Methylation profile study of CD14+ monocytesof multiple sclerosis-affected individuals.

Methylation is one of the most studied epigenetic mechanisms known to affect gene expression. It refers to the covalent binding of a methyl group to the fifth position of cytosine residues in the CpG dinucleotide context in mammals. In our study we analysed 26 CD14+ monocyte samples coming from relapsing remitting-multiple sclerosis (MS) patients anc controls. DNA libraries were prepared by SeqCap Epi Enrichment System (Roche) with enzymatic fragmentation and bisulfite conversion of 26 DNAs (pool 1) and then sequenced by Illumina Next-Generation Sequencing platform. The aim was to estimate the epigenetic profile and investigate differentially methylated regions between cases and controls. Our preliminary results showed an unexpected epigenetic pattern (~2.5 million CpGs after QC steps) lacking many methylation signals, suggesting that the enzymatic fragmentation disrupted somehow most of methylated cytosines. To evaluate whether the method of DNA fragmentation had an impact on the observed results, eight samples (pool 2) belonging to pool 1 were then analysed using mechanical fragmentation of DNA as a second and independent method. Pool 2 samples showed the expected methylation profile, with many loci either fully methylated or non-methylated. Methylation profiles from samples common to pool 1 and pool 2 were then compared to one another. Through bioinformatic and statistical tools the data were processed to infer any correlations between the methylation signals (β values) of the two pools and then to recover as many lost methylation signals as possible from the pool 1 samples, using the pool 2 samples as reference. Preliminary results showed that most fully methylated loci in pool 2 showed a lower β value in pool 1 samples, while for hypomethylated loci the two pools show a concordance of ~99%. Moreover, differentially methylated loci between MS cases and controls show a signal of differential methylation (nominal pvalue threshold 1%) for 1.359 CpG loci, a part of them map on the DIP2C gene. Further analyses need to be done to investigate the impact of enzymatic fragmentation on methylation estimation and to get the epigenetic profiles on the dataset of 26 MS samples. In addition, miRNA expression from this dataset will be integrated with methylation signals.