Cell-type and transcription factor specific enrichment of transcriptional cofactor motifs in ENCODE ChIP-seq data

Single-Cell Transcriptome Analysis Reveals Development-Specific Networks at Distinct Synchronized Antral Follicle Sizes in Sheep Oocytes

The development of the ovarian antral follicle is a complex, highly regulated process. Oocytes orchestrate and coordinate the development of mammalian ovarian follicles, and the rate of follicular development is governed by a developmental program intrinsic to the oocyte. Characterizing oocyte signatures during this dynamic process is critical for understanding oocyte maturation and follicular development. Although the transcriptional signature of sheep oocytes matured in vitro and preovulatory oocytes have been previously described, the transcriptional changes of oocytes in antral follicles have not. Here, we used single-cell transcriptomics (SmartSeq2) to characterize sheep oocytes from small, medium, and large antral follicles. We characterized the transcriptomic landscape of sheep oocytes during antral follicle development, identifying unique features in the transcriptional atlas, stage-specific molecular signatures, oocyte-secreted factors, and transcription factor networks. Notably, we identified the specific expression of 222 genes in the LO, 8 and 6 genes that were stage-specific in the MO and SO, respectively. We also elucidated signaling pathways in each antral follicle size that may reflect oocyte quality and in vitro maturation competency. Additionally, we discovered key biological processes that drive the transition from small to large antral follicles, revealing hub genes involved in follicle recruitment and selection. Thus, our work provides a comprehensive characterization of the single-oocyte transcriptome, filling a gap in the mapping of the molecular landscape of sheep oogenesis. We also provide key insights into the transcriptional regulation of the critical sizes of antral follicular development, which is essential for understanding how the oocyte orchestrates follicular development.

Dissecting the functional significance of HSP90AB1 and other heat shock proteins in countering glioblastomas and ependymomas using omics analysis and drug prediction using virtual screening

Heat shock proteins (HSPs) are the evolutionary family of proteins that are highly conserved and present widely in various organisms and play an array of important roles and cellular functions. Currently, very few or no studies are based on the systematic analysis of the HSPs in Glioblastoma (GBMs) and ependymomas. We performed an integrated omics analysis to predict the mutual regulatory differential HSP signatures that were associated with both glioblastoma and ependymomas. Further, we explored the various common dysregulated biological processes operating in both the tumors, and were analyzed using functional enrichment, gene ontology along with the pathway analysis of the predicted HSPs. We established an interactome network of protein-protein interaction (PPIN) to identify the hub HSPs that were commonly associated with GBMs and ependymoma. To understand the mutual molecular mechanism of the HSPs in both malignancies, transcription factors, and miRNAs overlapping with both diseases were explored. Moreover, a transcription factor-miRNAs-HSPs coregulatory network was constructed along with the prediction of potential candidate drugs that were based on perturbation-induced gene expression analysis. Based on the RNA-sequencing data, HSP90AB1 was identified as the most promising target among other predicted HSPs. Finally, the ranking of the drugs was arranged based on various drug scores. In conclusion, this study gave a spotlight on the mutual targetable HSPs, biological pathways, and regulatory signatures associated with GBMs and ependymoma with an improved understanding of crosstalk involved. Additionally, the role of therapeutics was also explored against HSP90AB1. These findings could potentially be able to explain the interplay of HSP90AB1 and other HSPs within these two malignancies.

Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

cis-regulatory modules(CRMs) formed by clusters of transcription factor (TF) binding sites (TFBSs) are as important as coding sequences in specifying phenotypes of humans. It is essential to categorize all CRMs and constituent TFBSs in the genome. In contrast to most existing methods that predict CRMs in specific cell types using epigenetic marks, we predict a largely cell type agonistic but more comprehensive map of CRMs and constituent TFBSs in the gnome by integrating all available TF ChIP-seq datasets. Our method is able to partition 77.47% of genome regions covered by available 6092 datasets into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, the predicted CRMCs are under strong evolutionary constraints, while the non-CRMCs are largely selectively neutral, strongly suggesting that the CRMCs are likely cis-regulatory, while the non-CRMCs are not. Our predicted CRMs are under stronger evolutionary constraints than three state-of-the-art predictions (GeneHancer, EnhancerAtlas and ENCODE phase 3) and substantially outperform them for recalling VISTA enhancers and non-coding ClinVar variants. We estimated that the human genome might encode about 1.47M CRMs and 68M TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought.

Pioneer factors and their in vitro identification methods

Pioneer transcription factors are a special group of transcription factors that can interact with nucleosomal DNA and initiate regulatory events. Their binding to regulatory regions is the first event in gene activation and can occur in silent or heterochromatin regions. Several research groups have endeavored to define pioneer factors and study their binding characteristics using various techniques. In this review, we describe the in vitro methods used to define and characterize pioneer factors, paying particular attention to differences in methodologies and how these differences can affect results.

