Effects of post-transcriptional regulation on phenotypic noise in Escherichia coli

Abstract

Cell-to-cell variations in protein abundance, called noise, give rise to phenotypic variability between isogenic cells. Studies of noise have focused on stochasticity introduced at transcription, yet the effects of post-transcriptional regulatory processes on noise remain unknown. We study the effects of RyhB, a small-RNA of Escherichia coli produced on iron stress, on the phenotypic variability of two of its downregulated target proteins, using dual chromosomal fusions to fluorescent reporters and measurements in live individual cells. The total noise of each of the target proteins is remarkably constant over a wide range of RyhB production rates despite cells being in stress. In fact, coordinate downregulation of the two target proteins by RyhB reduces the correlation between their levels. Hence, an increase in phenotypic variability under stress is achieved by decoupling the expression of different target proteins in the same cell, rather than by an increase in the total noise of each. Extrinsic noise provides the dominant contribution to the total protein noise over the total range of RyhB production rates. Stochastic simulations reproduce qualitatively key features of our observations and show that a feed-forward loop formed by transcriptional extrinsic noise, an sRNA and its target genes exhibits strong noise filtration capabilities.

Stochastic holin expression can account for lysis time variation in the bacteriophageλ

“…Indeed, l variant IN62 (no antiholin expression) lyses 10 min faster but with a twofold higher CV 2 FPT compared with wild-type [34]. Our results show that as in other systems [57][58][59], antiholinmediated incoherent feed-forward loop is a key regulatory motif in l's lytic pathway for buffering LT from stochastic gene expression.…”

Small RNAs in Bacteria and Archaea

“…Thus, sRNAs, in a threshold-linear model, are predicted to suppress stochastic fluctuations and to filter out transcriptional noise (Levine & Hwa, 2008). An experimental study supports noise filtration (Arbel-Goren et al, 2013), as does a comprehensive theoretical study (Mehta, Goyal, & Wingreen, 2008). On the other hand, ultrasensitivity near a threshold might generate cell-to-cell differences that generate phenotypic heterogeneity, maybe somewhat akin to phenomena such as heterogeneous competence gene expression, dependent on positive autoregulation by the TF ComK (Leisner, Kuhr, Radler, Frey, & Maier, 2009).…”

“…Several papers have addressed the features of (antisense-type) sRNA-mediated control, often in comparison with that by TFs (Arbel-Goren et al, 2013;Hussein & Lim, 2012;Levine & Hwa, 2008;Nitzan et al, 2015;Shimoni et al, 2007). For instance, a linear-threshold model was postulated based on theoretical/experimental studies in the Hwa lab (Levine & Hwa, 2008;Levine, Zhang, Kuhlman, & Hwa, 2007).…”

Phenotypic noise: effects of post‐transcriptional regulatory processes affecting mRNA

“…(b) The embedding of sRNAs within genetic networks in which extrinsic sources are dominant can be viewed as formation of an incoherent feed‐forward loop ( FFL ) motif in which transcriptional extrinsic sources act directly on the expression of target genes and indirectly via an sRNA . A typical example is furnished by the sRNA RyhB . Other sRNAs (e.g., RybB ) form mixed incoherent FFLs along with transcription factors ( TF s).…”

“…The most salient feature emerging from these studies is the prediction that the intrinsic contribution to protein noise —where σ p and μ p are the standard deviation and mean of protein number, respectively—exhibits a maximum in a regime in which the production rates of the sRNA and target transcripts are comparable (in most studies of phenotypic variability, noise is quantified by the nondimensional ratio σ 2 / μ 2 of protein or transcript distributions; alternatively, noise can be quantified by the Fano factor σ 2 / μ , which has the advantage of being equal to 1 for Poisson processes, serving as a useful behavior of reference). A recent study has addressed the effects of sRNA regulation on protein noise from an experimental point of view, focusing on the iron homeostasis network of E. coli as a model system . This work showed that the total protein noise of two different targets of the sRNA in the network remained independent of the sRNA production rate for most of the natural range of expression of the two target genes studied, and that extrinsic noise provides the main contribution to the total noise, even at the highest levels of sRNA production induced.…”