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Regulation of gene expression by small non‐coding RNAs: a quantitative view

2007; Springer Nature; Volume: 3; Issue: 1 Linguagem: Inglês

10.1038/msb4100181

1744-4292

Yishai Shimoni, Gilgi Friedlander, Guy Hetzroni, Gali Niv, Shoshy Altuvia, Ofer Biham, Hanah Margalit,

Bacterial Genetics and Biotechnology

Article25 September 2007Open Access Regulation of gene expression by small non-coding RNAs: a quantitative view Yishai Shimoni Yishai Shimoni Racah Institute of Physics, The Hebrew University, Jerusalem, Israel Search for more papers by this author Gilgi Friedlander Gilgi Friedlander Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem, Israel Search for more papers by this author Guy Hetzroni Guy Hetzroni Racah Institute of Physics, The Hebrew University, Jerusalem, Israel Search for more papers by this author Gali Niv Gali Niv Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem, Israel Search for more papers by this author Shoshy Altuvia Shoshy Altuvia Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem, Israel Search for more papers by this author Ofer Biham Corresponding Author Ofer Biham Racah Institute of Physics, The Hebrew University, Jerusalem, Israel Search for more papers by this author Hanah Margalit Corresponding Author Hanah Margalit Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem, Israel Search for more papers by this author Yishai Shimoni Yishai Shimoni Racah Institute of Physics, The Hebrew University, Jerusalem, Israel Search for more papers by this author Gilgi Friedlander Gilgi Friedlander Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem, Israel Search for more papers by this author Guy Hetzroni Guy Hetzroni Racah Institute of Physics, The Hebrew University, Jerusalem, Israel Search for more papers by this author Gali Niv Gali Niv Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem, Israel Search for more papers by this author Shoshy Altuvia Shoshy Altuvia Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem, Israel Search for more papers by this author Ofer Biham Corresponding Author Ofer Biham Racah Institute of Physics, The Hebrew University, Jerusalem, Israel Search for more papers by this author Hanah Margalit Corresponding Author Hanah Margalit Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem, Israel Search for more papers by this author Author Information Yishai Shimoni1, Gilgi Friedlander2, Guy Hetzroni1, Gali Niv2, Shoshy Altuvia2, Ofer Biham 1 and Hanah Margalit 2 1Racah Institute of Physics, The Hebrew University, Jerusalem, Israel 2Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem, Israel *Corresponding authors. Racah Institute of Physics, The Hebrew University, Jerusalem 91904, Israel. Tel.: +972 2 658 4363; Fax: +972 2 652 0089; E-mail: [email protected] of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, PO Box 12272, Jerusalem 91120, Israel. Tel.: +972 2 675 8614; Fax: +972 2 675 7308; E-mail: [email protected] Molecular Systems Biology (2007)3:138https://doi.org/10.1038/msb4100181 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info The importance of post-transcriptional regulation by small non-coding RNAs has recently been recognized in both pro- and eukaryotes. Small RNAs (sRNAs) regulate gene expression post-transcriptionally by base pairing with the mRNA. Here we use dynamical simulations to characterize this regulation mode in comparison to transcriptional regulation mediated by protein–DNA interaction and to post-translational regulation achieved by protein–protein interaction. We show quantitatively that regulation by sRNA is advantageous when fast responses to external signals are needed, consistent with experimental data about its involvement in stress responses. Our analysis indicates that the half-life of the sRNA–mRNA complex and the ratio of their production rates determine the steady-state level of the target protein, suggesting that regulation by sRNA may provide fine-tuning of gene expression. We also describe the network of regulation by sRNA in Escherichia coli, and integrate it with the transcription regulation network, uncovering mixed regulatory circuits, such as mixed feed-forward loops. The integration of sRNAs in feed-forward loops provides tight repression, guaranteed by the combination of transcriptional and post-transcriptional regulations. Synopsis Living cells are self-regulated by interactions between different molecules. Until very recently, most research has focused on transcription regulation interactions and on protein–protein interactions, which in many cases are involved in post-translational regulation. During the last years it has become evident that another type of interaction plays a prominent role in the regulation of cellular processes, manifested by small RNA (sRNA) molecules that base pair with the mRNA and regulate gene expression post-transcriptionally, influencing