Browsing by Author "Reddy, Timothy E"
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Item Open Access Adaptive sequence divergence forged new neurodevelopmental enhancers in humans.(Cell, 2022-11) Mangan, Riley J; Alsina, Fernando C; Mosti, Federica; Sotelo-Fonseca, Jesús Emiliano; Snellings, Daniel A; Au, Eric H; Carvalho, Juliana; Sathyan, Laya; Johnson, Graham D; Reddy, Timothy E; Silver, Debra L; Lowe, Craig BSearches for the genetic underpinnings of uniquely human traits have focused on human-specific divergence in conserved genomic regions, which reflects adaptive modifications of existing functional elements. However, the study of conserved regions excludes functional elements that descended from previously neutral regions. Here, we demonstrate that the fastest-evolved regions of the human genome, which we term "human ancestor quickly evolved regions" (HAQERs), rapidly diverged in an episodic burst of directional positive selection prior to the human-Neanderthal split, before transitioning to constraint within hominins. HAQERs are enriched for bivalent chromatin states, particularly in gastrointestinal and neurodevelopmental tissues, and genetic variants linked to neurodevelopmental disease. We developed a multiplex, single-cell in vivo enhancer assay to discover that rapid sequence divergence in HAQERs generated hominin-unique enhancers in the developing cerebral cortex. We propose that a lack of pleiotropic constraints and elevated mutation rates poised HAQERs for rapid adaptation and subsequent susceptibility to disease.Item Open Access Developing Quantitative Models in Analyzing High-throughput Sequencing Data(2021) Kim, Young-SookDiverse functional genomics assays have been developed and helped to investigate complex gene regulations in various biological conditions. For example, RNA-seq has been used to capture gene expressions in diverse human tissues, helping to study tissue-common and tissue-specific gene regulation. ChIP-seq has been used to identify the genomic regions bound by numerous transcription factors, thus helping to identify collaborative and competitive binding mechanisms of the transcription factors. Despite this huge increase in the amount and the accessibility of genomic data, we have several challenges to analyze those data with proper statistical methods. Some assays such as STARR-seq do not have a proper statistical model that detects both activated and repressed regulatory elements, making researchers depend on the statistical models developed for other assays. Some assays such as ChIP-seq and RNA-seq have limited joint analysis models that are flexible and computationally scalable, resulting in the limited statistical power in identifying the genomic regions or genes shared by multiple biological conditions. To solve those challenges in analyzing high-throughput assays, we first developed a statistical model called correcting reads and analysis of differential active elements or CRADLE to analyze STARR-seq data. CRADLE removes technical biases that can confound quantification of regulatory activity and then detects both activated and repressed regulatory elements. We observed the corrected read counts improved the visualization of regulatory activity, allowing for more accurate detection of regulatory elements. Indeed, through simulation study, we showed CRADLE significantly improved precision and recall in detecting regulatory elements compared to the previous statistical approaches and that improvement was especially prominent in identifying repressed regulatory elements. Based on our work on developing CRADLE, we adapted the statistical framework of CRADLE and developed a joint analysis model of multiple data for biology or JAMMY that can be applied to diverse high-throughput sequencing data. JAMMY is a flexible statistical model that jointly analyzes multiple conditions, identifies condition-shared and condition-specific genomic regions, and then quantifies the preferential activity of a subset of biological conditions for each genomic region. We applied JAMMY to STARR-seq, ChIP-seq, and RNA-seq data, and observed JAMMY overall improved the precision and recall in identifying condition-shared activity compared to the traditional condition-by-condition analysis. This gain of statistical power from the joint analysis led us to find a novel co-binding of two transcription factors in our study. Those results show the substantial advantages of using joint analysis model in integrating genomic data from multiple biological conditions.
