Pharmacogenomics of clinical response to Natalizumab in multiple sclerosis: a genome-wide multi-centric association study.

BACKGROUND: Inter-individual differences in treatment response are marked in multiple sclerosis (MS). This is true for Natalizumab (NTZ), to which a subset of patients displays sub-optimal treatment response. We conducted a multi-centric genome-wide association study (GWAS), with additional pathway and network analysis to identify genetic predictors of response to NTZ. METHODS: MS patients from three different centers were included. Response to NTZ was dichotomized, nominating responders (R) relapse-free patients and non-responders (NR) all the others, over a follow-up of 4 years. Association analysis on ~ 4.7 M imputed autosomal common single-nucleotide polymorphisms (SNPs) was performed fitting logistic regression models, adjusted for baseline covariates, followed by meta-analysis at SNP and gene level. Finally, these signals were projected onto STRING interactome, to elicit modules and hub genes linked to response. RESULTS: Overall, 1834 patients were included: 119 from Italy (R = 94, NR = 25), 81 from Germany (R = 61, NR = 20), and 1634 from Sweden (R = 1349, NR = 285). The top-associated variant was rs11132400T (p = 1.33 × 10-6, OR = 0.58), affecting expression of several genes in the locus, like KLKB1. The interactome analysis implicated a module of 135 genes, with over-representation of terms like canonical WNT signaling pathway (padjust = 7.08 × 10-6). Response-associated genes like GRB2 and LRP6, already implicated in MS pathogenesis, were topologically prioritized within the module. CONCLUSION: This GWAS, the largest pharmacogenomic study of response to NTZ, suggested MS-implicated genes and Wnt/ß-catenin signaling pathway, an essential component for blood-brain barrier formation and maintenance, to be related to treatment response.

A genetic-epigenetic interplay at 1q21.1 locus underlies CHD1L-mediated vulnerability to primary progressive multiple sclerosis.

Multiple Sclerosis (MS) is a heterogeneous inflammatory and neurodegenerative disease with an unpredictable course towards progressive disability. Treating progressive MS is challenging due to limited insights into the underlying mechanisms. We examined the molecular changes associated with primary progressive MS (PPMS) using a cross-tissue (blood and post-mortem brain) and multilayered data (genetic, epigenetic, transcriptomic) from independent cohorts. In PPMS, we found hypermethylation of the 1q21.1 locus, controlled by PPMS-specific genetic variations and influencing the expression of proximal genes (CHD1L, PRKAB2) in the brain. Evidence from reporter assay and CRISPR/dCas9 experiments supports a causal link between methylation and expression and correlation network analysis further implicates these genes in PPMS brain processes. Knock-down of CHD1L in human iPSC-derived neurons and knock-out of chd1l in zebrafish led to developmental and functional deficits of neurons. Thus, several lines of evidence suggest a distinct genetic-epigenetic-transcriptional interplay in the 1q21.1 locus potentially contributing to PPMS pathogenesis.

Physiological aging and inflammation-induced cellular senescence may contribute to oligodendroglial dysfunction in MS.

Aging affects all cell types in the CNS and plays an important role in CNS diseases. However, the underlying molecular mechanisms driving these age-associated changes and their contribution to diseases are only poorly understood. The white matter in the aging brain as well as in diseases, such as Multiple sclerosis is characterized by subtle abnormalities in myelin sheaths and paranodes, suggesting that oligodendrocytes, the myelin-maintaining cells of the CNS, lose the capacity to preserve a proper myelin structure and potentially function in age and certain diseases. Here, we made use of directly converted oligodendrocytes (dchiOL) from young, adult and old human donors to study age-associated changes. dchiOL from all three age groups differentiated in an comparable manner into O4 + immature oligodendrocytes, but the proportion of MBP + mature dchiOL decreased with increasing donor age. This was associated with an increased ROS production and upregulation of cellular senescence markers such as CDKN1A, CDKN2A in old dchiOL. Comparison of the transcriptomic profiles of dchiOL from adult and old donors revealed 1324 differentially regulated genes with limited overlap with transcriptomic profiles of the donors' fibroblasts or published data sets from directly converted human neurons or primary rodent oligodendroglial lineage cells. Methylome analyses of dchiOL and human white matter tissue samples demonstrate that chronological and epigenetic age correlate in CNS white matter as well as in dchiOL and resulted in the identification of an age-specific epigenetic signature. Furthermore, we observed an accelerated epigenetic aging of the myelinated, normal appearing white matter of multiple sclerosis (MS) patients compared to healthy individuals. Impaired differentiation and upregulation of cellular senescence markers could be induced in young dchiOL in vitro using supernatants from pro-inflammatory microglia. In summary, our data suggest that physiological aging as well as inflammation-induced cellular senescence contribute to oligodendroglial pathology in inflammatory demyelinating diseases such as MS.

