Abstract:Genetically identical cells exhibit large variability (noise) in gene expression, with important consequences for cellular function. Although the amount of noise decreases with and is thus partly determined by the mean expression level, the extent to which different promoter sequences can deviate away from this trend is not fully known. Here, we present a high-throughput method for measuring promoter-driven noise for thousands of designed synthetic promoters in parallel. We use it to investigate how promoters … Show more
“…The measurements were carried out using our previously described method that involves FACS sorting and deep sequencing of a barcoded pooled promoter library (Sharon et al 2012(Sharon et al , 2014. Briefly, uniquely barcoded promoters that drive a YFP reporter are FACS sorted into 12 bins of expression that subsequently receive an expression-bin barcode.…”
Section: Resultsmentioning
confidence: 99%
“…The pooled library was grown in synthetic media with a 2 11 -, 2 6 -, 2 4 -, 2 3 -, 2 2 -, or 2 0 -fold dilution of amino acids, and gene expression driven by each promoter was measured as previously described (Sharon et al 2014). …”
Transcription factors (TFs) are key mediators that propagate extracellular and intracellular signals through to changes in gene expression profiles. However, the rules by which promoters decode the amount of active TF into target gene expression are not well understood. To determine the mapping between promoter DNA sequence, TF concentration, and gene expression output, we have conducted in budding yeast a large-scale measurement of the activity of thousands of designed promoters at six different levels of TF. We observe that maximum promoter activity is determined by TF concentration and not by the number of binding sites. Surprisingly, the addition of an activator site often reduces expression. A thermodynamic model that incorporates competition between neighboring binding sites for a local pool of TF molecules explains this behavior and accurately predicts both absolute expression and the amount by which addition of a site increases or reduces expression. Taken together, our findings support a model in which neighboring binding sites interact competitively when TF is limiting but otherwise act additively.
“…The measurements were carried out using our previously described method that involves FACS sorting and deep sequencing of a barcoded pooled promoter library (Sharon et al 2012(Sharon et al , 2014. Briefly, uniquely barcoded promoters that drive a YFP reporter are FACS sorted into 12 bins of expression that subsequently receive an expression-bin barcode.…”
Section: Resultsmentioning
confidence: 99%
“…The pooled library was grown in synthetic media with a 2 11 -, 2 6 -, 2 4 -, 2 3 -, 2 2 -, or 2 0 -fold dilution of amino acids, and gene expression driven by each promoter was measured as previously described (Sharon et al 2014). …”
Transcription factors (TFs) are key mediators that propagate extracellular and intracellular signals through to changes in gene expression profiles. However, the rules by which promoters decode the amount of active TF into target gene expression are not well understood. To determine the mapping between promoter DNA sequence, TF concentration, and gene expression output, we have conducted in budding yeast a large-scale measurement of the activity of thousands of designed promoters at six different levels of TF. We observe that maximum promoter activity is determined by TF concentration and not by the number of binding sites. Surprisingly, the addition of an activator site often reduces expression. A thermodynamic model that incorporates competition between neighboring binding sites for a local pool of TF molecules explains this behavior and accurately predicts both absolute expression and the amount by which addition of a site increases or reduces expression. Taken together, our findings support a model in which neighboring binding sites interact competitively when TF is limiting but otherwise act additively.
“…In particular, whereas the excess noise levels of promoters without known regulatory inputs are very similar to those of our synthetic promoters, promoters with one or more regulatory inputs have clearly elevated noise levels (Figure 2C). The general association between elevated noise and gene regulation has recently been observed in eukaryotes as well (Sharon et al, 2014), and mutations that lower gene expression noise typically target TF binding sites (Hornung et al, 2012). 10.7554/eLife.05856.011Figure 2.Promoters with elevated noise exhibit high expression plasticity and large numbers of regulatory inputs.( A ) Native promoters were sorted by their excess noise x and, as a function of a cut-off on x (horizontal axis), we calculated the mean and standard error (vertical axis) of the variation in mRNA levels across different experimental conditions (data from http://genexpdb.ou.edu/) of all promoters with excess noise larger than x .…”
Although it is often tacitly assumed that gene regulatory interactions are finely tuned, how accurate gene regulation could evolve from a state without regulation is unclear. Moreover, gene expression noise would seem to impede the evolution of accurate gene regulation, and previous investigations have provided circumstantial evidence that natural selection has acted to lower noise levels. By evolving synthetic Escherichia coli promoters de novo, we here show that, contrary to expectations, promoters exhibit low noise by default. Instead, selection must have acted to increase the noise levels of highly regulated E. coli promoters. We present a general theory of the interplay between gene expression noise and gene regulation that explains these observations. The theory shows that propagation of expression noise from regulators to their targets is not an unwanted side-effect of regulation, but rather acts as a rudimentary form of regulation that facilitates the evolution of more accurate regulation.DOI:
http://dx.doi.org/10.7554/eLife.05856.001
“…Its ingredients are independent of specific biological mechanisms and rely on those general aspects of cellular events -exponential protein accumulation, division and feedback -that are likely to be common to all dividing cell populations. This marks a significant departure from the main current line of research on protein number variation, which investigates synthetically produced proteins while experimentally isolating the contribution of specific microscopic mechanisms [29][30][31][32][33][34].…”
Protein distributions measured under a broad set of conditions in bacteria and yeast were shown to exhibit a common skewed shape, with variances depending quadratically on means. For bacteria these properties were reproduced by temporal measurements of protein content, showing accumulation and division across generations. Here we present a stochastic growth-and-division model with feedback which captures these observed properties. The limiting copy number distribution is calculated exactly, and a single parameter is found to determine the distribution shape and the variance-to-mean relation. Estimating this parameter from bacterial temporal data reproduces the measured distribution shape with high accuracy, and leads to predictions for future experiments.
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