Item Embargo Dissecting the functional effects of non-coding gene regulatory elements(2024) Sankaranarayanan, LaavanyaOne of the most beautiful and challenging aspects in biology is deciphering the complexity of the genome, and how it functions or dysfunctions. It is this intricate complexity that is dependent on developmental stages, time of the day, and tissue types that allows for the proper development of an organism comprising of different tissue types with different functions. Amongst the many complexities, I focused on the gene-regulatory functions of the non-coding genome and its relation to diseases including disease risk, severity, and progression. Over the last few decades, there has been an increase in the research of genetic causes underlying several complex, common multifactorial diseases including metabolic and cardiovascular diseases. While these studies have identified genetic risk loci, they have not directly identified the genetic mechanisms behind what causes those diseases. Identifying genetic mechanisms for complex traits has been challenging because most of the variants are located outside of protein-coding regions, and determining the effects of such non-coding variants remains difficult. Previous studies that explore the genetic mechanism of complex diseases have underscored the need to develop new methods to study non-coding regions and to systematically identify the effects of non-coding variants towards a disease.
In this dissertation, I evaluate the hypothesis that non-coding regulatory elements can contribute to disease-relevant traits by altering gene expression levels. I will specifically focus on a common complex disease, polycystic ovary syndrome (PCOS) which is the most prevalent endocrine disorder among menstruating people. Family- and twin-studies have demonstrated a genetic basis to PCOS. Previous studies have identified non-coding genetic variation associated with PCOS risk across populations with different ancestries. However, the functional follow up of these risk loci has been limited. Therefore, there is a gap in addressing the functional effects of genetic variants and regulatory elements impacting PCOS phenotypes. We identified gene regulatory mechanisms that help explain genetic association with PCOS in several loci using high throughput reporter assays, CRISPR-based epigenome editing, and genetic association analysis. To develop approaches to study regulatory elements, I implemented reporter assays at three different scales to create a framework for the regulatory elements across PCOS risk loci. I also implemented experimental approaches that measured changes in gene expression at single cell levels to identify target genes of regulatory elements identified by the reporter assays. Specifically, we identified regulatory elements across PCOS genetic risk loci in cell models of steroidogenesis, H295R cells and COV434 cells. We then identified regulatory elements that controlled the expression of the gene DENND1A, which altered the levels of testosterone produced by the cell models upon perturbation. Lastly, we quantified the regulatory effects of allele-specific genetic variants from a population of PCOS cases and controls. Taken together, we have identified regulatory elements that could contribute to PCOS pathogenesis. More broadly, my results demonstrate the strengths of combining experimental and statistical approaches to identify molecular mechanisms of genetic risk loci contributing to disease pathogenesis.
Item Open Access Dissecting the Functional Impacts of Non-Coding Genetic Variation(2016) Guo, CongA large proportion of the variation in traits between individuals can be attributed to variation in the nucleotide sequence of the genome. The most commonly studied traits in human genetics are related to disease and disease susceptibility. Although scientists have identified genetic causes for over 4,000 monogenic diseases, the underlying mechanisms of many highly prevalent multifactorial inheritance disorders such as diabetes, obesity, and cardiovascular disease remain largely unknown. Identifying genetic mechanisms for complex traits has been challenging because most of the variants are located outside of protein-coding regions, and determining the effects of such non-coding variants remains difficult. In this dissertation, I evaluate the hypothesis that such non-coding variants contribute to human traits and diseases by altering the regulation of genes rather than the sequence of those genes. I will specifically focus on studies to determine the functional impacts of genetic variation associated with two related complex traits: gestational hyperglycemia and fetal adiposity. At the genomic locus associated with maternal hyperglycemia, we found that genetic variation in regulatory elements altered the expression of the HKDC1 gene. Furthermore, we demonstrated that HKDC1 phosphorylates glucose in vitro and in vivo, thus demonstrating that HKDC1 is a fifth human hexokinase gene. At the fetal-adiposity associated locus, we identified variants that likely alter VEPH1 expression in preadipocytes during differentiation. To make such studies of regulatory variation high-throughput and routine, we developed POP-STARR, a novel high throughput reporter assay that can empirically measure the effects of regulatory variants directly from patient DNA. By combining targeted genome capture technologies with STARR-seq, we assayed thousands of haplotypes from 760 individuals in a single experiment. We subsequently used POP-STARR to identify three key features of regulatory variants: that regulatory variants typically have weak effects on gene expression; that the effects of regulatory variants are often coordinated with respect to disease-risk, suggesting a general mechanism by which the weak effects can together have phenotypic impact; and that nucleotide transversions have larger impacts on enhancer activity than transitions. Together, the findings presented here demonstrate successful strategies for determining the regulatory mechanisms underlying genetic associations with human traits and diseases, and value of doing so for driving novel biological discovery.
