Dataset Information

Upstream distal regulatory elements contact the Lmo2 promoter in mouse erythroid cells.

ABSTRACT: The Lim domain only 2 (Lmo2) gene encodes a transcriptional cofactor critical for the development of hematopoietic stem cells. Several distal regulatory elements have been identified upstream of the Lmo2 gene in the human and mouse genomes that are capable of enhancing reporter gene expression in erythroid cells and may be responsible for the high level transcription of Lmo2 in the erythroid lineage. In this study we investigate how these elements regulate transcription of Lmo2 and whether or not they function cooperatively in the endogenous context. Chromosome conformation capture (3C) experiments show that chromatin-chromatin interactions exist between upstream regulatory elements and the Lmo2 promoter in erythroid cells but that these interactions are absent from kidney where Lmo2 is transcribed at twelve fold lower levels. Specifically, long range chromatin-chromatin interactions occur between the Lmo2 proximal promoter and two broad regions, 3-31 and 66-105 kb upstream of Lmo2, which we term the proximal and distal control regions for Lmo2 (pCR and dCR respectively). Each of these regions is bound by several transcription factors suggesting that multiple regulatory elements cooperate in regulating high level transcription of Lmo2 in erythroid cells. Binding of CTCF and cohesin which support chromatin loops at other loci were also found within the dCR and at the Lmo2 proximal promoter. Intergenic transcription occurs throughout the dCR in erythroid cells but not in kidney suggesting a role for these intergenic transcripts in regulating Lmo2, similar to the broad domain of intergenic transcription observed at the human ?-globin locus control region. Our data supports a model in which the dCR functions through a chromatin looping mechanism to contact and enhance Lmo2 transcription specifically in erythroid cells. Furthermore, these chromatin loops are supported by the cohesin complex recruited to both CTCF and transcription factor bound regions.

SUBMITTER: Bhattacharya A

PROVIDER: S-EPMC3528669 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Upstream distal regulatory elements contact the Lmo2 promoter in mouse erythroid cells.

Bhattacharya Anandi A Chen Chih-Yu CY Ho Sara S Mitchell Jennifer A JA

PloS one 20121221 12

The Lim domain only 2 (Lmo2) gene encodes a transcriptional cofactor critical for the development of hematopoietic stem cells. Several distal regulatory elements have been identified upstream of the Lmo2 gene in the human and mouse genomes that are capable of enhancing reporter gene expression in erythroid cells and may be responsible for the high level transcription of Lmo2 in the erythroid lineage. In this study we investigate how these elements regulate transcription of Lmo2 and whether or no ...[more]

PMID: 23285212

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Project description:BackgroundThe BRCA1 gene plays an important role in the maintenance of genomic stability. BRCA1 inactivation contributes to breast cancer tumorigenesis. An increasing number of transcription factors have been shown to regulate BRCA1 expression. c-Myc can act as a transcriptional activator, regulating up to 15% of all genes in the human genome and results from a high throughput screen suggest that BRCA1 is one of its targets. In this report, we used cultured breast cancer cells to examine the mechanisms of transcriptional activation of BRCA1 by c-Myc.Methodsc-Myc was depleted using c-Myc-specific siRNAs in cultured breast cancer cells. BRCA1 mRNA expression and BRCA1 protein expression were determined by quantitative RT-PCR and western blot, respectively and BRCA1 promoter activities were examined under these conditions. DNA sequence analysis was conducted to search for high similarity to E boxes in the BRCA1 promoter region. The association of c-Myc with the BRCA1 promoter in vivo was tested by a chromatin immunoprecipitation assay. We investigated the function of the c-Myc binding site in the BRCA1 promoter region by a promoter assay with nucleotide substitutions in the putative E boxes. BRCA1-dependent DNA repair activities were measured by a GFP-reporter assay.ResultsDepletion of c-Myc was found to be correlated with reduced expression levels of BRCA1 mRNA and BRCA1 protein. Depletion of c-Myc decreased BRCA1 promoter activity, while ectopically expressed c-Myc increased BRCA1 promoter activity. In the distal BRCA1 promoter, DNA sequence analysis revealed two tandem clusters with high similarity, and each cluster contained a possible c-Myc binding site. c-Myc bound to these regions in vivo. Nucleotide substitutions in the c-Myc binding sites in these regions abrogated c-Myc-dependent promoter activation. Furthermore, breast cancer cells with reduced BRCA1 expression due to depletion of c-Myc exhibited impaired DNA repair activity.ConclusionsThe distal BRCA1 promoter region is associated with c-Myc and contributes to BRCA1 gene activation.

Project description:BackgroundThe data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).ResultsWe develop a suite of ML models, under the banner AIKYATAN, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-'a-vis the competition. Specifically, AIKYATAN-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.ConclusionsOur exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our AIKYATAN suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Dataset Information

Upstream distal regulatory elements contact the Lmo2 promoter in mouse erythroid cells.

Publications

Upstream distal regulatory elements contact the Lmo2 promoter in mouse erythroid cells.

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