A genome-wide analysis of promoter-mediated phenotypic noise in Escherichia coli

Abstract

Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as "phenotypic noise." In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alone.

Figure 1. Dependence of variation in mRNA expression on mean mRNA expression level and derivation of a noise metric.

A. The observed variance in mRNA expression increases with increasing mean expression level. Shown are five promoters with various levels of mean and variance in expression (from left to right: rho transcription termination factor; prpR transcriptional dual regulator, bolA transcriptional dual regulator; tyrosyl-tRNA synthetase; and dps, an iron sequestration and DNA damage protein). B. The expression level and observed standard deviation for all 1522 promoters used in the analysis. The genes shown in panel A are highlighted in red (the left-most red dot is rho, the right-most dot is dps). C. The coefficient of variation decreases initially with increasing expression, but plateaus at higher expression levels. D–F. Analogous histograms and graphs to those shown in panels A–C), but calculated from log-transformed data. As discussed in the text, our focus is on variation in expression; we thus derived a measure of variation in mRNA expression that is independent of the mean level of mRNA expression, and any measurement artifacts associated with changes in the mean. This allows us to test whether mean and variation in expression can be decoupled due to selection or changes in the promoter sequence. The noise metric is the vertical deviation from a smooth spline (blue) calculated from the running median (orange) of mean log expression level versus the CV of log expression. The slight decrease in CV at low expression levels (panels C and E) is because fluorescence values lower than one cannot occur. Thus, for weakly expressed genes, the distribution specifying the variation in expression levels is truncated at one, decreasing the CV.

Figure 2. Noise in gene expression is dependent on the functional importance of the downstream gene.

Conserved non-essential genes exhibit less noise. Conservation is calculated as the number of gamma-proteobacterial taxa in which an orthologous gene copy is present. Promoters were binned according to the number of taxa in which an orthologue was found; the relationship is highly significant (for an unbinned analysis, Spearman's rho = −0.19, p = 7.2e-12, n = 1350 (Figure S5)). A nonparametric linear fit using Thiel's method is shown in black.

Figure 3. Noise in gene expression is related to the specific functional role.

Genes in different functional categories exhibit high or low levels of noise. We considered eight of the major categories delineated by MultiFun (metabolism, information transfer, regulation, transport, cell processes, cell structure, location, and extra-chromosomal origin) . Within each of these categories, we asked whether there were consistent differences in the amount of noise exhibited by genes of different function. Major categories and subcategories are ranked by the amount of noise exhibited by genes in that category; within each major category, subcategories are colored relative to the average amount of noise exhibited by all genes in the major category. The color indicates the probability of the null hypothesis (that genes in a given subcategory have the same level of noise as genes in other subcategories; two-sided Wilcox rank sum test). Two stars indicates that the subcategory exhibits a significantly higher or lower level of noise than other subcategories after correcting for multiple comparisons; one star indicates that the subcategory exhibits a higher or lower level of noise with p<0.05. Regulation is the only major functional category that exhibits higher noise, although this result is of only marginal significance.

Figure 4. E. coli and yeast orthologues have similar noise levels.

Orthologues in S. cerevisiae were determined through a reciprocal best-hit analysis for all the genes in E. coli. The number of reciprocal best-hit orthologues depended on the e-value cut-off that was set; this plot shows all orthologues with an e-value lower than e-35 (rho = 0.31, p = 0.015, n = 60). The data on yeast noise are from ; noise values for both S. cerevisiae and E. coli have been scaled such that the mean is zero and the standard deviation in noise is one.