Item Open Access Genome Engineering Tools to Dissect Gene Regulation(2019) Kocak, Daniel DewranOver the past several years genome and epigenome engineering has been propelled forward by CRISPR-Cas technologies. These prokaryotic defense systems work well in mammalian cells in a manner that is remarkably robust: they are non-toxic, fold into a catalytically active state, localize to targeted cellular compartments, and act on the eukaryotic genome, which is heavily compacted in chromatin. While all these are true, CRISPR-cas nucleases did not evolve to function as highly specific genome engineering tools. Thus, the major goals of the work presented herein are to i) refine the specificity of CRISPR-Cas enzymes, ii) develop methods that facilitate genome engineering in human cells, and iii) apply these technologies toward outstanding problems in human gene regulation. With regard to the first goal, we set out to develop a method that could be easily applied to increase the specificity of diverse CRISPR systems. Adopting RNA-engineering to achieve this goal, we modulate the kinetics of DNA strand invasion to increase the specificity of Cas enzymes. Since the guide RNA is a feature that is common across all CRISPR systems, we expect that this new method to tune the activity and specificity of Cas enzymes will be broadly useful. To address the second goal, we set out to develop an experimental pipeline for the high throughput, precise modification of mammalian genomes. Specifically, we modify the C-termini of genes to include an epitope tag for the genome-wide profiling of transcription factor binding sites. We apply this method to over 30 genes, encoding a variety of transcription factors, chromatin modifying enzymes, and gene regulatory proteins. Out of the large number of genes we focus particularly on members of the AP-1 transcription factor family and nuclear receptor co-activator and co-repressor families. Using this ChIP-seq data, which profiles genome wide binding, and integrating a variety of other genomic information, including chromatin modifications, chromatin accessibility, other TF binding, and inherent regulatory activity, we investigate the dimerization preferences of AP-1 subunits, their genomic binding patterns, and the regulatory potential of theses subunits. Toward addressing the third goal, we decided to focus on the glucocorticoid receptor (GR). The dual activating and repressive function of the GR is incompletely understood, and this duality is a property of many other stimuli responsive transcriptional responses (e.g. NFKB signaling). Thus, how one transcription factor is biochemically endowed with the ability to both activate and repress gene expression is an outstanding problem in gene regulation. It is hypothesized that the GR recruits a variety of distinct protein complexes in order to mediate its diverse function. We used CRISPR based loss of function screening in order to discover new GR cofactors. Using this method, we find a number of cofactors, both canonical and novel, that regulate this response in A549 cells. Ongoing work investigates how general these cofactors are across the transcriptome and whether they provide an avenue to decouple GR’s dual function, which has been a major goal in drug development. Through these studies we have found a way to make CRISPR systems more specific, developed and applied CRISPR based method to define AP-1 binding and function, and used unbiased CRISPR based screens to discover novel regulators of the glucocorticoid drug response.
Chapter 1 broadly introduces this work, its motivations, and aims of research presented herein.
Chapter 2 provides an introduction to both genome engineering and gene regulation. Specifically, it describes the development and application of CRISPR-cas tools and details outstanding problems in gene regulation through the lens of nuclear receptors.
Chapter 3 describes the purification of Cas9 protein and its characterization biochemically. Specifically, we use AFM to determine the DNA binding properties of Cas9 in vitro.
Chapter 4 introduces a new method to modulate the specificity of CRISPR systems in human cells. Therein we show that RNA secondary structure can be applied to diverse CRISPR systems to tune their activity.
