Adaptive sequence divergence forged new neurodevelopmental enhancers in humans.

Searches 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.

Direct GR Binding Sites Potentiate Clusters of TF Binding across the Human Genome.

The glucocorticoid receptor (GR) binds the human genome at >10,000 sites but only regulates the expression of hundreds of genes. To determine the functional effect of each site, we measured the glucocorticoid (GC) responsive activity of nearly all GR binding sites (GBSs) captured using chromatin immunoprecipitation (ChIP) in A549 cells. 13% of GBSs assayed had GC-induced activity. The responsive sites were defined by direct GR binding via a GC response element (GRE) and exclusively increased reporter-gene expression. Meanwhile, most GBSs lacked GC-induced reporter activity. The non-responsive sites had epigenetic features of steady-state enhancers and clustered around direct GBSs. Together, our data support a model in which clusters of GBSs observed with ChIP-seq reflect interactions between direct and tethered GBSs over tens of kilobases. We further show that those interactions can synergistically modulate the activity of direct GBSs and may therefore play a major role in driving gene activation in response to GCs.

From Prescription to Transcription: Genome Sequence as Drug Target.

Personalizing treatments to account for genetically mediated differences in drug responses is an exciting opportunity to improve patient outcomes. In this issue, Soccio et al. reveal new mechanisms by which non-coding variants alter the activity of the anti-diabetic drug rosiglitazone.

Multicenter integrated analysis of noncoding CRISPRi screens.

The ENCODE Consortium's efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE-gene links in K562 cells, we established guidelines for screening endogenous noncoding elements with CRISPR interference (CRISPRi), including accurate detection of CREs that exhibit variable, often low, transcriptional effects. Benchmarking five screen analysis tools, we find that CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity single guide RNAs. We uncover a subtle DNA strand bias for CRISPRi in transcribed regions with implications for screen design and analysis. Together, we provide an accessible data resource, predesigned single guide RNAs for targeting 3,275,697 ENCODE SCREEN candidate CREs with CRISPRi and screening guidelines to accelerate functional characterization of the noncoding genome.

Cooperative regulation of coupled oncoprotein synthesis and stability in triple-negative breast cancer by EGFR and CDK12/13.

Evidence has long suggested that epidermal growth factor receptor (EGFR) may play a prominent role in triple-negative breast cancer (TNBC) pathogenesis, but clinical trials of EGFR inhibitors have yielded disappointing results. Using a candidate drug screen, we identified that inhibition of cyclin-dependent kinases 12 and 13 (CDK12/13) dramatically sensitizes diverse models of TNBC to EGFR blockade. This combination therapy drives cell death through the 4E-BP1-dependent suppression of the translation and translation-linked turnover of driver oncoproteins, including MYC. A genome-wide CRISPR/Cas9 screen identified the CCR4-NOT complex as a major determinant of sensitivity to the combination therapy whose loss renders 4E-BP1 unresponsive to drug-induced dephosphorylation, thereby rescuing MYC translational suppression and promoting MYC stability. The central roles of CCR4-NOT and 4E-BP1 in response to the combination therapy were further underscored by the observation of CNOT1 loss and rescue of 4E-BP1 phosphorylation in TNBC cells that naturally evolved therapy resistance. Thus, pharmacological inhibition of CDK12/13 reveals a long-proposed EGFR dependence in TNBC that functions through the cooperative regulation of translation-coupled oncoprotein stability.

Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.

Over a thousand different transcription factors (TFs) bind with varying occupancy across the human genome. Chromatin immunoprecipitation (ChIP) can assay occupancy genome-wide, but only one TF at a time, limiting our ability to comprehensively observe the TF occupancy landscape, let alone quantify how it changes across conditions. We developed TF occupancy profiler (TOP), a Bayesian hierarchical regression framework, to profile genome-wide quantitative occupancy of numerous TFs using data from a single chromatin accessibility experiment (DNase- or ATAC-seq). TOP is supervised, and its hierarchical structure allows it to predict the occupancy of any sequence-specific TF, even those never assayed with ChIP. We used TOP to profile the quantitative occupancy of hundreds of sequence-specific TFs at sites throughout the genome and examined how their occupancies changed in multiple contexts: in approximately 200 human cell types, through 12 h of exposure to different hormones, and across the genetic backgrounds of 70 individuals. TOP enables cost-effective exploration of quantitative changes in the landscape of TF binding.

