scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.

Exploring the mRNA and Plasma Protein Levels of BDNF, NT4, SIRT1, HSP27, and HSP70 in Multiple Sclerosis Patients and Healthy Controls

Multiple sclerosis (MS) is a chronic, autoimmune neurodegenerative disease affecting the central nervous system. It is a major cause of non-traumatic neurological disability among young adults in North America and Europe. This study focuses on neuroprotective genes (BDNF, NT4/5, SIRT1, HSP70, and HSP27). Gene expression and protein levels of these markers were compared between MS patients and healthy controls. Blood samples were collected from 42 patients with multiple sclerosis (MS) and 48 control subjects without MS. Quantitative real-time PCR was performed to measure the expression of specific genes. The samples were analyzed in duplicate, and the abundance of mRNA was quantified using the 2-ΔCt method. ELISA assay was used to measure the concentration of specific proteins in the plasma samples. The results show that a 3.5-fold decrease in the gene expression of BDNF corresponds to a 1.5-fold downregulation in the associated plasma protein concentration (p < 0.001). Similar trends were observed with NT-4 (five-fold decrease, slight elevation in protein), SIRT1 (two-fold decrease, two-fold protein decrease), HSP70 (four-fold increase, nearly two-fold protein increase), and HSP27 (four-fold increase, two-fold protein increase) (p < 0.001). This study reveals strong correlations between gene expression and protein concentration in MS patients, emphasizing the relevance of these neuroprotective markers in the disease.

Simultaneous detection of DNA variation and methylation at HLA class II locus and immune gene promoters using targeted SureSelect Methyl-Sequencing

The Human Leukocyte Antigen (HLA) locus associates with a variety of complex diseases, particularly autoimmune and inflammatory conditions. The HLA-DR15 haplotype, for example, confers the major risk for developing Multiple Sclerosis in Caucasians, pinpointing an important role in the etiology of this chronic inflammatory disease of the central nervous system. In addition to the protein-coding variants that shape the functional HLA-antigen-T cell interaction, recent studies suggest that the levels of HLA molecule expression, that are epigenetically controlled, also play a role in disease development. However, deciphering the exact molecular mechanisms of the HLA association has been hampered by the tremendous genetic complexity of the locus and a lack of robust approaches to investigate it. Here, we developed a method to specifically enrich the genomic DNA from the HLA class II locus (chr6:32,426,802-34,167,129) and proximal promoters of 2,157 immune-relevant genes, utilizing the Agilent RNA-based SureSelect Methyl-Seq Capture related method, followed by sequencing to detect genetic and epigenetic variation. We demonstrated successful simultaneous detection of the genetic variation and quantification of DNA methylation levels in HLA locus. Moreover, by the detection of differentially methylated positions in promoters of immune-related genes, we identified relevant pathways following stimulation of cells. Taken together, we present a method that can be utilized to study the interplay between genetic variance and epigenetic regulation in the HLA class II region, potentially, in a wide disease context.

Genetic risk variants for multiple sclerosis are linked to differences in alternative pre-mRNA splicing

BACKGROUND

Multiple sclerosis (MS) is a chronic immune-mediated disease of the central nervous system to which a genetic predisposition contributes. Over 200 genetic regions have been associated with increased disease risk, but the disease-causing variants and their functional impact at the molecular level are mostly poorly defined. We hypothesized that single-nucleotide polymorphisms (SNPs) have an impact on pre-mRNA splicing in MS.

METHODS

Our study focused on 10 bioinformatically prioritized SNP-gene pairs, in which the SNP has a high potential to alter alternative splicing events (ASEs). We tested for differential gene expression and differential alternative splicing in B cells from MS patients and healthy controls. We further examined the impact of the SNP genotypes on ASEs and on splice isoform expression levels. Novel genotype-dependent effects on splicing were verified with splicing reporter minigene assays.

RESULTS

We were able to confirm previously described findings regarding the relation of MS-associated SNPs with the ASEs of the pre-mRNAs from GSDMB and SP140. We also observed an increased IL7R exon 6 skipping when comparing relapsing and progressive MS patients to healthy subjects. Moreover, we found evidence that the MS risk alleles of the SNPs rs3851808 (EFCAB13), rs1131123 (HLA-C), rs10783847 (TSFM), and rs2014886 (TSFM) may contribute to a differential splicing pattern. Of particular interest is the genotype-dependent exon skipping of TSFM due to the SNP rs2014886. The minor allele T creates a donor splice site, resulting in the expression of the exon 3 and 4 of a short TSFM transcript isoform, whereas in the presence of the MS risk allele C, this donor site is absent, and thus the short transcript isoform is not expressed.

