title: Characterization of DNA methylation and its association with other biological systems in lymphoblastoid cell lines.
abstract: Lymphoblastoid cell line (LCL) is a common tool to study genetic disorders. However, it has not been fully characterized to what degree LCLs preserve the in vivo status of non-genetic biological systems, such as DNA methylation and gene transcription. We previously reported that DNA methylation in LCLs is highly variable in a data set of ~27,000 CpG dinucleotide sites around transcription start site (TSS) and 63 human subjects including healthy controls and probands of genetic disorders. Disease-causing mutations are linked to differential methylation at some CpG sites, but account for a small proportion of the total variance. In this study, we repeated the experiments to ensure that the high variance is not due to technical error and scrutinized the characteristics of DNA methylation and its association with other biological systems. Using sequence information and ChIP-seq data, we conclude that local CpG density and histone modifications not only correlate to baseline methylation level, but also affect the direction of methylation change in LCLs. Integrative analysis of gene transcription and DNA methylation data of the same subjects shows that medium or high methylation around TSS blocks the transcription while low methylation is a necessary, but not sufficient condition of downstream gene transcription. We utilized epigenetic information around TSS to predict active gene transcription via logistic regression models. The multivariate model using DNA methylation, eight histone modifications, and two regulatory protein complexes (CTCF and cohesin) as predictors has better performance (accuracy=95.1%) than any univariate models of single predictors. Linear regression analysis further shows that the transcriptional levels predicted by epigenetic markers have significant correlation to microarray measurements (p=2.2e-10). This study provides new insights into the epigenetic systems of LCLs and suggests that more specifically designed experiments are needed to improve our understanding on this topic.
authors.affiliation: Center for Biomedical Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA. zhangz@email.chop.edu
authors.forename: Zhe
authors.initials: Z
authors.lastname: Zhang
authors.forename: Jinglan
authors.initials: J
authors.lastname: Liu
authors.forename: Maninder
authors.initials: M
authors.lastname: Kaur
authors.forename: Ian D
authors.initials: ID
authors.lastname: Krantz
mesh_terms: D002460:Cell Line; D018899:CpG Islands / Q000235:genetics; D019175:DNA Methylation / Q000235:genetics*; D044127:Epigenesis, Genetic; D057890:Epigenomics / Q000379:methods; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008954:Models, Biological; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D049490:Systems Biology / Q000379:methods*; D024363:Transcription Initiation Site