Dynamic regulation of transcriptional states by chromatin and transcription factors
TL;DR: This Review discusses emerging concepts regarding the function of regulatory elements in living cells and the involvement of these dynamic and stochastic processes in the evolution of fluctuating transcriptional activity states that are now commonly reported in eukaryotic systems.
Abstract: The interaction of regulatory proteins with the complex nucleoprotein structures that are found in mammalian cells involves chromatin reorganization at multiple levels. Mechanisms that support these transitions are complex on many timescales, which range from milliseconds to minutes or hours. In this Review, we discuss emerging concepts regarding the function of regulatory elements in living cells. We also explore the involvement of these dynamic and stochastic processes in the evolution of fluctuating transcriptional activity states that are now commonly reported in eukaryotic systems.
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TL;DR: Basset offers a powerful computational approach to annotate and interpret the noncoding genome using a recent machine learning advance-deep convolutional neural networks to learn the functional activity of DNA sequences from genomics data.
Abstract: The process of identifying genomic sites that show statistical relationships to phenotypes holds great promise for human health and disease (Hindorff et al. 2009). However, our current inability to efficiently interpret noncoding variants impedes progress toward using personal genomes in medicine. Coordinated efforts to survey the noncoding genome have shown that sequences marked by DNA accessibility and certain histone modifications are enriched for variants that are statistically related to phenotypes (The ENCODE Project Consortium 2012; Roadmap Epigenomics Consortium et al. 2015). The first stages of a mechanistic hypothesis can now be assigned to variants that directly overlap these annotations (Fu et al. 2014; Kircher et al. 2014; Ritchie et al. 2014).
However, simply considering the overlap of a variant with annotations underutilizes these data; more can be extracted by understanding the DNA–protein interactions as a function of the underlying sequence. Proteins that recognize specific signals in the DNA influence its accessibility and histone modifications (Voss and Hager 2014). Given training data, models parameterized by machine learning can effectively predict protein binding, DNA accessibility, histone modifications, and DNA methylation from the sequence (Das et al. 2006; Arnold et al. 2013; Benveniste et al. 2014; Pinello et al. 2014; Lee et al. 2015; Setty and Leslie 2015; Whitaker et al. 2015). A trained model can then annotate the influence of every nucleotide (and variant) on these regulatory attributes. This upgrades previous approaches in two ways. First, variants can be studied at a finer resolution; researchers can prioritize variants predicted to drive the regulatory activity and devalue those predicted to be irrelevant bystanders. Second, rare variants that introduce a gain of function will often not overlap regulatory annotations in publicly available data. An accurate model for regulatory activity can predict the gain of function, allowing follow-up consideration of the site.
In recent years, artificial neural networks with many stacked layers have achieved breakthrough advances on benchmark data sets in image analysis (Krizhevsky et al. 2012) and natural language processing (Collobert et al. 2011). Rather than choose features manually or in a preprocessing step, convolutional neural networks (CNNs) adaptively learn them from the data during training. They apply nonlinear transformations to map input data to informative high-dimensional representations that trivialize classification or regression (Bengio et al. 2013). Early applications of CNNs to DNA sequence analysis surpass more established algorithms, such as support vector machines or random forests, at predicting protein binding and accessibility from DNA sequence (Alipanahi et al. 2015; Zhou and Troyanskaya 2015). More accurate models can more precisely dissect regulatory sequences, thus improving noncoding variant interpretation. However, to fully exploit the value of these models, it is essential that they are technically and conceptually accessible to the researchers who can take advantage of their potential.
