scholarly journals Nascent RNA sequencing reveals a dynamic global transcriptional response at genes and enhancers to the natural medicinal compound celastrol

2017 ◽  
Author(s):  
Noah Dukler ◽  
Gregory T. Booth ◽  
Yi-Fei Huang ◽  
Nathaniel Tippens ◽  
Charles G. Danko ◽  
...  
Keyword(s):  
Time Course ◽  
Transcriptional Response ◽  
Distinct Group ◽  
K562 Cells ◽  
Careful Analysis ◽  
Transcriptional Responses ◽  
Time Course Data ◽  
Nascent Rna ◽  
Polymerase Pausing ◽  
Key Aspects

AbstractMost studies of responses to transcriptional stimuli measure changes in cellular mRNA concentrations. By sequencing nascent RNA instead, it is possible to detect changes in transcription in minutes rather than hours, and thereby distinguish primary from secondary responses to regulatory signals. Here, we describe the use of PRO-seq to characterize the immediate transcriptional response in human cells to celastrol, a compound derived from traditional Chinese medicine that has potent anti-inflammatory, tumor-inhibitory and obesity-controlling effects. Our analysis of PRO-seq data for K562 cells reveals dramatic transcriptional effects soon after celastrol treatment at a broad collection of both coding and noncoding transcription units. This transcriptional response occurred in two major waves, one within 10 minutes, and a second 40-60 minutes after treatment. Transcriptional activity was generally repressed by celastrol, but one distinct group of genes, enriched for roles in the heat shock response, displayed strong activation. Using a regression approach, we identified key transcription factors that appear to drive these transcriptional responses, including members of the E2F and RFX families. We also found sequence-based evidence that particular TFs drive the activation of enhancers. We observed increased polymerase pausing at both genes and enhancers, suggesting that pause release may be widely inhibited during the celastrol response. Our study demonstrates that a careful analysis of PRO-seq time course data can disentangle key aspects of a complex transcriptional response, and it provides new insights into the activity of a powerful pharmacological agent.

scholarly journals The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

2019 ◽  
Author(s):  
Jennifer E. L. Diaz ◽  
Mehmet Eren Ahsen ◽  
Thomas Schaffter ◽  
Xintong Chen ◽  
Ronald B. Realubit ◽  
...  
Keyword(s):  
Time Course ◽  
Transcriptional Response ◽  
Unrelated Individual ◽  
Drug Combinations ◽  
Prediction Algorithm ◽  
Transcriptional Responses ◽  
Transcriptomic Response ◽  
Transcriptional Signature ◽  
Activation Of Transcription ◽  
Sequential Activation

AbstractOur ability to predict the effects of drug combinations is limited, in part by limited understanding of how the transcriptional response of two monotherapies results in that of their combination. We performed the first analysis of matched time course RNAseq profiling of cells treated with both single drugs and their combinations. The transcriptional signature of the synergistic combination we studied had unique gene expression not seen in either constituent monotherapy. This can be explained mechanistically by the sequential activation of transcription factors in time in the gene regulatory network. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.84 in the prediction of synergistic drug combinations in an independent dataset.

TSHZ2 is an EGF-regulated tumor suppressor that binds to the cytokinesis regulator PRC1 and inhibits metastasis

2021 ◽  
Vol 14 (688) ◽  
pp. eabe6156
Author(s):  
Mary L. Uribe ◽  
Maik Dahlhoff ◽  
Rajbir N. Batra ◽  
Nishanth B. Nataraj ◽  
Yuya Haga ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Tumor Suppressors ◽  
Mammalian Cells ◽  
Transcriptional Response ◽  
Distinct Group ◽  
Cyclin B1 ◽  
Prolonged Treatment ◽  
Transcriptional Responses ◽  
Tumor Growth And Metastasis ◽  
Transcription Factor P53