Dual Cross-Linking Chromatin Immunoprecipitation Protocol for Next-Generation Sequencing (ChIPseq) in Macrophages

Macrophages arise from distinct progenitor cell populations throughout development and are one of the most diverse cell types, capable of performing discrete functions, undergoing distinct modes of activation, and infiltrating or residing in numerous niches in the body. In adapting to their environments, macrophages display high levels of plasticity which is associated with profound epigenomic and transcriptional changes. Understanding these changes has been greatly facilitated by the next-generation sequencing (NGS)-based approaches such as RNAseq and chromatin immunoprecipitation (ChIP)seq. Despite the recent advances, obtaining quality ChIPseq data in macrophages for endogenous factors and especially coregulators recruited to DNA indirectly has proved to be extremely challenging. Here, we describe a dual crosslinking protocol for ChIPseq in macrophages that we developed for difficult-to-ChIP transcription factors, coregulators, and their posttranslational modifications. Further, we provide guidance on crucial optimization steps throughout this protocol. Although our experience has been predominantly in murine and human macrophages, we believe our protocols can be modified and optimized to study signal-induced epigenomic changes in any cell type of choice.

GSMC: Combining Parallel Gibbs Sampling with Maximal Cliques for Hunting DNA Motif

Regulatory elements are responsible for regulating gene transcription. Therefore, identification of these elements is a tremendous challenge in the field of gene expression. Transcription factors (TFs) play a key role in gene regulation by binding to target promoter sequences. A set of conserved sequence patterns with a highly similar structure that is bound by a TF is called a motif. Motif discovery has been a difficult problem over the past decades. Meanwhile, it is a foundation stone in meeting this challenge. Recent advances in obtaining genomic sequences and high-throughput gene expression analysis techniques have enabled the rapid development of computational methods for motif discovery. As a result, a large number of motif-finding algorithms aiming at various motif models have sprung up in the past few years. However, most of them are not suitable for analysis of the large data sets generated by next-generation sequencing. To better handle large-scale ChIP-Seq data and achieve better performance in computational time and motif detection accuracy, we propose an excellent motif-finding algorithm known as GSMC (Combining Parallel Gibbs Sampling with Maximal Cliques for hunting DNA Motif). The GSMC algorithm consists of two steps. First, we employ the commonly used Gibbs sampling to generating initial motifs. Second, we utilize maximal cliques to cluster motifs according to Similarity with Position Information Contents (SPIC). Consequently, we raise the detection accuracy in a great degree, in the meantime holding comparative computation efficiency. In addition, we can find much more credible cofactor interacting motifs.

Genome-wide association study identifies the SERPINB gene cluster as a susceptibility locus for food allergy

Genetic factors and mechanisms underlying food allergy are largely unknown. Due to heterogeneity of symptoms a reliable diagnosis is often difficult to make. Here, we report a genome-wide association study on food allergy diagnosed by oral food challenge in 497 cases and 2387 controls. We identify five loci at genome-wide significance, the clade B serpin (SERPINB) gene cluster at 18q21.3, the cytokine gene cluster at 5q31.1, the filaggrin gene, the C11orf30/LRRC32 locus, and the human leukocyte antigen (HLA) region. Stratifying the results for the causative food demonstrates that association of the HLA locus is peanut allergy-specific whereas the other four loci increase the risk for any food allergy. Variants in the SERPINB gene cluster are associated with SERPINB10 expression in leukocytes. Moreover, SERPINB genes are highly expressed in the esophagus. All identified loci are involved in immunological regulation or epithelial barrier function, emphasizing the role of both mechanisms in food allergy.

Glucocorticoids and Reproduction: Traffic Control on the Road to Reproduction

Glucocorticoids are steroid hormones that regulate diverse cellular functions and are essential to facilitate normal physiology. However, stress-induced levels of glucocorticoids result in several pathologies including profound reproductive dysfunction. Compelling new evidence indicates that glucocorticoids are crucial to the establishment and maintenance of reproductive function. The fertility-promoting or -inhibiting activity of glucocorticoids depends on timing, dose, and glucocorticoid responsiveness within a given tissue, which is mediated by the glucocorticoid receptor (GR). The GR gene and protein are subject to cellular processing, contributing to signaling diversity and providing a mechanism by which both physiological and stress-induced levels of glucocorticoids function in a cell-specific manner. Understanding how glucocorticoids regulate fertility and infertility may lead to novel approaches to the regulation of reproductive function.

