Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape

Pioneer transcription factors in cell reprogramming

Biochemical and genomic studies have shown that transcription factors with the highest reprogramming activity often have the special ability to engage their target sites on nucleosomal DNA, thus behaving as “pioneer factors” to initiate events in closed chromatin. This review by Iwafuchi-Doi and Zaret focuses on the most recent studies of pioneer factors in cell programming and reprogramming, how pioneer factors have special chromatin-binding properties, and facilitators and impediments to chromatin binding.

A subset of eukaryotic transcription factors possesses the remarkable ability to reprogram one type of cell into another. The transcription factors that reprogram cell fate are invariably those that are crucial for the initial cell programming in embryonic development. To elicit cell programming or reprogramming, transcription factors must be able to engage genes that are developmentally silenced and inappropriate for expression in the original cell. Developmentally silenced genes are typically embedded in “closed” chromatin that is covered by nucleosomes and not hypersensitive to nuclease probes such as DNase I. Biochemical and genomic studies have shown that transcription factors with the highest reprogramming activity often have the special ability to engage their target sites on nucleosomal DNA, thus behaving as “pioneer factors” to initiate events in closed chromatin. Other reprogramming factors appear dependent on pioneer factors for engaging nucleosomes and closed chromatin. However, certain genomic domains in which nucleosomes are occluded by higher-order chromatin structures, such as in heterochromatin, are resistant to pioneer factor binding. Understanding the means by which pioneer factors can engage closed chromatin and how heterochromatin can prevent such binding promises to advance our ability to reprogram cell fates at will and is the topic of this review.

Dissecting neural differentiation regulatory networks through epigenetic footprinting

Models derived from human pluripotent stem cells that accurately recapitulate neural development in vitro and allow for the generation of specific neuronal subtypes are of major interest to the stem cell and biomedical community. Notch signalling, particularly through the Notch effector HES5, is a major pathway critical for the onset and maintenance of neural progenitor cells in the embryonic and adult nervous system. Here we report the transcriptional and epigenomic analysis of six consecutive neural progenitor cell stages derived from a HES5::eGFP reporter human embryonic stem cell line. Using this system, we aimed to model cell-fate decisions including specification, expansion and patterning during the ontogeny of cortical neural stem and progenitor cells. In order to dissect regulatory mechanisms that orchestrate the stage-specific differentiation process, we developed a computational framework to infer key regulators of each cell-state transition based on the progressive remodelling of the epigenetic landscape and then validated these through a pooled short hairpin RNA screen. We were also able to refine our previous observations on epigenetic priming at transcription factor binding sites and suggest here that they are mediated by combinations of core and stage-specific factors. Taken together, we demonstrate the utility of our system and outline a general framework, not limited to the context of the neural lineage, to dissect regulatory circuits of differentiation.

Promoter or enhancer, what's the difference? Deconstruction of established distinctions and presentation of a unifying model

Gene transcription is strictly controlled by the interplay of regulatory events at gene promoters and gene-distal regulatory elements called enhancers. Despite extensive studies of enhancers, we still have a very limited understanding of their mechanisms of action and their restricted spatio-temporal activities. A better understanding would ultimately lead to fundamental insights into the control of gene transcription and the action of regulatory genetic variants involved in disease. Here, I review and discuss pros and cons of state-of-the-art genomics methods to localize and infer the activity of enhancers. Among the different approaches, profiling of enhancer RNAs yields the highest specificity and may be superior in detecting in vivo activity. I discuss their apparent similarities to promoters, which challenge the established view of enhancers and promoters as distinct entities, and present a unifying model of regulatory elements in transcriptional regulation, in which activity, transcriptional output and regulatory function is context specific.

