SNIC
SUPR
SNIC SUPR
A reference methylome of cerebrospinal fluid cells from multiple sclerosis patients
Dnr:

SNIC 2017/1-479

Type:

SNAC Medium

Principal Investigator:

Francesco Marabita

Affiliation:

Karolinska Institutet

Start Date:

2017-11-29

End Date:

2018-12-01

Primary Classification:

10610: Bioinformatics and Systems Biology (methods development to be 10203)

Secondary Classification:

10602: Biochemistry and Molecular Biology

Tertiary Classification:

30207: Neurology

Allocation

Abstract

Multiple Sclerosis (MS), is a chronic inflammatory disease characterized by autoimmune destruction of myelin sheaths and subsequent neuronal death. Epidemiological data establish MS as a complex disease influenced by genetic and environmental factors, although the contribution of different innate and adaptive immune cells in MS pathogenesis is not entirely disclosed. Immune cells from the cerebrospinal fluid (CSF) can provide insight into the pathogenic processes occurring in the inaccessible target organ, but they have not been sufficiently utilized due their low number. Epigenetic mechanisms, such as DNA methylation, integrate both intrinsic and environmental signals and are associated to stable changes in gene expression. Thus, regardless of a complete mechanistic understanding, DNA methylation changes might be evaluated as novel robust biomarkers of MS progression and treatment response. Specifically, we aim at investigating the DNA methylation changes of CSF cells in MS with the prospect of better understanding disease pathogenesis and heterogeneity. We included CSF samples of relapsing-remitting MS and secondary progressive MS patients, as well as age- and sex-matched other neurological disease controls (OND). Genome-wide methylation analysis was performed with a modification of low-input post-bisulfite adaptor tagging (scBS-seq, Smallwood et al. Nat Methods 2014). We have obtained CpG methylation estimates after processing of the aligned reads and performed initial analysis. Statistical modeling will reveal candidate differentially methylated regions between the MS and the OND controls. Furthermore, machine learning approaches will be used both to impute methylation values and to find a set of predictive features for the disease class prediction. In summary, genome-wide methylation analysis, based on a low cell-number library preparation, will enable us to study MS heterogeneity and progression using samples that are inaccessible to many other methods and chart a reference methylome of CSF cells during a chronic inflammatory disease.