CTCF-Mediated Genome Architecture Regulates the Dosage of Mitotically Stable Mono-allelic Expression of Autosomal Genes.

The mechanisms that guide the clonally stable random mono-allelic expression of autosomal genes remain enigmatic. We show that (1) mono-allelically expressed (MAE) genes are assorted and insulated from bi-allelically expressed (BAE) genes through CTCF-mediated chromatin loops; (2) the cell-type-specific dynamics of mono-allelic expression coincides with the gain and loss of chromatin insulator sites; (3) dosage of MAE genes is more sensitive to the loss of chromatin insulation than that of BAE genes; and (4) inactive alleles of MAE genes are significantly more insulated than active alleles and are de-repressed upon CTCF depletion. This alludes to a topology wherein the inactive alleles of MAE genes are insulated from the spatial interference of transcriptional states from the neighboring bi-allelic domains via CTCF-mediated loops. We propose that CTCF functions as a typical insulator on inactive alleles, but facilitates transcription through enhancer-linking on active allele of MAE genes, indicating widespread allele-specific regulatory roles of CTCF.

Biased visibility in Hi-C datasets marks dynamically regulated condensed and decondensed chromatin states genome-wide.

BACKGROUND: Proximity ligation based techniques, like Hi-C, involve restriction digestion followed by ligation of formaldehyde cross-linked chromatin. Distinct chromatin states can impact the restriction digestion, and hence the visibility in the contact maps, of engaged loci. Yet, the extent and the potential impact of digestion bias remain obscure and under-appreciated in the literature. RESULTS: Through analysis of 45 Hi-C datasets, lamina-associated domains (LADs), inactive X-chromosome in mammals, and polytene bands in fly, we first established that the DNA in condensed chromatin had lesser accessibility to restriction endonucleases used in Hi-C as compared to that in decondensed chromatin. The observed bias was independent of known systematic biases, was not appropriately corrected by existing computational methods, and needed an additional optimization step. We then repurposed this bias to identify novel condensed domains outside LADs, which were bordered by insulators and were dynamically associated with the polycomb mediated epigenetic and transcriptional states during development. CONCLUSIONS: Our observations suggest that the corrected one-dimensional read counts of existing Hi-C datasets can be reliably repurposed to study the gene-regulatory dynamics associated with chromatin condensation and decondensation, and that the existing Hi-C datasets should be interpreted with cautions.

Comprehensive profiling of transcriptional networks specific for lactogenic differentiation of HC11 mammary epithelial stem-like cells.

The development of mammary gland as a lactogenic tissue is a highly coordinated multistep process. The epithelial cells of lactiferous tubules undergo profound changes during the developmental window of puberty, pregnancy, and lactation. Several hormones including estrogen, progesterone, glucocorticoids and prolactin act in concert, and orchestrate the development of mammary gland. Understanding the gene regulatory networks that coordinate proliferation and differentiation of HC11 Mammary Epithelial stem-like Cells (MEC) under the influence of lactogenic hormones is critical for elucidating the mechanism of lactogenesis in detail. In this study, we analyzed transcriptome profiles of undifferentiated MEC (normal) and compared them with Murine Embryonic Stem Cells (ESC) using next-generation mRNA sequencing. Further, we analyzed the transcriptome output during lactogenic differentiation of MEC following treatment with glucocorticoids (primed state) and both glucocorticoids and prolactin together (prolactin state). We established stage-specific gene regulatory networks in ESC and MEC (normal, priming and prolactin states). We validated the top up-and downregulated genes in each stage of differentiation of MEC by RT-PCR and found that they are comparable with that of RNA-seq data. HC11 MEC display decreased expression of Pou5f1 and Sox2, which is crucial for the differentiation of MEC, which otherwise ensure pluripotency to ESC. Cited4 is induced during priming and is involved in milk secretion. MEC upon exposure to both glucocorticoids and prolactin undergo terminal differentiation, which is associated with the expression of several genes, including Xbp1 and Cbp that are required for cell growth and differentiation. Our study also identified differential expression of transcription factors and epigenetic regulators in each stage of lactogenic differentiation. We also analyzed the transcriptome data for the pathways that are selectively activated during lactogenic differentiation. Further, we found that selective expression of chromatin modulators (Dnmt3l, Chd9) in response to glucocorticoids suggests a highly coordinated stage-specific lactogenic differentiation of MEC.

RNA sequencing of murine mammary epithelial stem-like cells (HC11) undergoing lactogenic differentiation and its comparison with embryonic stem cells.

OBJECTIVES: Understanding of transcriptional networks specifying HC11 murine mammary epithelial stem cell-like cells (MEC) in comparison with embryonic stem cells (ESCs) and their rewiring, under the influence of glucocorticoids (GC) and prolactin (PRL) hormones, is critical for elucidating the mechanism of lactogenesis. In this data note, we provide RNA sequencing data from murine MECs and ESCs, MECs treated with steroid hormone alone and in combination with PRL. This data could help in understanding temporal dynamics of mRNA transcription that impact the process of lactogenesis associated with mammary gland development. Further integration of these data sets with existing datasets of cells derived from various stages of mammary gland development and different types of breast tumors, should pave the way for effective prognosis and to develop therapies for breast cancer. DATA DESCRIPTION: We have generated RNA-sequencing data representing steady-state levels of mRNAs from murine ESCs, normal MECs (N), MECs primed (P) with hydrocortisone (HC) alone and in combination with PRL hormone by using Illumina sequencing platform. We have generated ~ 58 million reads for ESCs with an average length of ~ 100 nt and an average 115 million good quality mapped reads with an average length of ~ 150 nt for different stages of MECs differentiation.