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

Clinical Epigenetics

Research

Maria Pia Campagna¹  Alexandre Xavier²  Vicki E. Maltby²  Jeannette Lechner-Scott³  Jim Stankovich⁴  Vilija G. Jokubaitis⁵  Helmut Butzkueven⁶  Rodney A. Lea⁷  Rodney J. Scott⁸ 

[1] Central Clinical School, Monash University, Melbourne, VIC, Australia;Hunter Medical Research Institute, University of Newcastle, Newcastle, NSW, Australia;Hunter Medical Research Institute, University of Newcastle, Newcastle, NSW, Australia;Neurology Department, John Hunter Hospital, Hunter New England Health, Newcastle, NSW, Australia;Monash University, Melbourne, VIC, Australia;Monash University, Melbourne, VIC, Australia;University of Melbourne, Melbourne, VIC, Australia;Royal Melbourne Hospital, Melbourne, VIC, Australia;Neurology Department, Alfred Health, Melbourne, VIC, Australia;Monash University, Melbourne, VIC, Australia;University of Melbourne, Melbourne, VIC, Australia;Royal Melbourne Hospital, Melbourne, VIC, Australia;Neurology Department, Eastern Health, Melbourne, VIC, Australia;Neurology Department, Alfred Health, Melbourne, VIC, Australia;Queensland University of Technology, Brisbane, QLD, Australia;University of Melbourne, Melbourne, VIC, Australia;School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia;Division of Molecular Medicine, New South Wales Health Pathology North, Newcastle, NSW, Australia;

关键词: DNA methylation;    Epigenetics;    Prognostics;    Machine learning;    Epigenome-wide association study;    Genomics;   

DOI  :  10.1186/s13148-022-01397-2

 received in 2022-08-08, accepted in 2022-12-02,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodsWe 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.ResultsFemales 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.ConclusionRMS 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.

【 授权许可】

CC BY   
© Crown 2022

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