Base pairing small RNAs and their roles in global regulatory networks

Abstract

Bacteria use a range of RNA regulators collectively termed small RNAs (sRNAs) to help respond to changes in the environment. Many sRNAs regulate their target mRNAs through limited base-pairing interactions. Ongoing characterization of base-pairing sRNAs in bacteria has started to reveal how these sRNAs participate in global regulatory networks. These networks can be broken down into smaller regulatory circuits that have characteristic behaviors and functions. In this review, we describe the specific regulatory circuits that incorporate base-pairing sRNAs and the importance of each circuit in global regulation. Because most of these circuits were originally identified as network motifs in transcriptional networks, we also discuss why sRNAs may be used over protein transcription factors to help transduce environmental signals.

Fig. 1

Overview of regulatory circuits in which sRNAs are found in E. coli and Salmonella. The regulator controlling sRNA expression is sensitive to an environmental stimuli, such as the build-up of intracellular iron activating Fur, or osmotic shock inducing the phosphorylation of OmpR by the cognate surface receptor EnvZ. (a) The single-input module (SIM) coordinates the expression of multiple genes through a single regulator. The sRNA incorporated into the SIM can directly target multiple genes or target a global regulator. (b) The dense overlapping regulon (DOR) controls the expression of a common set of genes in response to multiple signals. Examples include the regulation of outer membrane proteins and the alternative sigma factor σ^S. (c) The negative feedback loop allows an sRNA to repress its own expression. Feedback can be direct or indirect depending on whether the sRNA targets its own regulator or relieves the stress responsible for activating sRNA expression. (d) The feedforward loop integrates two regulatory branches to control the expression of one target gene. The two known examples control the levels of two major outer membrane proteins through the combined regulation by OmpR and an OmpR-regulated sRNA. Arrows designate activation, bars designate repression, and dashed lines designate indirect regulation. Transcription regulators are in blue, sRNA regulators are in red, and target genes and operons are in gray.

Fig. 2

Feedback loops in the Vibrio quorum sensing phosphorelay cascade. In both V. harveyi and V. cholerae, a build-up of autoinducers leads to deactivation of LuxO through dephosphorylation by the histidine kinase two-component system. In the absence of autoinducers, the histidine kinase two-component system phosphorylates LuxO to LuxO-P, which activates the expression of the Qrr family of sRNAs. In turn, the Qrr RNAs repress the expression of the quorum sensing master regulator LuxR in V. hareyi and HapR in V. cholerae. Two of the identified feedback loops involve the Qrrs, while the other two involve autorepression by each transcription regulator. Autorepression by LuxO occurs independently of its phosphorylation state.

Fig. 3

The regulatory properties of sRNAs. (a) Response curve for a target gene negatively regulated by a protein repressor. Target protein levels depend on repressor levels and the affinity of the repressor for the target gene promoter. Target levels are more susceptible to high-affinity repressors (blue) than low-affinity repressors (green). (b) Noise profile for a gene negatively regulated by a protein repressor. Noise reflects the cell-cell variability in target protein levels across a bacterial population. The amount of noise decreases with increasing repressor levels and is generally independent of the affinity between the repressor and the target gene promoter. (c) Response curve for a gene negatively regulated by an sRNA. Target protein levels depend on the ratio of mRNA and sRNA production rates and the relative speed of sRNA action. When the speed of sRNA action is fast, the curve follows a ‘threshold-linear’ response (red). This response can be divided into two regimes based on whether sRNA or mRNA production dominates with a sharp transition in between (sRNA/mRNA production = 1). When the speed of sRNA action is slow, the transition between regimes is graded (orange). (d) Noise profile for a gene negatively regulated by an sRNA. Noise is dampened when repressor levels are high because all transcribed mRNAs are bound by sRNAs and silenced. Noise is maximized in the transition between regimes because of ultrasensitivity near sRNA/mRNA production = 1. (e) Dynamics of target gene induction after the input signal is shut off for a protein repressor that directly senses the signal (blue) or for a negatively-acting sRNA whose transcription is controlled by the signal (red). sRNA-based regulation introduces a time lag following increased transcription of the target gene or reduced production of the sRNA. (f) Dynamics of target gene regulation under a fluctuating input signal for a protein repressor directly sensing the signal (blue) or for a negatively-acting sRNA whose transcription is controlled by the signal (red). sRNA-based regulation buffers against signal fluctuations due to the time lag when sRNA production is shut off. All axes are linear and each plot is a pictorial representation of results from previous computational studies (Levine et al., 2007; Legewie et al., 2008; Levine & Hwa, 2008; Mehta et al., 2008).