Targeted long-read sequencing identifies missing disease-causing variation.

Despite widespread clinical genetic testing, many individuals with suspected genetic conditions lack a precise diagnosis, limiting their opportunity to take advantage of state-of-the-art treatments. In some cases, testing reveals difficult-to-evaluate structural differences, candidate variants that do not fully explain the phenotype, single pathogenic variants in recessive disorders, or no variants in genes of interest. Thus, there is a need for better tools to identify a precise genetic diagnosis in individuals when conventional testing approaches have been exhausted. We performed targeted long-read sequencing (T-LRS) using adaptive sampling on the Oxford Nanopore platform on 40 individuals, 10 of whom lacked a complete molecular diagnosis. We computationally targeted up to 151 Mbp of sequence per individual and searched for pathogenic substitutions, structural variants, and methylation differences using a single data source. We detected all genomic aberrations-including single-nucleotide variants, copy number changes, repeat expansions, and methylation differences-identified by prior clinical testing. In 8/8 individuals with complex structural rearrangements, T-LRS enabled more precise resolution of the mutation, leading to changes in clinical management in one case. In ten individuals with suspected Mendelian conditions lacking a precise genetic diagnosis, T-LRS identified pathogenic or likely pathogenic variants in six and variants of uncertain significance in two others. T-LRS accurately identifies pathogenic structural variants, resolves complex rearrangements, and identifies Mendelian variants not detected by other technologies. T-LRS represents an efficient and cost-effective strategy to evaluate high-priority genes and regions or complex clinical testing results.

Correcting signal biases and detecting regulatory elements in STARR-seq data.

High-throughput reporter assays such as self-transcribing active regulatory region sequencing (STARR-seq) have made it possible to measure regulatory element activity across the entire human genome at once. The resulting data, however, present substantial analytical challenges. Here, we identify technical biases that explain most of the variance in STARR-seq data. We then develop a statistical model to correct those biases and to improve detection of regulatory elements. This approach substantially improves precision and recall over current methods, improves detection of both activating and repressive regulatory elements, and controls for false discoveries despite strong local correlations in signal.

AP-1 subunits converge promiscuously at enhancers to potentiate transcription.

The AP-1 transcription factor (TF) dimer contributes to many biological processes and environmental responses. AP-1 can be composed of many interchangeable subunits. Unambiguously determining the binding locations of these subunits in the human genome is challenging because of variable antibody specificity and affinity. Here, we definitively establish the genome-wide binding patterns of five AP-1 subunits by using CRISPR to introduce a common antibody tag on each subunit. We find limited evidence for strong dimerization preferences between subunits at steady state and find that, under a stimulus, dimerization patterns reflect changes in the transcriptome. Further, our analysis suggests that canonical AP-1 motifs indiscriminately recruit all AP-1 subunits to genomic sites, which we term AP-1 hotspots. We find that AP-1 hotspots are predictive of cell type-specific gene expression and of genomic responses to glucocorticoid signaling (more so than super-enhancers) and are significantly enriched in disease-associated genetic variants. Together, these results support a model where promiscuous binding of many AP-1 subunits to the same genomic location play a key role in regulating cell type-specific gene expression and environmental responses.

Transgenic mice for in vivo epigenome editing with CRISPR-based systems.

