The role of epigenetics in Multiple Sclerosis development, progression and treatment

The overall aim of this thesis was to determine epigenetic changes in peripheral immune cells from Multiple Sclerosis (MS) patients. MS is a chronic inflammatory neurodegenerative disease, which initially presents itself during young adulthood. Big consortia have identified over 230 different polymorphisms contributing to the risk of developing disease, with many of these polymorphisms located in immune genes. However, the odds ratios of these polymorphisms are small and many known environmental risk factors are contributing to the disease. This indicates that the risk may partially be conferred through epigenetic changes such as DNA methylation. In this thesis, we investigate the role of DNA methylation in different peripheral immune cells using genome-wide DNA methylation arrays. We first characterized DNA methylation patterns in four different immune cell types form relapsing-remitting (RRMS), secondary-progressive (SPMS) patients and healthy controls (HC) and compared them with each other. Here we found a shared signature between all cells types, and in SPMS we found a specific neurodegenerative signal, while in MS patients, we saw lymphocyte signaling and T cell activation being affected. The top changes in CD4+ T cells indicate a change in the VMP1/MIR21 locus. We functionally investigated this and found lower miR-21 expression and an increase of miR-21 target genes. Because the most numerous methylation changes were found in CD19+ B cells, we further investigated CD19+ cells in a second larger cohort. After meta-analysis, the changes in B cells indicate differences in metabolism and activation between RRMS and HC. To analyze the shared pathway data, we developed a method to cluster pathways, which we further developed into an R package called GeneSetCluster. We investigated the effects of dimethyl fumarate (DMF) and rituximab treatment on DNA methylation in CD4+ and CD14+ cells. The different treatments had a different cell type specific signature as well as different kinetics. After DMF treatment, we found changes in reactive oxygen species (ROS) signaling and T cell subtype associated genes. Furthermore, we identified a polymorphism associated with treatment outcome and ROS production that does not associate with disease susceptibility. After rituximab treatment, we found differences in activation, metabolism and motility associated genes. Our findings collectively underline the importance of investigating epigenetic changes in multiple cell types to identify novel, potentially modifiable

Transcriptomic Profiling Reveals That HMGB1 Induces Macrophage Polarization Different from Classical M1

Macrophages are key inflammatory immune cells that display dynamic phenotypes and functions in response to their local microenvironment. In different conditions, macrophage polarization can be induced by high-mobility group box 1 (HMGB1), a nuclear DNA-binding protein that activates innate immunity via the Toll-like receptor (TLR) 4, the receptor for advanced glycation end products (RAGE), and C-X-C chemokine receptor (CXCR) 4. This study investigated the phenotypes of murine bone-marrow-derived macrophages (BMDMs) stimulated with different HMGB1 redox isoforms using bulk RNA sequencing (RNA-Seq). Disulfide HMGB1 (dsHMGB1)-stimulated BMDMs showed a similar but distinct transcriptomic profile to LPS/IFNγ- and LPS-stimulated BMDMs. Fully reduced HMGB1 (frHMGB1) did not induce any significant transcriptomic change. Interestingly, compared to LPS/IFNγ- and LPS-, dsHMGB1-stimulated BMDMs showed lipid metabolism and foam cell differentiation gene set enrichment, and oil red O staining revealed that both dsHMGB1 and frHMGB1 alleviated oxidized low-density lipoprotein (oxLDL)-induced foam cells formation. Overall, this work, for the first time, used transcriptomic analysis by RNA-Seq to investigate the impact of HMGB1 stimulation on BMDM polarization. Our results demonstrated that dsHMGB1 and frHMGB1 induced distinct BMDM polarization phenotypes compared to LPS/IFNγ- and LPS- induced phenotypes.publishedVersio

Epigenetic clock indicates accelerated aging in glial cells of progressive multiple sclerosis patients