The astrocyte-produced growth factor HB-EGF limits autoimmune CNS pathology.

Central nervous system (CNS)-resident cells such as microglia, oligodendrocytes and astrocytes are gaining increasing attention in respect to their contribution to CNS pathologies including multiple sclerosis (MS). Several studies have demonstrated the involvement of pro-inflammatory glial subsets in the pathogenesis and propagation of inflammatory events in MS and its animal models. However, it has only recently become clear that the underlying heterogeneity of astrocytes and microglia can not only drive inflammation, but also lead to its resolution through direct and indirect mechanisms. Failure of these tissue-protective mechanisms may potentiate disease and increase the risk of conversion to progressive stages of MS, for which currently available therapies are limited. Using proteomic analyses of cerebrospinal fluid specimens from patients with MS in combination with experimental studies, we here identify Heparin-binding EGF-like growth factor (HB-EGF) as a central mediator of tissue-protective and anti-inflammatory effects important for the recovery from acute inflammatory lesions in CNS autoimmunity. Hypoxic conditions drive the rapid upregulation of HB-EGF by astrocytes during early CNS inflammation, while pro-inflammatory conditions suppress trophic HB-EGF signaling through epigenetic modifications. Finally, we demonstrate both anti-inflammatory and tissue-protective effects of HB-EGF in a broad variety of cell types in vitro and use intranasal administration of HB-EGF in acute and post-acute stages of autoimmune neuroinflammation to attenuate disease in a preclinical mouse model of MS. Altogether, we identify astrocyte-derived HB-EGF and its epigenetic regulation as a modulator of autoimmune CNS inflammation and potential therapeutic target in MS.

The aging mouse CNS is protected by an autophagy-dependent microglia population promoted by IL-34.

Microglia harness an unutilized health-promoting potential in age-related neurodegenerative and neuroinflammatory diseases, conditions like progressive multiple sclerosis (MS). Our research unveils an microglia population emerging in the cortical brain regions of aging mice, marked by ERK1/2, Akt, and AMPK phosphorylation patterns and a transcriptome indicative of activated autophagy - a process critical for cellular adaptability. By deleting the core autophagy gene Ulk1 in microglia, we reduce this population in the central nervous system of aged mice. Notably, this population is found dependent on IL-34, rather than CSF1, although both are ligands for CSF1R. When aging mice are exposed to autoimmune neuroinflammation, the loss of autophagy-dependent microglia leads to neural and glial cell death and increased mortality. Conversely, microglial expansion mediated by IL-34 exhibits a protective effect. These findings shed light on an autophagy-dependent neuroprotective microglia population as a potential target for treating age-related neuroinflammatory conditions, including progressive MS.

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.

Smoking affects epigenetic ageing of lung bronchoalveolar lavage cells in Multiple Sclerosis.