Cell-lineage specificity and role of AP-1 in the prostate fibroblast androgen receptor cistrome

Androgen receptor (AR) signalling in fibroblasts is important in prostate development and carcinogenesis, and is inversely related to prostate cancer mortality. However, the molecular mechanisms of AR action in fibroblasts and other non-epithelial cell types are largely unknown. The genome-wide DNA binding profile of AR in human prostate fibroblasts was identified by chromatin immunoprecipitation sequencing (ChIP-Seq), and found to be common to other fibroblast lines but disparate from AR cistromes of prostate cancer cells and tissue. Although AR binding sites specific to fibroblasts were less well conserved evolutionarily than those shared with cancer epithelia, they were likewise correlated with androgen regulation of fibroblast gene expression. Whereas FOXA1 is the key pioneer factor of AR in cancer epithelia, our data indicated that AP-1 likely plays a more important role in the AR cistrome in fibroblasts. The specificity of AP-1 and FOXA1 to binding in these cells is demonstrated using immunoblot and immunohistochemistry. Importantly, we find the fibroblast cistrome is represented in whole tissue/in vivo ChIP-seq studies at both genomic and resulting protein levels, highlighting the importance of the stroma in whole tissue -omic studies. This is the first nuclear receptor ChIP-seq study in prostatic fibroblasts, and provides novel insight into the action of fibroblast AR in prostate cancer.

Differential effects of HTLV-1 Tax oncoprotein on the different estrogen-induced-ER α-mediated transcriptional activities

The activated estrogen (E2) receptor α (ERα) is a potent transcription factor that is involved in the activation of various genes by 2 different pathways; a classical and non-classical. In classical pathway, ERα binds directly to E2-responsive elements (EREs) located in the appropriate genes promoters and stimulates their transcription. However, in non-classical pathway, the ERα can indirectly bind with promoters and enhance their activity. For instance, ERα activates BRCA1 expression by interacting with jun/fos complex bound to the AP-1 site in BRCA1 promoter. Interference with the expression and/or functions of BRCA1, leads to high risk of breast or/and ovarian cancer. HTLV-1Tax was found to strongly inhibit BRCA1 expression by preventing the binding of E2-ERα complex to BRCA1 promoter. Here we examined Tax effect on ERα induced activation of genes by the classical pathway by testing its influence on E2-induced expression of ERE promoter-driven luciferase reporter (ERE-Luc). Our findings showed that E2 profoundly stimulated this reporter expression and that HTLV-1Tax significantly induced this stimulation. This result is highly interesting because in our previous study Tax was found to strongly block the E2-ERα-mediated activation of BRCA1 expression. ERα was found to produce a big complex by recruiting various cofactors in the nucleus before binding to the ERE region. We also found that only part of the reqruited cofactors are required for the transcriptional activity of ERα complex. Chip assay revealed that the binding of Tax to the ERα complex, did not interfere with its link to ERE region.

Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets

Motivation: The motif discovery problem consists of finding recurring patterns of short strings in a set of nucleotide sequences. This classical problem is receiving renewed attention as most early motif discovery methods lack the ability to handle large data of recent genome-wide ChIP studies. New ChIP-tailored methods focus on reducing computation time and pay little regard to the accuracy of motif detection. Unlike such methods, our method focuses on increasing the detection accuracy while maintaining the computation efficiency at an acceptable level. The major advantage of our method is that it can mine diverse multiple motifs undetectable by current methods.

Results: The repulsive parallel Markov chain Monte Carlo (RPMCMC) algorithm that we propose is a parallel version of the widely used Gibbs motif sampler. RPMCMC is run on parallel interacting motif samplers. A repulsive force is generated when different motifs produced by different samplers near each other. Thus, different samplers explore different motifs. In this way, we can detect much more diverse motifs than conventional methods can. Through application to 228 transcription factor ChIP-seq datasets of the ENCODE project, we show that the RPMCMC algorithm can find many reliable cofactor interacting motifs that existing methods are unable to discover.

Availability and implementation: A C++ implementation of RPMCMC and discovered cofactor motifs for the 228 ENCODE ChIP-seq datasets are available from http://daweb.ism.ac.jp/yoshidalab/motif.

Contact:ikebata.hisaki@ism.ac.jp, yoshidar@ism.ac.jp

Supplementary information:Supplementary data are available from Bioinformatics online.

Comprehensive discovery of DNA motifs in 349 human cells and tissues reveals new features of motifs

Comprehensive motif discovery under experimental conditions is critical for the global understanding of gene regulation. To generate a nearly complete list of human DNA motifs under given conditions, we employed a novel approach to de novo discover significant co-occurring DNA motifs in 349 human DNase I hypersensitive site datasets. We predicted 845 to 1325 motifs in each dataset, for a total of 2684 non-redundant motifs. These 2684 motifs contained 54.02 to 75.95% of the known motifs in seven large collections including TRANSFAC. In each dataset, we also discovered 43 663 to 2 013 288 motif modules, groups of motifs with their binding sites co-occurring in a significant number of short DNA regions. Compared with known interacting transcription factors in eight resources, the predicted motif modules on average included 84.23% of known interacting motifs. We further showed new features of the predicted motifs, such as motifs enriched in proximal regions rarely overlapped with motifs enriched in distal regions, motifs enriched in 5' distal regions were often enriched in 3' distal regions, etc. Finally, we observed that the 2684 predicted motifs classified the cell or tissue types of the datasets with an accuracy of 81.29%. The resources generated in this study are available at http://server.cs.ucf.edu/predrem/.