Pei Yin Hsu

Xenoestrogen-induced epigenetic repression of microRNA-9-3 in breast epithelial cells.

Early exposure to xenoestrogens may predispose to breast cancer risk later in adult life. It is likely that long-lived, self-regenerating epithelial progenitor cells are more susceptible to these exposure injuries over time and transmit the injured memory through epigenetic mechanisms to their differentiated progeny. Here, we used progenitor-containing mammospheres as an in vitro exposure model to study this epigenetic effect. Expression profiling identified that, relative to control cells, 9.1% of microRNAs (82 of 898 loci) were altered in epithelial progeny derived from mammospheres exposed to a synthetic estrogen, diethylstilbestrol. Repressive chromatin marks, trimethyl Lys27 of histone H3 (H3K27me3) and dimethyl Lys9 of histone H3 (H3K9me2), were found at a down-regulated locus, miR-9-3, in epithelial cells preexposed to diethylstilbestrol. This was accompanied by recruitment of DNA methyltransferase 1 that caused an aberrant increase in DNA methylation of its promoter CpG island in mammosphere-derived epithelial cells on diethylstilbestrol preexposure. Functional analyses suggest that miR-9-3 plays a role in the p53-related apoptotic pathway. Epigenetic silencing of this gene, therefore, reduces this cellular function and promotes the proliferation of breast cancer cells. Promoter hypermethylation of this microRNA may be a hallmark for early breast cancer development, and restoration of its expression by epigenetic and microRNA-based therapies is another viable option for future treatment of this disease.

Epigenetic influences of low-dose bisphenol A in primary human breast epithelial cells

Substantial evidence indicates that exposure to bisphenol A (BPA) during early development may increase breast cancer risk later in life. The changes may persist into puberty and adulthood, suggesting an epigenetic process being imposed in differentiated breast epithelial cells. The molecular mechanisms by which early memory of BPA exposure is imprinted in breast progenitor cells and then passed onto their epithelial progeny are not well understood. The aim of this study was to examine epigenetic changes in breast epithelial cells treated with low-dose BPA. We also investigated the effect of BPA on the ERα signaling pathway and global gene expression profiles. Compared to control cells, nuclear internalization of ERα was observed in epithelial cells preexposed to BPA. We identified 170 genes with similar expression changes in response to BPA. Functional analysis confirms that gene suppression was mediated in part through an ERα-dependent pathway. As a result of exposure to BPA or other estrogen-like chemicals, the expression of lysosomal-associated membrane protein 3 (LAMP3) became epigenetically silenced in breast epithelial cells. Furthermore, increased DNA methylation in the LAMP3 CpG island was this repressive mark preferentially occurred in ERα-positive breast tumors. These results suggest that the in vitro system developed in our laboratory is a valuable tool for exposure studies of BPA and other xenoestrogens in human cells. Individual and geographical differences may contribute to altered patterns of gene expression and DNA methylation in susceptible loci. Combination of our exposure model with epigenetic analysis and other biochemical assays can give insight into the heritable effect of low-dose BPA in human cells.

Epigenetic silencing mediated through activated PI3K/AKT signaling in breast cancer.

Trimethylation of histone 3 lysine 27 (H3K27me3) is a critical epigenetic mark for the maintenance of gene silencing. Additional accumulation of DNA methylation in target loci is thought to cooperatively support this epigenetic silencing during tumorigenesis. However, molecular mechanisms underlying the complex interplay between the two marks remain to be explored. Here we show that activation of PI3K/AKT signaling can be a trigger of this epigenetic processing at many downstream target genes. We also find that DNA methylation can be acquired at the same loci in cancer cells, thereby reinforcing permanent repression in those losing the H3K27me3 mark. Because of a link between PI3K/AKT signaling and epigenetic alterations, we conducted epigenetic therapies in conjunction with the signaling-targeted treatment. These combined treatments synergistically relieve gene silencing and suppress cancer cell growth in vitro and in xenografts. The new finding has important implications for improving targeted cancer therapies in the future.

Hierarchical Modularity in ERα Transcriptional Network Is Associated with Distinct Functions and Implicates Clinical Outcomes

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes, and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ERα-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.

Estrogen Induces Global Reorganization of Chromatin Structure in Human Breast Cancer Cells

In the cell nucleus, each chromosome is confined to a chromosome territory. This spatial organization of chromosomes plays a crucial role in gene regulation and genome stability. An additional level of organization has been discovered at the chromosome scale: the spatial segregation into open and closed chromatins to form two genome-wide compartments. Although considerable progress has been made in our knowledge of chromatin organization, a fundamental issue remains the understanding of its dynamics, especially in cancer. To address this issue, we performed genome-wide mapping of chromatin interactions (Hi-C) over the time after estrogen stimulation of breast cancer cells. To biologically interpret these interactions, we integrated with estrogen receptor (ERα) binding events, gene expression and epigenetic marks. We show that gene-rich chromosomes as well as areas of open and highly transcribed chromatins are rearranged to greater spatial proximity, thus enabling genes to share transcriptional machinery and regulatory elements. At a smaller scale, differentially interacting loci are enriched for cancer proliferation and estrogen-related genes. Moreover, these loci are correlated with higher ERα binding events and gene expression. Taken together these results reveal the role of a hormone - estrogen - on genome organization, and its effect on gene regulation in cancer.

