Jeffrey J.
Tabor‡
a,
Travis S.
Bayer
b,
Zachary B.
Simpson
a,
Matthew
Levy
a and
Andrew D.
Ellington
*ac
aCenter for Systems and Synthetic Biology and Institute for Cell and Molecular Biology, University of Texas at Austin, Austin. E-mail: andy.ellington@mail.utexas.edu
bDivision of Biology, California Institute of Technology, Pasadena, California 91125
cDepartment of Chemistry and Biochemistry, University of Texas at Austin, Austin, TX 78712
First published on 1st May 2008
Stochastic fluctuations (noise) in gene expression can cause members of otherwise genetically identical populations to display drastically different phenotypes. An understanding of the sources of noise and the strategies cells employ to function reliably despite noise is proving to be increasingly important in describing the behavior of natural organisms and will be essential for the engineering of synthetic biological systems. Here we describe the design of synthetic constructs, termed ribosome competing RNAs (rcRNAs), as a means to rationally perturb noise in cellular gene expression. We find that noise in gene expression increases in a manner proportional to the ability of an rcRNA to compete for the cellular ribosome pool. We then demonstrate that operons significantly buffer noise between coexpressed genes in a natural cellular background and can even reduce the level of rcRNA enhanced noise. These results demonstrate that synthetic genetic constructs can significantly affect the noise profile of a living cell and, importantly, that operons are a facile genetic strategy for buffering against noise.
Synthetic biology, or the (re)construction of gene networks with defined performance characteristics, has proven a useful paradigm for studying the principles which govern cellular function.24–26 Several synthetic studies have demonstrated that noise can drive identical gene regulatory networks to encode significant variation across cell populations.6,27–32 It is now apparent that the study and construction of genetic regulatory circuits will require strategies for understanding and controlling intracellular noise. To this end, we constructed ribosome-competing RNA constructs (rcRNAs) as a synthetic biological tool to study the impact of synthetic genetic elements on noise in living cells, and to begin to develop methods for engineering noise buffering.
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Fig. 1 rcRNA strategy for ribosome competition. (a) Cellular mRNAs (blue) associate with ribosomes probabilistically at a rate dependent upon the concentration of the mRNA and ribosomes in that cell. (Top) “Wild-type” gene expression scenario. (Bottom) An exogenous rcRNA competes with bulk cellular mRNAs for translational machinery. A reduction in the number of available ribosomes results in a decreased probability of a given cellular mRNA associating with a ribosome. (b) A series of rcRNAs based on rc1 (shown) were engineered to contain ribosome binding sites (green) of increasingly single-stranded nature. Mutations of 2, 3, 4 or 6 mismatches were made in the antisense stem (bracketed) such that the region in and around the RBS became destabilized. (c) In-line probing of the rcRNA structure. Each rcRNA molecule was transcribed in vitro. The 5′-most nucleotide was then radioactively labeled with a phosphate group containing a 32P atom and the RNAs were incubated under conditions which promote spontaneous hydrolysis of the phosphodiester backbone.37 Nucleotide residues which tend to be single-stranded undergo hydrolysis significantly faster than nucleotides involved in a base pair. Hydrolysis results in two truncated RNAs, a labeled 5′-fragment and the remaining 3′ fragment. The RNA population is then separated on a polyacrylamide gel matrix and runs as a pattern of degradation products of different sizes, based on the position of the hydrolysis event relative to the 5′-end of the RNA. Nucleotide residues which are more single stranded show up as more intense bands while residues which are more double stranded show less intense bands. Equivalent counts of 32P containing RNA were added to each lane of a 10% denaturing polyacrylamide gel. Schematic of the rcRNA transcript is shown at right. (d) In vitro translation competition assays. A fixed amount of gfp DNA template was added to a coupled in vitro transcription–translation reaction along with increasing molar ratios of rcRNA DNA templates. The unpaired rcRNA, rc6, is represented as grey bars while rc1 is represented as white bars. Data are normalized to a gfp only control. Error bars represent 95% confidence intervals derived from 3 experiments run in parallel. |
To examine whether rcRNAs could directly compete for translation in a manner dependent on the availability of the RBS, we performed two in vitro assays. First, the predicted secondary structures of rcRNAs were verified by limited hydrolysis of single-stranded regions (in-line probing).37 As predicted, an rcRNA variant engineered to have a perfectly base-paired RBS/anti-RBS stem (rc1), showed the greatest double-strandedness in both regions (Fig. 1c). Thus, rc1 was predicted to have minimal capacity for translation competition amongst the rcRNA series. As bulges were designed into the anti-RBS stem in different rcRNA variants, both the RBS and anti-RBS regions became less structured, rationally allowing for increased ribosome binding (Fig. 1c).