CONCLUSION

In summary, we found that genetic variants from MS risk loci affect pre-mRNA splicing. Our findings substantiate the role of ASEs with respect to the genetics of MS. Further studies on how disease-causing genetic variants may modify the interactions between splicing regulatory sequence elements and RNA-binding proteins can help to deepen our understanding of the genetic susceptibility to MS.

RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases

Profiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. We characterised time-series of primary human naïve CD4+ T cells during early T helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-seq datasets to evaluate biomarker discovery in five T-cell associated diseases, further validating the findings for multiple sclerosis (MS) and asthma. The new models significantly out-performing models not including the usage of multiple splice variants and time delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was validated in MS using two independent cohorts for evaluating response to treatment and disease prognosis. In summary, our splice variant and time delay models substantially improved the prediction of protein abundance from mRNA expression in three different immune cell types. The models provided valuable biomarker candidates, which were further validated in MS and asthma.

Proximal and distal effects of genetic susceptibility to multiple sclerosis on the T cell epigenome

Identifying the effects of genetic variation on the epigenome in disease-relevant cell types can help advance our understanding of the first molecular contributions of genetic susceptibility to disease onset. Here, we establish a genome-wide map of DNA methylation quantitative trait loci in CD4⁺ T-cells isolated from multiple sclerosis patients. Utilizing this map in a colocalization analysis, we identify 19 loci where the same haplotype drives both multiple sclerosis susceptibility and local DNA methylation. We also identify two distant methylation effects of multiple sclerosis susceptibility loci: a chromosome 16 locus affects PRDM8 methylation (a chromosome 4 region not previously associated with multiple sclerosis), and the aggregate effect of multiple sclerosis-associated variants in the major histocompatibility complex influences DNA methylation near PRKCA (chromosome 17). Overall, we present a new resource for a key cell type in inflammatory disease research and uncover new gene targets for the study of predisposition to multiple sclerosis.

Cladribine Alters Immune Cell Surface Molecules for Adhesion and Costimulation: Further Insights to the Mode of Action in Multiple Sclerosis

Cladribine (CLAD) is a deoxyadenosine analogue prodrug which is given in multiple sclerosis (MS) as two short oral treatment courses 12 months apart. Reconstitution of adaptive immune function following selective immune cell depletion is the presumed mode of action. In this exploratory study, we investigated the impact of CLAD tablets on immune cell surface molecules for adhesion (CAMs) and costimulation (CoSs) in people with MS (pwMS). We studied 18 pwMS who started treatment with CLAD and 10 healthy controls (HCs). Peripheral blood mononuclear cells were collected at baseline and every 3 months throughout a 24-month period. We analysed ICAM-1, LFA-1, CD28, HLADR, CD154, CD44, VLA-4 (CD49d/CD29), PSGL-1 and PD-1 with regard to their expression on B and T cells (T helper (Th) and cytotoxic T cells (cT)) and surface density (mean fluorescence intensity, MFI) by flow cytometry. The targeted analysis of CAM and CoS on the surface of immune cells in pwMS revealed a higher percentage of ICAM-1 (B cells, Th, cT), LFA-1 (B cells, cT), HLADR (B cells, cT), CD28 (cT) and CD154 (Th). In pwMS, we found lower frequencies of Th and cT cells expressing PSGL-1 and B cells for the inhibitory signal PD-1, whereas the surface expression of LFA-1 on cT and of HLADR on B cells was denser. Twenty-four months after the first CLAD cycle, the frequencies of B cells expressing CD44, CD29 and CD49d were lower compared with the baseline, together with decreased densities of ICAM-1, CD44 and HLADR. The rate of CD154 expressing Th cells dropped at 12 months. For cT, no changes were seen for frequency or density. Immune reconstitution by oral CLAD was associated with modification of the pro-migratory and -inflammatory surface patterns of CAMs and CoSs in immune cell subsets. This observation pertains primarily to B cells, which are key cells underlying MS pathogenesis.