Background: Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disease of the central nervous system (CNS) characterized by irreversible disability at later progressive stages. A growing body of evidence suggests that disease progression depends on age and inflammation within the CNS. We aimed to investigate epigenetic aging in bulk brain tissue and sorted nuclei from MS patients using DNA methylation-based epigenetic clocks. Methods: We applied Horvath’s multi-tissue and Shireby’s brain-specific Cortical clock on bulk brain tissue (n = 46), sorted neuronal (n = 54), and glial nuclei (n = 66) from post-mortem brain tissue of progressive MS patients and controls. Results: We found a significant increase in age acceleration residuals, corresponding to 3.6 years, in glial cells of MS patients compared to controls (P = 0.0024) using the Cortical clock, which held after adjustment for covariates (Padj = 0.0263). The 4.8-year age acceleration found in MS neurons (P = 0.0054) did not withstand adjustment for covariates and no significant difference in age acceleration residuals was observed in bulk brain tissue between MS patients and controls. Conclusion: While the findings warrant replication in larger cohorts, our study suggests that glial cells of progressive MS patients exhibit accelerated biological aging.This study was supported by grants from the Swedish Research Council, the Swedish Association for Persons with Neurological Disabilities, the Swedish Brain Foundation, the Swedish MS Foundation, the Stockholm County Council – ALF project, the European Union’s Horizon 2020 research, innovation program (grant agreement No. 733161) and the European Research Council (ERC, grant agreement No. 818170), the Knut and Alice Wallenberg Foundation grant, Åke Wilberg Foundation, and Karolinska Institute’s funds. LK was supported by a fellowship from the Margaretha af Ugglas Foundation. DK was supported by an Erasmus fellowship. The funders of the study had no role in study design, sample acquisition, data collection, data analysis, data interpretation, or writing of the manuscript. AU-C was supported by “Doctorados industriales 2018–2020” and “Contrato predoctoral en investigación en ciencias y tecnologías de la salud en el periodo 2019–2022” fellowships, both funded by the Government of Navarra and by an Erasmus fellowship. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant agreement No. 2018-05973

STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications

BACKGROUND AND METHODS Systematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain steps in systematic reviews, such as data extraction, are labour-intensive, which hampers their feasibility, especially with the rapidly expanding body of biomedical literature. To bridge this gap, we aimed to develop a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n = 45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n = 31 publications; multiple sclerosis, n = 244 publications). RESULTS Our data mining tool, STEED (STructured Extraction of Experimental Data), successfully extracted key experimental parameters such as animal models and species, as well as risk of bias items like randomization or blinding, from in vivo studies. Sensitivity and specificity were over 85% and 80%, respectively, for most items in both validation corpora. Accuracy and F1-score were above 90% and 0.9 for most items in the validation corpora, respectively. Time savings were above 99%. CONCLUSIONS Our text mining tool, STEED, can extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. This enables the tool's deployment for probing a field in a research improvement context or replacing one human reader during data extraction, resulting in substantial time savings and contributing towards the automation of systematic reviews

Microglial autophagy-associated phagocytosis is essential for recovery from neuroinflammation

Multiple sclerosis (MS) is a leading cause of incurable progressive disability in young adults caused by inflammation and neurodegeneration in the central nervous system (CNS). The capacity of microglia to clear tissue debris is essential for maintaining and restoring CNS homeostasis. This capacity diminishes with age, and age strongly associates with MS disease progression, although the underlying mechanisms are still largely elusive. Here, we demonstrate that the recovery from CNS inflammation in a murine model of MS is dependent on the ability of microglia to clear tissue debris. Microglia-specific deletion of the autophagy regulator Atg7, but not the canonical macroautophagy protein Ulk1, led to increased intracellular accumulation of phagocytosed myelin and progressive MS-like disease. This impairment correlated with a microglial phenotype previously associated with neurodegenerative pathologies. Moreover, Atg7-deficient microglia showed notable transcriptional and functional similarities to microglia from aged wild-type mice that were also unable to clear myelin and recover from disease. In contrast, induction of autophagy in aged mice using the disaccharide trehalose found in plants and fungi led to functional myelin clearance and disease remission. Our results demonstrate that a noncanonical form of autophagy in microglia is responsible for myelin degradation and clearance leading to recovery from MS-like disease and that boosting this process has a therapeutic potential for age-related neuroinflammatory conditions.Swedish Research CouncilSwedish Brain FoundationSwedish Association for Persons with Neurological DisabilitiesStockholm County Council (ALF project)AstraZeneca (AstraZeneca-Science for Life Laboratory collaboration)European Union Horizon 2020/European Research Council Consolidator Grant (Epi4MS)Knut and Alice Wallenbergs FoundationMargeretha af Ugglas FoundationAlltid Litt SterkereFoundation of Swedish MS researchNEURO SwedenKarolinska InstitutetAccepte

