Factors predisposing to humoral autoimmunity against brain-antigens in health and disease Analysis of 49 autoantibodies in over 7000 subjects

Background:Circulating autoantibodies (AB) against brain-antigens, often deemed pathological, receive increasing attention. We assessed predispositions and seroprevalence/characteristics of 49 AB in >7000 individuals.Methods:Exploratory cross-sectional cohort study, investigating deeply phenotyped neuropsychiatric patients and healthy individuals of GRAS Data Collection for presence/characteristics of 49 brain-directed serum-AB. Predispositions were evaluated through GWAS of NMDAR1-AB carriers, analyses of immune check-point genotypes, APOE4 status, neurotrauma. Chi-square, Fisher’s exact tests and logistic regression analyses were used.Results:Study of N=7025 subjects (55.8% male; 41±16 years) revealed N=1133 (16.13%) carriers of any AB against 49 defined brain-antigens. Overall, age dependence of seroprevalence (OR=1.018/year; 95% CI [1.015-1.022]) emerged, but no disease association, neither general nor with neuropsychiatric subgroups. Males had higher AB seroprevalence (OR=1.303; 95% CI [1.144-1.486]). Immunoglobulin class (N for IgM:462; IgA:487; IgG:477) and titers were similar. Abundant were NMDAR1-AB (7.7%). Low seroprevalence (1.25%-0.02%) was seen for most AB (e.g. amphiphysin, KCNA2, ARHGAP26, GFAP, CASPR2, MOG, Homer-3, KCNA1, GLRA1b, GAD65). Non-detectable were others. GWAS of NMDAR1-AB carriers revealed three genome-wide significant SNPs, two intergenic, one in TENM3, previously autoimmune disease-associated. Targeted analysis of immune check-point genotypes (CTLA4, PD1, PD-L1) uncovered effects on humoral anti-brain autoimmunity (OR=1.55; 95% CI [1.058-2.271]) and disease likelihood (OR=1.43; 95% CI [1.032-1.985]). APOE4 carriers (∼19%) had lower seropositivity (OR=0.766; 95% CI [0.625-0.933]). Neurotrauma predisposed to NMDAR1-AB seroprevalence (IgM: OR=1.599; 95% CI [1.022-2.468]).Conclusions:Humoral autoimmunity against brain-antigens, frequent across health and disease, is predicted by age, gender, genetic predisposition, and brain injury. Seroprevalence, immunoglobulin class, or titers do not predict disease

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation

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

DeepWAS: multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

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

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

Genome-wide association studies (GWAS) identify genetic variants associated with quantitative traits or disease. Thus, GWAS never directly link variants to regulatory mechanisms, which, in turn, are typically inferred during post-hoc analyses. In parallel, a recent deep learning-based method allows for prediction of regulatory effects per variant on currently up to 1,000 cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that directly integrates predictions of these regulatory effects of single variants into a multivariate GWAS setting. As a result, single variants associated with a trait or disease are, by design, coupled to their impact on a chromatin feature in a cell type. Up to 40,000 regulatory single-nucleotide polymorphisms (SNPs) 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) to each identify 43-61 regulatory SNPs, called deepSNPs, which are shown to reach at least nominal significance in large GWAS. MS- and height-specific deepSNPs resided in active chromatin and introns, whereas MDD-specific deepSNPs located mostly to intragenic regions and repressive chromatin states. We found deepSNPs to be enriched in public or cohort-matched expression and methylation quantitative trait loci and demonstrate the potential of the DeepWAS method to directly generate testable functional hypotheses based on genotype data alone. DeepWAS is an innovative GWAS approach with the power to identify individual SNPs in non-coding regions with gene regulatory capacity with a joint contribution to disease risk. DeepWAS is available at https://github.com/cellmapslab/DeepWAS

Clinical implications of serum neurofilament in newly diagnosed MS patients: a longitudinal multicentre cohort study

BACKGROUND: We aim to evaluate serum neurofilament light chain (sNfL), indicating neuroaxonal damage, as a biomarker at diagnosis in a large cohort of early multiple sclerosis (MS) patients. METHODS: In a multicentre prospective longitudinal observational cohort, patients with newly diagnosed relapsing-remitting MS (RRMS) or clinically isolated syndrome (CIS) were recruited between August 2010 and November 2015 in 22 centers. Clinical parameters, MRI, and sNfL levels (measured by single molecule array) were assessed at baseline and up to four-year follow-up. FINDINGS: Of 814 patients, 54.7% (445) were diagnosed with RRMS and 45.3% (369) with CIS when applying 2010 McDonald criteria (RRMS[2010] and CIS[2010]). After reclassification of CIS[2010] patients with existing CSF analysis, according to 2017 criteria, sNfL levels were lower in CIS[2017] than RRMS[2017] patients (9.1 pg/ml, IQR 6.2-13.7 pg/ml, n = 45; 10.8 pg/ml, IQR 7.4-20.1 pg/ml, n = 213; p = 0.036). sNfL levels correlated with number of T2 and Gd+ lesions at baseline and future clinical relapses. Patients receiving disease-modifying therapy (DMT) during the first four years had higher baseline sNfL levels than DMT-naïve patients (11.8 pg/ml, IQR 7.5-20.7 pg/ml, n = 726; 9.7 pg/ml, IQR 6.4-15.3 pg/ml, n = 88). Therapy escalation decisions within this period were reflected by longitudinal changes in sNfL levels. INTERPRETATION: Assessment of sNfL increases diagnostic accuracy, is associated with disease course prognosis and may, particularly when measured longitudinally, facilitate therapeutic decisions

