Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: DNA methylation of promoter CpG islands is associated with gene suppression, and its unique genome-wide profiles have been linked to tumor progression. Coupled with high-throughput sequencing technologies, it can now efficiently determine genome-wide methylation profiles in cancer cells. Also, experimental and computational technologies make it possible to find the functional relationship between cancer-specific methylation patterns and their clinicopathological parameters. Methodology/Principal Findings: Cancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. 191 patient samples (169 tumor and 22 normal specimen) and 41 breast cancer cell-lines are deposited in the database, comprising about 6.6 billion uniquely mapped sequence reads. This provides comprehensive and genome-wide epigenetic portraits of human breast cancer and endometrial cancer to date. Two views are proposed for users to better understand methylation structure at the genomic level or systemic methylation alteration at the gene level. In addition, a variety of annotation tracks are provided to cover genomic information. CMS includes important analytic functions for interpretation of methylation data, such as the detection of differentially methylated regions, statistical calculation of global methylation intensities, multiple gene sets of biologically significant categories, interactivity with UCSC via custom-track data. We also present examples of discoveries utilizing the framework. Conclusions/Significance: CMS provides visualization and analytic functions for cancer methylome datasets. A comprehensive collection of datasets, a variety of embedded analytic functions and extensive applications with biological and translational significance make this system powerful and unique in cancer methylation research. CMS is freely accessible at: http://cbbiweb.uthscsa.edu/KMethylomes/. [ABSTRACT FROM AUTHOR]

The transcriptional response driven by Hypoxia-inducible factor (HIF) is central to the adaptation to oxygen restriction. Despite recent characterization of genome-wide HIF DNA binding locations and hypoxia-regulated transcripts in different cell types, the molecular bases of HIF target selection remain unresolved. Herein, we combined multi-level experimental data and computational predictions to identify sequence motifs that may contribute to HIF target selectivity. We obtained a core set of bona fide HIF binding regions by integrating multiple HIF1 DNA binding and hypoxia expression profiling datasets. This core set exhibits evolutionarily conserved binding regions and is enriched in functional responses to hypoxia. Computational prediction of enriched transcription factor binding sites identified sequence motifs corresponding to several stress-responsive transcription factors, such as activator protein 1 (AP1), cAMP response element-binding (CREB), or CCAAT-enhancer binding protein (CEBP). Experimental validations on HIF-regulated promoters suggest a functional role of the identified motifs in modulating HIF-mediated transcription. Accordingly, transcriptional targets of these factors are over-represented in a sorted list of hypoxia-regulated genes. Altogether, our results implicate cooperativity among stress-responsive transcription factors in fine-tuning the HIF transcriptional response. [ABSTRACT FROM AUTHOR]

Deregulation of the transforming growth factor-b (TGFb) signaling pathway in epithelial ovarian cancer has been reported, but the precise mechanism underlying disrupted TGFb signaling in the disease remains unclear. We performed chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate genome-wide screening of TGFb-induced SMAD4 binding in epithelial ovarian cancer. Following TGFb stimulation of the A2780 epithelial ovarian cancer cell line, we identified 2,362 SMAD4 binding loci and 318 differentially expressed SMAD4 target genes. Comprehensive examination of SMAD4-bound loci, revealed four distinct binding patterns: 1) Basal; 2) Shift; 3) Stimulated Only; 4) Unstimulated Only. TGFb stimulated SMAD4-bound loci were primarily classified as either Stimulated only (74%) or Shift (25%), indicating that TGFbstimulation alters SMAD4 binding patterns in epithelial ovarian cancer cells. Furthermore, based on gene regulatory network analysis, we determined that the TGFb-induced, SMAD4-dependent regulatory network was strikingly different in ovarian cancer compared to normal cells. Importantly, the TGFb/SMAD4 target genes identified in the A2780 epithelial ovarian cancer cell line were predictive of patient survival, based on in silico mining of publically available patient data bases. In conclusion, our data highlight the utility of next generation sequencing technology to identify genome-wide SMAD4 target genes in epithelial ovarian cancer and link aberrant TGFb/SMAD signaling to ovarian tumorigenesis. Furthermore, the identified SMAD4 binding loci, combined with gene expression profiling and in silico data mining of patient cohorts, may provide a powerful approach to determine potential gene signatures with biological and future translational research in ovarian and other cancers. [ABSTRACT FROM AUTHOR]