translation or mRNA stability. This mode of regulation was found in both pro- and eukaryotes (for review see Storz et al, 2005). In this paper, we focus on bacterial sRNAs, and in particular on regulatory interactions found in Escherichia coli, for which most experimental data on sRNAs are available. At present there are about 80 known sRNAs in E. coli (for review see Gottesman, 2005; Storz et al, 2005). These molecules are 50–400 nucleotides long and many of them are evolutionary conserved (Hershberg et al, 2003). Most of those for which some functional knowledge has been acquired were often shown to act as inhibitors of translation by base pairing with the mRNA in the ribosome-binding site (for review see Gottesman, 2005). We describe the network of sRNA–target regulatory interactions in E. coli, and study the kinetics of this regulation mechanism in comparison to transcription regulation mediated by protein–DNA interaction and post-translational regulation mediated by protein–protein interaction. For this we describe the three regulatory mechanisms by mathematical models, followed by simulations using average kinetic parameters based on experimental data (Altuvia et al, 1997; Altuvia and Wagner, 2000; Alon, 2006). We show that there are measurable qualitative differences between the three regulation mechanisms, both in response time and in effectiveness. In regard to effectiveness, we find that transcriptional regulation is generally the most effective mechanism in the activation or repression of the target gene. It requires the least copies of the regulating molecule in order to achieve the same level of regulation, as achieved by the two other mechanisms. The regulation by sRNA has stoichiometric properties. We found that it may be effective for the regulation of a few dozens of genes, depending on the relative production rates of the sRNA and target RNA molecules. Our analysis indicates that the half-life of the sRNA–mRNA complex and the ratio of their production rates determine the steady-state level of the target protein, suggesting that regulation by sRNA may provide fine-tuning of gene expression. In regard to response time, protein–protein interaction provides the fastest response to external stimuli, transcription regulation provides the slowest response and sRNA regulation is intermediate. However, when the regulator's synthesis is induced upon an external signal, there is a time interval where regulation by sRNA provides the fastest response to external stimuli (Figure 1). This stems from its relatively quick synthesis relative to protein synthesis. We also analyzed the recovery time upon shutoff of the external signal. Two parameters determine the recovery time in case of regulation by sRNA: (1) the ratio between the production rates of the regulatory sRNA and target mRNA and (2) the degradation rate of the sRNA. For a wide range of these parameters, our analysis indicates that regulation by sRNA leads to a fast recovery. Thus, for stimuli that require fast responses in a short time interval, regulation by sRNA may be advantageous, as, for example, under transient stress conditions. Indeed, most of the regulatory sRNAs with known function are induced in response to various stress conditions. As some of the sRNAs are known to regulate the translation of transcriptional regulators, and the production of the sRNAs themselves is transcriptionally regulated, it is conceivable that there are regulatory modules that integrate these two levels of regulation. Indeed, our integrative analysis of the transcription regulation network and post-transcriptional regulation network identified interesting combinations of the two levels of regulation in regulatory circuits (Figure 6). These include mixed feed-forward loops (Figure 6A) and mixed feedback loops (Figure 6B). It is intriguing to understand the advantage of a regulatory circuit that involves the two levels of regulation in comparison to an equivalent circuit that involves only transcription regulation. To this end we examined the feed-forward loop OmpR-MicF-ompF (Figure 6) in comparison to an equivalent feed-forward loop containing only transcriptional regulators. In this feed-forward loop, a regulator A activates a second regulator B, and they both repress the target gene c. In loops composed of transcriptional regulation, both A and B are transcriptional regulators, while in the mixed feed-forward loop found in the E. coli network A is a transcriptional regulator (OmpR) and B is an sRNA (MicF). The mixed feed-forward loop provides tighter regulation, because it blocks both transcription and translation of the target gene. Thus, if a few transcripts escape the repression by the transcriptional repressor, they will be blocked post-transcriptionally by the sRNA. It is advantageous also in regard to response time, because for a wide range of parameters it provides both a fast shutdown of the