A digitally-augmented ground space with timed visual cues for facilitating forearm crutches’ mobility

Persuasive technologies for physical rehabilitation have been pro posed in a number of different health interventions such as post-stroke gait rehabilitation. We propose a new persuasive system, called Augmented Crut ches, aimed at helping people to walk with crutches. People with injuries, or with any sort of mobility problem typically use assistive devices such as crut ches, walkers or canes in order to be able to walk more independently. However, walking with crutches is a learning skill that needs continuous repetition and constant attention to detail in order to walk correctly with them and without suffering negative consequences, such as falls or injuries. In close collaboration with therapists, we identify the main issues that patients face when walking with crutches. These vary from person to person, but the most common and hardest challenges are the position and coordination of the crutches. Augmented Crut ches studies human behavior aspects in these situations and augments the ground space around the user with digital visual cues where timing is the most important factor, without the need for a constant therapist providing manual help. This is performed through a mini-projector connected to a smartphone, worn by the user in a portable, lightweight manner. Our system helps people to learn how to walk using crutches with increased self-confidence and motivation. Additionally, our work identifies timing, controllability and awareness as the key design dimensions for the successful creation of persuasive, interactive experiences for learning how to walk with crutches.info:eu-repo/semantics/publishedVersio

The Rare IL22RA2 Signal Peptide Coding Variant rs28385692 Decreases Secretion of IL-22BP Isoform-1, -2 and -3 and Is Associated with Risk for Multiple Sclerosis.

The IL22RA2 locus is associated with risk for multiple sclerosis (MS) but causative variants are yet to be determined. In a single nucleotide polymorphism (SNP) screen of this locus in a Basque population, rs28385692, a rare coding variant substituting Leu for Pro at position 16 emerged significantly (p = 0.02). This variant is located in the signal peptide (SP) shared by the three secreted protein isoforms produced by IL22RA2 (IL-22 binding protein-1(IL-22BPi1), IL-22BPi2 and IL-22BPi3). Genotyping was extended to a Europe-wide case-control dataset and yielded high significance in the full dataset (p = 3.17 × 10-4). Importantly, logistic regression analyses conditioning on the main known MS-associated SNP at this locus, rs17066096, revealed that this association was independent from the primary association signal in the full case-control dataset. In silico analysis predicted both disruption of the alpha helix of the H-region of the SP and decreased hydrophobicity of this region, ultimately affecting the SP cleavage site. We tested the effect of the p.Leu16Pro variant on the secretion of IL-22BPi1, IL-22BPi2 and IL-22BPi3 and observed that the Pro16 risk allele significantly lowers secretion levels of each of the isoforms to around 50%-60% in comparison to the Leu16 reference allele. Thus, our study suggests that genetically coded decreased levels of IL-22BP isoforms are associated with augmented risk for MS

Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state

Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility

We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and established a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 variants within the extended MHC. We used an ensemble of methods to prioritize 551 putative susceptibility genes that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we observed enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses

Assessment of microRNA-related SNP effects in the 3′ untranslated region of the IL22RA2 risk locus in multiple sclerosis

Abstract Recent large-scale association studies have identified over 100 MS risk loci. One of these MS risk variants is single-nucleotide polymorphism (SNP) rs17066096, located 14 kb downstream of IL22RA2. IL22RA2 represents a compelling MS candidate gene due to the role of IL-22 in autoimmunity; however, rs17066096 does not map into any known functional element. We assessed whether rs17066096 or a nearby proxy SNP may exert pathogenic effects by affecting microRNA-to-mRNA binding and thus IL22RA2 expression using comprehensive in silico predictions, in vitro reporter assays, and genotyping experiments in 6,722 individuals. In silico screening identified two predicted microRNA binding sites in the 3′UTR of IL22RA2 (for hsa-miR-2278 and hsamiR-411-5p) encompassing a SNP (rs28366) in moderate linkage disequilibrium with rs17066096 (r 2 =0.4). The binding of both microRNAs to the IL22RA2 3′UTR was confirmed in vitro, but their binding affinities were not significantly affected by rs28366. Association analyses revealed significant Electronic supplementary material The online version of this articl