Second, to directly assay the functionality of the rcRNAs, we performed translation competition assays in a reconstituted E. coli lysate (Experimental). A fixed amount of reporter gene (gfp) DNA template was added to a coupled in vitro transcription–translation reaction and increasing amounts of DNA templates for either a highly structured (rc1) or a largely unstructured (rc6) rcRNA were added to the reaction. Interestingly, addition of either rcRNA template at low levels resulted in an increase in GFP protein abundance. This effect is likely due to stabilization of the gfpmRNAtranscript through RNAse competition. The rc1 template continued to increase GFP abundance when added at a 5 : 1 ratio relative to the gfp template, but resulted in no further increase in GFP production when added at a 10 : 1 ratio. In sharp contrast, the addition of increasing amounts of the relatively unstructured rc6 template resulted in a strong inhibition of translational capacity (Fig. 1d). These results demonstrated that rcRNAs are capable of reducing translation rate in a manner proportional to the availability of the RBS sequence within the engineered helix.
To measure the effect of ribosome competition on noise, we then followed the expression of a gfp gene in individual E. coli cells using flow cytometry. Individual rcRNA constructs that could compete for ribosomes to different extents were introduced into E. coli, and rcRNA expression was driven to high levels by T7 RNA polymerase (see Experimental). Noise was quantified as the standard deviation in GFP abundance divided by the mean over the cell populations, a metric also known as the coefficient of variation (CV). As predicted, the availability of the rcRNA RBS showed a strong positive correlation with noise, and a strong negative correlation with GFP abundance (Fig. 2a, Table 1). It is worthy to note that though the availability of the RBS from the in-line probing experiments had a largely predictable effect on in vivo expression data, the correlation between the two experiments was not perfect. For example, though rc1 appears to have a less available RBS than rc2 and rc3 (Fig. 1c), rc1 expression results in lower GFP abundance and a slightly higher noise profile than does the expression of rc2 and rc3 (Table 1). The RBS of rc4, however, was shown to be the most available in the in-line assays, and rc4 indeed resulted in the lowest GFP abundance and the greatest amount of noise in vivo. As was the case with the in vitro translation reactions above, expression of the highly structured rcRNAs (rc1–3) generally led to increased GFP abundance, while expression of the mostly unstructured rcRNAs (rc4, 5 and 6) decreased GFP.
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Fig. 2 The effect of ribosome competing RNAs (rcRNAs) on gene expression. (a) Flow cytometric histograms of E. coli populations expressing GFP as well a single rcRNA variant. rcRNA variants expressed (near to far): rc1, rc3, rc2, wild-type (uninduced cells carrying the plasmid for rc1), rc6, rc5, rc4. (b) relative GFP fluorescence versus noise (standard deviation divided by mean protein abundance) from the flow cytometry data in panel ‘a’. The data were fit to an equation of the form y = axb where the value of b is −0.83 (dashed line). Three cultures of each sample were grown in parallel and assayed under the conditions described in the Experimental. Error bars represent 95% confidence intervals. (c) Relative abundance of gfpmRNA in E. coli treated with 200 ng mL−1 anhydrotetracycline (aTc) as compared to cells treated with no inducer in the presence of a high affinity (rc6) or low affinity (rc1) rcRNA. RNA was prepared and quantitated from three cultures grown in parallel under the conditions described in Experimental. Error bars represent 95% confidence intervals. |
Construct | Designed bulges | Relative GFP (×10−1) | Noise (×10−1) |
---|---|---|---|
Wild-type | — | 5.8 ± 0.1 | 2.7 ± 0.1 |
rc1 | 0 | 10 ± 0.1 | 5.3 ± 0.04 |
rc2 | 2 | 7.6 ± 0.9 | 4.1 ± 0.7 |
rc3 | 4 | 9.1 ± 0.1 | 4.9 ± 0.1 |
rc4 | 4 | 0.4 ± 0.1 | 55 ± 3.5 |
rc5 | 6 | 0.8 ± 0.1 | 28 ± 4.2 |
rc6 | 6 | 1.2 ± 0.007 | 28 ± 0.2 |
The in vivo rcRNA expression data showed that noise and GFP abundance obeyed an inverse power law relationship (Fig. 2b), a scaling previously observed in studies in which the rate of transcription was modulated.17,18 Given that mRNAs are known to compete for cellular ribosome pools38 and that highly expressed mRNAs are capable of sequestering nearly all cellular ribosomes,39 one interpretation of these results is that rcRNA-induced noise results from probabilistic mRNA–ribosome interactions which become infrequent as ribosomes become rare. It is very important to note, however, that there are many downstream effects resulting from the initial competition for ribosomes which likely contribute to the noise profiles observed in our experiments. For example, competition for ribosome pools would reduce the expression level of all cellular proteins , including RNA polymerases. A reduction in RNA polymerase abundance should itself increase total noise in gene expression, due to a reduction in transcription rate. Though it has previously been shown that noise scales inversely with net expression level,8,40 the results of these experiments demonstrate that the same scaling can arise from ribosome competition as a result of the expression of exogenous genes.
