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

Gladstone Institutes, San Francisco, CA 94158.
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 10/2012; 109(43):17454-9. DOI: 10.1073/pnas.1213530109
Source: PubMed

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.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Gene expression in individual cells is highly variable and sporadic, often resulting in the synthesis of mRNAs and proteins in bursts. Bursting in gene expression is known to impact cell-fate in diverse systems ranging from latency in HIV-1 viral infections to cellular differentiation. It is generally assumed that bursts are geometrically distributed and that they arrive according to a Poisson process. On the other hand, recent single-cell experiments provide evidence for complex burst arrival processes, highlighting the need for more general stochastic models. To address this issue, we invoke a mapping between general models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions. These moments are then used to derive explicit conditions, based entirely on experimentally measurable quantities, that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process. For non-Poisson arrivals, we develop approaches for accurate estimation of burst parameters.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Stochasticity in the gene-expression process can create variability in the level of a given mRNA/protein across a homogenous population of living cells. Random fluctuations between different promoter states have been implicated as a major source of noise in the expression of many genes. These fluctuations are typically modeled through a two-state promoter architecture, where the promoter of a gene transitions between an active (ON) and inactive (OFF) state, spending an exponentially distributed time-interval in each state. High levels of mRNA production occur from the active state, while the inactive state allows for a low basal rate of production. Recent data has shown the existence of three-state promoter architectures with memory, where the time spent in the inactive state is gamma distributed. Here we analyze stochastic models of both two-state and three-state promoter architectures and identify key differences in their stochastic dynamics. Quantifying distance between probability distributions using standard metrics reveals that the difference in the mRNA copy number distributions for both promoter architectures is maximum when the stability of the active promoter state is comparable to the stability of the mRNA transcript. Our results further show that mRNA auto-correlations decay more rapidly for a three-state promoter architecture compared to a two-state promoter architecture. Interestingly, we find that in certain parameter regimes the three-state promoter architecture can yield negative mRNA auto-correlations. Finally, we discuss how these results can be useful for identifying genetic promoter architectures from single-cell mRNA data.
    2013 IEEE 52nd Annual Conference on Decision and Control (CDC); 12/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Stochastic promoter switching between transcriptionally active (ON) and inactive (OFF) states is a major source of noise in gene expression. It is often implicitly assumed that transitions between promoter states are memoryless, i.e., promoters spend an exponentially-distributed time interval in each of the two states. However, increasing evidence suggests that promoter ON/OFF times can be non-exponential, hinting at more complex transcriptional regulatory architectures. Given the essential role of gene expression in all cellular functions, efficient computational techniques for characterizing promoter architectures are critically needed. We have developed a novel model reduction for promoters with arbitrary numbers of ON and OFF states, allowing us to approximate complex promoter switching behavior with Weibull-distributed ON/OFF times. Using this model reduction, we created "bursty MCEM2", an efficient parameter estimation and model selection technique for inferring the number and configuration of promoter states from single-cell gene expression data. Application of bursty MCEM(2) to data from the endogenous mouse glutaminase promoter reveals nearly deterministic promoter OFF times, consistent with a multi-step activation mechanism consisting of 10 or more inactive states. Our novel approach to modeling promoter fluctuations together with bursty MCEM(2) provides powerful tools for characterizing transcriptional bursting across genes under different environmental conditions. © The Author (2015). Published by Oxford University Press. All rights reserved. For Permissions, please email:
    Bioinformatics 01/2015; DOI:10.1093/bioinformatics/btv007 · 4.62 Impact Factor

Full-text (2 Sources)

Available from
Jun 30, 2014