Dataset Information

CENP-A nucleosomes localize to transcription factor hotspots and subtelomeric sites in human cancer cells.

ABSTRACT:

Background

The histone H3 variant CENP-A is normally tightly regulated to ensure only one centromere exists per chromosome. Native CENP-A is often found overexpressed in human cancer cells and a range of human tumors. Consequently, CENP-A misregulation is thought to contribute to genome instability in human cancers. However, the consequences of such overexpression have not been directly elucidated in human cancer cells.

Results

To investigate native CENP-A overexpression, we sought to uncover CENP-A-associated defects in human cells. We confirm that CENP-A is innately overexpressed in several colorectal cancer cell lines. In such cells, we report that a subset of structurally distinct CENP-A-containing nucleosomes associate with canonical histone H3, and with the transcription-coupled chaperones ATRX and DAXX. Furthermore, such hybrid CENP-A nucleosomes localize to DNase I hypersensitive and transcription factor binding sites, including at promoters of genes across the human genome. A distinct class of CENP-A hotspots also accumulates at subtelomeric chromosomal locations, including at the 8q24/Myc region long-associated with genomic instability. We show this 8q24 accumulation of CENP-A can also be seen in early stage primary colorectal tumors.

Conclusions

Our data demonstrate that excess CENP-A accumulates at noncentromeric locations in the human cancer genome. These findings suggest that ectopic CENP-A nucleosomes could alter the state of the chromatin fiber, potentially impacting gene regulation and chromosome fragility.

SUBMITTER: Athwal RK

PROVIDER: S-EPMC4363203 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

CENP-A nucleosomes localize to transcription factor hotspots and subtelomeric sites in human cancer cells.

Athwal Rajbir K RK Walkiewicz Marcin P MP Baek Songjoon S Fu Song S Bui Minh M Camps Jordi J Ried Thomas T Sung Myong-Hee MH Dalal Yamini Y

Epigenetics & chromatin 20150113

<h4>Background</h4>The histone H3 variant CENP-A is normally tightly regulated to ensure only one centromere exists per chromosome. Native CENP-A is often found overexpressed in human cancer cells and a range of human tumors. Consequently, CENP-A misregulation is thought to contribute to genome instability in human cancers. However, the consequences of such overexpression have not been directly elucidated in human cancer cells.<h4>Results</h4>To investigate native CENP-A overexpression, we sough ...[more]

PMID: 25788983

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Project description:In computational methods, position weight matrices (PWMs) are commonly applied for transcription factor binding site (TFBS) prediction. Although these matrices are more accurate than simple consensus sequences to predict actual binding sites, they usually produce a large number of false positive (FP) predictions and so are impoverished sources of information. Several studies have employed additional sources of information such as sequence conservation or the vicinity to transcription start sites to distinguish true binding regions from random ones. Recently, the spatial distribution of modified nucleosomes has been shown to be associated with different promoter architectures. These aligned patterns can facilitate DNA accessibility for transcription factors. We hypothesize that using data from these aligned and periodic patterns can improve the performance of binding region prediction. In this study, we propose two effective features, "modified nucleosomes neighboring" and "modified nucleosomes occupancy", to decrease FP in binding site discovery. Based on these features, we designed a logistic regression classifier which estimates the probability of a region as a TFBS. Our model learned each feature based on Sp1 binding sites on Chromosome 1 and was tested on the other chromosomes in human CD4+T cells. In this work, we investigated 21 histone modifications and found that only 8 out of 21 marks are strongly correlated with transcription factor binding regions. To prove that these features are not specific to Sp1, we combined the logistic regression classifier with the PWM, and created a new model to search TFBSs on the genome. We tested the model using transcription factors MAZ, PU.1 and ELF1 and compared the results to those using only the PWM. The results show that our model can predict Transcription factor binding regions more successfully. The relative simplicity of the model and capability of integrating other features make it a superior method for TFBS prediction.

Dataset Information

CENP-A nucleosomes localize to transcription factor hotspots and subtelomeric sites in human cancer cells.

Background

Results

Conclusions

Publications

CENP-A nucleosomes localize to transcription factor hotspots and subtelomeric sites in human cancer cells.

Similar Datasets

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