Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise

Abstract

Individual cells from a genetically identical population exhibit substantial variation in gene expression. A significant part of this variation is due to noise in the process of transcription that is intrinsic to each gene, and is determined by factors such as the rate with which the promoter transitions between transcriptionally active and inactive states, and the number of transcripts produced during the active state. However, we have a limited understanding of how the DNA sequence affects such promoter dynamics. Here, we used single-cell time-lapse microscopy to compare the effect on transcriptional dynamics of two distinct types of sequence changes in the promoter that can each increase the mean expression of a cell population by similar amounts but through different mechanisms. We show that increasing expression by strengthening a transcription factor binding site results in slower promoter dynamics and higher noise as compared with increasing expression by adding nucleosome-disfavoring sequences. Our results suggest that when achieving the same mean expression, the strategy of using stronger binding sites results in a larger number of transcripts produced from the active state, whereas the strategy of adding nucleosome-disfavoring sequences results in a higher frequency of promoter transitions between active and inactive states. In the latter strategy, this increased sampling of the active state likely reduces the expression variability of the cell population. Our study thus demonstrates the effect of cis-regulatory elements on expression variability and points to concrete types of sequence changes that may allow partial decoupling of expression level and noise.

Single-cell nucleosome mapping reveals the molecular basis of gene expression heterogeneity

“…57), which refers to the fact that nucleosomes are dynamic, and spontaneous unwrapping at the ends could allow for access to sequence-specific transcription factors to bind the DNA and interfere with nucleosome positioning. Furthermore, our data support a model where fluctuations in promoter chromatin structure could trigger pulses of transcriptional activity (30,44,58,59).…”

Probing the effect of promoters on noise in gene expression using thousands of designed sequences

“…In this model, noise is affected by both the rate of transcriptional activation and the size of the transcriptional bursts (the number of mRNA molecules produced at each instance of promoter activation) (Sherman and Cohen 2014) and the number of proteins produced from each mRNA. Therefore, according to this model, promoters can encode different combinations of expression mean and noise by modulating transcriptional bursting, as shown experimentally in several studies (Ozbudak et al 2002;Choi and Kim 2009;Amit et al 2011;Hornung et al 2012;Raveh-Sadka et al 2012;Dadiani et al 2013). However, we have a poor understanding of the extent to which promoters regulate noise beyond the level that is dictated by the mean and the sequence features by which such regulation is encoded.…”

“…One way to isolate this effect is to integrate the tested promoter upstream of a reporter gene and within a fixed genomic context. Moreover, since the sequence of native promoters differs by many parameters, mutated versions of native promoters (Hornung et al 2012), synthetic promoters built by random ligation of several building blocks (Mogno et al 2010), or designed synthetic promoters (Murphy et al 2007;Amit et al 2011;Raveh-Sadka et al 2012;Carey et al 2013;Dadiani et al 2013) are more suitable for studying the rules by which promoter sequence affects noise. Previous such studies show that nucleosome-disfavoring sequences increase expression and reduce noise (Choi and Kim 2009;Raveh-Sadka et al 2012;Dadiani et al 2013), whereas an equivalent increase in mean expression that results from the addition of an activator binding site increases noise (Dadiani et al 2013), and TATA boxes have little effect on noise (Mogno et al 2010).…”

Transcriptional refractoriness is dependent on core promoter architecture

“…Thus, many genes are transcribed in bursts, with brief periods of high activity (gene 'on') interspersed by long periods of inactivity (gene 'off'). Burst size, that is, the number of transcripts per burst, and burst frequency, that is, the number of transcriptional bursts per time unit are gene specific and appear to depend on the promoter architecture, such as the presence of a CAAT box, a TATA box, the size of the nucleosome-free region as well as the location and number of transcription factor binding sites [7][8][9][10][11][12] . Burst sizes of between 1 and 450 transcripts per burst have been observed followed by periods of inactivity of up to several hours 7,8,[12][13][14][15][16][17][18][19] .…”