Methyl-CpG binding domain protein sequencing (MBD-seq) is widely used to survey DNA methylation patterns. However, the optimal experimental parameters for MBD-seq remain unclear and the data analysis remains challenging. In this study, we generated high depth MBD-seq data in MCF-7 cell and developed a bi-asymmetric-Laplace model (BALM) to perform data analysis. We found that optimal efficiency of MBD-seq experiments was achieved by sequencing 100 million unique mapped tags from a combination of 500 mM and 1000 mM salt concentration elution in MCF-7 cells. Clonal bisulfite sequencing results showed that the methylation status of each CpG dinucleotides in the tested regions was accurately detected with high resolution using the proposed model. These results demonstrated the combination of MBD-seq and BALM could serve as a useful tool to investigate DNA methylome due to its low cost, high specificity, efficiency and resolution. [ABSTRACT FROM AUTHOR]

MicroRNAs are small non-coding RNAs involved in post-transcriptional regulation of gene expression. Due to the poor annotation of primary microRNA (pri-microRNA) transcripts, the precise location of promoter regions driving expression of many microRNA genes is enigmatic. This deficiency hinders our understanding of microRNA-mediated regulatory networks. In this study, we develop a computational approach to identify the promoter region and transcription start site (TSS) of primicroRNAs actively transcribed using genome-wide RNA Polymerase II (RPol II) binding patterns derived from ChIP-seq data. Based upon the assumption that the distribution of RPol II binding patterns around the TSS of microRNA and protein coding genes are similar, we designed a statistical model to mimic RPol II binding patterns around the TSS of highly expressed, well-annotated promoter regions of protein coding genes. We used this model to systematically scan the regions upstream of all intergenic microRNAs for RPol II binding patterns similar to those of TSS from protein coding genes. We validated our findings by examining the conservation, CpG content, and activating histone marks in the identified promoter regions. We applied our model to assess changes in microRNA transcription in steroid hormone-treated breast cancer cells. The results demonstrate many microRNA genes have lost hormone-dependent regulation in tamoxifen-resistant breast cancer cells. MicroRNA promoter identification based upon RPol II binding patterns provides important temporal and spatial measurements regarding the initiation of transcription, and therefore allows comparison of transcription activities between different conditions, such as normal and disease states. [ABSTRACT FROM AUTHOR]

Background: Tumor suppressor gene (TSG) RASSF1A and candidate TSG BLU are two tandem head-to-tail genes located at 3p21.3. We hypothesized that there may be a concordance on their gene expression and promoter methylation status. If not, then there may be an insulator located between RASSF1A and BLU genes that provides a barrier activity. Methodology/Principal Findings: We first identified potential transcriptionally important CpG sites using the methylationspecific oligonucleotide array in relation to mRNA expression of RASSF1A and BLU genes in primary lung tumors. We demonstrated that E2F1 bound to the potential transcriptionally important CpG sites in RASSF1A gene of a normal lung cell line expressing RASSF1A transcripts, whereas loss of E2F1 binding to RASSF1A in A549 cancer cell line was the result of DNA methylation. Both RASSF1A and BLU genes had their own potential transcriptionally important CpG regions. However, there was no correlation of methylation status between RASSF1A and BLU. Using gel shift assay and chromatin immunoprecipitation-PCR (ChIP-PCR), we found that CCCTC-binding factor (CTCF) bound to insulator sequences located between these two genes. Bisulfite sequencing and ChIP-PCR revealed distinct methylation and chromatin boundaries separated by the CTCF binding domains in normal cells, whereas such distinct epigenetic domains were not observed in cancer cells. Note that demethylation reagent and histone deacetylase inhibitor treatments led to CTCF binding and recovery of barrier effect for RASSF1A and BLU genes in cancer cells. Conclusions/Significance: Our study dissects the potential transcriptionally important CpG sites for RASSF1A and BLU genes at the sequence level and demonstrates that CTCF binding to the insulator of BLU gene provides a barrier activity within separate epigenetic domains of the juxtaposed BLU and RASSF1A loci in the 3p21.3 gene cluster region. [ABSTRACT FROM AUTHOR]

