Human traits and diseases are heavily influenced by genetic and non-genetic factors. Rather than directly altering an individual's genetic blueprint, non-genetic or """"""""epigenetic"""""""" factors change how cells read and interpret this blueprint. While the epigenetic landscape of a cell is typically precisely controlled by developmental programs, variability between individuals can result from environmental and stochastic influences such as nutritional status (Barker 1989). These external influences utilize a wide array of non- genetic cellular mechanisms to leave their epigenetic marks, including: DNA methylation (Busslinger 1983);altered transcription factor networks (Iliopoulos 2009);and histone modifications (Jenuwein 2001). Collectively, these variable epigenetic marks form an epigenetic memory that influences future cellular behavior (Feinberg 2010). The extent to which the epigenetic landscape of a tissue is influenced by individual-specific epigenetic memory events is unknown. Consequently, this proposal aims to identify the causes and consequences of epigenetic variation between individuals with the goal of identifying sites of epigenetic memory. We will specifically focus on epigenetic variation in primary human hematopoietic progenitor cells due to their central role in forming a diverse array of tissue types and diseases and the wealth of tools that already exist for isolating them and differentiating them in vitro. Using high-throughput technologies to characterize the active chromatin landscape of 11 human primary hematopoietic progenitor cell samples we have already established that 3-5% of all active chromatin sites show a significant amount of variability between individuals. With this proposal we will identify which of these sites;(1) are caused by epigenetic memory events, as opposed to genetic variation;(2) are stably maintained by the cell;and (3) cause changes in the regulation of neighboring genes. This proposal will utilize a number of established and novel genomic and proteomic technologies including targeted capture resequencing, high-throughput DNaseI-seq and targeted proteomics. Overall, this research will provide a comprehensive picture of epigenetic memory and its effects on transcriptional regulation. Additionally, this study will provide a baseline for clinicians and researchers intending to use genome-wide active chromatin data to elucidate pathological or physiological processes.
This proposal aims to expand our knowledge of how the epigenetic landscape of human blood cells varies between individuals and is expected to yield fundamental insights into the nature and prevalence of inter-individual epigenetic variation in human blood cells. The specific focus of the proposal will be on hematopoietic progenitor cells, which are of major therapeutic and biological importance. The project combines multiple cutting-edge technologies including genome-scale mapping of chromatin remodeling, targeted analysis of genetic variation and DNA methylation by massively parallel sequencing, and targeted quantitative proteomics.
|Mose, Lisle E; Wilkerson, Matthew D; Hayes, D Neil et al. (2014) ABRA: improved coding indel detection via assembly-based realignment. Bioinformatics 30:2813-5|
|Stergachis, Andrew B; Haugen, Eric; Shafer, Anthony et al. (2013) Exonic transcription factor binding directs codon choice and affects protein evolution. Science 342:1367-72|
|Stergachis, Andrew B; Neph, Shane; Reynolds, Alex et al. (2013) Developmental fate and cellular maturity encoded in human regulatory DNA landscapes. Cell 154:888-903|
|Neph, Shane; Stergachis, Andrew B; Reynolds, Alex et al. (2012) Circuitry and dynamics of human transcription factor regulatory networks. Cell 150:1274-86|
|Neph, Shane; Vierstra, Jeff; Stergachis, Andrew B et al. (2012) An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489:83-90|
|Thurman, Robert E; Rynes, Eric; Humbert, Richard et al. (2012) The accessible chromatin landscape of the human genome. Nature 489:75-82|
|Stergachis, Andrew B; MacLean, Brendan; Lee, Kristen et al. (2011) Rapid empirical discovery of optimal peptides for targeted proteomics. Nat Methods 8:1041-3|