target gene upon an external signal and a fast recovery when the signal has terminated. These properties make this circuit a preferred regulatory module when fast responses to changes in the environment are needed, and further emphasize the advantages of sRNAs in responses to stress conditions. Introduction Living cells are self-regulated by interactions between different molecules. Until very recently, most research has focused on transcription regulation interactions and on protein–protein interactions, which in many cases are involved in post-translational regulation. During the last years it has become evident that another type of interaction plays a prominent role in the regulation of cellular processes, manifested by small RNA (sRNA) molecules that base pair with the mRNA and regulate gene expression post-transcriptionally. This mode of regulation was found in both pro- and eukaryotes (for review see Storz et al, 2005). Although there are differences in the characteristics of the eukaryotic and prokaryotic regulatory RNAs and in the fine-details of their mechanism of action, both exert their regulatory function mostly by base pairing with the mRNA and influencing translation or mRNA stability. It is intriguing to study the properties of this type of regulatory interactions in comparison to the other types of interactions, and to understand their integration in the cellular circuitry. In this paper we focus on bacterial sRNAs, and particularly on regulatory interactions found in Escherichia coli, for which most experimental data on sRNAs are available. At present there are about 80 known sRNAs in E. coli (for review see Gottesman, 2005; Storz et al, 2005). These molecules are 50–400 nucleotides long and many of them are evolutionary conserved (Hershberg et al, 2003), hinting to their important roles in the cellular mechanisms. Still, for many of the sRNAs, their cellular and molecular functions have not yet been determined. Many of those, for which some functional knowledge has been acquired, were often shown to act as inhibitors of translation by base pairing with the mRNA in the ribosome-binding site (for review see Gottesman, 2005). However, in E. coli there are also a couple of examples where the sRNAs play a role as translational activators, promoting ribosome binding to the mRNA by exposing its binding site (Majdalani et al, 1998, 2001; Prevost et al, 2007). In many cases the sRNA–mRNA interactions are assisted by the RNA chaperone Hfq (Valentin-Hansen et al, 2004). The acknowledgment that post-transcriptional regulation by sRNAs is a global phenomenon has raised many interesting questions and speculations regarding their roles in the cellular regulatory networks. It was suggested that it would be cost-effective for the cell to use this mode of regulation, because these molecules are small and are not translated, and therefore the energetic cost of their synthesis is smaller in comparison to synthesis of regulatory proteins (Altuvia and Wagner, 2000). The ease of synthesis led to the suggestion that it would be beneficial for the cell to use these molecules for quick responses to environmental stresses. In this paper we describe this regulatory mechanism by dynamical simulations, and analyze quantitatively these intuitive conjectures. Furthermore, we compare the properties of post-transcriptional regulation by sRNA–mRNA base pairing to those of transcriptional regulation by protein–DNA interaction and post-translational regulation by protein–protein interaction. We show that there are measurable differences between the three regulation modes and describe the situations when regulation by sRNA is advantageous. The interactions between molecules within the cell can be described as a network in which nodes represent genes (or their products) and edges represent the interactions between them. Recently, a considerable effort has been put in deducing the structure of these networks from experimental data, aiming at a systematic understanding of regulation mechanisms and cell function (Milo et al, 2002; Shen-Orr et al, 2002; Yeger-Lotem et al, 2004). Here we describe the network of post-transcriptional regulation by sRNAs in E. coli, where nodes represent either sRNA genes or their targets, and edges point from sRNA genes to their targets. By integrating this network with the transcription regulation network, we discover intriguing regulatory circuits involving both transcriptional regulation and post-transcriptional regulation. The different properties of transcription regulation and regulation by sRNAs have important implications in these mixed regulatory circuits. We demonstrate this by comparing analogous feed-forward loops that are either composed of transcription regulation per se or involve also regulation by sRNA. Results and discussion We analyze different types of regulation of gene expression mediated by three different interaction