Biology subjects

Reddy, T. E.

Publications and source records attributed to Reddy, T. E..

30 records · Page 2Linked to original sources

bioRxiv2022

Mediator Kinase Inhibition Impedes Transcriptional Plasticity and Prevents Resistance to ERK/MAPK-Targeted Therapy in KRAS-Mutant Cancers

Acquired resistance remains a major challenge for therapies targeting oncogene activated pathways. KRAS is the most frequently mutated oncogene in human cancers, yet strategies targeting its downstream signaling kinases have failed to produce durable treatment responses. Here, we developed multiple models of acquired resistance to dual-mechanism ERK/MAPK inhibitors across KRAS-mutant pancreatic, colorectal, and lung cancers, and then probed the long-term events enabling survival against this novel class of drugs. These studies revealed that resistance emerges secondary to large-scale transcriptional adaptations that are diverse and tumor-specific. Transcriptional reprogramming extends beyond the well-established early response, and instead represents a dynamic, evolved population-level process that is refined to attain a stably resistant phenotype. Mechanistic and translational studies reveal that resistance to dual-mechanism ERK/MAPK inhibition is broadly susceptible to manipulation of the epigenetic machinery, and that Mediator kinase, in particular, can be co-targeted at a bottleneck point to prevent diverse, tumor-specific resistance programs.

cancer biology↗

bioRxiv2021

Genome-wide annotation of gene regulatory elements linked to cell fitness

Noncoding regulatory elements control gene expression and thus govern nearly all biological processes. Epigenomic profiling assays have identified millions of putative regulatory elements, but systematically determining the function of those regulatory elements remains a substantial challenge. Here we adapt CRISPR screening by epigenetic repression to screen all 111,619 putative non-coding regulatory elements defined by open chromatin sites in human K562 leukemia cells for their role in regulating essential cellular processes and proliferation. In an initial screen containing 1,084,704 gRNAs, we implemented an analysis framework to quantify perturbation effects, and nominate 1,108 regulatory elements that strongly impact cell fitness. We tested 8,845 of the primary screen elements in a secondary screen, evaluated their cell-type specificity in a second cancer cell line, and then used a single-cell RNA-seq CRISPR screen to discover 63 connections between distal regulatory elements and target genes. This comprehensive and quantitative genome-wide map of essential gene regulatory elements presents a framework for extensive characterization of noncoding regulatory elements that drive complex cell phenotypes and for prioritizing non-coding genetic variants that may contribute to common traits and disease risk.

genomics↗

bioRxiv2021

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

The discovery, characterization, and adaptation of the RNA-guided clustered regularly interspersed short palindromic repeat (CRISPR)-Cas9 system has greatly increased the ease with which genome and epigenome editing can be performed. Fusion of chromatin-modifying domains to the nuclease-deactivated form of Cas9 (dCas9) has enabled targeted gene activation or repression in both cultured cells and in vivo in animal models. However, delivery of the large dCas9 fusion proteins to target cell types and tissues is an obstacle to widespread adoption of these tools for in vivo studies. Here we describe the generation and validation of two conditional transgenic mouse lines for targeted gene regulation, Rosa26:LSL-dCas9-p300 for gene activation and Rosa26:LSL-dCas9-KRAB for gene repression. Using the dCas9p300 and dCas9KRAB transgenic mice we demonstrate activation or repression of genes in both the brain and liver in vivo, and T cells and fibroblasts ex vivo. We show gene regulation and targeted epigenetic modification with gRNAs targeting either transcriptional start sites (TSS) or distal enhancer elements, as well as corresponding changes to downstream phenotypes. These mouse lines are convenient and valuable tools for facile, temporally controlled, and tissue-restricted epigenome editing and manipulation of gene expression in vivo.

genomics↗

bioRxiv2021

Cooperative regulation of coupled oncoprotein translation and stability in triple-negative breast cancer by EGFR and CDK12

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 discovered that inhibition of CDK12 dramatically sensitizes diverse models of TNBC to EGFR blockade. Instead of functioning through CDK12s well-established roles proximal to transcription, this combination therapy drives cell death through the 4E-BP1-dependent suppression of the translation and consequent stability 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, rescuing MYC translational suppression and 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 reveals a long proposed EGFR dependence in TNBC that functions through the cooperative regulation of translation-coupled oncoprotein stability.

cancer biology↗

bioRxiv2020

Detecting regulatory elements in high-throughput reporter assays

High-throughput reporter assays such as self-transcribing active regulatory region sequencing (STARR-seq) have made it possible to measure genome-wide regulatory element activity across the human genome. The assays, however, also present substantial analytical challenges. Here, we identify technical biases that explain most of the variance in STARR-seq signals. 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 signal correlations.

bioinformatics↗

bioRxiv2020

Quantitative occupancy of myriad transcription factors from one DNase experiment enables efficient comparisons across conditions

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 TOP, a Bayesian hierarchical regression framework, to profile genome-wide quantitative occupancy of numerous TFs using data from a single DNase-seq experiment. 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 nearly 1500 human TF motifs, and examined how their occupancies changed genome-wide in multiple contexts: across 178 cell types, over 12 hours 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.