Genome-wide analysis uncovers high frequency, strong differential chromosomal interactions and their associated epigenetic patterns in E2-mediated gene regulation.

BackgroundAn emerging Hi-C protocol has the ability to probe three-dimensional (3D) architecture and capture chromatin interactions in a genome-wide scale. It provides informative results to address how chromatin organization changes contribute to disease/tumor occurrence and progression in response to stimulation of environmental chemicals or hormones.ResultsIn this study, using MCF7 cells as a model system, we found estrogen stimulation significantly impact chromatin interactions, leading to alteration of gene regulation and the associated histone modification states. Many chromosomal interaction regions at different levels of interaction frequency were identified. In particular, the top 10 hot regions with the highest interaction frequency are enriched with breast cancer specific genes. Furthermore, four types of E2-mediated strong differential (gain- or loss-) chromosomal (intra- or inter-) interactions were classified, in which the number of gain-chromosomal interactions is less than the number of loss-chromosomal interactions upon E2 stimulation. Finally, by integrating with eight histone modification marks, DNA methylation, regulatory elements regions, ERα and Pol-II binding activities, associations between epigenetic patterns and high chromosomal interaction frequency were revealed in E2-mediated gene regulation.ConclusionsThe work provides insight into the effect of chromatin interaction on E2/ERα regulated downstream genes in breast cancer cells.

LOcating non-unique matched tags (LONUT) to improve the detection of the enriched regions for ChIP-seq data.

One big limitation of computational tools for analyzing ChIP-seq data is that most of them ignore non-unique tags (NUTs) that match the human genome even though NUTs comprise up to 60% of all raw tags in ChIP-seq data. Effectively utilizing these NUTs would increase the sequencing depth and allow a more accurate detection of enriched binding sites, which in turn could lead to more precise and significant biological interpretations. In this study, we have developed a computational tool, LOcating Non-Unique matched Tags (LONUT), to improve the detection of enriched regions from ChIP-seq data. Our LONUT algorithm applies a linear and polynomial regression model to establish an empirical score (ES) formula by considering two influential factors, the distance of NUTs to peaks identified using uniquely matched tags (UMTs) and the enrichment score for those peaks resulting in each NUT being assigned to a unique location on the reference genome. The newly located tags from the set of NUTs are combined with the original UMTs to produce a final set of combined matched tags (CMTs). LONUT was tested on many different datasets representing three different characteristics of biological data types. The detected sites were validated using de novo motif discovery and ChIP-PCR. We demonstrate the specificity and accuracy of LONUT and show that our program not only improves the detection of binding sites for ChIP-seq, but also identifies additional binding sites.

Cancer omics: From regulatory networks to clinical outcomes

Current limitation in cancer genomic studies is a lack of the integration of various omics data generated through next generation sequencing technologies, as well as a lack of the sounding and comprehensive epigenomic and genomic information about a particular cancer cell type. In this review, we will discuss main aspects of current genomics research with its application in cancer topics. We will first overview the next-generation sequencing technologies, then outline the major computational approaches, particularly focusing on ChIP-based omics data, and list several remaining open questions facing computational biologists, further present regulatory network analysis inferred from the ChIP-based omics data; finally implicate the clinical outcomes from the network and pathway analysis.

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC.

Detection of DNA Methylation by MeDIP and MBDCap Assays: An Overview of Techniques

DNA methylation has been characterized as the representative example of epigenetic modifications and implicated in numerous biological processes, such as genomic imprinting and X chromosome inactivation. It primarily occurs at CpG dinucleotides in mammals and plays a critical role in transcriptional regulations. Examination of DNA methylation patterns in gene(s) or across a genome is vital to understand the role of epigenetic modulation in the progress of development and tumorigenesis. Currently, lots of approaches have been developed to investigate DNA methylation patterns for either limited regions or for genome-scale studies, but some of them rely on using restriction enzymes. In this chapter, we describe two commonly used protocols to detect enrichment of methylated DNA regions, namely, methylated DNA immunoprecipitation (MeDIP) and capture of methylated DNA by methyl-CpG binding domain-based (MBD) proteins (MBDCap). They are the most economical and effective methods to study DNA methylation either at a single locus or in genome-wide scale.