Investigating the shared genetic architecture between multiple sclerosis and inflammatory bowel diseases

An epidemiological association between multiple sclerosis (MS) and inflammatory bowel disease (IBD) is well established, but whether this reflects a shared genetic aetiology, and whether consistent genetic relationships exist between MS and the two predominant IBD subtypes, ulcerative colitis (UC) and Crohn’s disease (CD), remains unclear. Here, we use large-scale genome-wide association study summary data to investigate the shared genetic architecture between MS and IBD overall and UC and CD independently. We find a significantly greater genetic correlation between MS and UC than between MS and CD, and identify three SNPs shared between MS and IBD (rs13428812), UC (rs116555563) and CD (rs13428812, rs9977672) in cross-trait meta-analyses. We find suggestive evidence for a causal effect of MS on UC and IBD using Mendelian randomization, but no or weak and inconsistent evidence for a causal effect of IBD or UC on MS. We observe largely consistent patterns of tissue-specific heritability enrichment for MS and IBDs in lung, spleen, whole blood and small intestine, and identify cell-type-specific enrichment for MS and IBDs in CD4⁺ T cells in lung and CD8⁺ cytotoxic T cells in lung and spleen. Our study sheds light on the biological basis of comorbidity between MS and IBD.

An epidemiological association between multiple sclerosis (MS) and inflammatory bowel disease (IBD) is well-established, but a genetic link is unclear. Here, the authors investigate the shared genetic architecture between MS and IBD to shed light on the biological basis of comorbidity.

ETV7 limits antiviral gene expression and control of influenza viruses

The type I interferon (IFN) response is an important component of the innate immune response to viral infection. Precise control of IFN responses is critical because insufficient expression of IFN-stimulated genes (ISGs) can lead to a failure to restrict viral spread, whereas excessive ISG activation can result in IFN-related pathologies. Although both positive and negative regulatory factors control the magnitude and duration of IFN signaling, it is also appreciated that several ISGs regulate aspects of the IFN response themselves. In this study, we performed a CRISPR activation screen to identify previously unknown regulators of the type I IFN response. We identified the strongly induced ISG encoding ETS variant transcription factor 7 (ETV7) as a negative regulator of the type I IFN response. However, ETV7 did not uniformly suppress ISG transcription. Instead, ETV7 preferentially targeted a subset of antiviral ISGs that were particularly important for IFN-mediated control of influenza viruses. Together, our data assign a function for ETV7 as an IFN response regulator and also identify ETV7 as a potential therapeutic target to increase innate antiviral responses and enhance IFN-based antiviral therapies.

Expression Quantitative Trait Locus Analysis in Systemic Sclerosis Identifies New Candidate Genes Associated With Multiple Aspects of Disease Pathology

OBJECTIVE

To identify the genetic variants that affect gene expression (expression quantitative trait loci [eQTLs]) in systemic sclerosis (SSc) and to investigate their role in the pathogenesis of the disease.

METHODS

We performed an eQTL analysis using whole-blood sequencing data from 333 SSc patients and 524 controls and integrated them with SSc genome-wide association study (GWAS) data. We integrated our findings from expression modeling, differential expression analysis, and transcription factor binding site enrichment with key clinical features of SSc.

RESULTS

We detected 49,123 validated cis-eQTLs from 4,539 SSc-associated single-nucleotide polymorphisms (SNPs) (P_GWAS < 10^-5 ). A total of 1,436 genes were within 1 Mb of the 4,539 SSc-associated SNPs. Of those 1,436 genes, 565 were detected as having ≥1 eQTL with an SSc-associated SNP. We developed a strategy to prioritize disease-associated genes based on their expression variance explained by SSc eQTLs (r² > 0.05). As a result, 233 candidates were identified, 134 (58%) of them associated with hallmarks of SSc and 105 (45%) of them differentially expressed in the blood cells, skin, or lung tissue of SSc patients. Transcription factor binding site analysis revealed enriched motifs of 24 transcription factors (5%) among SSc eQTLs, 5 of which were found to be differentially regulated in the blood cells (ELF1 and MGA), skin (KLF4 and ID4), and lungs (TBX4) of SSc patients. Ten candidate genes (4%) can be targeted by approved medications for immune-mediated diseases, of which only 3 have been tested in clinical trials in patients with SSc.

CONCLUSION

The findings of the present study indicate a new layer to the molecular complexity of SSc, contributing to a better understanding of the pathogenesis of the disease.

Tyro3, Axl, Mertk receptor-mediated efferocytosis and immune regulation in the tumor environment

Three structurally related tyrosine receptor cell surface kinases, Tyro3, Axl, and Mertk (TAM) have been recognized to modulate immune function, tissue homeostasis, cardiovasculature, and cancer. The TAM receptor family appears to operate in adult mammals across multiple cell types, suggesting both widespread and specific regulation of cell functions and immune niches. TAM family members regulate tissue homeostasis by monitoring the presence of phosphatidylserine expressed on stressed or apoptotic cells. The detection of phosphatidylserine on apoptotic cells requires intermediary molecules that opsonize the dying cells and tether them to TAM receptors on phagocytes. This complex promotes the engulfment of apoptotic cells, also known as efferocytosis, that leads to the resolution of inflammation and tissue healing. The immune mechanisms dictating these processes appear to fall upon specific family members or may involve a complex of different receptors acting cooperatively to resolve and repair damaged tissues. Here, we focus on the role of TAM receptors in triggering efferocytosis and its consequences in the regulation of immune responses in the context of inflammation and cancer.

STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.

Molecular analysis of pediatric CNS-PNET revealed nosologic heterogeneity and potent diagnostic markers for CNS neuroblastoma with FOXR2-activation

Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly malignant neoplasms posing diagnostic challenge due to a lack of defining molecular markers. CNS neuroblastoma with forkhead box R2 (FOXR2) activation (CNS_NBL) emerged as a distinct pediatric brain tumor entity from a pool previously diagnosed as primitive neuroectodermal tumors of the central nervous system (CNS-PNETs). Current standard of identifying CNS_NBL relies on molecular analysis. We set out to establish immunohistochemical markers allowing safely distinguishing CNS_NBL from morphological mimics. To this aim we analyzed a series of 84 brain tumors institutionally diagnosed as CNS-PNET. As expected, epigenetic analysis revealed different methylation groups corresponding to the (1) CNS-NBL (24%), (2) glioblastoma IDH wild-type subclass H3.3 G34 (26%), (3) glioblastoma IDH wild-type subclass MYCN (21%) and (4) ependymoma with RELA_C11orf95 fusion (29%) entities. Transcriptome analysis of this series revealed a set of differentially expressed genes distinguishing CNS_NBL from its mimics. Based on RNA-sequencing data we established SOX10 and ANKRD55 expression as genes discriminating CNS_NBL from other tumors exhibiting CNS-PNET. Immunohistochemical detection of combined expression of SOX10 and ANKRD55 clearly identifies CNS_NBL discriminating them to other hemispheric CNS neoplasms harboring “PNET-like” microscopic appearance. Owing the rarity of CNS_NBL, a confirmation of the elaborated diagnostic IHC algorithm will be necessary in prospective patient series.

Summary-data-based Mendelian randomization prioritizes potential druggable targets for multiple sclerosis

Multiple sclerosis is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study findings into therapeutics and effective preventive strategies has been limited to date. We used summary-data-based Mendelian randomization to synthesize findings from public expression quantitative trait locus, methylation quantitative trait locus and Multiple Sclerosis Genome-Wide Association Study datasets. By correlating the effects of methylation on multiple sclerosis, methylation on expression and expression on multiple sclerosis susceptibility, we prioritize genetic loci with evidence of influencing multiple sclerosis susceptibility. We overlay these findings onto a list of 'druggable' genes, i.e. genes which are currently, or could theoretically, be targeted by therapeutic compounds. We use GeNets and search tool for the retrieval of interacting genes/proteins to identify protein-protein interactions and druggable pathways enriched in our results. We extend these findings to a model of Epstein-Barr virus-infected B cells, lymphoblastoid cell lines. We conducted a systematic review of prioritized genes using the Open Targets platform to identify completed and planned trials targeting prioritized genes in multiple sclerosis and related disease areas. Expression of 45 genes in peripheral blood was strongly associated with multiple sclerosis susceptibility (False discovery rate 0.05). Of these 45 genes, 20 encode a protein which is currently targeted by an existing therapeutic compound. These genes were enriched for Gene Ontology terms pertaining to immune system function and leucocyte signalling. We refined this prioritized gene list by restricting to loci where CpG site methylation was associated with multiple sclerosis susceptibility, with gene expression and where expression was associated with multiple sclerosis susceptibility. This approach yielded a list of 15 prioritized druggable target genes for which there was evidence of a pathway linking methylation, expression and multiple sclerosis. Five of these 15 genes are targeted by existing drugs and three were replicated in a smaller expression Quantitative Trait Loci dataset (CD40, MERTK and PARP1). In lymphoblastoid cell lines, this approach prioritized 7 druggable gene targets, of which only one was prioritized by the multi-omic approach in peripheral blood (FCRL3). Systematic review of Open Targets revealed multiple early-phase trials targeting 13/20 prioritized genes in disorders related to multiple sclerosis. We use public datasets and summary-data-based Mendelian randomization to identify a list of prioritized druggable genetic targets in multiple sclerosis. We hope our findings could be translated into a platform for developing targeted preventive therapies.