BACKGROUND: A compelling body of evidence implicates cigarette smoking and lung inflammation in Multiple Sclerosis (MS) susceptibility and progression. Previous studies have reported epigenetic age (DNAm age) acceleration in blood immune cells and in glial cells of people with MS (pwMS) compared to healthy controls (HC). OBJECTIVES: We aimed to examine biological ageing in lung immune cells in the context of MS and smoking. METHODS: We analyzed age acceleration residuals in lung bronchoalveolar lavage (BAL) cells, constituted of mainly alveolar macrophages, from 17 pwMS and 22 HC in relation to smoking using eight DNA methylation-based clocks, namely AltumAge, Horvath, GrimAge, PhenoAge, Zhang, SkinBlood, Hannum, Monocyte clock as well as two RNA-based clocks, which capture different aspects of biological ageing. RESULTS: After adjustment for covariates, five epigenetic clocks showed significant differences between the groups. Four of them, Horvath (Padj = 0.028), GrimAge (Padj = 4.28 × 10-7), SkinBlood (Padj = 0.001) and Zhang (Padj = 0.02), uncovered the sole effect of smoking on ageing estimates, irrespective of the clinical group. The Horvath, SkinBlood and Zhang clocks showed a negative impact of smoking while GrimAge detected smoking-associated age acceleration in BAL cells. On the contrary, the AltumAge clock revealed differences between pwMS and HC and indicated that, in the absence of smoking, BAL cells of pwMS were epigenetically 5.4 years older compared to HC (Padj = 0.028). Smoking further affected epigenetic ageing in BAL cells of pwMS specifically as non-smoking pwMS exhibited a 10.2-year AltumAge acceleration compared to pwMS smokers (Padj = 0.0049). Of note, blood-derived monocytes did not show any MS-specific or smoking-related AltumAge differences. The difference between BAL cells of pwMS smokers and non-smokers was attributable to the differential methylation of 114 AltumAge-CpGs (Padj < 0.05) affecting genes involved in innate immune processes such as cytokine production, defense response and cell motility. These changes functionally translated into transcriptional differences in BAL cells between pwMS smokers and non-smokers. CONCLUSIONS: BAL cells of pwMS display inflammation-related and smoking-dependent changes associated to epigenetic ageing captured by the AltumAge clock. Future studies examining potential confounders, such as the distribution of distinct BAL myeloid cell types in pwMS compared to control individuals in relation to smoking may clarify the varying performance and DNAm age estimations among epigenetic clocks.

Evaluation of Cell-Specific Epigenetic Age Acceleration in People With Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: In multiple sclerosis (MS), accelerated aging of the immune system (immunosenescence) may be associated with disease onset or drive progression. DNA methylation (DNAm) is an epigenetic factor that varies among lymphocyte subtypes, and cell-specific DNAm is associated with MS. DNAm varies across the life span and can be used to accurately estimate biological age acceleration, which has been linked to a range of morbidities. The objective of this study was to test for cell-specific epigenetic age acceleration (EAA) in people with MS. METHODS: This was a case-control study of EAA using existing DNAm data from several independent previously published studies. Data were included if .idat files from Illumina 450K or EPIC arrays were available for both a case with MS and an age-matched and sex-matched control, from the same study. Multifactor statistical modeling was performed to assess the primary outcome of EAA. We explored the relationship of EAA and MS, including interaction terms to identify immune cell-specific effects. Cell-sorted DNA methylation data from 3 independent datasets were used to validate findings. RESULTS: We used whole blood DNA methylation data from 583 cases with MS and 643 non-MS controls to calculate EAA using the GrimAge algorithm. The MS group exhibited an increased EAA compared with controls (approximately 9 mths, 95% CI 3.6-14.4), p = 0.001). Statistical deconvolution showed that EAA is associated with MS in a B cell-dependent manner (ß int = 1.7, 95% CI 0.3-2.8), p = 0.002), irrespective of B-cell proportions. Validation analysis using 3 independent datasets enriched for B cells showed an EAA increase of 5.1 years in cases with MS compared with that in controls (95% CI 2.8-7.4, p = 5.5 × 10-5). By comparison, there was no EAA difference in MS in a T cell-enriched dataset. We found that EAA was attributed to the DNAm surrogates for Beta-2-microglobulin (difference = 47,546, 95% CI 10,067-85,026; p = 7.2 × 10-5), and smoking pack-years (difference = 8.1, 95% CI 1.9-14.2, p = 0.002). DISCUSSION: This study provides compelling evidence that B cells exhibit marked EAA in MS and supports the hypothesis that premature B-cell immune senescence plays a role in MS. Future MS studies should focus on age-related molecular mechanisms in B cells.

