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Transcriptional Burst Frequency and Burst Size Are Equally Modulated Across the Human Genome

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Transcriptional Burst Frequency and Burst Size Are Equally Modulated Across the Human Genome Transcriptional burst frequency and burst size are equally modulated across the human genome Roy D. Dara,b,c,1, Brandon S. Razookya,d,e,1, Abhyudai Singhd,2, Thomas V. Trimelonif, James M. McCollumf, Chris D. Coxg,h, Michael L. Simpsonb,I,3, and Leor S. Weinbergera,d,j,3 aGladstone Institutes, San Francisco, CA 94158; bCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831; cDepartment of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996; dDepartment of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093; eBiophysics Graduate Group, University of California, San Francisco, CA 94158; gCenter for Environmental Biotechnology, University of Tennessee, Knoxville, TN 37996;hDepartment of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996; IDepartment of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996; jDepartment of Biochemistry and Biophysics, University of California, San Francisco, CA 94158; and fDepartment of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284–3072 Edited by Jonathan S. Weissman, University of California, San Francisco, CA, and approved September 13, 2012 (received for review August 8, 2012) Gene expression occurs either as an episodic process, characterized autocorrelation time of expression fluctuations (as measured by pulsatile bursts, or as a constitutive process, characterized by by the noise autocorrelation time at half of its initial value, τ1∕2) a Poisson-like accumulation of gene products. It is not clear which (11, 12) (Fig. 1C). Although this three-dimensional noise space is mode of gene expression (constitutive versus bursty) predomi- impractical to analyze directly, different two-dimensional projec- nates across a genome or how transcriptional dynamics are influ- tions of noise space allow the quantification of rate parameters enced by genomic position and promoter sequence. Here, we use in gene-regulatory models and provide a convenient method to time-lapse fluorescence microscopy to analyze 8,000 individual differentiate between underlying gene-expression mechanisms, human genomic loci and find that at virtually all loci, episodic such as constitutive versus bursty transcription (Fig. 1C). For bursting—as opposed to constitutive expression—is the predomi- example, transcriptional bursting increases both noise magnitude nant mode of expression. Quantitative analysis of the expression and noise autocorrelation time and shifts points in the CV2 dynamics at these 8,000 loci indicates that both the frequency versus τ1∕2 plane to the upper right quadrant relative to a con- and size of the transcriptional bursts varies equally across the stitutive expression model (Fig. 1C, Bottom Left). Conversely, human genome, independent of promoter sequence. Strikingly, translational bursting shifts noise magnitude, but not the auto- weaker expression loci modulate burst frequency to increase correlation time (10, 13). activity, whereas stronger expression loci modulate burst size to Importantly, analysis of the τ1∕2 axis is critical to fully parame- increase activity. Transcriptional activators such as trichostatin A terize two-state transcriptional bursting models (Fig. 1C), which (TSA) and tumor necrosis factor α (TNF) only modulate burst size always include at least three unknown parameters: the rate of k and frequency along a constrained trend line governed by the transition to a transcriptionally active state ( on), the rate of tran- k promoter. In summary, transcriptional bursting dominates across sitioning to a transcriptionally inactive state ( off ), and the rate the human genome, both burst frequency and burst size vary by k of transcription once in the active state ( m) (13, 14). Analyses chromosomal location, and transcriptional activators alter burst of a single two-dimensional plane (e.g., CV2 versus expression frequency and burst size, depending on the expression level of level) cannot fully determine these three rate parameters. Con- 2 the locus. versely, analyses of CV versus expression level and τ1∕2 versus expression level allow the determination of these three para- ∣ ∣ 2 stochastic noise automated single-cell imaging human meters, and analysis of CV -versus-τ1∕2 facilitates direct compar- immunodeficiency virus ∣ long terminal repeat promoter isons of data containing widely varying expression levels, because it removes the reciprocal dependence of noise magnitude on here exists conflicting evidence over the predominant mode expression level (10). Tof gene expression in both prokaryotes and eukaryotes. The The ability to accurately quantify these