types, protein–DNA, protein–protein and sRNA–mRNA. To this end we described the regulatory mechanisms involving these interactions by mathematical models, followed by simulations, using average kinetic parameters based on experimental data (Altuvia et al, 1997; Altuvia and Wagner, 2000; Alon, 2006). We distinguished between two scenarios. In the first scenario, we assumed that the products of both the regulated gene (target) and the regulator are already present in the cell when an external signal turns on the regulation. In the second scenario, the target protein is already present when an external signal turns on the synthesis of the regulator. For both scenarios we compared the kinetics of regulation mediated by protein–DNA, protein–protein or sRNA–mRNA interaction. We describe in some detail the modeling of regulation by sRNA. Let the sRNA transcription rate be gs (molecules/second), and the target mRNA transcription rate be gm (molecules/second). The target mRNAs are translated into proteins at a rate gp. The degradation rates are ds, dm and dp, for the sRNAs, mRNAs and proteins, respectively. The sRNA base pairs with the target mRNA at a rate α. The base pairing blocks the binding of the ribosome to the mRNA, thus negatively regulating translation. This system is described by the following rate equations: where Ns, Nm and Np are the number of sRNA, mRNA and protein molecules per cell, respectively. In the analysis below, these equations are solved by direct numerical integration starting from suitable initial conditions, as specified. A similar model was recently used for the analysis of regulation by the sRNA RyhB (Levine et al, 2007). Analogous equations are used in the analysis of transcriptional regulation by protein–DNA interaction and post-translational regulation by protein–protein interaction. The parameters used in the simulations are based on experimental measurements in E. coli (Altuvia et al, 1997; Altuvia and Wagner, 2000; Alon, 2006). The transcription rate of mRNAs was taken to be gm=0.02 (molecules/second). Based on the high abundance of sRNAs, we assumed an average transcription rate of gs=1 (molecules/second), 50 times faster than that of mRNAs. The high abundance of sRNAs may be due to duplicated copies of their genes (Wilderman et al, 2004), strong promoters or high stability (Altuvia and Wagner, 2000). This difference in transcription rates is supported by experimental results obtained with oxyS (Altuvia et al, 1997). The translation rate was taken as gp=0.01 (s−1). The degradation rates for sRNAs, mRNAs and proteins were taken as ds=0.0025, dm=0.002 and dp=0.001 (s−1), respectively. The rate constants for binding of sRNA to mRNA, regulatory protein to promoter and protein to protein were all taken as α=1 (s−1/molecule). It should be noted that we ran the simulations for a range of biologically relevant parameters around these average values and obtained similar conclusions. In Figure 1 we present for each regulation type the level of the target protein versus time, starting from the time at which the regulation is turned on. At time t=0, a sudden change in the external conditions turns on the regulation. In case of transcriptional regulation, the regulatory protein binds to the promoter of the target gene and represses its transcription. In case of post-translational regulation mediated by protein–protein interaction, regulator proteins bind to the target proteins and form complexes, which do not exhibit the activity of the free target proteins (they may be degraded, as in the case of E. coli σ32, which is targeted to degradation by the binding of DnaKJ proteins; Straus et al, 1990). In case of post-transcriptional regulation by sRNA, the sRNA molecules bind the transcripts of the target gene and inhibit their translation. In these simulations it is assumed that the complex of regulator and target molecules does not dissociate back to its original components (Masse et al, 2003). We discuss below the case in which such dissociation takes place, and its effects. Figure 1.Repression of a single gene. Shown is the level of the target protein (number of molecules) versus time: transcriptional regulation (dashed line), post-translational regulation by protein–protein interaction (dashed-dotted line) and post-transcriptional regulation by sRNA (solid line). (A) Both the regulator and the target molecules are present in the cell when the regulation is turned on in response to an external stimulus. The post-translational regulation by protein–protein interaction results in a much faster response than the two other mechanisms. (B) The target protein is present while the regulator is produced in response to an external stimulus. The response mediated by sRNA regulation is the fastest at a time interval immediately after the stimulus takes place. Download figure Download PowerPoint The two panels in Figure 1 differ in their initial