DNA Methylation Signatures of Multiple Sclerosis Occur Independently of Known Genetic Risk and Are Primarily Attributed to B Cells and Monocytes.

Epigenetic mechanisms can regulate how DNA is expressed independently of sequence and are known to be associated with various diseases. Among those epigenetic mechanisms, DNA methylation (DNAm) is influenced by genotype and the environment, making it an important molecular interface for studying disease etiology and progression. In this study, we examined the whole blood DNA methylation profiles of a large group of people with (pw) multiple sclerosis (MS) compared to those of controls. We reveal that methylation differences in pwMS occur independently of known genetic risk loci and show that they more strongly differentiate disease (AUC = 0.85, 95% CI 0.82-0.89, p = 1.22 × 10-29) than known genetic risk loci (AUC = 0.72, 95% CI: 0.66-0.76, p = 9.07 × 10-17). We also show that methylation differences in MS occur predominantly in B cells and monocytes and indicate the involvement of cell-specific biological pathways. Overall, this study comprehensively characterizes the immune cell-specific epigenetic architecture of MS.

Perturbed epigenetic transcriptional regulation in AML with IDH mutations causes increased susceptibility to NK cells.

Isocitrate dehydrogenase (IDH) mutations are found in 20% of acute myeloid leukemia (AML) patients. However, only 30-40% of the patients respond to IDH inhibitors (IDHi). We aimed to identify a molecular vulnerability to tailor novel therapies for AML patients with IDH mutations. We characterized the transcriptional and epigenetic landscape with the IDH2i AG-221, using an IDH2 mutated AML cell line model and AML patient cohorts, and discovered a perturbed transcriptional regulatory network involving myeloid transcription factors that were partly restored after AG-221 treatment. In addition, hypermethylation of the HLA cluster caused a down-regulation of HLA class I genes, triggering an enhanced natural killer (NK) cell activation and an increased susceptibility to NK cell-mediated responses. Finally, analyses of DNA methylation data from IDHi-treated patients showed that non-responders still harbored hypermethylation in HLA class I genes. In conclusion, this study provides new insights suggesting that IDH mutated AML is particularly sensitive to NK cell-based personalized immunotherapy.

Sustained Low Relapse Rate With Highly Variable B-Cell Repopulation Dynamics With Extended Rituximab Dosing Intervals in Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: B cell-depleting therapies are highly effective in relapsing-remitting multiple sclerosis (RRMS) but are associated with increased infection risk and blunted humoral vaccination responses. Extension of dosing intervals may mitigate such negative effects, but its consequences on MS disease activity are yet to be ascertained. The objective of this study was to determine clinical and neuroradiologic disease activity, as well as B-cell repopulation dynamics, after implementation of extended rituximab dosing in RRMS. METHODS: We conducted a prospective observational study in a specialized-care, single-center setting, including patients with RRMS participating in the COMBAT-MS and MultipleMS observational drug trials, who had received at least 2 courses of rituximab (median follow-up 4.2 years, range 0.1-8.9 years). Using Cox regression, hazard ratios (HRs) of clinical relapse and/or contrast-enhancing lesions on MRI were calculated in relation to time since last dose of rituximab. RESULTS: A total of 3,904 dose intervals were accumulated in 718 patients and stratified into 4 intervals: <8, ≥8 to 12, ≥12 to 18, and ≥18 months. We identified 24 relapses of which 20 occurred within 8 months since previous infusion and 4 with intervals over 8 months. HRs for relapse when comparing ≥8 to 12, ≥12 to 18, and ≥18 months with <8 months since last dose were 0.28 (95% CI 0.04-2.10), 0.38 (95% CI 0.05-2.94), and 0.89 (95% CI 0.20-4.04), respectively, and thus nonsignificant. Neuroradiologic outcomes mirrored relapse rates. Dynamics of total B-cell reconstitution varied considerably, but median total B-cell counts reached lower level of normal after 12 months and median memory B-cell counts after 16 months. DISCUSSION: In this prospective cohort of rituximab-treated patients with RRMS exposed to extended dosing intervals, we could not detect a relation between clinical or neuroradiologic disease activity and time since last infusion. Total B- and memory B-cell repopulation kinetics varied considerably. These findings, relevant for assessing risk-mitigation strategies with anti-CD20 therapies in RRMS, suggest that relapse risk remains low with extended infusion intervals. Further studies are needed to investigate the relation between B-cell repopulation dynamics and adverse event risks associated with B-cell depletion.