Here, we introduce Basset, an open source package to apply deep CNNs to learn functional activities of DNA sequences. We used Basset to simultaneously predict the accessibility of DNA sequences in 164 cell types mapped by DNase-seq by the ENCODE Project Consortium and Roadmap Epigenomics Consortium (The ENCODE Project Consortium 2012; Roadmap Epigenomics Consortium et al. 2015). From these data sets, CNNs simultaneously learn the relevant sequence motifs and the regulatory logic with which they are combined to determine cell-specific DNA accessibility. We show that a model achieving this level of accuracy provides meaningful, nucleotide-precision measurements. Subsequently, we assign Genome-wide association study (GWAS) variants cell-type–specific scores that reflect the accessibility difference predicted by the model between the two alleles. These scores are highly predictive of the causal SNP among sets of linked variants. Importantly, Basset puts CNNs in the hands of the genome biology community, providing tools and strategies for researchers to train and analyze models on new data sets. In conjunction with genomic big data, Basset offers a promising future for understanding how the genome crafts phenotypes.
801 citations
Cites methods from "Dynamic regulation of transcription..."
...With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell’s chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility....
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...By computing the predicted accessibility of all possible mutations to a sequence, Basset can be used to perform an in silico saturation mutagenesis....
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TL;DR: Three-dimensional genome architecture has important roles in the regulation of gene expression and is therefore a key determinant of cell identity in normal development and in disease states.
Abstract: How cells adopt different identities has long fascinated biologists. Signal transduction in response to environmental cues results in the activation of transcription factors that determine the gene-expression program characteristic of each cell type. Technological advances in the study of 3D chromatin folding are bringing the role of genome conformation in transcriptional regulation to the fore. Characterizing this role of genome architecture has profound implications, not only for differentiation and development but also for diseases including developmental malformations and cancer. Here we review recent studies indicating that the interplay between transcription and genome conformation is a driving force for cell-fate decisions.
325 citations
TL;DR: It is found that MNase-accessible nucleosomes, bound by transcription factors, are retained more at liver-specific enhancers than at promoters and ubiquitous enhancers, suggesting nucleosome are not exclusively repressive to gene regulation when they are retained with, and exposed by, pioneer factors.
302 citations
TL;DR: The identification of widespread chromatin changes during SCLC progression reveals an unexpected global reprogramming during metastatic progression, and indicates that Nfib is necessary and sufficient to increase chromatin accessibility at a large subset of the intergenic regions.
296 citations
TL;DR: It is shown that sustained proximity of an enhancer to its target is required for transcription, and the approach offers quantitative insight into the spatial and temporal determinants of long-range gene regulation and their implications for cellular fates.
Abstract: A long-standing question in gene regulation is how remote enhancers communicate with their target promoters, and specifically how chromatin topology dynamically relates to gene activation. Here, we combine genome editing and multi-color live imaging to simultaneously visualize physical enhancer–promoter interaction and transcription at the single-cell level in Drosophila embryos. By examining transcriptional activation of a reporter by the endogenous even-skipped enhancers, which are located 150 kb away, we identify three distinct topological conformation states and measure their transition kinetics. We show that sustained proximity of the enhancer to its target is required for activation. Transcription in turn affects the three-dimensional topology as it enhances the temporal stability of the proximal conformation and is associated with further spatial compaction. Furthermore, the facilitated long-range activation results in transcriptional competition at the locus, causing corresponding developmental defects. Our approach offers quantitative insight into the spatial and temporal determinants of long-range gene regulation and their implications for cellular fates. The authors use genome editing and live imaging to visualize physical enhancer–promoter interaction and transcription at the single-cell level in Drosophila embryos. They show that sustained proximity of an enhancer to its target is required for transcription.
295 citations
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TL;DR: This work constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated and reveals how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.
Abstract: Clonal populations of cells exhibit substantial phenotypic variation. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. We constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Both stochasticity inherent in the biochemical process of gene expression (intrinsic noise) and fluctuations in other cellular components (extrinsic noise) contribute substantially to overall variation. Transcription rate, regulatory dynamics, and genetic factors control the amplitude of noise. These results establish a quantitative foundation for modeling noise in genetic networks and reveal how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.
5,209 citations
TL;DR: Network motifs are reviewed, suggesting that they serve as basic building blocks of transcription networks, including signalling and neuronal networks, in diverse organisms from bacteria to humans.
