Successful application of human-based methyl capture sequencing for methylome analysis in non-human primate models

Ja-Rang Lee, Dong-Sung Ryu, Sang-Je Park, Se-Hee Choe, Hyeon-Mu Cho, Sang-Rae Lee, Sun-Uk Kim, Young-Hyun Kim, Jae-Won Huh

The characterization of genomic or epigenomic variation in human and animal models could provide important insight into pathophysiological mechanisms of various diseases, and lead to new developments in disease diagnosis and clinical intervention. The African green monkey (AGM; Chlorocebus aethiops) and cynomolgus monkey (CM; Macaca fascicularis) have long been considered important animal models in biomedical research. However, non-human primate-specific methods applicable to epigenomic analyses in AGM and CM are lacking. The recent development of methyl-capture sequencing (MC-seq) has an unprecedented advantage of cost-effectiveness, and further allows for extending the methylome coverage compared to conventional sequencing approaches. Here, we used a human probe-designed MC-seq method to assay DNA methylation in DNA obtained from 13 CM and three AGM blood samples. To effectively adapt the human probe-designed target region for methylome analysis in non-human primates, we redefined the target regions, focusing on regulatory regions and intragenic regions with consideration of interspecific sequence homology and promoter region variation. Methyl-capture efficiency was controlled by the sequence identity between the captured probes based on the human reference genome and the AGM and CM genome sequences, respectively. Using reasonable guidelines, 56 and 62% of the human-based capture probes could be effectively mapped for DNA methylome profiling in the AGM and CM genome, respectively, according to numeric global statistics. In particular, our method could cover up to 89 and 87% of the regulatory regions of the AGM and CM genome, respectively. Use of human-based MC-seq methods provides an attractive, cost-effective approach for the methylome profiling of non-human primates at the single-base resolution level.