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 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 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 in either single locus or genome-wide scale.

Dual mTOR Kinase Inhibitor MLN0128 Sensitizes HR+/HER2+ Breast Cancer Patient-Derived Xenografts to Trastuzumab or Fulvestrant.

Purpose: Therapeutic strategies against hormonal receptor-positive (HR+)/HER2+ breast cancers with poor response to trastuzumab need to be optimized.Experimental Design: Two HR+/HER2+ patient-derived xenograft (PDX) models named as COH-SC1 and COH-SC31 were established to explore targeted therapies for HER2+ breast cancers. RNA sequencing and RPPA (reverse phase protein array) analyses were conducted to decipher molecular features of the two PDXs and define the therapeutic strategy of interest, validated by in vivo drug efficacy examination and in vitro cell proliferation analysis.Results: Estrogen acted as a growth driver of trastuzumab-resistant COH-SC31 tumors but an accelerator in the trastuzumab-sensitive COH-SC1 model. In vivo trastuzumab efficacy examination further confirmed the consistent responses between PDXs and the corresponding tumors. Integrative omics analysis revealed that mammalian target of rapamycin (mTOR) and ERα signaling predominantly regulate tumor growth of the two HR+/HER2+ PDXs. Combination of the dual mTOR complex inhibitor MLN0128 and anti-HER2 trastuzumab strongly suppressed tumor growth of COH-SC1 PDX accompanied by increasing ER-positive cell population in vivo Instead, MLN0128 in combination with antiestrogen fulvestrant significantly halted the growth of HR+/HER2+ cancer cells in vitro and trastuzumab-resistant COH-SC31 as well as trastuzumab-sensitive COH-SC1 tumors in vivoConclusions: Compared with the standard trastuzumab treatment, this study demonstrates alternative therapeutic strategies against HR+/HER2+ tumors through establishment of two PDXs coupled with integrative omics analyses and in vivo drug efficacy examination. This work presents a prototype of future "co-clinical" trials to tailor personalized medicine in clinical practice. Clin Cancer Res; 24(2); 395-406. ©2017 AACR.

Adaptations to chronic rapamycin in mice.

Rapamycin inhibits mechanistic (or mammalian) target of rapamycin (mTOR) that promotes protein production in cells by facilitating ribosome biogenesis (RiBi) and eIF4E-mediated 5'cap mRNA translation. Chronic treatment with encapsulated rapamycin (eRapa) extended health and life span for wild-type and cancer-prone mice. Yet, the long-term consequences of chronic eRapa treatment are not known at the organ level. Here, we report our observations of chronic eRapa treatment on mTORC1 signaling and RiBi in mouse colon and visceral adipose. As expected, chronic eRapa treatment decreased detection of phosphorylated mTORC1/S6K substrate, ribosomal protein (rpS6) in colon and fat. However, in colon, contrary to expectations, there was an upregulation of 18S rRNA and some ribosomal protein genes (RPGs) suggesting increased RiBi. Among RPGs, eRapa increases rpl22l1 mRNA but not its paralog rpl22. Furthermore, there was an increase in the cap-binding protein, eIF4E relative to its repressor 4E-BP1 suggesting increased translation. By comparison, in fat, there was a decrease in the level of 18S rRNA (opposite to colon), while overall mRNAs encoding ribosomal protein genes appeared to increase, including rpl22, but not rpl22l1 (opposite to colon). In fat, there was a decrease in eIF4E relative to actin (opposite to colon) but also an increase in the eIF4E/4E-BP1 ratio likely due to reductions in 4E-BP1 at our lower eRapa dose (similar to colon). Thus, in contrast to predictions of decreased protein production seen in cell-based studies, we provide evidence that colon from chronically treated mice exhibited an adaptive 'pseudo-anabolic' state, which is only partially present in fat, which might relate to differing tissue levels of rapamycin, cell-type-specific responses, and/or strain differences.

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.

Amplification of distant estrogen response elements deregulates target genes associated with tamoxifen resistance in breast cancer.

A causal role of gene amplification in tumorigenesis is well known, whereas amplification of DNA regulatory elements as an oncogenic driver remains unclear. In this study, we integrated next-generation sequencing approaches to map distant estrogen response elements (DEREs) that remotely control the transcription of target genes through chromatin proximity. Two densely mapped DERE regions located on chromosomes 17q23 and 20q13 were frequently amplified in estrogen receptor-α-positive luminal breast cancer. These aberrantly amplified DEREs deregulated target gene expression potentially linked to cancer development and tamoxifen resistance. Progressive accumulation of DERE copies was observed in normal breast progenitor cells chronically exposed to estrogenic chemicals. These findings may extend to other DNA regulatory elements, the amplification of which can profoundly alter target transcriptome during tumorigenesis.

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.

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

BACKGROUND: An 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. RESULTS: In 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. CONCLUSIONS: The work provides insight into the effect of chromatin interaction on E2/ERα regulated downstream genes in breast cancer cells.

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.

Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data.

BACKGROUND: Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF) partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks. RESULTS: In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2) were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM). A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes.We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study) and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes) and targeted TFs (25% of common TFs). The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells. CONCLUSIONS: Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to further study the underlying mechanisms in breast cancer cells or other human diseases.

Estrogen-mediated epigenetic repression of large chromosomal regions through DNA looping.

The current concept of epigenetic repression is based on one repressor unit corresponding to one silent gene. This notion, however, cannot adequately explain concurrent silencing of multiple loci observed in large chromosome regions. The long-range epigenetic silencing (LRES) can be a frequent occurrence throughout the human genome. To comprehensively characterize the influence of estrogen signaling on LRES, we analyzed transcriptome, methylome, and estrogen receptor alpha (ESR1)-binding datasets from normal breast epithelia and breast cancer cells. This "omics" approach uncovered 11 large repressive zones (range, 0.35 approximately 5.98 megabases), including a 14-gene cluster located on 16p11.2. In normal cells, estrogen signaling induced transient formation of multiple DNA loops in the 16p11.2 region by bringing 14 distant loci to focal ESR1-docking sites for coordinate repression. However, the plasticity of this free DNA movement was reduced in breast cancer cells. Together with the acquisition of DNA methylation and repressive chromatin modifications at the 16p11.2 loci, an inflexible DNA scaffold may be a novel determinant used by breast cancer cells to reinforce estrogen-mediated repression.