Interferon beta treatment is a potent and targeted epigenetic modifier in multiple sclerosis

IntroductionMultiple Sclerosis (MS) has a complex pathophysiology that involves genetic and environmental factors. DNA methylation (DNAm) is one epigenetic mechanism that can reversibly modulate gene expression. Cell specific DNAm changes have been associated with MS, and some MS therapies such as dimethyl fumarate can influence DNAm. Interferon Beta (IFNβ), was one of the first disease modifying therapies in multiple sclerosis (MS). However, how IFNβ reduces disease burden in MS is not fully understood and little is known about the precise effect of IFNβ treatment on methylation.MethodsThe objective of this study was to determine the changes in DNAm associated with INFβ use, using methylation arrays and statistical deconvolutions on two separate datasets (total ntreated = 64, nuntreated = 285).ResultsWe show that IFNβ treatment in people with MS modifies the methylation profile of interferon response genes in a strong, targeted, and reproducible manner. Using these identified methylation differences, we constructed a methylation treatment score (MTS) that is an accurate discriminator between untreated and treated patients (Area under the curve = 0.83). This MTS is time-sensitive and in consistent with previously identified IFNβ treatment therapeutic lag. This suggests that methylation changes are required for treatment efficacy. Overrepresentation analysis found that IFNβ treatment recruits the endogenous anti-viral molecular machinery. Finally, statistical deconvolution revealed that dendritic cells and regulatory CD4+ T cells were most affected by IFNβ induced methylation changes.DiscussionIn conclusion, our study shows that IFNβ treatment is a potent and targeted epigenetic modifier in multiple sclerosis

Conceiving complexity: Biological mechanisms underpinning the lasting effect of pregnancy on multiple sclerosis outcomes

Multiple sclerosis (MS) is an autoimmune, demyelinating disease with the highest incidence in women of childbearing age. The effect of pregnancy on disease activity and progression is a primary concern for women with MS and their clinical teams. It is well established that inflammatory disease activity is naturally suppressed during pregnancy, followed by an increase postpartum. However, the long-term effect of pregnancy on disease progression is less understood. Having had a pregnancy before MS onset has been associated with an older age at first demyelinating event, an average delay of 3.4 years. After MS onset, there is conflicting evidence about the impact of pregnancy on long-term outcomes. The study with the longest follow-up to date showed that pregnancy was associated with a 0.36-point lower disability score after 10-years of disease in 1830 women. Understanding the biological mechanism by which pregnancy induces long-term beneficial effects on MS outcomes could provide mechanistic insights into the elusive determinants of secondary progression. Here, we review potential biological processes underlying this effect, including evidence that acute sex hormone exposure induces lasting changes to neurobiological and DNA methylation patterns, and how sustained methylation changes in immune cells can alter immune composition and function long-term.</p

Effect of cardiologist care on 6-month outcomes in patients discharged with heart failure: Results from an observational study based on administrative data

Objectives To evaluate the effect of cardiologist care on adherence to evidence-based secondary prevention medications, mortality and readmission within 6 months of discharge in patients with heart failure (HF). Design Retrospective observational study based on administrative data. Setting Local Healthcare Authority (LHA) of Bologna, one of the largest LHAs of Italy with ~870 000 inhabitants. Participants All patients residing in the LHA of Bologna discharged from hospital with a diagnosis of HF between 1 January 2015 and 31 December 2015. Primary and secondary outcome measures Multivariable regression analysis was used to assess the association of inpatient and outpatient cardiologist care with adherence to evidence-based medications, all-cause mortality and hospital readmission (including emergency room visits) within 6 months of discharge. Results The study population included 2650 patients (mean age 82.3 years). 340 (12.8%) patients were discharged from cardiology wards, while 635 (24.0%) were seen by a cardiologist during follow-up. Inpatient and outpatient cardiologist care was associated with an increased likelihood of adherence to ACE inhibitors/ angiotensin receptor blockers (ACEIs/ARBs), \uce\ub2-blockers and aldosterone antagonists after discharge. The risk of mortality was significantly lower among patients adherent to ACEIs/ARBs and/or \uce\ub2-blockers (\ue2\u80\u9353% and \ue2\u80\u9328%, respectively); the risk of hospital readmission was significantly lower among patients adherent to ACEIs/ARBs (\ue2\u80\u9328%). Conclusions Compared with non-specialist care, cardiologist care improves patient adherence to evidence-based medications and might thus favourably affect mortality and readmission following HF