bioinformatics↗

bioRxiv2020

Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth

While a genetic component of preterm birth (PTB) has long been recognized and recently mapped by genome-wide association studies (GWAS), the molecular determinants underlying PTB remain elusive. This stems in part from an incomplete availability of comprehensive functional genomic annotations in human cell types relevant to pregnancy and PTB. Here, we generated extensive transcriptional and chromatin annotations of cultured primary decidua-derived mesenchymal stromal/stem cells (MSCs) and in vitro differentiated decidual stromal cells (DSCs) and developed a computational framework to integrate these functional annotations with results from a GWAS of gestational duration in 56,384 women. This resulted in a significant enrichment of heritability estimates in functional noncoding regions in stromal cells, as well as in the discovery of additional loci associated with gestational duration and target genes of associated loci. Our strategy illustrates how systematic functional annotations in pregnancy-relevant cell types aid in the experimental follow-up of GWAS for PTB and, likely, other pregnancy-related conditions.

genomics↗

bioRxiv2019

Glucocorticoids regulate cancer cell dormancy

The glucocorticoid receptor directly regulates thousands of genes across the human genome in a cell-type specific manner, governing various aspects of homeostasis. The influence of the glucocorticoid receptor is also seen in various pathologies, including cancer, where it has been linked to tumorigenesis, metastasis, apoptosis resistance, and therapy bypass. Nonetheless, the direct genetic and molecular underpinnings of glucocorticoid action in cancer remain elusive. Here, we dissected the glucocorticoid receptor signalling axis and uncovered the mechanism of glucocorticoid-mediated cancer cell dormancy. Upon glucocorticoid receptor activation cancer cells undergo quiescence, subserved by cell cycle arrest through CDKN1C and reprogramming of signalling orchestrated via FOXO1/IRS2. Strikingly, co-expression of these three genes, directly regulated by glucocorticoid-induced chromatin looping, correlates with a benign molecular phenotype across human cancers, whereas triple loss is associated with increased expression of proliferation/aggressiveness markers. Finally, we show that the glucocorticoid receptor signalling axis is inactivated by alterations of either the chromatin remodelling complex or TP53 in vitro and in vivo. Our results indicate that the activation of the glucocorticoid receptor leads to cancer cell dormancy, which has several implications in terms of glucocorticoid use in cancer therapy.

cancer biology↗

bioRxiv2019

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 paving the way for 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 but additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time series data of 2, 768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2, 768 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 freely available as an open source software package at https://github.com/lujonathanh/BETS.

bioinformatics↗

bioRxiv2017

Genome-wide quantification of the effects of DNA methylation on human gene regulation

Changes in DNA methylation are important in development and disease, but not all regulatory elements act in a methylation-dependent (MD) manner. Here, we developed mSTARR-seq, a high-throughput approach to quantify the effects of DNA methylation on regulatory element function. We assay MD activity in 14% of the euchromatic human genome, identify 2,143 MD regulatory elements, and predict MD activity using sequence and chromatin state information. We identify transcription factors associated with higher activity in unmethylated or methylated states, including an association between pioneer transcription factors and methylated DNA. Finally, we use mSTARR-seq to predict DNA methylation-gene expression correlations in primary cells. Our findings provide a map of MD regulatory activity across the human genome, facilitating interpretation of the many emerging associations between methylation and trait variation.

genomics↗

bioRxiv2017

Evaluation Of Chromatin Accessibility In Prefrontal Cortex Of Schizophrenia Cases And Controls

Schizophrenia genome-wide association (GWA) studies have identified over 150 regions of the genome that are associated with disease risk, yet there is little evidence that coding mutations contribute to this disorder. To explore the mechanism of non-coding regulatory elements in schizophrenia, we performed ATAC-seq on adult prefrontal cortex brain samples from 135 individuals with schizophrenia and 137 controls, and identified 118,152 ATAC-seq peaks. These accessible chromatin regions in brain are highly enriched for SNP-heritability for schizophrenia (10.6 fold enrichment, P=2.4x10-4, second only to genomic regions conserved in Eutherian mammals) and replicated in an independent dataset (9.0 fold enrichment, P=2.7x10-4). This degree of enrichment of schizophrenia heritability was higher than in open chromatin found in 138 different cell and tissue types. Brain open chromatin regions that overlapped highly conserved regions exhibited an even higher degree of heritability enrichment, indicating that conservation can identify functional subsets within regulatory elements active in brain. However, we did not identify chromatin accessibility differences between schizophrenia cases and controls, nor did we find an interaction of chromatin QTLs with case-control status. This indicates that although causal variants map within regulatory elements, mechanisms other than differential chromatin may govern the contribution of regulatory element variation to schizophrenia risk. Our results strongly implicate gene regulatory processes involving open chromatin in the pathogenesis of schizophrenia, and suggest a strategy to understand the hundreds of common variants emerging from large genomic studies of complex brain diseases.

genomics↗

bioRxiv2017

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 cluster number with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison with 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 novel regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

bioinformatics↗