Our results are also consistent with the hypothesis that inefficiently translated rcRNAs function as RNAse competitors, stabilizing the gfpmRNA, increasing GFP abundance and in turn decreasing noise in GFP levels. The intracellular stability of mRNAs is thought to be governed in part by a competition between ribosome binding and RNAseE-induced degradation.41 We therefore expected that when ribosome competition increases, mRNA levels should decline, increasing the significance of mRNA fluctuations and therefore noise in protein levels. To examine this effect, we quantitated gfpmRNA levels in strains expressing different rcRNAs. Consistent with the mRNA stability model, the magnitude of induction of gfpmRNA (see Experimental) was approximately 12-fold lower in E. coli expressing an rcRNA that could effectively compete for ribosomes as compared with a control rcRNA with an occluded RBS (Fig. 2c). This is likely a combined effect of ribosome competition and lower mRNA half-life. These two factors can feedback on one another, as the observed decline in mRNA levels should further reduce the frequency of mRNA–ribosome interactions, which could in turn reduce transcriptional or metabolic capability and thus mRNA production. The possibility that such a noise amplification cascade exists would argue strongly that there should be mechanisms for the reduction or regulation of noise in cells.
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Fig. 3 Noise in monocistronic and bicistronic expression platforms. (a) Schematic for engineered monocistronic (left) or bicistronic (right) CFP/YFP expression platforms. The monocistronic plasmid, pASKJ13065, was assembled such that both genes were expressed from the same TetR-repressed promoter (Ptet), carried the same RBS (green), and were followed by the same transcription terminator (not shown after YFP). On both plasmids, both genes contained double stop codons at the end of the respective open reading frame (asterisks shown in bicistronic version). The bicistronic plasmid, pASKJ13004, was designed exactly as pASKJ13065 except that both genes were expressed from a single Ptet promoter. (b) Intrinsic noise of mono and bicistronic constructs. E. coli carrying the monocistronic (black squares) and bicistronic (red dots) plasmids were induced to different extents by the addition of increasing amounts of the inducer anhydrotetracycline (aTc; between 0 and 500 ng mL−1) to the media. Reporter gene expression and noise were calculated as described in the Experimental. Relative fluorescence indicates CFP values from which E. coli autofluorescence and YFP bleedthrough were corrected. The data used to compare low level expression from the two constructs is the point at which each produces 0.012 relative fluorescence units (2nd point from left in each data set). Lines connecting data points are guides to the eye. (c) CFP fluorescence and intrinsic noise data in E. coli populations expressing mono or bicistronic CFP and YFP as well as the noisy rcRNA, rc6. High level CFP/YFP expression was matched between cells carrying the two constructs by inducing pASKJ13004 carrying cells with 250 ng mL−1 aTc (equivalent to green arrow in Fig. 3b) and pASKJ13065 carrying cells with 37.5 ng mL−1 aTc. Noise was quantitated from three cultures grown in parallel. Error values represent 95% confidence intervals. |
E. coli populations carrying the monocistronic and bicistronic constructs were grown with increasing concentrations of the transcriptional inducer anhydrotetracycline (aTc) and populations were analyzed by multi-channel flow cytometry (see Experimental). In agreement with the model,11 the bicistronic expression platform resulted in significantly less noise than the monocistronic version over a large range of low protein expression levels (Fig. 3b). To directly compare the noise profiles of two platforms, the mono and bicistronic constructs were induced to the same level of protein expression (0.012 relative fluorescence units) and the noise was quantified. At this low expression level, the operon showed ∼70% less noise than the monocistronic construct (Fig. 3b).