Chapter 5 details a method for the high throughput tagging of transcription factors. It specifically investigates members of the AP-1 transcription factor complex.
Chapter 6 is an investigation of the glucocorticoid receptor and its cofactors. We apply a variety of genome engineering and genomic methods to characterize known co-factors and discover new ones.
Chapter 7 is an outlook on the fields of genome-engineering and gene regulation. It describes key questions that are still unanswered and possible lines of attack to address them.
Item Open Access Genome-wide Metabolic Reconstruction and Flux Balance Analysis Modeling of Haloferax volcanii(2018) Rosko, Andrew SThe Archaea are an understudied domain of the tree of life, and consist of single-celled microorganisms possessing rich metabolic diversity. Archaeal metabolic capabilities are of interest for industry and basic understanding of the early evolution of metabolism. However, archaea possess many unusual pathways that remain unknown or unclear. To address this knowledge gap, here I built a whole-genome metabolic reconstruction of a model archaeal species, Haloferax volcanii, which included several atypical reactions and pathways in this organism. I then use flux balance analysis to predict fluxes through central carbon metabolism during growth on minimal media containing two different sugars. This establishes a foundation for the future study of the regulation of metabolism in H. volcanii and evolutionary comparison with other archaea.
Item Open Access Glucocorticoid-Mediated Transcriptional Regulation in the Human Genome(2021) Seo, JungkyunGlucocorticoids (GCs) are a class of steroid hormones released from adrenal gland to mediate multiple physiological processes including the immune responses, cognitive functions and development. Glucocorticoids exert their gene regulatory effects through a ligand-activated transcription factor, glucocorticoid receptor (GR). Upon GC activation, GRs are particularly recruited to promoter-distal regulatory elements enriched with other transcription factors (TFs) and co-regulators including active protein 1 (AP-1). AP-1 is a heterodimeric TF potentially composed of subunits belongs to JUN, FOS and activating protein TF family. While the genomic function of AP-1 in response to GCs is well studied, the effect of specific configurations of AP-1 subunits on GR-mediated transcription remains unknown. In chapter 1, I introduce various regulatory components for transcriptional regulation. In chapter 2, I demonstrate that AP-1 subunits may not form preferential dimers between specific subunits, but rather bind each other promiscuously. I further show that the convergence of AP-1 subunits to enhancers is a key determinant for GR-mediated transcription and, by extension, cell-type specific environmental responses. GR binds DNA both directly and indirectly. While genome wide binding activity of GR can be effectively characterized by ChIP-seq, the binding mode (i.e. direct vs. indirect) at a specific site can’t be directly inferred. In chapter 3, I describe a machine learning approach to predict direct and indirect GR-DNA interactions using Protein Binding Microarray data. I demonstrate that motif-directed GR binding remains to be persistent after stimulus whereas indirect GR binding is likely transient. I further illustrate that robust transcriptional activation requires persistent GR binding and direct GR binding have the higher regulatory potential than indirect GR binding. GR activation represses certain genes. Along with the regulatory actions of GR cofactors, histone deacetylation is thought to regulate gene expression, especially gene repression. Therefore, I hypothesize that limiting histone deacetylases (HDAC) activity promotes robust gene activation. To test this hypothesis, in chapter 4, I delve into the GC-mediated transcriptional change after inhibiting the activity of histone deacetylases by HDACi to determine HDAC effect on GC-meditated transcriptional outputs. I demonstrate that the inhibition of HDAC activity reduces the magnitude of GC-mediated repression as well as activation in transcription. I also show that HDACi x GR-mediated intronic changes quantified from RNA-seq are minimally confounded by mRNA half-life linked to exonic changes, thereby accurately capturing transcriptional activity. Throughout the dissertation, I investigate GR-mediated transcriptional regulation by integrative analyses for numerous functional genomic datasets and by a predictive modeling for differential GR-DNA binding modes. In particular, the dissertation demonstrates that TF cooperativity, especially from subunits of AP-1 TF family, is a key determinant that drives the control of transcriptional output in cell-type specific manner. The dissertation further shows the potential for the generality of this regulatory mechanism beyond glucocorticoid stimulus and human cell types, suggesting that the TF convergence to a site, especially from the same TF family, may determine their functional specificity in a given cellular context.