C-type lectin receptors Mcl and Mincle control development of multiple sclerosis-like neuroinflammation

Pattern recognition receptors (PRRs) are crucial for responses to infections and tissue damage; however, their role in autoimmunity is less clear. Herein we demonstrate that 2 C-type lectin receptors (CLRs) Mcl and Mincle play an important role in the pathogenesis of experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis (MS). Congenic rats expressing lower levels of Mcl and Mincle on myeloid cells exhibited a drastic reduction in EAE incidence. In vivo silencing of Mcl and Mincle or blockade of their endogenous ligand SAP130 revealed that these receptors’ expression in the central nervous system is crucial for T cell recruitment and reactivation into a pathogenic Th17/GM-CSF phenotype. Consistent with this, we uncovered MCL- and MINCLE-expressing cells in brain lesions of MS patients and we further found an upregulation of the MCL/MINCLE signaling pathway and an increased response following MCL/MINCLE stimulation in peripheral blood mononuclear cells from MS patients. Together, these data support a role for CLRs in autoimmunity and implicate the MCL/MINCLE pathway as a potential therapeutic target in MS.

Multiple sclerosis risk variants regulate gene expression in innate and adaptive immune cells

At least 200 single-nucleotide polymorphisms (SNPs) are associated with multiple sclerosis (MS) risk. A key function that could mediate SNP-encoded MS risk is their regulatory effects on gene expression. We performed microarrays using RNA extracted from purified immune cell types from 73 untreated MS cases and 97 healthy controls and then performed Cis expression quantitative trait loci mapping studies using additive linear models. We describe MS risk expression quantitative trait loci associations for 129 distinct genes. By extending these models to include an interaction term between genotype and phenotype, we identify MS risk SNPs with opposing effects on gene expression in cases compared with controls, namely, rs2256814 MYT1 in CD4 cells (q = 0.05) and rs12087340 RF00136 in monocyte cells (q = 0.04). The rs703842 SNP was also associated with a differential effect size on the expression of the METTL21B gene in CD8 cells of MS cases relative to controls (q = 0.03). Our study provides a detailed map of MS risk loci that function by regulating gene expression in cell types relevant to MS.

B and T Cells Driving Multiple Sclerosis: Identity, Mechanisms and Potential Triggers

Historically, multiple sclerosis (MS) has been viewed as being primarily driven by T cells. However, the effective use of anti-CD20 treatment now also reveals an important role for B cells in MS patients. The results from this treatment put forward T-cell activation rather than antibody production by B cells as a driving force behind MS. The main question of how their interaction provokes both B and T cells to infiltrate the CNS and cause local pathology remains to be answered. In this review, we highlight key pathogenic events involving B and T cells that most likely contribute to the pathogenesis of MS. These include (1) peripheral escape of B cells from T cell-mediated control, (2) interaction of pathogenic B and T cells in secondary lymph nodes, and (3) reactivation of B and T cells accumulating in the CNS. We will focus on the functional programs of CNS-infiltrating lymphocyte subsets in MS patients and discuss how these are defined by mechanisms such as antigen presentation, co-stimulation and cytokine production in the periphery. Furthermore, the potential impact of genetic variants and viral triggers on candidate subsets will be debated in the context of MS.

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

Horizontal integration of summary statistics from different GWAS traits can be used to evaluate evidence for their shared genetic causality. One popular method to do this is a Bayesian method, coloc, which is attractive in requiring only GWAS summary statistics and no linkage disequilibrium estimates and is now being used routinely to perform thousands of comparisons between traits. Here we show that while most users do not adjust default software values, misspecification of prior parameters can substantially alter posterior inference. We suggest data driven methods to derive sensible prior values, and demonstrate how sensitivity analysis can be used to assess robustness of posterior inference. The flexibility of coloc comes at the expense of an unrealistic assumption of a single causal variant per trait. This assumption can be relaxed by stepwise conditioning, but this requires external software and an LD matrix aligned to study alleles. We have now implemented conditioning within coloc, and propose a new alternative method, masking, that does not require LD and approximates conditioning when causal variants are independent. Importantly, masking can be used in combination with conditioning where allelically aligned LD estimates are available for only a single trait. We have implemented these developments in a new version of coloc which we hope will enable more informed choice of priors and overcome the restriction of the single causal variant assumptions in coloc analysis.

Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder

Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein-protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes.