Background: Classical NF-κB signaling functions as a negative regulator of skeletal myogenesis through potentially multiple mechanisms. The inhibitory actions of TNFa on skeletal muscle differentiation are mediated in part through sustained NF-κB activity. In dystrophic muscles, NF-κB activity is compartmentalized to myofibers to inhibit regeneration by limiting the number of myogenic progenitor cells. This regulation coincides with elevated levels of muscle derived TNFα that is also under IKKb and NF-κB control. Methodology/Principal Findings: Based on these findings we speculated that in DMD, TNFa secreted from myotubes inhibits regeneration by directly acting on satellite cells. Analysis of several satellite cell regulators revealed that TNFα is capable of inhibiting Notch-1 in satellite cells and C2C12 myoblasts, which was also found to be dependent on NF-κB. Notch-1 inhibition occurred at the mRNA level suggesting a transcriptional repression mechanism. Unlike its classical mode of action, TNFa stimulated the recruitment of Ezh2 and Dnmt-3b to coordinate histone and DNA methylation, respectively. Dnmt-3b recruitment was dependent on Ezh2. Conclusions/Significance: We propose that in dystrophic muscles, elevated levels of TNFα and NF-κB inhibit the regenerative potential of satellite cells via epigenetic silencing of the Notch-1 gene. [ABSTRACT FROM AUTHOR]

It is now established that, as compared to normal cells, the cancer cell genome has an overall inverse distribution of DNA methylation ("methylome"), i.e., predominant hypomethylation and localized hypermethylation, within "CpG islands" (CGIs). Moreover, although cancer cells have reduced methylation "fidelity" and genomic instability, accurate maintenance of aberrant methylomes that underlie malignant phenotypes remains necessary. However, the mechanism(s) of cancer methylome maintenance remains largely unknown. Here, we assessed CGI methylation patterns propagated over 1, 3, and 5 divisions of A2780 ovarian cancer cells, concurrent with exposure to the DNA cross-linking chemotherapeutic cisplatin, and observed cell generation-successive increases in total hyper- and hypo-methylated CGIs. Empirical bayesian modeling revealed five distinct modes of methylation propagation: (1) heritable (i.e., unchanged) high-methylation (1186 probe loci in CGI microarray); (2) heritable (i.e., unchanged) low-methylation (286 loci); (3) stochastic hypermethylation (i.e., progressively increased, 243 loci); (4) stochastic hypomethylation (i.e., progressively decreased, 247 loci); and (5) considerable "random" methylation (582 loci). These results support a "stochastic model" of DNA methylation equilibrium deriving from the efficiency of two distinct processes, methylation maintenance and de novo methylation. A role for cis-regulatory elements in methylation fidelity was also demonstrated by highly significant (p<2.2×10(-5)) enrichment of transcription factor binding sites in CGI probe loci showing heritably high (118 elements) and low (47 elements) methylation, and also in loci demonstrating stochastic hyper-(30 elements) and hypo-(31 elements) methylation. Notably, loci having "random" methylation heritability displayed nearly no enrichment. These results demonstrate an influence of cis-regulatory elements on the nonrandom propagation of both strictly heritable and stochastically heritable CGIs.