Item Open Access Integrative Analysis of the Genomics of the Human Glucocorticoid Response(2017) McDowell, Ian ChristopherGlucocorticoids (GCs) are potent steroid hormones that regulate immunity and metabolism, and do so primarily by activating the transcription factor glucocorticoid receptor (GR). Once activated, GR binds to thousands of promoter-distal sites and regulates gene expression. Chromatin accessibility is believed to predetermine GR binding; however, several observations suggest preprogramming is more complex. For example, accessible sites far outnumber GR sites, suggesting additional discriminating influences. To investigate the relative importance of the component factors and marks in determining GR binding and the effects of GR binding on those factors and marks, I integrated hundreds of genome-wide measurements of transcription factor binding, epigenetic state, and gene expression across a 12-hour time course of GC exposure and reanalyzed complementary data in diverse cellular contexts. I found that GC treatment induces GR to bind preferentially to enhancers, which initiates a cascade of highly coordinated changes in occupancy of transcription factors and histone modifications. While GR recruits to most enhancers, the strength and persistence of binding—which ultimately determines enhancer dynamics—depends on motif content and spatial interactions between enhancers.
Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, I present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models cluster number with a Dirichlet process and temporal dependencies with Gaussian processes. I demonstrate the accuracy of DPGP in comparison with state-of-the-art approaches using hundreds of simulated data sets. To further test our method, I apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the GC dexamethasone. I validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal novel regulatory mechanisms.
Item Open Access Maternal and Fetal Genetic Contributions to Preterm Birth(2022) Cunningham, Sarah JeanPreterm birth is a major public health issue, affecting approximately 10% of pregnancies in the United States. The causes of preterm birth include the genetics of the mother, the genetics of the fetus, the environment, and the interplay of any combination of these factors. Maternal genetics is a significant contributor to preterm birth but few genome wide association studies (GWAS) have identified associated genetic variants. Recently, the largest GWAS on preterm birth to date found several loci associated with preterm birth that suggest impaired decidualization of the endometrium may play a role. To investigate regulatory activity in genomic regions identified via GWAS, we used the massively parallel reporter assay STARR-seq. Motifs enriched in peaks of significant activity include many transcription factors known to have critical roles in decidualization of the endometrium. An additional factor leading to preterm birth is maternal psychological stress. Maternal psychological stress is particularly linked to PPROM, the leading identifiable cause of preterm birth. PPROM is a pregnancy complication in which the chorion and amnion weaken and rupture prior to 37 weeks of pregnancy and before contractions have begun. PPROM is responsible for 30-40% of preterm birth cases. PPROM is closely linked to maternal psychological stress, leading us to hypothesize that glucocorticoid signaling may contribute to PPROM. As a step towards testing that hypothesis, we investigated the gene expression effects of glucocorticoids in primary amnion cells using RNA-seq. KCNA5 emerged as a potential GR regulated gene. KCNA5 has previously been reported to be a cell stress sensor that regulates proliferation and apoptosis. KCNA5 knockdown significantly increased cell proliferation without appearing to impact apoptosis and CRISPR-mediated over expression of endogenous KCNA5 decreased in cell proliferation. Taken together, these results suggest that glucocorticoid-mediated activation of KCNA5 contributes to decreased cell proliferation in the amnion epithelium. Decreases in amnion epithelial proliferation could impair the ability of these cells to repair microfractures in the membrane and lead to overall membrane weakening. We were able to study endocrine responses in pregnancy relevant cells to understand more broadly how these interactions affect preterm birth. Greater understanding of the interaction between preterm birth risk factors will move our understanding of the field forward.