Transcribed B lymphocyte genes and multiple sclerosis risk genes are underrepresented in Epstein-Barr Virus hypomethylated regions

Epstein-Barr Virus (EBV) infection appears to be necessary for the development of Multiple Sclerosis (MS), although the specific mechanisms are unknown. More than 200 single-nucleotide polymorphisms (SNPs) are known to be associated with the risk of developing MS. About a quarter of these are also highly associated with proximal gene expression in B cells infected with EBV (lymphoblastoid cell lines-LCLs). The DNA of LCLs is hypomethylated compared with both uninfected and activated B cells. Since methylation can affect gene expression, and so cell differentiation and immune evasion, we hypothesised that EBV-driven hypomethylation may affect the interaction between EBV infection and MS. We interrogated an existing dataset comprising three individuals with whole-genome bisulfite sequencing data from EBV transformed B cells and CD40L-activated B cells. DNA methylation surrounding MS risk SNPs associated with gene expression in LCLs (LCLeQTL) was less likely to be hypomethylated than randomly selected chromosomal regions. Differential methylation was independent of genomic features such as promoter regions, but genes preferentially expressed in EBV-infected B cells, including the LCLeQTL genes, were underrepresented in the hypomethylated regions. Our data does not indicate MS genetic risk is affected by EBV hypomethylation.

Reworking GWAS Data to Understand the Role of Nongenetic Factors in MS Etiopathogenesis

Genome-wide association studies have identified more than 200 multiple sclerosis (MS)-associated loci across the human genome over the last decade, suggesting complexity in the disease etiology. This complexity poses at least two challenges: the definition of an etiological model including the impact of nongenetic factors, and the clinical translation of genomic data that may be drivers for new druggable targets. We reviewed studies dealing with single genes of interest, to understand how MS-associated single nucleotide polymorphism (SNP) variants affect the expression and the function of those genes. We then surveyed studies on the bioinformatic reworking of genome-wide association studies (GWAS) data, with aggregate analyses of many GWAS loci, each contributing with a small effect to the overall disease predisposition. These investigations uncovered new information, especially when combined with nongenetic factors having possible roles in the disease etiology. In this context, the interactome approach, defined as “modules of genes whose products are known to physically interact with environmental or human factors with plausible relevance for MS pathogenesis”, will be reported in detail. For a future perspective, a polygenic risk score, defined as a cumulative risk derived from aggregating the contributions of many DNA variants associated with a complex trait, may be integrated with data on environmental factors affecting the disease risk or protection.

Changes in Deoxyribonucleic Acid Methylation Contribute to the Pathophysiology of Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system characterized by loss of coordination, weakness, dysfunctions in bladder capacity, bowel movement, and cognitive impairment. Thus, the disease leads to a significant socioeconomic burden. In the pathophysiology of the disease, both genetic and environmental risk factors are involved. Gene x environment interaction is modulated by epigenetic mechanisms. Epigenetics refers to a sophisticated system that regulates gene expression with no changes in the DNA sequence. The most studied epigenetic mechanism is the DNA methylation. In this review, we summarize the data available from the current literature by grouping sets of differentially methylated genes in distinct biological categories: the immune system including innate and adaptive response, the DNA damage, and the central nervous system.

Interactome of the Autoimmune Risk Protein ANKRD55

The ankyrin repeat domain-55 (ANKRD55) gene contains intronic single nucleotide polymorphisms (SNPs) associated with risk to contract multiple sclerosis, rheumatoid arthritis or other autoimmune disorders. Risk alleles of these SNPs are associated with higher levels of ANKRD55 in CD4⁺ T cells. The biological function of ANKRD55 is unknown, but given that ankyrin repeat domains constitute one of the most common protein-protein interaction platforms in nature, it is likely to function in complex with other proteins. Thus, identification of its protein interactomes may provide clues. We identified ANKRD55 interactomes via recombinant overexpression in HEK293 or HeLa cells and mass spectrometry. One hundred forty-eight specifically interacting proteins were found in total protein extracts and 22 in extracts of sucrose gradient-purified nuclei. Bioinformatic analysis suggested that the ANKRD55-protein partners from total protein extracts were related to nucleotide and ATP binding, enriched in nuclear transport terms and associated with cell cycle and RNA, lipid and amino acid metabolism. The enrichment analysis of the ANKRD55-protein partners from nuclear extracts is related to sumoylation, RNA binding, processes associated with cell cycle, RNA transport, nucleotide and ATP binding. The interaction between overexpressed ANKRD55 isoform 001 and endogenous RPS3, the cohesins SMC1A and SMC3, CLTC, PRKDC, VIM, β-tubulin isoforms, and 14-3-3 isoforms were validated by western blot, reverse immunoprecipitaton and/or confocal microscopy. We also identified three phosphorylation sites in ANKRD55, with S436 exhibiting the highest score as likely 14-3-3 binding phosphosite. Our study suggests that ANKRD55 may exert function(s) in the formation or architecture of multiple protein complexes, and is regulated by (de)phosphorylation reactions. Based on interactome and subcellular localization analysis, ANKRD55 is likely transported into the nucleus by the classical nuclear import pathway and is involved in mitosis, probably via effects associated with mitotic spindle dynamics.

