models. We also investigated the association between MS genetic risk scores and relapse hazard and performed a gene ontology pathway analysis. Results The low-frequency genetic variant rs11871306 within WNT9B reached genomewide significance in predicting relapse hazard and replicated (meta-analysis hazard ratio (HR) = 2.15, 95% confidence interval (CI) = 1.70-2.78, p = 2.07 x 10(-10)). A pathway analysis identified an association of the pathway response to vitamin D with relapse hazard (p = 4.33 x 10(-6)). The MS genetic risk scores, however, were not associated with relapse hazard. Interpretation Genetic factors underlying disease heterogeneity differ from variants associated with MS susceptibility. Our findings imply that genetic variation within the Wnt signaling and vitamin D pathways contributes to differences in relapse occurrence. The present study highlights these cross-talking pathways as potential modulators of MS disease activity. ANN NEUROL 202

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

OBJECTIVES: Juvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed. METHODS: Here we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches. RESULTS: Immune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy. CONCLUSIONS: These results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.status: publishe

Genetic Architecture of Adaptive Immune System Identifies Key Immune Regulators

The immune system is highly diverse, but characterization of its genetic architecture has lagged behind the vast progress made by genome-wide association studies (GWASs) of emergent diseases. Our GWAS for 54 functionally relevant phenotypes of the adaptive immune system in 489 healthy individuals identifies eight genome-wide significant associations explaining 6%-20% of variance. Coding and splicing variants in PTPRC and COMMD10 are involved in memory T cell differentiation. Genetic variation controlling disease-relevant T helper cell subsets includes RICTOR and STON2 associated with Th2 and Th17, respectively, and the interferon-lambda locus controlling regulatory T cell proliferation. Early and memory B cell differentiation stages are associated with variation in LARP1B and SP4. Finally, the latrophilin family member ADGRL2 correlates with baseline pro-inflammatory interleukin-6 levels. Suggestive associations reveal mechanisms of autoimmune disease associations, in particular related to pro-inflammatory cytokine production. Pinpointing these key human immune regulators offers attractive therapeutic perspectives.status: publishe

Genetic Architecture of Adaptive Immune System Identifies Key Immune Regulators

Summary: The immune system is highly diverse, but characterization of its genetic architecture has lagged behind the vast progress made by genome-wide association studies (GWASs) of emergent diseases. Our GWAS for 54 functionally relevant phenotypes of the adaptive immune system in 489 healthy individuals identifies eight genome-wide significant associations explaining 6%–20% of variance. Coding and splicing variants in PTPRC and COMMD10 are involved in memory T cell differentiation. Genetic variation controlling disease-relevant T helper cell subsets includes RICTOR and STON2 associated with Th2 and Th17, respectively, and the interferon-lambda locus controlling regulatory T cell proliferation. Early and memory B cell differentiation stages are associated with variation in LARP1B and SP4. Finally, the latrophilin family member ADGRL2 correlates with baseline pro-inflammatory interleukin-6 levels. Suggestive associations reveal mechanisms of autoimmune disease associations, in particular related to pro-inflammatory cytokine production. Pinpointing these key human immune regulators offers attractive therapeutic perspectives. : Lagou et al. identify genetic factors explaining interindividual variation in composition of the adaptive immune system. Factors pinpoint key human immune regulators controlling B and T cell differentiation and levels of disease-relevant T helper and regulatory cells. These findings shed light on mechanisms of autoimmune disease and offer therapeutic perspectives. Keywords: adaptive immune system, immune phenotype, genetics, association, genome-wide association, autoimmunity, susceptibilit