conditions. In Figure 1A both the regulator and the target are already present in the cell when the regulation is turned on due to some external stimulus. In Figure 1B the regulator is initially absent and is produced due to an external stimulus, while the target gene is expressed independent of the stimulus. The first scenario may be regarded as turning the regulator on by a conformational change exerted by the external stimulus (e.g., phosphorylation of OmpR by EnvZ under high osmolarity; Pratt et al, 1996). In the second scenario, the regulator's synthesis is turned on following the stimulus (e.g., induction of synthesis of the sRNA OxyS by OxyR under oxidative stress; Altuvia et al, 1997). When both the regulator and the target are present in the cell, protein–protein interaction provides the fastest response to the external stimulus (Figure 1A). The regulator proteins are available to carry out the regulation and they quickly bind to the target proteins and suppress their activity. When the regulation is mediated by sRNA–mRNA base pairing, the sRNA molecules quickly bind to the mRNA molecules and prevent their translation. However, the already present target proteins are active until they degrade. As a result, the regulation by sRNA results in a slower response than that exerted by protein–protein interaction. In case of transcriptional regulation, the regulatory protein binds the promoter of the target and represses its transcription. However, the target proteins that are already present are active until they degrade. Moreover, already transcribed mRNA molecules continue to be translated into proteins until they degrade too. As a result, transcriptional regulation leads to the slowest response. We now turn to analyze the second scenario, in which the regulator is produced in response to the external signal while the target protein is already present. In case of transcription regulation, the regulation process remains virtually the same as in Figure 1A and even slower. This is because at the time of the stimulus the regulatory protein is absent and needs to be transcribed and translated. The post-translational regulation by protein–protein interaction results in a faster response. Once the regulatory proteins are formed, they bind to the target proteins and deactivate them, regardless of the degradation times. However, in this situation, unlike the previous scenario, the regulatory proteins are not available at t=0 to carry out the regulation, and therefore the response time depends on their production rate. The response time in case of regulation by sRNA is intermediate. It consists of the time it takes to produce the sRNA molecules and the degradation time of the target proteins that remain after the sRNAs bind to their target mRNAs and suppress their translation. However, since sRNA production rate is extremely fast, the kinetics of the regulation by sRNAs in both scenarios is very similar. It is noteworthy that shortly after the regulation is turned on, no mRNA molecules of the regulatory proteins are present. Thus, the initial production rate of regulatory proteins is much lower than that of sRNAs. As a result, shortly after t=0, regulation by sRNA exerts a faster response than regulation by protein–protein interaction. Hence, when the regulator is not present in the cell and a fast response is needed in a short time interval, such as upon an external stress, regulation by sRNA has an advantage over the two other regulation types. Indeed, several of the sRNAs with known functions play a role in response to sudden changes in environmental conditions (Altuvia and Wagner, 2000). These include OxyS that is induced in response to oxidative stress and regulates ∼40 genes, as suggested by genetic screens (Altuvia et al, 1997), and RyhB that is induced in response to iron depletion and regulates genes involved in iron metabolism (Masse et al, 2007). Another difference between the various regulation mechanisms is considered below. In case of transcriptional regulation, a single bound repressor is sufficient to shutdown the expression of the target gene. In this case, the regulation effectiveness does not depend on the transcription rate of the target gene. It depends only on the production rate of the regulatory protein and on its binding/dissociation rates to the promoter of the target. Thus, with suitable binding/dissociation rates, transcriptional regulation enables using a protein of low concentration to regulate a protein of high concentration. In case of protein–protein interaction, the regulation effectiveness is determined by the relative production rates of the regulator and target proteins. If the production rate of the regulatory protein is faster than that of the target protein, the regulation will be very effective. On the other hand, when the production rates of these two proteins are comparable, it enables fine-tuning of the regulation