Quantitative proteomic analyses of CD4+ and CD8+ T cells reveal differentially expressed proteins in multiple sclerosis patients and healthy controls

Background

Multiple sclerosis (MS) is an autoimmune, neuroinflammatory disease, with an unclear etiology. However, T cells play a central role in the pathogenesis by crossing the blood–brain-barrier, leading to inflammation of the central nervous system and demyelination of the protective sheath surrounding the nerve fibers. MS has a complex inheritance pattern, and several studies indicate that gene interactions with environmental factors contribute to disease onset.

Methods

In the current study, we evaluated T cell dysregulation at the protein level using electrospray liquid chromatography–tandem mass spectrometry to get novel insights into immune-cell processes in MS. We have analyzed the proteomic profiles of CD4⁺ and CD8⁺ T cells purified from whole blood from 13 newly diagnosed, treatment-naive female patients with relapsing–remitting MS and 14 age- and sex-matched healthy controls.

Results

An overall higher protein abundance was observed in both CD4⁺ and CD8⁺ T cells from MS patients when compared to healthy controls. The differentially expressed proteins were enriched for T-cell specific activation pathways, especially CTLA4 and CD28 signaling in CD4⁺ T cells. When selectively analyzing proteins expressed from the genes most proximal to > 200 non-HLA MS susceptibility polymorphisms, we observed differential expression of eight proteins in T cells between MS patients and healthy controls, and there was a correlation between the genotype at three MS genetic risk loci and protein expressed from proximal genes.

Conclusion

Our study provides evidence for proteomic differences in T cells from relapsing–remitting MS patients compared to healthy controls and also identifies dysregulation of proteins encoded from MS susceptibility genes.

Electronic supplementary material

The online version of this article (10.1186/s12014-019-9241-5) contains supplementary material, which is available to authorized users.

Multi-drug use among patients with multiple sclerosis: A cross-sectional study of associations to clinicodemographic factors

Multiple sclerosis (MS) is the most prevalent immune-mediated disease affecting the central nervous system. A treatment strategy with multiple therapies is a frequent clinical scenario. Unmonitored multi-drug use can lead to adverse outcomes, higher health care costs and medication non-adherence. The primary aim of this study was to evaluate the frequency of polypharmacy and related clinicodemographic factors in a single-center MS patient cohort. Furthermore, medication aspects of therapy management were examined. After the patients agreed to participate in the study, data were collected through patient interviews, patient records and clinical investigations. Subsequently, a statistical data analysis regarding various medication subgroups and polypharmacy (use of at least five drugs) was performed. Polypharmacy was observed in 56.5% of the patients (N = 306). High degrees of disability (odds ratio [OR] = 1.385), comorbidities (OR = 4.879) and inpatient treatment (OR = 5.146) were associated with a significantly higher risk of polypharmacy (p ≤ 0.001). Among patients with polypharmacy, disease-modifying drugs, antihypertensives, gastrointestinal drugs, thrombosis prophylactics, osteoporosis medications and sedatives were frequently used. In summary, polypharmacy plays a large role in MS patients, especially in those with higher degrees of disability, those with comorbidities and those treated in an inpatient setting.

A genetic variant associated with multiple sclerosis inversely affects the expression of CD58 and microRNA-548ac from the same gene

Genome-wide association studies have identified more than 200 genetic variants to be associated with an increased risk of developing multiple sclerosis (MS). Still, little is known about the causal molecular mechanisms that underlie the genetic contribution to disease susceptibility. In this study, we investigated the role of the single-nucleotide polymorphism (SNP) rs1414273, which is located within the microRNA-548ac stem-loop sequence in the first intron of the CD58 gene. We conducted an expression quantitative trait locus (eQTL) analysis based on public RNA-sequencing and microarray data of blood-derived cells of more than 1000 subjects. Additionally, CD58 transcripts and mature hsa-miR-548ac molecules were measured using real-time PCR in peripheral blood samples of 32 MS patients. Cell culture experiments were performed to evaluate the efficiency of Drosha-mediated stem-loop processing dependent on genotype and to determine the target genes of this underexplored microRNA. Across different global populations and data sets, carriers of the MS risk allele showed reduced CD58 mRNA levels but increased hsa-miR-548ac levels. We provide evidence that the SNP rs1414273 might alter Drosha cleavage activity, thereby provoking partial uncoupling of CD58 gene expression and microRNA-548ac production from the shared primary transcript in immune cells. Moreover, the microRNA was found to regulate genes, which participate in inflammatory processes and in controlling the balance of protein folding and degradation. We thus uncovered new regulatory implications of the MS-associated haplotype of the CD58 gene locus, and we remind that paradoxical findings can be encountered in the analysis of eQTLs upon data aggregation. Our study illustrates that a better understanding of RNA processing events might help to establish the functional nature of genetic variants, which predispose to inflammatory and neurological diseases.