Unlike early transcriptional responses to mitogens, later events are less well-characterized. Here, we identified delayed down-regulated genes (DDGs) in mammary cells after prolonged treatment with epidermal growth factor (EGF). The expression of these DDGs was low in mammary tumors and correlated with prognosis. The proteins encoded by several DDGs directly bind to and inactivate oncoproteins and might therefore act as tumor suppressors. The transcription factor teashirt zinc finger homeobox 2 (TSHZ2) is encoded by a DDG, and we found that overexpression of TSHZ2 inhibited tumor growth and metastasis and accelerated mammary gland development in mice. Although the gene TSHZ2 localizes to a locus (20q13.2) that is frequently amplified in breast cancer, we found that hypermethylation of its promoter correlated with down-regulation of TSHZ2 expression in patients. Yeast two-hybrid screens and protein-fragment complementation assays in mammalian cells indicated that TSHZ2 nucleated a multiprotein complex containing PRC1/Ase1, cyclin B1, and additional proteins that regulate cytokinesis. TSHZ2 increased the inhibitory phosphorylation of PRC1, a key driver of mitosis, mediated by cyclin-dependent kinases. Furthermore, similar to the tumor suppressive transcription factor p53, TSHZ2 inhibited transcription from the PRC1 promoter. By recognizing DDGs as a distinct group in the transcriptional response to EGF, our findings uncover a group of tumor suppressors and reveal a role for TSHZ2 in cell cycle regulation.

scholarly journals The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Jennifer EL Diaz ◽  
Mehmet Eren Ahsen ◽  
Thomas Schaffter ◽  
Xintong Chen ◽  
Ronald B Realubit ◽  
...  
Keyword(s):  
Time Course ◽  
Transcriptional Response ◽  
Unrelated Individual ◽  
Drug Combinations ◽  
Prediction Algorithm ◽  
Transcriptional Responses ◽  
Transcriptomic Response ◽  
Transcriptional Signature ◽  
Activation Of Transcription ◽  
Sequential Activation

Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.

scholarly journals Plasticity of fibroblast transcriptional response to physical and biochemical cues revealed by dynamic network analysis

2020 ◽  
Author(s):  
Pilhwa Lee
Keyword(s):  
Regulatory Networks ◽  
Time Course ◽  
Inflammatory Responses ◽  
Dynamic Network ◽  
Transcriptional Response ◽  
Live Cells ◽  
Human Skin Fibroblast ◽  
Transcriptional Responses ◽  
External Cues ◽  
Transcription Factor Expression

Data on human skin fibroblast transcriptional responses to external cues were used to reconstruct dynamic gene regulatory networks. The goal of the reconstruction was to determine dynamic network interactions (quantitative predictive relationships of mutual regulatory influences of and on transcription factor expression) from time course data on 56 transcript expression levels obtained following different external cues. The inherently under-determined nature of this problem was addressed in part by excluding putative regulatory motifs that did not appear to be functional in multiple independent experiments from different independent external perturbations. Data were obtained from a previously published experiment in which the 56 transcripts were assayed by bioluminescence in live cells cultured on substrates of varying levels of stiffness and exposed to different levels of arginylglycylaspartic acid (RGD) peptide. The inferred dynamical networks were validated via comparison of predictions to known interactions from gene databases. We discovered that exposure to different substrate stiffnesses and to RGD stimulate responses that are mediated through GATA4, SMAD3/4, ETS-1, and STAT5 and other genes, which can initiate hypertrophic, fibrotic, and inflammatory responses.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Verônica R. de Melo Costa ◽  
Julianus Pfeuffer ◽  
Annita Louloupi ◽  
Ulf A. V. Ørom ◽  
Rosario M. Piro
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Time Course ◽  
Progressive Increase ◽  
Rna Seq ◽  
Transcript Processing ◽  
Aberrant Transcript ◽  
Genome Wide ◽  
Splicing Efficiency ◽  
Nascent Rna

Abstract Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

scholarly journals Copper Availability Influences the Transcriptomic Response of Candida albicans to Fluconazole Stress

