The past year has seen continued application of next-Gen sequencing approaches in studies that use human tissues from several clinical sources. We obtained cord blood monocytes from newborns, while monocytes from adults have been available through the NIH Department of Transfusion Medicine. These cells were analyzed either directly or after differentiation in vitro into dendritic cells. We previously obtained human skin fibroblasts from newborns and adults, as needed, under a protocol approved by the NICHD Institutional Review Board. Third, through collaborative work with Dr. Alan DeCherney (NICHD), we obtained human ovarian granulosa cells harvested in association with ART services at Shady Grove Hospital (Gaithersburg, MD). Our current work focuses on whole genome surveys to explore the epigenome features that underlie changes in gene function. In mammalian cells, cis-dependent epigenetic states are, of course, maintained by both chromatin structure and DNA methylation. Chromatin states are measured with respect to histone methylation and histone acetylation levels, as well as the topologies of the latter patterns extending over domains that typically encompass one or more genes. DNA methylation is characterized at the whole genome level (MethylCap-seq), as well as at nucleotide resolution within CpG-enriched regions (RRBS). Of interest are interactions between the chromatin- and DNA methylation-based components of epigenetic control. Given the rapid progress of epigenomics and the very large data sets generated by chromatin immunoprecipitation (ChIP) combined with next-Gen sequencing-based (ChIP-seq) analyses, continuing refinement of bioinformatics tools is absolutely essential. Along these lines, we constantly improve and extend our genome annotation, pattern recognition, and pattern comparison algorithms. Bioinformatic tools have been developed and applied to efficiently link patterns in RNA expression data sets with epigenome features. Further, new code to localize non-randomly clustered DNA methylation patterns (variegation, imprinting, and random monoallelic states) has revealed important new insights into age-related epigenome change. Most recently, algorithms that measure conservation of topological structure of chromatin domains (rather than peak height or domain borders) have revealed widespread and previously unreported developmental changes. If evidence for such changes can be confirmed and extended, this new dimension in epigenome properties could assume considerable importance. The emerging goal is to generalize our paradigm of dynamic postnatal epigenome structure to address a range of current problems in maternal reproductive health, pediatrics and areas of medicine relating to age-related disease.