CRISPR-Cas9 technologies have dramatically increased the ease of targeting DNA sequences in the genomes of living systems. The fusion of chromatin-modifying domains to nuclease-deactivated Cas9 (dCas9) has enabled targeted epigenome editing in both cultured cells and animal models. However, delivering large dCas9 fusion proteins to target cells and tissues is an obstacle to the widespread adoption of these tools for in vivo studies. Here, we describe the generation and characterization of two conditional transgenic mouse lines for epigenome editing, Rosa26:LSL-dCas9-p300 for gene activation and Rosa26:LSL-dCas9-KRAB for gene repression. By targeting the guide RNAs to transcriptional start sites or distal enhancer elements, we demonstrate regulation of target genes and corresponding changes to epigenetic states and downstream phenotypes in the brain and liver in vivo, and in T cells and fibroblasts ex vivo. These mouse lines are convenient and valuable tools for facile, temporally controlled, and tissue-restricted epigenome editing and manipulation of gene expression in vivo.

Causal network inference from gene transcriptional time-series response to glucocorticoids.

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.

Glucocorticoid receptor recruits to enhancers and drives activation by motif-directed binding.

Glucocorticoids are potent steroid hormones that regulate immunity and metabolism by activating the transcription factor (TF) activity of glucocorticoid receptor (GR). Previous models have proposed that DNA binding motifs and sites of chromatin accessibility predetermine GR binding and activity. However, there are vast excesses of both features relative to the number of GR binding sites. Thus, these features alone are unlikely to account for the specificity of GR binding and activity. To identify genomic and epigenetic contributions to GR binding specificity and the downstream changes resultant from GR binding, we performed hundreds of genome-wide measurements of TF binding, epigenetic state, and gene expression across a 12-h time course of glucocorticoid exposure. We found that glucocorticoid treatment induces GR to bind to nearly all pre-established enhancers within minutes. However, GR binds to only a small fraction of the set of accessible sites that lack enhancer marks. Once GR is bound to enhancers, a combination of enhancer motif composition and interactions between enhancers then determines the strength and persistence of GR binding, which consequently correlates with dramatic shifts in enhancer activation. Over the course of several hours, highly coordinated changes in TF binding and histone modification occupancy occur specifically within enhancers, and these changes correlate with changes in the expression of nearby genes. Following GR binding, changes in the binding of other TFs precede changes in chromatin accessibility, suggesting that other TFs are also sensitive to genomic features beyond that of accessibility.

Bayesian estimation of genetic regulatory effects in high-throughput reporter assays.

MOTIVATION: High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA. RESULTS: We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which integrates prior information and explicitly accounts for variability between experimental replicates. The model produces substantially more accurate predictions than existing methods when allele frequencies are low, which is of clear advantage in the search for disease-causing variants in DNA captured from patient cohorts. Using the model, we demonstrate a clear tradeoff between variant sequencing coverage and numbers of biological replicates, and we show that the use of additional biological replicates decreases variance in estimates of effect size, due to the properties of the Poisson-binomial distribution. We also provide a power and sample size calculator, which facilitates decision making in experimental design parameters. AVAILABILITY AND IMPLEMENTATION: The software is freely available from www.geneprediction.org/bird. The experimental design web tool can be accessed at http://67.159.92.22:8080. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distinct properties of cell-type-specific and shared transcription factor binding sites.

Most human transcription factors bind a small subset of potential genomic sites and often use different subsets in different cell types. To identify mechanisms that govern cell-type-specific transcription factor binding, we used an integrative approach to study estrogen receptor α (ER). We found that ER exhibits two distinct modes of binding. Shared sites, bound in multiple cell types, are characterized by high-affinity estrogen response elements (EREs), inaccessible chromatin, and a lack of DNA methylation, while cell-specific sites are characterized by a lack of EREs, co-occurrence with other transcription factors, and cell-type-specific chromatin accessibility and DNA methylation. These observations enabled accurate quantitative models of ER binding that suggest tethering of ER to one-third of cell-specific sites. The distinct properties of cell-specific binding were also observed with glucocorticoid receptor and for ER in primary mouse tissues, representing an elegant genomic encoding scheme for generating cell-type-specific gene regulation.

Predicting gene structure changes resulting from genetic variants via exon definition features.