transcriptional rate classical view of gene expression as a constitutive, Poisson-like parameters is essential for answering basic questions about the accumulation of gene products (Fig. 1A) is supported by a com- mechanisms that regulate transcription. Previous studies ele- prehensive large-scale study in bacteria, demonstrating that >400 gantly applied flow cytometry (6, 7) and time-lapse microscopy genes appear to follow constitutive (or Poisson-like) gene expres- (1, 15, 16) to analyze gene-expression noise in large subsets of sion (1). Constitutive expression has also been reported for genes. However, a tedious experimental bottleneck of subcloning subsets of human genes (2). Conversely, several elegant studies and expansion of isogenic populations necessarily limits the showed that specific promoters in bacteria and yeast express throughput of these noise-analysis approaches. Here, we circum- gene products in an episodic process (Fig. 1B), characterized by vent this subcloning requirement to globally apply the analytical pulsatile bursts in transcription (3–9). Given this conflicting evi- dence, it remains unclear if episodic bursting is the predominant mode of gene expression across a genome or just a highlighted Author contributions: R.D.D., B.S.R., C.D.C., M.L.S., and L.S.W. designed research; R.D.D., B.S.R., T.V.T., and J.M.M. performed research; R.D.D., B.S.R., C.D.C., M.L.S., and L.S.W. exception. If bursting is predominant, it is not clear if or how it contributed new reagents/analytic tools; R.D.D., B.S.R., A.S., T.V.T., J.M.M., M.L.S., and depends on genomic location. L.S.W. analyzed data; and R.D.D., B.S.R., C.D.C., M.L.S., and L.S.W. wrote the paper. To globally determine if constitutive Poisson-like expression The authors declare no conflict of interest. or episodic bursty expression dominates throughout the human This article is a PNAS Direct Submission. genome, we capitalize on a recently proposed theoretical frame- 1R.D.D. and B.S.R. contributed equally to this work. work (10) for extracting the details of gene regulation from the 2Present address: Department of Electrical and Computer Engineering, University of time-resolved structure of fluctuations (i.e., noise) in gene expres- Delaware, Newark, DE 19716. sion. This analysis quantifies time-lapse expression trajectories to 3To whom correspondence may be addressed: E-mail: [email protected] or obtain three orthogonal measures of expression: the average [email protected]. expression level, the magnitude of expression fluctuations (as This article contains supporting information online at www.pnas.org/lookup/suppl/ 2 measured by the coefficient of variation squared, CV ), and the doi:10.1073/pnas.1213530109/-/DCSupplemental. 17454–17459 ∣ PNAS ∣ October 23, 2012 ∣ vol. 109 ∣ no. 43 www.pnas.org/cgi/doi/10.1073/pnas.1213530109 Downloaded by guest on September 27, 2021 tions (approximately 69%) occur within transcriptionally active regions (20, 21). Jurkat T lymphocytes were infected with HIV- based lentiviral vectors encoding a short-lived, two-hour half-life version of green fluorescent protein (referred to as d2GFP), to generate a library of cells in which the vector is integrated at a different genomic position in each individual cell (i.e., a poly- clonal library) (Fig. 2A). To focus on measuring the intrinsic fluc- tuation dynamics of the genomic region surrounding the vector- integration site, we utilized a vector encoding the HIV-1 LTR promoter, which is relatively weak and heavily influenced by the expression dynamics of the local chromatin environment (22). Initially, the analytical framework for distinguishing bursty expression was applied to five isoclonal populations: Each popu- lation was grown from a single parent cell, and all daughter cells, therefore, share the same LTR genomic integration site (Fig. 2 A–D). Cell fluorescence was then imaged for 18 h, and the resulting fluorescence intensity trajectories were used to con- struct a three-dimensional noise space with three axes: noise mag- nitude as measured by the coefficient of variation squared (CV2), noise autocorrelation represented by the half-autocorrelation time (τ1∕2), and mean expression level (hGFPi) (Fig. 2 B–C and SI Appendix). Analysis of these trajectories in the noise space allows comparisons between isoclonal populations and differen- tiation between isoclones that exhibit constitutive transcription versus episodic bursts of transcription (Fig. 2D). To generate an initial baseline origin for noise-space analysis of the single-cell data, we identified isoclones that were the most Poissonian in their expression fluctuation profiles from a library of isoclonal populations (17) (SI Appendix). Two isoclonal populations exhi- biting the fastest fluctuation autocorrelation decays (i.e., shortest
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