MicroRNAs are small non-coding RNAs involved in post-transcriptional regulation of gene expression. Due to the poor annotation of primary microRNA (pri-microRNA) transcripts, the precise location of promoter regions driving expression of many microRNA genes is enigmatic. This deficiency hinders our understanding of microRNA-mediated regulatory networks. In this study, we develop a computational approach to identify the promoter region and transcription start site (TSS) of pri-microRNAs actively transcribed using genome-wide RNA Polymerase II (RPol II) binding patterns derived from ChIP-seq data. Based upon the assumption that the distribution of RPol II binding patterns around the TSS of microRNA and protein coding genes are similar, we designed a statistical model to mimic RPol II binding patterns around the TSS of highly expressed, well-annotated promoter regions of protein coding genes. We used this model to systematically scan the regions upstream of all intergenic microRNAs for RPol II binding patterns similar to those of TSS from protein coding genes. We validated our findings by examining the conservation, CpG content, and activating histone marks in the identified promoter regions. We applied our model to assess changes in microRNA transcription in steroid hormone-treated breast cancer cells. The results demonstrate many microRNA genes have lost hormone-dependent regulation in tamoxifen-resistant breast cancer cells. MicroRNA promoter identification based upon RPol II binding patterns provides important temporal and spatial measurements regarding the initiation of transcription, and therefore allows comparison of transcription activities between different conditions, such as normal and disease states.

Background: Tumor suppressor gene (TSG) RASSF1A and candidate TSG BLU are two tandem head-to-tail genes located at 3p21.3. We hypothesized that there may be a concordance on their gene expression and promoter methylation status. If not, then there may be an insulator located between RASSF1A and BLU genes that provides a barrier activity., Methodology/principal Findings: We first identified potential transcriptionally important CpG sites using the methylation-specific oligonucleotide array in relation to mRNA expression of RASSF1A and BLU genes in primary lung tumors. We demonstrated that E2F1 bound to the potential transcriptionally important CpG sites in RASSF1A gene of a normal lung cell line expressing RASSF1A transcripts, whereas loss of E2F1 binding to RASSF1A in A549 cancer cell line was the result of DNA methylation. Both RASSF1A and BLU genes had their own potential transcriptionally important CpG regions. However, there was no correlation of methylation status between RASSF1A and BLU. Using gel shift assay and chromatin immunoprecipitation-PCR (ChIP-PCR), we found that CCCTC-binding factor (CTCF) bound to insulator sequences located between these two genes. Bisulfite sequencing and ChIP-PCR revealed distinct methylation and chromatin boundaries separated by the CTCF binding domains in normal cells, whereas such distinct epigenetic domains were not observed in cancer cells. Note that demethylation reagent and histone deacetylase inhibitor treatments led to CTCF binding and recovery of barrier effect for RASSF1A and BLU genes in cancer cells., Conclusions/significance: Our study dissects the potential transcriptionally important CpG sites for RASSF1A and BLU genes at the sequence level and demonstrates that CTCF binding to the insulator of BLU gene provides a barrier activity within separate epigenetic domains of the juxtaposed BLU and RASSF1A loci in the 3p21.3 gene cluster region.

Background: Alterations in DNA methylation in cancer include global hypomethylation and gene-specific hypermethylation. It is not clear whether these two epigenetic errors are mechanistically linked or occur independently. This study was performed to determine the relationship between DNA hypomethylation, hypermethylation and microsatellite instability in cancer., Methodology/principal Findings: We examined 61 cancer cell lines and 60 colorectal carcinomas and their adjacent tissues using LINE-1 bisulfite-PCR as a surrogate for global demethylation. Colorectal carcinomas with sporadic microsatellite instability (MSI), most of which are due to a CpG island methylation phenotype (CIMP) and associated MLH1 promoter methylation, showed in average no difference in LINE-1 methylation between normal adjacent and cancer tissues. Interestingly, some tumor samples in this group showed increase in LINE-1 methylation. In contrast, MSI-showed a significant decrease in LINE-1 methylation between normal adjacent and cancer tissues (P<0.001). Microarray analysis of repetitive element methylation confirmed this observation and showed a high degree of variability in hypomethylation between samples. Additionally, unsupervised hierarchical clustering identified a group of highly hypomethylated tumors, composed mostly of tumors without microsatellite instability. We extended LINE-1 analysis to cancer cell lines from different tissues and found that 50/61 were hypomethylated compared to peripheral blood lymphocytes and normal colon mucosa. Interestingly, these cancer cell lines also exhibited a large variation in demethylation, which was tissue-specific and thus unlikely to be resultant from a stochastic process., Conclusion/significance: Global hypomethylation is partially reversed in cancers with microsatellite instability and also shows high variability in cancer, which may reflect alternative progression pathways in cancer.