Epigenome-wide association studies: current knowledge, strategies and recommendations

The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.</p

Whole-blood methylation signatures are associated with and accurately classify multiple sclerosis disease severity

Background: The variation in multiple sclerosis (MS) disease severity is incompletely explained by genetics, suggesting genetic and environmental interactions are involved. Moreover, the lack of prognostic biomarkers makes it difficult for clinicians to optimise care. DNA methylation is one epigenetic mechanism by which gene–environment interactions can be assessed. Here, we aimed to identify DNA methylation patterns associated with mild and severe relapse-onset MS (RMS) and to test the utility of methylation as a predictive biomarker. Methods: We conducted an epigenome-wide association study between 235 females with mild (n = 119) or severe (n = 116) with RMS. Methylation was measured with the Illumina methylationEPIC array and analysed using logistic regression. To generate hypotheses about the functional consequence of differential methylation, we conducted gene set enrichment analysis using ToppGene. We compared the accuracy of three machine learning models in classifying disease severity: (1) clinical data available at baseline (age at onset and first symptoms) built using elastic net (EN) regression, (2) methylation data using EN regression and (3) a weighted methylation risk score of differentially methylated positions (DMPs) from the main analysis using logistic regression. We used a conservative 70:30 test:train split for classification modelling. A false discovery rate threshold of 0.05 was used to assess statistical significance. Results: Females with mild or severe RMS had 1472 DMPs in whole blood (839 hypermethylated, 633 hypomethylated in the severe group). Differential methylation was enriched in genes related to neuronal cellular compartments and processes, and B-cell receptor signalling. Whole-blood methylation levels at 1708 correlated CpG sites classified disease severity more accurately (machine learning model 2, AUC = 0.91) than clinical data (model 1, AUC = 0.74) or the wMRS (model 3, AUC = 0.77). Of the 1708 selected CpGs, 100 overlapped with DMPs from the main analysis at the gene level. These overlapping genes were enriched in neuron projection and dendrite extension, lending support to our finding that neuronal processes, rather than immune processes, are implicated in disease severity. Conclusion: RMS disease severity is associated with whole-blood methylation at genes related to neuronal structure and function. Moreover, correlated whole-blood methylation patterns can assign disease severity in females with RMS more accurately than clinical data available at diagnosis.</p

Interferon beta treatment is a potent and targeted epigenetic modifier in multiple sclerosis

Introduction: Multiple Sclerosis (MS) has a complex pathophysiology that involves genetic and environmental factors. DNA methylation (DNAm) is one epigenetic mechanism that can reversibly modulate gene expression. Cell specific DNAm changes have been associated with MS, and some MS therapies such as dimethyl fumarate can influence DNAm. Interferon Beta (IFNβ), was one of the first disease modifying therapies in multiple sclerosis (MS). However, how IFNβ reduces disease burden in MS is not fully understood and little is known about the precise effect of IFNβ treatment on methylation. Methods: The objective of this study was to determine the changes in DNAm associated with INFβ use, using methylation arrays and statistical deconvolutions on two separate datasets (total ntreated = 64, nuntreated = 285). Results: We show that IFNβ treatment in people with MS modifies the methylation profile of interferon response genes in a strong, targeted, and reproducible manner. Using these identified methylation differences, we constructed a methylation treatment score (MTS) that is an accurate discriminator between untreated and treated patients (Area under the curve = 0.83). This MTS is time-sensitive and in consistent with previously identified IFNβ treatment therapeutic lag. This suggests that methylation changes are required for treatment efficacy. Overrepresentation analysis found that IFNβ treatment recruits the endogenous anti-viral molecular machinery. Finally, statistical deconvolution revealed that dendritic cells and regulatory CD4+ T cells were most affected by IFNβ induced methylation changes. Discussion: In conclusion, our study shows that IFNβ treatment is a potent and targeted epigenetic modifier in multiple sclerosis.</p