Optimization of transcription factor binding map accuracy utilizing knockout-mouse models

Genome-wide assessment of protein-DNA interaction by chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) is a key technology for studying transcription factor (TF) localization and regulation of gene expression. Signal-to-noise-ratio and signal specificity in ChIP-seq studies depend on many variables, including antibody affinity and specificity. Thus far, efforts to improve antibody reagents for ChIP-seq experiments have focused mainly on generating higher quality antibodies. Here we introduce KOIN (knockout implemented normalization) as a novel strategy to increase signal specificity and reduce noise by using TF knockout mice as a critical control for ChIP-seq data experiments. Additionally, KOIN can identify 'hyper ChIPable regions' as another source of false-positive signals. As the use of the KOIN algorithm reduces false-positive results and thereby prevents misinterpretation of ChIP-seq data, it should be considered as the gold standard for future ChIP-seq analyses, particularly when developing ChIP-assays with novel antibody reagents.

Machine learning for Big Data analytics in plants

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences.

Predicting expression: the complementary power of histone modification and transcription factor binding data

Background

Transcription factors (TFs) and histone modifications (HMs) play critical roles in gene expression by regulating mRNA transcription. Modelling frameworks have been developed to integrate high-throughput omics data, with the aim of elucidating the regulatory logic that results from the interactions of DNA, TFs and HMs. These models have yielded an unexpected and poorly understood result: that TFs and HMs are statistically redundant in explaining mRNA transcript abundance at a genome-wide level.

Results

We constructed predictive models of gene expression by integrating RNA-sequencing, TF and HM chromatin immunoprecipitation sequencing and DNase I hypersensitivity data for two mammalian cell types. All models identified genome-wide statistical redundancy both within and between TFs and HMs, as previously reported. To investigate potential explanations, groups of genes were constructed for ontology-classified biological processes. Predictive models were constructed for each process to explore the distribution of statistical redundancy. We found significant variation in the predictive capacity of TFs and HMs across these processes and demonstrated the predictive power of HMs to be inversely proportional to process enrichment for housekeeping genes.

Conclusions

It is well established that the roles played by TFs and HMs are not functionally redundant. Instead, we attribute the statistical redundancy reported in this and previous genome-wide modelling studies to the heterogeneous distribution of HMs across chromatin domains. Furthermore, we conclude that statistical redundancy between individual TFs can be readily explained by nucleosome-mediated cooperative binding. This could possibly help the cell confer regulatory robustness by rejecting signalling noise and allowing control via multiple pathways.

Electronic supplementary material

The online version of this article (doi:10.1186/1756-8935-7-36) contains supplementary material, which is available to authorized users.

Identifying and mitigating bias in next-generation sequencing methods for chromatin biology

Next-generation sequencing (NGS) technologies have been used in diverse ways to investigate various aspects of chromatin biology by identifying genomic loci that are bound by transcription factors, occupied by nucleosomes or accessible to nuclease cleavage, or loci that physically interact with remote genomic loci. However, reaching sound biological conclusions from such NGS enrichment profiles requires many potential biases to be taken into account. In this Review, we discuss common ways in which biases may be introduced into NGS chromatin profiling data, approaches to diagnose these biases and analytical techniques to mitigate their effect.

Comparative evaluation of DNase-seq footprint identification strategies

DNase I is an enzyme preferentially cleaving DNA in highly accessible regions. Recently, Next-Generation Sequencing has been applied to DNase I assays (DNase-seq) to obtain genome-wide maps of these accessible chromatin regions. With high-depth sequencing, DNase I cleavage sites can be identified with base-pair resolution, revealing the presence of protected regions ("footprints"), corresponding to bound molecules on the DNA. Integrating footprint positions close to transcription start sites with motif analysis can reveal the presence of regulatory interactions between specific transcription factors (TFs) and genes. However, this inference heavily relies on the accuracy of the footprint call and on the sequencing depth of the DNase-seq experiment. Using ENCODE data, we comprehensively evaluate the performances of two recent footprint callers (Wellington and DNaseR) and one metric (the Footprint Occupancy Score, or FOS), and assess the consequences of different footprint calls on the reconstruction of TF-TF regulatory networks. We rate Wellington as the method of choice among those tested: not only its predictions are the best in terms of accuracy, but also the properties of the inferred networks are robust against sequencing depth.