Abstract: Transcription regulation networks control the expression of genes. The transcription networks of well-studied microorganisms appear to be made up of a small set of recurring regulation patterns, called network motifs. The same network motifs have recently been found in diverse organisms from bacteria to humans, suggesting that they serve as basic building blocks of transcription networks. Here I review network motifs and their functions, with an emphasis on experimental studies. Network motifs in other biological networks are also mentioned, including signalling and neuronal networks.
3,076 citations
University of Washington1, Duke University2, Fred Hutchinson Cancer Research Center3, University of Massachusetts Medical School4, University of Texas at Austin5, Chinese Academy of Sciences6, University of North Carolina at Chapel Hill7, University of Bergen8, Harvard University9, Imperial College London10
TL;DR: The first extensive map of human DHSs identified through genome-wide profiling in 125 diverse cell and tissue types is presented, revealing novel relationships between chromatin accessibility, transcription, DNA methylation and regulatory factor occupancy patterns.
Abstract: DNase I hypersensitive sites (DHSs) are markers of regulatory DNA and have underpinned the discovery of all classes of cis-regulatory elements including enhancers, promoters, insulators, silencers and locus control regions. Here we present the first extensive map of human DHSs identified through genome-wide profiling in 125 diverse cell and tissue types. We identify ∼2.9 million DHSs that encompass virtually all known experimentally validated cis-regulatory sequences and expose a vast trove of novel elements, most with highly cell-selective regulation. Annotating these elements using ENCODE data reveals novel relationships between chromatin accessibility, transcription, DNA methylation and regulatory factor occupancy patterns. We connect ∼580,000 distal DHSs with their target promoters, revealing systematic pairing of different classes of distal DHSs and specific promoter types. Patterning of chromatin accessibility at many regulatory regions is organized with dozens to hundreds of co-activated elements, and the transcellular DNase I sensitivity pattern at a given region can predict cell-type-specific functional behaviours. The DHS landscape shows signatures of recent functional evolutionary constraint. However, the DHS compartment in pluripotent and immortalized cells exhibits higher mutation rates than that in highly differentiated cells, exposing an unexpected link between chromatin accessibility, proliferative potential and patterns of human variation. An extensive map of human DNase I hypersensitive sites, markers of regulatory DNA, in 125 diverse cell and tissue types is described; integration of this information with other ENCODE-generated data sets identifies new relationships between chromatin accessibility, transcription, DNA methylation and regulatory factor occupancy patterns. This paper describes the first extensive map of human DNaseI hypersensitive sites — markers of regulatory DNA — in 125 diverse cell and tissue types. Integration of this information with other data sets generated by ENCODE (Encyclopedia of DNA Elements) identified new relationships between chromatin accessibility, transcription, DNA methylation and regulatory-factor occupancy patterns. Evolutionary-conservation analysis revealed signatures of recent functional constraint within DNaseI hypersensitive sites.
2,628 citations
TL;DR: Next-generation sequencing is providing a window for visualizing the human epigenome and how it is altered in cancer, including linking epigenetic abnormalities to mutations in genes that control DNA methylation, the packaging and the function of DNA in chromatin, and metabolism.
Abstract: The past decade has highlighted the central role of epigenetic processes in cancer causation, progression and treatment. Next-generation sequencing is providing a window for visualizing the human epigenome and how it is altered in cancer. This view provides many surprises, including linking epigenetic abnormalities to mutations in genes that control DNA methylation, the packaging and the function of DNA in chromatin, and metabolism. Epigenetic alterations are leading candidates for the development of specific markers for cancer detection, diagnosis and prognosis. The enzymatic processes that control the epigenome present new opportunities for deriving therapeutic strategies designed to reverse transcriptional abnormalities that are inherent to the cancer epigenome.
2,483 citations
TL;DR: Stochasticity in gene expression can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
Abstract: Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
2,381 citations