Non-parametric combination analysis of multiple data types enables detection of novel regulatory mechanisms in T cells of multiple sclerosis patients

Multiple Sclerosis (MS) is an autoimmune disease of the central nervous system with prominent neurodegenerative components. The triggering and progression of MS is associated with transcriptional and epigenetic alterations in several tissues, including peripheral blood. The combined influence of transcriptional and epigenetic changes associated with MS has not been assessed in the same individuals. Here we generated paired transcriptomic (RNA-seq) and DNA methylation (Illumina 450 K array) profiles of CD4+ and CD8+ T cells (CD4, CD8), using clinically accessible blood from healthy donors and MS patients in the initial relapsing-remitting and subsequent secondary-progressive stage. By integrating the output of a differential expression test with a permutation-based non-parametric combination methodology, we identified 149 differentially expressed (DE) genes in both CD4 and CD8 cells collected from MS patients. Moreover, by leveraging the methylation-dependent regulation of gene expression, we identified the gene SH3YL1, which displayed significant correlated expression and methylation changes in MS patients. Importantly, silencing of SH3YL1 in primary human CD4 cells demonstrated its influence on T cell activation. Collectively, our strategy based on paired sampling of several cell-types provides a novel approach to increase sensitivity for identifying shared mechanisms altered in CD4 and CD8 cells of relevance in MS in small sized clinical materials

Hypermethylation of MIR21 in CD4+ T cells from patients with relapsing-remitting multiple sclerosis associates with lower miRNA-21 levels and concomitant up-regulation of its target genes

BACKGROUND: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system caused by genetic and environmental factors. DNA methylation, an epigenetic mechanism that controls genome activity, may provide a link between genetic and environmental risk factors.OBJECTIVE: We sought to identify DNA methylation changes in CD4+ T cells in patients with relapsing-remitting (RR-MS) and secondary-progressive (SP-MS) disease and healthy controls (HC).METHODS: We performed DNA methylation analysis in CD4+ T cells from RR-MS, SP-MS, and HC and associated identified changes with the nearby risk allele, smoking, age, and gene expression.RESULTS: We observed significant methylation differences in the VMP1/MIR21 locus, with RR-MS displaying higher methylation compared to SP-MS and HC. VMP1/MIR21 methylation did not correlate with a known MS risk variant in VMP1 or smoking but displayed a significant negative correlation with age and the levels of mature miR-21 in CD4+ T cells. Accordingly, RR-MS displayed lower levels of miR-21 compared to SP-MS, which might reflect differences in age between the groups, and healthy individuals and a significant enrichment of up-regulated miR-21 target genes.CONCLUSION: Disease-related changes in epigenetic marking of MIR21 in RR-MS lead to differences in miR-21 expression with a consequence on miR-21 target genes.</p

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 packag

GeneSetCluster: a tool for summarizing and integrating gene-set analysis results

Abstract Background Gene-set analysis tools, which make use of curated sets of molecules grouped based on their shared functions, aim to identify which gene-sets are over-represented in the set of features that have been associated with a given trait of interest. Such tools are frequently used in gene-centric approaches derived from RNA-sequencing or microarrays such as Ingenuity or GSEA, but they have also been adapted for interval-based analysis derived from DNA methylation or ChIP/ATAC-sequencing. Gene-set analysis tools return, as a result, a list of significant gene-sets. However, while these results are useful for the researcher in the identification of major biological insights, they may be complex to interpret because many gene-sets have largely overlapping gene contents. Additionally, in many cases the result of gene-set analysis consists of a large number of gene-sets making it complicated to identify the major biological insights. Results We present GeneSetCluster, a novel approach which allows clustering of identified gene-sets, from one or multiple experiments and/or tools, based on shared genes. GeneSetCluster calculates a distance score based on overlapping gene content, which is then used to cluster them together and as a result, GeneSetCluster identifies groups of gene-sets with similar gene-set definitions (i.e. gene content). These groups of gene-sets can aid the researcher to focus on such groups for biological interpretations. Conclusions GeneSetCluster is a novel approach for grouping together post gene-set analysis results based on overlapping gene content. GeneSetCluster is implemented as a package in R. The package and the vignette can be downloaded at https://github.com/TranslationalBioinformaticsUnit </jats:sec