Absence of a simple code: how transcription factors read the genome

Transcription factors (TFs) influence cell fate by interpreting the regulatory DNA within a genome. TFs recognize DNA in a specific manner; the mechanisms underlying this specificity have been identified for many TFs based on 3D structures of protein-DNA complexes. More recently, structural views have been complemented with data from high-throughput in vitro and in vivo explorations of the DNA-binding preferences of many TFs. Together, these approaches have greatly expanded our understanding of TF-DNA interactions. However, the mechanisms by which TFs select in vivo binding sites and alter gene expression remain unclear. Recent work has highlighted the many variables that influence TF-DNA binding, while demonstrating that a biophysical understanding of these many factors will be central to understanding TF function.

When genetics meets epigenetics: deciphering the mechanisms controlling inter-individual variation in immune responses to infection

The response of host immune cells to microbial stimuli is dependent on robust and coordinated gene expression programs involving the transcription of thousands of genes. The dysregulation of such regulatory programs is likely to significantly contribute to the marked differences in susceptibility to infectious diseases observed among individuals and between human populations. Although the specific factors leading to a dysfunctional immune response to infection remain largely unknown, we are increasingly appreciating the importance of genetic variants in altering the expression levels of immune-related genes, possibly via epigenetic changes. This review describes how recent technological advances have profoundly contributed to our current understanding of the genetic architecture and the epigenetic rules controlling immune responses to infectious agents and how genetic and epigenetic data can be combined to unravel the mechanisms associated with host variation in transcriptional responses to infection.

Mechanistic heterogeneity in site recognition by the structurally homologous DNA-binding domains of the ETS family transcription factors Ets-1 and PU.1

ETS family transcription factors regulate diverse genes through binding at cognate DNA sites that overlap substantially in sequence. The DNA-binding domains of ETS proteins (ETS domains) are highly conserved structurally yet share limited amino acid homology. To define the mechanistic implications of sequence diversity within the ETS family, we characterized the thermodynamics and kinetics of DNA site recognition by the ETS domains of Ets-1 and PU.1, which represent the extremes in amino acid divergence among ETS proteins. Even though the two ETS domains bind their optimal sites with similar affinities under physiologic conditions, their nature of site recognition differs strikingly in terms of the role of hydration and counter ion release. The data suggest two distinct mechanisms wherein Ets-1 follows a "dry" mechanism that rapidly parses sites through electrostatic interactions and direct protein-DNA contacts, whereas PU.1 utilizes hydration to interrogate sequence-specific sites and form a long-lived complex relative to the Ets-1 counterpart. The kinetic persistence of the high affinity PU.1 · DNA complex may be relevant to an emerging role of PU.1, but not Ets-1, as a pioneer transcription factor in vivo. In addition, PU.1 activity is critical to the development and function of macrophages and lymphocytes, which present osmotically variable environments, and hydration-dependent specificity may represent an important regulatory mechanism in vivo, a hypothesis that finds support in gene expression profiles of primary murine macrophages.

Chromatin features and the epigenetic regulation of pluripotency states in ESCs

In pluripotent stem cells, the interplay between signaling cues, epigenetic regulators and transcription factors orchestrates developmental potency. Flexibility in gene expression control is imparted by molecular changes to the nucleosomes, the building block of chromatin. Here, we review the current understanding of the role of chromatin as a plastic and integrative platform to direct gene expression changes in pluripotent stem cells, giving rise to distinct pluripotent states. We will further explore the concept of epigenetic asymmetry, focusing primarily on histone stoichiometry and their associated modifications, that is apparent at both the nucleosome and chromosome-wide levels, and discuss the emerging importance of these asymmetric chromatin configurations in diversifying epigenetic states and their implications for cell fate control.