strength, which is not possible in transcriptional regulation. A similar property characterizes regulation by sRNA. The regulation effectiveness strongly depends on the relative production rates of the sRNA and the target mRNA. Since the rate of production of sRNAs is up to two orders of magnitude faster than of typical mRNAs, it enables effective regulation. It also enables a single sRNA-encoding gene to regulate dozens of other genes. As long as the sRNA is produced at a faster rate than the combined production rate of all the target mRNAs, the regulation is strong. It gradually weakens when the combined production rate of the target mRNAs exceeds that of the sRNA. As an example, we consider an sRNA-encoding gene that regulates n other genes. In this case, the rate equations shown above are modified such that the second and third equations are copied into n equations, accounting for the number of sRNA molecules and the number of protein molecules of each of the n target genes. In addition, the first equation is modified such that Nm is replaced by the total number of mRNA molecules of all the target genes. For simplicity, the parameter values of all the target genes are taken to be identical. In Figure 2 we present the number of molecules of each of the target proteins versus n. In this example, when n exceeds 50, the regulation weakens and the number of molecules of each target protein increases. Indeed, there are a few examples where a single sRNA-encoding gene regulates several genes involved in the same physiological process, hinting for the existence of sRNA regulons in accord with the regulons governed by transcriptional regulatory proteins (Altuvia, 2004). Our results suggest that for appropriate relations between the production rates of the regulator sRNA and its target genes in the regulon, the simultaneous regulation of these genes will be very effective. The applicable parameter range for production rates of sRNA and mRNA in E. coli suggests that in order to be effective, such a regulon should contain only several dozens of genes. In general, the targets may differ from each other in their transcription and translation rates, as well as in their affinities to the sRNA. These differences may provide a hierarchy of regulation. Figure 2.A single sRNA-encoding gene may be responsible for the regulation of many genes. Shown is the protein level (number of molecules) of each of the n target genes regulated by a single sRNA-encoding gene. Here, the production of the sRNA is 50 times faster than that of each of the target mRNAs. In this case, as long as n<50, the regulation is effective. It gradually weakens as n exceeds 50, and the level of each of the target proteins increases. Download figure Download PowerPoint Kinetic studies indicated that the sRNA–mRNA complexes might dissociate back into their original components (Argaman and Altuvia, 2000; Wagner et al, 2002), with dissociation rates γ in the range between 0.02 and 0.1 s−1, which is much faster than the degradation rate of the complex. To address this additional scenario, we added one more equation to the model, which accounts for the copy number Nx of the complex. This equation takes the form dNx/dt=αNsNm−(dx+γ)Nx, where dx is the degradation rate of the complex. For simplicity, we chose the degradation rate of the complex to be equal to that of the free mRNA molecule, namely dx=dm. In addition, we added the term +γNx to the equations that describe the time derivatives of Ns and Nm. As the dissociation rate increases, the regulation effectiveness is reduced. As a result, there are more mRNA molecules available for translation into proteins, and the protein level increases. In Figure 3 we present the number of the target protein molecules Np versus the dissociation rate of the complex γ for four different values of the ratio between the production rates of the sRNA and target mRNA, gs/gm. When sRNAs are produced much faster than mRNAs, there is a large surplus of sRNAs and the regulation remains strong even when dissociation takes place. However, when the sRNA production rate is close to that of the mRNA, even small dissociation rates significantly weaken the regulation and the protein level increases. Delicate control of the dissociation rate enables fine-tuning and maintenance of the target protein level at a desired steady-state level. Figure 3.Effect of sRNA–mRNA dissociation. Shown is the target protein level (number of molecules) versus the dissociation rate of the sRNA–mRNA complex. Four different ratios of sRNA to mRNA production rates (gs/gm) were considered. When the ratio is high, the regulation remains effective even when dissociation takes place. However, when the ratio is low, dissociation significantly reduces the effectiveness of the regulation. Download figure Download PowerPoint Another post-transcriptional regulation mechanism is manifested by mRNA-binding proteins (or meta