Li, Hu

Abstract Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were ‘unmasked’ only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

Abstract The post-genomic era ushered in a cadre of techniques to study epigenetic mechanisms involved in disease progression and cancer. One understudied mechanism of gene expression/transcriptional control is 3-dimensional architecture of the genome. Several high-throughput technologies have been adapted to examine 3D genome architecture; in particular those derived from chromatin conformation capture (3C) sequencing technologies. Of 3C-derived techniques, HiC-seq, is the most extensive, and examines all chromatin-chromatin contact sites within a genome, elucidating the entire 3D genome architecture. Although, HiC-seq technique was published 2009, it has been under-utilized in research laboratories due to its technical complexity, expense, and requirement for large volumes of starting material (50 million cells/500 μg of DNA). Other barriers to use of HiC-seq include the requirement for specialized equipment and laborious extraction techniques. However, the benefits of HiC-seq are high for cancer researchers because a genome-wide "snap-shot" of the entire 3D genome of the cancer is displayed. This allows for analysis of higher-order epigenetic control of gene expression. Furthermore, understanding the chromatin conformation of the genome provides new insights into the cellular disease state and potentially novel therapeutic approaches to drug-resistant cancers. Our study presents significant technical innovations, permitting HiC-seq technique to be adopted in laboratories without extensive experience in 3C-seq technologies, and with the use of standard equipment and reagents, as well as smaller volumes of starting materials. Our modified technique utilizes up to 100 fold lower amounts of starting material (500,000 cells/5-10 μg of DNA) per reaction. This adaptation of HiC-seq preparation offers the potential for simultaneous extraction of total RNA and Hi-C processed chromatin for sequencing. To optimize our technique we utilized the estrogen-responsive breast cancer cell line, MCF7. This cell line is typically utilized to study potential drug therapies in the context of estrogen-sensitive cancers. We stream lined HiC-seq by optimizing nuclei extraction and allowing for collection of a cytoplasmic fraction for mRNA extraction and sequencing analysis. We also optimized enzymatic digestion, ligation and DNA extraction methods by utilizing newer enzymatic chemistries, commercially available silica column extraction methods and reagents in place of multiple phenol-based DNA extraction methods. Our quality control results suggest that sample processing for reduced amounts of material is similar if not superior to conventional HiC-seq. We leverage this powerful genomic high-throughput technique in order to simultaneously examine the 3D architecture and transcriptional profiles of MCF7 breast cancer cells. Our computational workflow includes further quality controls using HiC-User Pipeline software from the Bahraham Institute and the R Bioconductor package HiTC package. Overall our optimizations in MCF7 breast cancer cells, and post-acquisition work-flow, make HiC-seq an accessible technology. These innovations permit simultaneous analysis of architectural and transcriptional profiles in cancer cells. Citation Format: Jennifer D Davis, Christian Ross, Hu Li, Amy Brock. Examining 3-dimensional genome architecture and transcriptional profiles using an optimized HiC-seq protocol in breast cancer cells [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-03-16.