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.

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.

Human genome-wide measurement of drug-responsive regulatory activity.

Environmental stimuli commonly act via changes in gene regulation. Human-genome-scale assays to measure such responses are indirect or require knowledge of the transcription factors (TFs) involved. Here, we present the use of human genome-wide high-throughput reporter assays to measure environmentally-responsive regulatory element activity. We focus on responses to glucocorticoids (GCs), an important class of pharmaceuticals and a paradigmatic genomic response model. We assay GC-responsive regulatory activity across >108 unique DNA fragments, covering the human genome at >50×. Those assays directly detected thousands of GC-responsive regulatory elements genome-wide. We then validate those findings with measurements of transcription factor occupancy, histone modifications, chromatin accessibility, and gene expression. We also detect allele-specific environmental responses. Notably, the assays did not require knowledge of GC response mechanisms. Thus, this technology can be used to agnostically quantify genomic responses for which the underlying mechanism remains unknown.

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.

Pre-established Chromatin Interactions Mediate the Genomic Response to Glucocorticoids.

The glucocorticoid receptor (GR) is a hormone-inducible transcription factor involved in metabolic and anti-inflammatory gene expression responses. To investigate what controls interactions between GR binding sites and their target genes, we used in situ Hi-C to generate high-resolution, genome-wide maps of chromatin interactions before and after glucocorticoid treatment. We found that GR binding to the genome typically does not cause new chromatin interactions to target genes but instead acts through chromatin interactions that already exist prior to hormone treatment. Both glucocorticoid-induced and glucocorticoid-repressed genes increased interactions with distal GR binding sites. In addition, while glucocorticoid-induced genes increased interactions with transcriptionally active chromosome compartments, glucocorticoid-repressed genes increased interactions with transcriptionally silent compartments. Lastly, while the architectural DNA-binding proteins CTCF and RAD21 were bound to most chromatin interactions, we found that glucocorticoid-responsive chromatin interactions were depleted for CTCF binding but enriched for RAD21. Together, these findings offer new insights into the mechanisms underlying GC-mediated gene activation and repression.

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.

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

A long-range flexible billboard model of gene activation.

Gene regulation is fundamentally important for the coordination of diverse biologic processes including homeostasis and responses to developmental and environmental stimuli. Transcription factor (TF) binding sites are one of the major functional subunits of gene regulation. They are arranged in cis-regulatory modules (CRMs) that can be more active than the sum of their individual effects. Recently, we described a mechanism of glucocorticoid (GC)-induced gene regulation in which the glucocorticoid receptor (GR) binds coordinately to multiple CRMs that are 10s of kilobases apart in the genome. In those results, the minority of GR binding sites appear to involve direct TF:DNA interactions. Meanwhile, other GR binding sites in a cluster interact with those direct binding sites to tune their gene regulatory activity. Here, we consider the implications of those and related results in the context of existing models of gene regulation. Based on our analyses, we propose that the billboard and regulatory grammar models of cis-regulatory element activity be expanded to consider the influence of long-range interactions between cis-regulatory modules.

Transversions have larger regulatory effects than transitions.

BACKGROUND: Transversions (Tv's) are more likely to alter the amino acid sequence of proteins than transitions (Ts's), and local deviations in the Ts:Tv ratio are indicative of evolutionary selection on genes. Whether the two different types of mutations have different effects in non-protein-coding sequences remains unknown. Genetic variants primarily impact gene expression by disrupting the binding of transcription factors (TFs) and other DNA-binding proteins. Because Tv's cause larger changes in the shape of a DNA backbone, we hypothesized that Tv's would have larger impacts on TF binding and gene expression. RESULTS: Here, we provide multiple lines of evidence demonstrating that Tv's have larger impacts on regulatory DNA including analyses of TF binding motifs and allele-specific TF binding. In these analyses, we observed a depletion of Tv's within TF binding motifs and TF binding sites. Using massively parallel population-scale reporter assays, we also provided empirical evidence that Tv's have larger effects than Ts's on the activity of human gene regulatory elements. CONCLUSIONS: Tv's are more likely to disrupt TF binding, resulting in larger changes in gene expression. Although the observed differences are small, these findings represent a novel, fundamental property of regulatory variation. Understanding the features of functional non-coding variation could be valuable for revealing the genetic underpinnings of complex traits and diseases in future studies.

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.

Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.

Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.

Multi-species protein similarity clustering reveals novel expanded immune gene families in the eastern oyster Crassostrea virginica.

