Literature DB >> 23064634

Transcriptional burst frequency and burst size are equally modulated across the human genome.

Roy D Dar1, Brandon S Razooky, Abhyudai Singh, Thomas V Trimeloni, James M McCollum, Chris D Cox, Michael L Simpson, Leor S Weinberger.   

Abstract

Gene expression occurs either as an episodic process, characterized by pulsatile bursts, or as a constitutive process, characterized by a Poisson-like accumulation of gene products. It is not clear which mode of gene expression (constitutive versus bursty) predominates across a genome or how transcriptional dynamics are influenced by genomic position and promoter sequence. Here, we use time-lapse fluorescence microscopy to analyze 8,000 individual human genomic loci and find that at virtually all loci, episodic bursting--as opposed to constitutive expression--is the predominant mode of expression. Quantitative analysis of the expression dynamics at these 8,000 loci indicates that both the frequency and size of the transcriptional bursts varies equally across the human genome, independent of promoter sequence. Strikingly, weaker expression loci modulate burst frequency to increase activity, whereas stronger expression loci modulate burst size to increase activity. Transcriptional activators such as trichostatin A (TSA) and tumor necrosis factor α (TNF) only modulate burst size and frequency along a constrained trend line governed by the promoter. In summary, transcriptional bursting dominates across the human genome, both burst frequency and burst size vary by chromosomal location, and transcriptional activators alter burst frequency and burst size, depending on the expression level of the locus.

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Year:  2012        PMID: 23064634      PMCID: PMC3491463          DOI: 10.1073/pnas.1213530109

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  43 in total

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Authors:  Abhyudai Singh; Brandon Razooky; Chris D Cox; Michael L Simpson; Leor S Weinberger
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Journal:  Science       Date:  2008-11-20       Impact factor: 47.728

4.  Using noise to probe and characterize gene circuits.

Authors:  Chris D Cox; James M McCollum; Michael S Allen; Roy D Dar; Michael L Simpson
Journal:  Proc Natl Acad Sci U S A       Date:  2008-07-31       Impact factor: 11.205

5.  Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells.

Authors:  Yuichi Taniguchi; Paul J Choi; Gene-Wei Li; Huiyi Chen; Mohan Babu; Jeremy Hearn; Andrew Emili; X Sunney Xie
Journal:  Science       Date:  2010-07-30       Impact factor: 47.728

6.  Mammalian genes are transcribed with widely different bursting kinetics.

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Journal:  EMBO J       Date:  2001-04-02       Impact factor: 11.598

8.  HIV promoter integration site primarily modulates transcriptional burst size rather than frequency.

Authors:  Ron Skupsky; John C Burnett; Jonathan E Foley; David V Schaffer; Adam P Arkin
Journal:  PLoS Comput Biol       Date:  2010-09-30       Impact factor: 4.475

Review 9.  Regulation and function of NF-kappaB transcription factors in the immune system.

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10.  Dynamic analysis of stochastic transcription cycles.

Authors:  Claire V Harper; Bärbel Finkenstädt; Dan J Woodcock; Sönke Friedrichsen; Sabrina Semprini; Louise Ashall; David G Spiller; John J Mullins; David A Rand; Julian R E Davis; Michael R H White
Journal:  PLoS Biol       Date:  2011-04-12       Impact factor: 8.029

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  172 in total

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2.  A hardwired HIV latency program.

Authors:  Brandon S Razooky; Anand Pai; Katherine Aull; Igor M Rouzine; Leor S Weinberger
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Journal:  Proc Natl Acad Sci U S A       Date:  2013-08-01       Impact factor: 11.205

5.  Neurog3-Independent Methylation Is the Earliest Detectable Mark Distinguishing Pancreatic Progenitor Identity.

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Journal:  Dev Cell       Date:  2019-01-07       Impact factor: 12.270

6.  Transcriptional Bursting and Co-bursting Regulation by Steroid Hormone Release Pattern and Transcription Factor Mobility.

Authors:  Diana A Stavreva; David A Garcia; Gregory Fettweis; Prabhakar R Gudla; George F Zaki; Vikas Soni; Andrew McGowan; Geneva Williams; Anh Huynh; Murali Palangat; R Louis Schiltz; Thomas A Johnson; Diego M Presman; Matthew L Ferguson; Gianluca Pegoraro; Arpita Upadhyaya; Gordon L Hager
Journal:  Mol Cell       Date:  2019-08-14       Impact factor: 17.970

7.  Transient changes in intercellular protein variability identify sources of noise in gene expression.

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Journal:  Biophys J       Date:  2014-11-04       Impact factor: 4.033

8.  Inferring single-cell gene expression mechanisms using stochastic simulation.

Authors:  Bernie J Daigle; Mohammad Soltani; Linda R Petzold; Abhyudai Singh
Journal:  Bioinformatics       Date:  2015-01-07       Impact factor: 6.937

9.  LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.

Authors:  Changlin Wan; Wennan Chang; Yu Zhang; Fenil Shah; Xiaoyu Lu; Yong Zang; Anru Zhang; Sha Cao; Melissa L Fishel; Qin Ma; Chi Zhang
Journal:  Nucleic Acids Res       Date:  2019-10-10       Impact factor: 16.971

10.  BET bromodomain-targeting compounds reactivate HIV from latency via a Tat-independent mechanism.

Authors:  Daniela Boehm; Vincenzo Calvanese; Roy D Dar; Sifei Xing; Sebastian Schroeder; Laura Martins; Katherine Aull; Pao-Chen Li; Vicente Planelles; James E Bradner; Ming-Ming Zhou; Robert F Siliciano; Leor Weinberger; Eric Verdin; Melanie Ott
Journal:  Cell Cycle       Date:  2012-02-01       Impact factor: 4.534

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