Item Open Access Quantifying Eukaryotic Gene Regulation in Hormone Response and Disease.(2016) Vockley, Christopher VockleyQuantifying the function of mammalian enhancers at the genome or population scale has been longstanding challenge in the field of gene regulation. Studies of individual enhancers have provided anecdotal evidence on which many foundational assumptions in the field are based. Genome-scale studies have revealed that the number of sites bound by a given transcription factor far outnumber the genes that the factor regulates. In this dissertation we describe a new method, chromatin immune-enriched reporter assays (ChIP-reporters), and use that approach to comprehensively test the enhancer activity of genomic loci bound by the glucocorticoid receptor (GR). Integrative genomics analyses of our ChIP-reporter data revealed an unexpected mechanism of glucocorticoid (GC)-induced gene regulation. In that mechanism, only the minority of GR bound sites acts as GC-inducible enhancers. Many non-GC-inducible GR binding sites interact with GC-induced sites via chromatin looping. These interactions can increase the activity of GC-induced enhancers. Finally, we describe a method that enables the detection and characterization of the functional effects of non-coding genetic variation on enhancer activity at the population scale. Taken together, these studies yield both mechanistic and genetic evidence that provides context that informs the understanding of the effects of multiple enhancer variants on gene expression.
Item Open Access Statistical Modeling of Genetic and Epigenetic Factors in Gene Structures and Transcriptional Enhancers(2017) Majoros, William HPredicting the phenotypic effects of genetic variants is a major goal in modern genetics, with direct applicability in both the study of diseases in humans and animals, and the breeding of agriculturally important plants. Computational methods for interpreting genetic variants are still in their infancy, and rely heavily on annotations of functional genomic elements. Importantly, functional annotations inform the interpretation of genetic variants, but the locations and boundaries of such annotations can be altered by the presence of specific alleles, either singly or in combination, so that variant interpretation and genomic annotation should ideally be performed jointly. Such joint interpretation would enable predictions to account for the influence that one or more variants may have on the phenotypic impacts of other variants.
In this dissertation I describe computational methods for variant interpretation in both gene bodies and, separately, in transcriptional enhancers that regulate the expression of genes. In the case of gene bodies, I describe novel methods for joint modeling of multiple variants and gene structures. Whereas gene structure prediction methods have to date focused exclusively on annotation of reference genomes, I introduce the novel problem of annotating personal genomes of individuals or strains, and I describe and evaluate novel methods for addressing that problem. I show that these methods are able to predict complex changes in gene structures that result from genetic variants, that they are able to jointly interpret multiple variants that are not independent in their effects, and that predictions are supported by both RNA-seq data and patterns of intolerance to mutation across human populations.
In the case of transcriptional enhancers, I describe experimental and associated computational methods for assessing the impacts of genetic variants on the ability of an enhancer to drive gene expression in an episomal reporter assay. I show that these methods are able to identify variants impacting enhancer function, and I show that the functional score assigned by these methods can be used to fine-map gene expression associations.
I also describe a statistical pattern recognition method for efficiently identifying stimulus-responsive regulatory elements genome-wide and parsing those elements into functional sub-components. I show that this model is able to identify stimulus-responsive enhancers with high accuracy. I show that sub-components identified by this method are enriched for distinct sets of binding motifs for transcription factors known to mediate the response to treatment by glucocorticoids. Applying this model to timecourse data, I was able to cluster predicted enhancers into sets having distinct trajectories of activity over time in response to treatment by glucocorticoids. Using experimental chromatin conformation data, I show that these trajectories associate with distinct patterns of expression for genes in physical association with these enhancers.