Integration of small RNAs from plasma and cerebrospinal fluid for classification of multiple sclerosis.

Multiple Sclerosis (MS) is an autoimmune, neurological disease, commonly presenting with a relapsing-remitting form, that later converts to a secondary progressive stage, referred to as RRMS and SPMS, respectively. Early treatment slows disease progression, hence, accurate and early diagnosis is crucial. Recent advances in large-scale data processing and analysis have progressed molecular biomarker development. Here, we focus on small RNA data derived from cell-free cerebrospinal fluid (CSF), cerebrospinal fluid cells, plasma and peripheral blood mononuclear cells as well as CSF cell methylome data, from people with RRMS (n = 20), clinically/radiologically isolated syndrome (CIS/RIS, n = 2) and neurological disease controls (n = 14). We applied multiple co-inertia analysis (MCIA), an unsupervised and thereby unbiased, multivariate method for simultaneous data integration and found that the top latent variable classifies RRMS status with an Area Under the Receiver Operating Characteristics (AUROC) score of 0.82. Variable selection based on Lasso regression reduced features to 44, derived from the small RNAs from plasma (20), CSF cells (8) and cell-free CSF (16), with a marginal reduction in AUROC to 0.79. Samples from SPMS patients (n = 6) were subsequently projected on the latent space and differed significantly from RRMS and controls. On contrary, we found no differences between relapse and remission or between inflammatory and non-inflammatory disease controls, suggesting that the latent variable is not prone to inflammatory signals alone, but could be MS-specific. Hence, we here showcase that integration of small RNAs from plasma and CSF can be utilized to distinguish RRMS from SPMS and neurological disease controls.

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 (P adj = 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.

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.

DNA methylation changes in glial cells of the normal-appearing white matter in Multiple Sclerosis patients.

Multiple Sclerosis (MS), the leading cause of non-traumatic neurological disability in young adults, is a chronic inflammatory and neurodegenerative disease of the central nervous system (CNS). Due to the poor accessibility to the target organ, CNS-confined processes underpinning the later progressive form of MS remain elusive thereby limiting treatment options. We aimed to examine DNA methylation, a stable epigenetic mark of genome activity, in glial cells to capture relevant molecular changes underlying MS neuropathology. We profiled DNA methylation in nuclei of non-neuronal cells, isolated from 38 post-mortem normal-appearing white matter (NAWM) specimens of MS patients (n = 8) in comparison to white matter of control individuals (n = 14), using Infinium MethylationEPIC BeadChip. We identified 1,226 significant (genome-wide adjusted P-value < 0.05) differentially methylated positions (DMPs) between MS patients and controls. Functional annotation of the altered DMP-genes uncovered alterations of processes related to cellular motility, cytoskeleton dynamics, metabolic processes, synaptic support, neuroinflammation and signaling, such as Wnt and TGF-ß pathways. A fraction of the affected genes displayed transcriptional differences in the brain of MS patients, as reported by publically available transcriptomic data. Cell type-restricted annotation of DMP-genes attributed alterations of cytoskeleton rearrangement and extracellular matrix remodelling to all glial cell types, while some processes, including ion transport, Wnt/TGF-ß signaling and immune processes were more specifically linked to oligodendrocytes, astrocytes and microglial cells, respectively. Our findings strongly suggest that NAWM glial cells are highly altered, even in the absence of lesional insult, collectively exhibiting a multicellular reaction in response to diffuse inflammation.

Comparison of EM-seq and PBAT methylome library methods for low-input DNA.