Comparative genomics research in non-model species has highlighted how invertebrate hosts possess complex diversified repertoires of immune molecules. The levels of diversification in particular immune gene families appear to differ between invertebrate lineages and even between species within lineages, reflecting differences not only in evolutionary histories, but also in life histories, environmental niches, and pathogen exposures. The goal of this research was to identify immune-related gene families experiencing high levels of diversification in eastern oysters, Crassostrea virginica. Families containing 1) transcripts differentially expressed in eastern oysters in response to bacterial challenge and 2) a larger number of transcripts compared to other species included those coding for the C1q and C-type lectin domain containing proteins (C1qDC and CTLDC), GTPase of the immune-associated proteins (GIMAP), scavenger receptors (SR), fibrinogen-C domain containing proteins (also known as FREPs), dopamine beta-hydrolase (DBH), interferon-inducible 44 (IFI44), serine protease inhibitors, apextrin, and dermatopontin. Phylogenetic analysis of two of the families significantly expanded in bivalves, IFI44 and GIMAP, showed a patchy distribution within both protostomes and deuterostomes, suggesting multiple independent losses and lineage-specific expansions. Increased availability of genomic information for a broader range of non-model species broadly distributed through vertebrate and invertebrate phyla will likely lead to improved knowledge on mechanisms of immune-gene diversification.

Transcriptome of American oysters, Crassostrea virginica, in response to bacterial challenge: insights into potential mechanisms of disease resistance.

The American oyster Crassostrea virginica, an ecologically and economically important estuarine organism, can suffer high mortalities in areas in the Northeast United States due to Roseovarius Oyster Disease (ROD), caused by the gram-negative bacterial pathogen Roseovarius crassostreae. The goals of this research were to provide insights into: 1) the responses of American oysters to R. crassostreae, and 2) potential mechanisms of resistance or susceptibility to ROD. The responses of oysters to bacterial challenge were characterized by exposing oysters from ROD-resistant and susceptible families to R. crassostreae, followed by high-throughput sequencing of cDNA samples from various timepoints after disease challenge. Sequence data was assembled into a reference transcriptome and analyzed through differential gene expression and functional enrichment to uncover genes and processes potentially involved in responses to ROD in the American oyster. While susceptible oysters experienced constant levels of mortality when challenged with R. crassostreae, resistant oysters showed levels of mortality similar to non-challenged oysters. Oysters exposed to R. crassostreae showed differential expression of transcripts involved in immune recognition, signaling, protease inhibition, detoxification, and apoptosis. Transcripts involved in metabolism were enriched in susceptible oysters, suggesting that bacterial infection places a large metabolic demand on these oysters. Transcripts differentially expressed in resistant oysters in response to infection included the immune modulators IL-17 and arginase, as well as several genes involved in extracellular matrix remodeling. The identification of potential genes and processes responsible for defense against R. crassostreae in the American oyster provides insights into potential mechanisms of disease resistance.

Identification of potential general markers of disease resistance in American oysters, Crassostrea virginica through gene expression studies.

Several diseases have a significant impact on American oyster populations in the Atlantic coasts of North America. Knowledge about the responses of oysters to pathogenic challenge could help in identifying potential markers of disease resistance and biomarkers of the health status of an oyster population. A previous analysis of the transcriptome of resistant and susceptible American oysters in response to challenge with the bacterial pathogen Roseovarius crassostreae, as well as sequencing of suppression subtractive hybridization libraries from oysters challenged with the protozoan parasite Perkinsus marinus, provided a list of genes potentially involved in disease resistance or susceptibility. We investigated the patterns of inducible gene expression of several of these genes in response to experimental challenge with the oyster pathogens R. crassostreae, Vibrio tubiashii, and P. marinus. Oysters showing differential susceptibility to R. crassostreae demonstrated differential patterns of expression of genes coding for immune (serine protease inhibitor-1, SPI1) and stress-related (heat shock protein 70, HSP70; arginine kinase) proteins 30 days after challenge with this bacterial pathogen. Differential patterns of expression of immune (spi1, galectin and a matrix metalloproteinase) and stress-related (hsp70, histone H4, and arginine kinase) genes was observed in hemocytes from adult oysters challenged with P. marinus, but not with V. tubiashii. While levels of spi1 expression in hemocytes collected 8 and 21 days after P. marinus challenge were negatively correlated with parasite load in oysters tissues at the end of the challenge (62 days), levels of expression of hsp70 in hemocytes collected 1-day after challenge were positively correlated with oyster parasite load at 62 days. Our results confirm previous research on the role of serine protease inhibitor-1 in immunity and disease resistance in oysters. They also suggest that HSP70 and histone H4 could be used as a markers of health status or disease susceptibility in oysters.