GCC 2017

➔ Poster

Authors
Deepak Tanwar 1, Romain Lambrot 1, Keith Siklenka  2, Mahmoud Aarabi 3, Donovan Chan 3, Clifford Librach 4, Sergey Moskovtsev 4, Jianguo Xia 1,5, Jacquetta Trasler 3, Sarah Kimmins 1,2

1 : Department of Animal Science, McGill University, Ste Anne-de-Bellevue, QC -  Canada
2 : Department of Pharmacology and Therapeutics, McGill University, Montreal, QC -  Canada
3 : Research Institute of the McGill University Health Centre at the Montreal Children's Hospital, Montreal, QC -  Canada
4 : Department of Obstetrics and Gynaecology, University of Toronto, Toronto, ON -  Canada
5 : Institute of Parasitology, McGill University – Montreal, QC, Canada 



Abstract
Sperm has a unique chromatin conformation with the majority of somatic histones being replaced with protamines. Thus, unlike a typical ChIP-seq profile generated from targeting a histone modification there are fewer histone peaks and these tend to be distributed over CpG (5'-C-phosphate-G-3') enriched regions. Effects of the paternal environment including stress, diet and toxicants have been linked to negative outcomes for offspring including birth defects and increased risks for complex diseases. These paternal effects may occur via non-genetic inheritance, through epigenetic mechanisms including DNA methylation, post-translational modifications of histones and noncoding RNAs. We hypothesize that, the sperm epigenome in men, specifically histone methylation, can influence offspring development and health. The challenges in analyzing and quantitating ChIP-seq data from sperm with currently available software is the ability to detect and quantify differences not just in peak enrichment but also the broad domains. Our objective is to develop the most suitable bioinformatics pipeline for semi-quantitative/quantitative comparison of histone methylation levels in sperm from fertile and infertile, men of varying folate status, BMI and toxicant exposures. To perform an optimal pre-processing and to address other challenges in data analysis, I am developing an efficient bioinformatics pipeline for analyzing sperm epigenome data, by using currently available tools (Bowtie2, Trimmomatic, Picard tools, MACS2, etc.), to address the challenges of identification of the most reliable peak calling method with appropriate parameters while taking into account the unique chromatin configuration in sperm.