2021 ◽  
Vol 11 (4) ◽  
Author(s):  
Elizabeth W Hunsaker ◽  
Chen-Hsin Albert Yu ◽  
Katherine J Franz
Keyword(s):  
Candida Albicans ◽  
Time Course ◽  
Transcriptional Response ◽  
Metal Homeostasis ◽  
Metal Availability ◽  
Drug Induced ◽  
Transcriptomic Response ◽  
Opportunistic Fungal Pathogen ◽  
Whole Transcriptome Analysis ◽  
Whole Transcriptome

Abstract The ability of pathogens to maintain homeostatic levels of essential biometals is known to be important for survival and virulence in a host, which itself regulates metal availability as part of its response to infection. Given this importance of metal homeostasis, we sought to address how the availability of copper in particular impacts the response of the opportunistic fungal pathogen Candida albicans to treatment with the antifungal drug fluconazole. The present study reports whole transcriptome analysis via time-course RNA-seq of C. albicans cells exposed to fluconazole with and without 10 µM supplemental CuSO4 added to the growth medium. The results show widespread impacts of small changes in Cu availability on the transcriptional response of C. albicans to fluconazole. Of the 2359 genes that were differentially expressed under conditions of cotreatment, 50% were found to be driven uniquely by exposure to both Cu and fluconazole. The breadth of metabolic processes that were affected by cotreatment illuminates a fundamental intersectionality between Cu metabolism and fungal response to drug stress. More generally, these results show that seemingly minor fluctuations in Cu availability are sufficient to shift cells’ transcriptional response to drug stress. Ultimately, the findings may inform the development of new strategies that capitalize on drug-induced vulnerabilities in metal homeostasis pathways.

scholarly journals Tumor hypoxia induces nuclear paraspeckle formation through HIF-2α dependent transcriptional activation of NEAT1 leading to cancer cell survival

Oncogene ◽  
2014 ◽  
Vol 34 (34) ◽  
pp. 4482-4490 ◽  
Author(s):  
H Choudhry ◽  
A Albukhari ◽  
M Morotti ◽  
S Haider ◽  
D Moralli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cell ◽  
Transcriptional Activation ◽  
Cellular Proliferation ◽  
Hypoxia Inducible Factor ◽  
Tumor Hypoxia ◽  
Transcriptional Response ◽  
Clonogenic Survival ◽  
Breast Cancer Cell Lines ◽  
Transcriptional Responses

Abstract Activation of cellular transcriptional responses, mediated by hypoxia-inducible factor (HIF), is common in many types of cancer, and generally confers a poor prognosis. Known to induce many hundreds of protein-coding genes, HIF has also recently been shown to be a key regulator of the non-coding transcriptional response. Here, we show that NEAT1 long non-coding RNA (lncRNA) is a direct transcriptional target of HIF in many breast cancer cell lines and in solid tumors. Unlike previously described lncRNAs, NEAT1 is regulated principally by HIF-2 rather than by HIF-1. NEAT1 is a nuclear lncRNA that is an essential structural component of paraspeckles and the hypoxic induction of NEAT1 induces paraspeckle formation in a manner that is dependent upon both NEAT1 and on HIF-2. Paraspeckles are multifunction nuclear structures that sequester transcriptionally active proteins as well as RNA transcripts that have been subjected to adenosine-to-inosine (A-to-I) editing. We show that the nuclear retention of one such transcript, F11R (also known as junctional adhesion molecule 1, JAM1), in hypoxia is dependent upon the hypoxic increase in NEAT1, thereby conferring a novel mechanism of HIF-dependent gene regulation. Induction of NEAT1 in hypoxia also leads to accelerated cellular proliferation, improved clonogenic survival and reduced apoptosis, all of which are hallmarks of increased tumorigenesis. Furthermore, in patients with breast cancer, high tumor NEAT1 expression correlates with poor survival. Taken together, these results indicate a new role for HIF transcriptional pathways in the regulation of nuclear structure and that this contributes to the pro-tumorigenic hypoxia-phenotype in breast cancer.

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