DNA methylation is the most studied epigenetic mark involved in regulation of gene expression. For low input samples, a limited number of methods for quantifying DNA methylation genome-wide has been evaluated. Here, we compared a series of input DNA amounts (1-10ng) from two methylome library preparation protocols, enzymatic methyl-seq (EM-seq) and post-bisulfite adaptor tagging (PBAT) adapted from single-cell PBAT. EM-seq takes advantage of enzymatic activity while PBAT relies on conventional bisulfite conversion for detection of DNA methylation. We found that both methods accurately quantified DNA methylation genome-wide. They produced expected distribution patterns around genomic features, high C-T transition efficiency at non-CpG sites and high correlation between input amounts. However, EM-seq performed better in regard to library and sequencing quality, i.e. EM-seq produced larger insert sizes, higher alignment rates and higher library complexity with lower duplication rate compared to PBAT. Moreover, EM-seq demonstrated higher CpG coverage, better CpG site overlap and higher consistency between input series. In summary, our data suggests that EM-seq overall performed better than PBAT in whole-genome methylation quantification of low input samples.

Deep characterization of paired chromatin and transcriptomes in four immune cell types from multiple sclerosis patients.

Background: The putative involvement of chromatin states in multiple sclerosis (MS) is thus far unclear. Here we determined the association of chromatin-accessibility with concurrent genetic, epigenetic and transcriptional events. Material & methods: We generated paired assay for transposase-accessible chromatin sequencing and RNA-sequencing profiles from sorted blood immune CD4+ and CD8+ T cells, CD14+ monocytes and CD19+ B cells from healthy controls (HCs) and MS patients. Results: We identified differentially accessible regions between MS patients and HCs, primarily in CD4+ and CD19+. CD4+ regions were enriched for MS-associated single nucleotide polymorphisms and differentially methylated loci. In the vicinity of differentially accessible regions of CD4+ cells, 42 differentially expressed genes were identified. The top two dysregulated genes identified in this multilayer analysis were CCDC114 and SERTAD1. Conclusion: These findings provide new insight into the primary role of CD4+ and CD19+ cells in MS.

Lay abstract Multiple sclerosis (MS) is a devastating disease that affects individuals at a young age and gradually worsens over their lifespan. Currently, treatment for MS is broad, meaning it treats the symptoms but not the cause of the disease. Treating symptoms means that patients may feel better, but their general quality of life is not normal. In addition, treating symptoms can lead to the underlying cause still being present, which can come back once treatment is stopped. What we are striving to do in this article is to better understand the cause. If we can do that, we can have targeted treatment that will get rid of the disease without the fear of it coming back and drastically improve quality of life and life span. Here, we have identified the complex nature of MS and made an effort to identify certain genes that are different in MS patients and present a way to better understand MS using advanced genome study methodologies.

A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis.

BACKGROUND: There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. RESULT: We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10- 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. CONCLUSIONS: We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.

Small noncoding RNA profiling across cellular and biofluid compartments and their implications for multiple sclerosis immunopathology.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease affecting the central nervous system (CNS). Small non-coding RNAs (sncRNAs) and, in particular, microRNAs (miRNAs) have frequently been associated with MS. Here, we performed a comprehensive analysis of all classes of sncRNAs in matching samples of peripheral blood mononuclear cells (PBMCs), plasma, cerebrospinal fluid (CSF) cells, and cell-free CSF from relapsing-remitting (RRMS, n = 12 in relapse and n = 11 in remission) patients, secondary progressive (SPMS, n = 6) MS patients, and noninflammatory and inflammatory neurological disease controls (NINDC, n = 11; INDC, n = 5). We show widespread changes in miRNAs and sncRNA-derived fragments of small nuclear, nucleolar, and transfer RNAs. In CSF cells, 133 out of 133 and 115 out of 117 differentially expressed sncRNAs were increased in RRMS relapse compared to remission and RRMS compared to NINDC, respectively. In contrast, 65 out of 67 differentially expressed PBMC sncRNAs were decreased in RRMS compared to NINDC. The striking contrast between the periphery and CNS suggests that sncRNA-mediated mechanisms, including alternative splicing, RNA degradation, and mRNA translation, regulate the transcriptome of pathogenic cells primarily in the CNS target organ.

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.