Item Open Access The Glucocorticoid-Mediated Dynamics of Genome Architecture(2018) D'Ippolito, AnthonyHuman cells are perpetually receiving and responding to a variety of intrinsic and extrinsic signals. A primary mechanism by which cells carry out these responses is via changes in the regulation of gene expression. Many studies have examined gene regulation in steady state systems, but few have investigated the genomic response to stimuli. Therefore, it is less well understood how cellular stimuli elicit dynamic gene expression responses. Here, we investigate how extracellular stimuli mediate gene expression responses via: 1) Changes in transcription factor configurations at enhancer elements; and 2) Changes in chromatin looping between putative enhancers and their target gene promoters. To study these phenomena, we used glucocorticoid (GC) treatment as a model transcriptional stimulus. This hormone steroid is known to bind to and activate the GC receptor (GR), a ligand-induced transcription factor (TF), and is therefore a highly tractable system for studying stimulus responsive gene regulation. Using this model system, we first used high-resolution TF-binding site mapping approaches to elucidate the genomic binding locations of GR and its associated cofactors. Using these approaches, we found evidence that: 1) The GR binds to the genome as both a monomer and dimer; and 2) The GR binds to the genome with AP-1 in a more relaxed configuration, while it binds FOXA1 in a more constrained configuration. We next interrogated the role of chromatin looping in mediating dynamic transcriptional responses. For this work we used high-throughput genomics methods to assay chromatin conformation across a time course of GC treatment. These studies resulted in several main findings: 1) Chromatin loops do not form in response to stimulus, but are instead pre-formed before GC treatment; 2) Chromatin looping interactions increase between distal GR binding sites and GC-responsive genes; 3) The insulator protein CTCF is depleted at stimulus responsive looping interactions; and 4) GC treatment mediates changes in higher-order chromosome compartmentalization that are concordant with gene expression responses. Together these results provide evidence for a genome topology that is pre-wired to respond to stimulus, and that subsequent transcriptional responses are mediated through GR binding to putative enhancer elements with other TFs, in a variety of genomic binding configurations.
Item Open Access The PsychENCODE project.(Nat Neurosci, 2015-12) PsychENCODE Consortium; Akbarian, Schahram; Liu, Chunyu; Knowles, James A; Vaccarino, Flora M; Farnham, Peggy J; Crawford, Gregory E; Jaffe, Andrew E; Pinto, Dalila; Dracheva, Stella; Geschwind, Daniel H; Mill, Jonathan; Nairn, Angus C; Abyzov, Alexej; Pochareddy, Sirisha; Prabhakar, Shyam; Weissman, Sherman; Sullivan, Patrick F; State, Matthew W; Weng, Zhiping; Peters, Mette A; White, Kevin P; Gerstein, Mark B; Amiri, Anahita; Armoskus, Chris; Ashley-Koch, Allison E; Bae, Taejeong; Beckel-Mitchener, Andrea; Berman, Benjamin P; Coetzee, Gerhard A; Coppola, Gianfilippo; Francoeur, Nancy; Fromer, Menachem; Gao, Robert; Grennan, Kay; Herstein, Jennifer; Kavanagh, David H; Ivanov, Nikolay A; Jiang, Yan; Kitchen, Robert R; Kozlenkov, Alexey; Kundakovic, Marija; Li, Mingfeng; Li, Zhen; Liu, Shuang; Mangravite, Lara M; Mangravite, Lara M; Mattei, Eugenio; Markenscoff-Papadimitriou, Eirene; Navarro, Fábio CP; North, Nicole; Omberg, Larsson; Panchision, David; Parikshak, Neelroop; Poschmann, Jeremie; Price, Amanda J; Purcaro, Michael; Reddy, Timothy E; Roussos, Panos; Schreiner, Shannon; Scuderi, Soraya; Sebra, Robert; Shibata, Mikihito; Shieh, Annie W; Skarica, Mario; Sun, Wenjie; Swarup, Vivek; Thomas, Amber; Tsuji, Junko; van Bakel, Harm; Wang, Daifeng; Wang, Yongjun; Wang, Kai; Werling, Donna M; Willsey, A Jeremy; Witt, Heather; Won, Hyejung; Wong, Chloe CY; Wray, Gregory A; Wu, Emily Y; Xu, Xuming; Yao, Lijing; Senthil, Geetha; Lehner, Thomas; Sklar, Pamela; Sestan, Nenad