Motivation: Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed and produce functional proteins. Results: We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and non-coding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or non-coding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products and we propose that they may commonly act as cryptic factors in disease. Availability and implementation: The software is available from geneprediction.org/SGRF. Supplementary information: Supplementary information is available at Bioinformatics online.

Clustering gene expression time series data using an infinite Gaussian process mixture model.

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, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we 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 glucocorticoid dexamethasone. We 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 shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

Genomic approaches for understanding the genetics of complex disease.

There are thousands of known associations between genetic variants and complex human phenotypes, and the rate of novel discoveries is rapidly increasing. Translating those associations into knowledge of disease mechanisms remains a fundamental challenge because the associated variants are overwhelmingly in noncoding regions of the genome where we have few guiding principles to predict their function. Intersecting the compendium of identified genetic associations with maps of regulatory activity across the human genome has revealed that phenotype-associated variants are highly enriched in candidate regulatory elements. Allele-specific analyses of gene regulation can further prioritize variants that likely have a functional effect on disease mechanisms; and emerging high-throughput assays to quantify the activity of candidate regulatory elements are a promising next step in that direction. Together, these technologies have created the ability to systematically and empirically test hypotheses about the function of noncoding variants and haplotypes at the scale needed for comprehensive and systematic follow-up of genetic association studies. Major coordinated efforts to quantify regulatory mechanisms across genetically diverse populations in increasingly realistic cell models would be highly beneficial to realize that potential.

Genome-wide specificity of DNA binding, gene regulation, and chromatin remodeling by TALE- and CRISPR/Cas9-based transcriptional activators.

Genome engineering technologies based on the CRISPR/Cas9 and TALE systems are enabling new approaches in science and biotechnology. However, the specificity of these tools in complex genomes and the role of chromatin structure in determining DNA binding are not well understood. We analyzed the genome-wide effects of TALE- and CRISPR-based transcriptional activators in human cells using ChIP-seq to assess DNA-binding specificity and RNA-seq to measure the specificity of perturbing the transcriptome. Additionally, DNase-seq was used to assess genome-wide chromatin remodeling that occurs as a result of their action. Our results show that these transcription factors are highly specific in both DNA binding and gene regulation and are able to open targeted regions of closed chromatin independent of gene activation. Collectively, these results underscore the potential for these technologies to make precise changes to gene expression for gene and cell therapies or fundamental studies of gene function.

Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort.

We report a novel high-throughput method to empirically quantify individual-specific regulatory element activity at the population scale. The approach combines targeted DNA capture with a high-throughput reporter gene expression assay. As demonstration, we measured the activity of more than 100 putative regulatory elements from 95 individuals in a single experiment. In agreement with previous reports, we found that most genetic variants have weak effects on distal regulatory element activity. Because haplotypes are typically maintained within but not between assayed regulatory elements, the approach can be used to identify causal regulatory haplotypes that likely contribute to human phenotypes. Finally, we demonstrate the utility of the method to functionally fine map causal regulatory variants in regions of high linkage disequilibrium identified by expression quantitative trait loci (eQTL) analyses.

Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements.

Epigenome editing with the CRISPR (clustered, regularly interspaced, short palindromic repeats)-Cas9 platform is a promising technology for modulating gene expression to direct cell phenotype and to dissect the causal epigenetic mechanisms of gene regulation. Fusions of nuclease-inactive dCas9 to the Krüppel-associated box (KRAB) repressor (dCas9-KRAB) can silence target gene expression, but the genome-wide specificity and the extent of heterochromatin formation catalyzed by dCas9-KRAB are not known. We targeted dCas9-KRAB to the HS2 enhancer, a distal regulatory element that orchestrates the expression of multiple globin genes, and observed highly specific induction of H3K9 trimethylation (H3K9me3) at the enhancer and decreased chromatin accessibility of both the enhancer and its promoter targets. Targeted epigenetic modification of HS2 silenced the expression of multiple globin genes, with minimal off-target changes in global gene expression. These results demonstrate that repression mediated by dCas9-KRAB is sufficiently specific to disrupt the activity of individual enhancers via local modification of the epigenome.