More than 200 genetic loci have been associated with an increased risk of developing multiple sclerosis (MS). Here, we investigated the role of a single-nucleotide polymorphism (SNP), which is located within the microRNA-548ac stem-loop sequence in the first intron of the CD58 gene. We analyzed expression data of blood-derived cells of about 1000 subjects and observed that MS risk allele carriers have reduced CD58 mRNA levels but increased hsa-miR-548ac levels. Our findings suggest that Drosha cleavage activity is affected, perhaps attributable to the specific SNP. This may contribute to partial uncoupling of CD58 gene expression and hsa-miR-548ac production from the shared primary transcript in immune cells. We discovered that the mature microRNA downregulates genes involved in inflammatory processes and in controlling the balance of protein folding and degradation. Our study exemplifies that paradoxical findings can be encountered in the analysis of genetic variants regulating transcription and/or RNA processing.

Polypharmacy in outpatients with relapsing-remitting multiple sclerosis: A single-center study

Background

Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system. Given the chronic and heterogenous nature of the disease, treatment with various therapies is a frequent scenario in clinical practice. In persons with chronic morbidity such as MS patients, polypharmacy can give rise to considerable health problems.

Objectives

The aim of the present study was to examine the frequency of polypharmacy among relapsing-remitting (RR) MS patients as well as to analyse sociodemographic and clinical factors, which might be associated with polypharmacy (use of five or more medications). Differences in medication between MS patients with and without secondary illnesses (PwSI and Pw/oSI), between men and women and between patients with and without polypharmacy (PwP and Pw/oP) were examined.

Methods

For 145 RRMS outpatients, we prospectively collected data by means of anamnesis, patient records, clinical examination and a structured patient interview. This was followed by comparative analyses of various patient subgroups (PwP vs. Pw/oP, PwSI vs. Pw/oSI, men vs. women).

Results

The proportion of included MS patients with polypharmacy (use of ≥5 medications) was 30.3%. PwP were significantly older than Pw/oP (45.9 vs. 41.7 years), had a lower level of education and showed a significantly higher median EDSS score (3.0 vs. 2.0). Comorbidities (p<0.001; odds ratio [OR] = 6.293) and higher EDSS scores (p = 0.029; OR = 1.440) were associated with a higher risk of polypharmacy. The proportion of polypharmacy among PwSI was approximately four times higher than among Pw/oSI (46.8% vs. 11.8%). Particularly in the use of antihypertensives, gastrointestinal drugs and dietary supplements, there were differences between Pw/oP and PwP.

Conclusion

We found a high burden of polypharmacy in patients with RRMS. This particularly applies to more severely disabled MS patients who suffer from comorbidities.

DNA methylation as a mediator of HLA-DRB1*15:01 and a protective variant in multiple sclerosis

The human leukocyte antigen (HLA) haplotype DRB1*15:01 is the major risk factor for multiple sclerosis (MS). Here, we find that DRB1*15:01 is hypomethylated and predominantly expressed in monocytes among carriers of DRB1*15:01. A differentially methylated region (DMR) encompassing HLA-DRB1 exon 2 is particularly affected and displays methylation-sensitive regulatory properties in vitro. Causal inference and Mendelian randomization provide evidence that HLA variants mediate risk for MS via changes in the HLA-DRB1 DMR that modify HLA-DRB1 expression. Meta-analysis of 14,259 cases and 171,347 controls confirms that these variants confer risk from DRB1*15:01 and also identifies a protective variant (rs9267649, p < 3.32 × 10^-8, odds ratio = 0.86) after conditioning for all MS-associated variants in the region. rs9267649 is associated with increased DNA methylation at the HLA-DRB1 DMR and reduced expression of HLA-DRB1, suggesting a modulation of the DRB1*15:01 effect. Our integrative approach provides insights into the molecular mechanisms of MS susceptibility and suggests putative therapeutic strategies targeting a methylation-mediated regulation of the major risk gene.