The mathematical model,11 predicts that the noise buffering properties of the operon will be greatest at low protein expression levels, and that noise in mono and bicistronic systems will converge as protein abundance increases. We investigated this prediction by varying protein expression from each construct from very low to very high levels. In agreement with the model, the noise output of the two constructs converged as protein abundance increased, and that at the highest expression levels the operon had no noise buffering effect (Fig. 3b). The contribution of mRNA fluctuations to noise is expected to be greatest at low expression levels, and this was also the regime under which the operon served to most efficiently buffer noise. At higher expression levels, mRNA fluctuations become less significant, diminishing the noise buffering effect of bicistronic encoding. Taken together, the noise trends of the mono and bicistronic constructs are in strong agreement with the predictions of the model over the protein abundances in our experiments.
We then introduced rcRNAs with the mono and bicistronic reporter constructs in order to assay the tolerance of the different expression systems to genetically-encoded noise. To better compare the mono and bicistronic systems, we induced both to the same high expression level, where their respective noise levels were low. This was achieved by inducing cells carrying the bicistronic plasmid to the maximal expression level (Fig. 3b) and varying the aTc concentration added to cells carrying the monocistronic plasmid to achieve the same level of fluorescence. In this high expression regime, noise in the monocistronic construct is naturally lower than the bicistronic version (Fig. 3b, green arrow). Even so, the introduction of a noisy rcRNA, rc6, resulted in 20% more noise in the monocistronic construct than in the bicistronic version (Fig. 3c). That is, the operon was significantly more tolerant to rcRNA induced noise than the monocistronic construct. These results bolster the hypothesis that operons function as genetic noise insulators, and demonstrate their efficacy in buffering artificially enhanced noise resulting from the expression of foreign genes.
Here we show that noise in gene expression can be rationally engineered and that noise is strongly affected by the introduction of exogenous mRNAs which compete for ribosomes. Previous studies have shown that bulk cellular mRNAs compete for access to the ribosome,38 and that highly expressed mRNAs are capable of occupying nearly all cellular ribosomes.39 Our artificial ribosome-depleted states may mimic naturally occurring cellular states that arise when certain genes, such as those involved in stress response, are expressed to high levels or translated preferentially.47–54 The relationship between competition for translation and noise may prove to be universal for any of a number of factors required for gene expression (e.g., transcription factors), a hypothesis that is directly amenable to experimental evaluation. Indeed, competition for ribosomes may affect the translation and availability of other gene expression machinery and thereby initiate feedback cascades that further exacerbate noise in gene expression.
We also demonstrate that operons strongly buffer against noise in the coexpression of multiple genes, reducing noise ∼70% (nearly 4-fold) at low expression levels. While the cotranscription of multiple genes on a single mRNA undoubtedly reduces against noise generated by random promoter transitions and mRNA birth and death events,3–5,10 we demonstrate here that operons also buffer against noise arising from diminished ribosome pools. The noise buffering properties of the operon were particularly strong at low expression levels in our experiments. These low expression levels are likely to be more representative of natural protein abundances than the higher expression levels, as the genes were expressed from a multicopy plasmid. This suggests that noise buffering may be a relevant property of many natural operons. It is possible, then, that operons represent a reliable strategy by which cells can stoichiometrically couple the expression of multiple genes, even in translationally compromised or stressed environments.
The finding that operons have noise buffering properties has fundamental evolutionary implications. Though operons are one of the most ubiquitous forms of gene organization in nature, they are relatively unstable over evolutionary time. Operons frequently decompose into multiple genetic loci which are regulated by the same transcription factors, but are independently transcribed and translated.55 The most stable operons share an intriguing feature: they encode genes whose products physically interact.56 When gene products interact (e.g. in multi-protein complexes), stoichiometric coupling becomes critical, as the over- or underproduction of any single product will squander cellular resources and have deleterious fitness effects, and polycistronic encoding may provide a strong selective advantage against these effects.
Our results are also relevant to many approaches in synthetic biology. The engineering of synthetic biological systems with complex behaviors is proving to be challenging, as noise in the expression of certain gene products thwarts efforts to forward engineer deterministic phenotypes.29 Moreover, as the size (in DNA base pairs) of the synthetic biological constructs introduced into living cells is increased, competition for cellular resources, including ribosomes, will become greater and noise will concomitantly increase. Our experiments suggest that operons may offer a robust design strategy for those attempting to engineer synthetic pathways and behaviors that require reliable stoichiometric coupling of multiple gene products (such as the efficient production of foreign metabolites).57
Ultimately, these results nicely emphasize the increasing crossover between systems and synthetic biology. It was a synthetic tool (and orthogonal noise generator) that allowed the rational manipulation of the noise inherent in genetic expression. Novel synthetic circuits are frequently at the mercy of the cellular backgrounds in which they are implanted, and it is thus difficult to predict and model their performance. Our synthetic tool can now also be applied to any synthetic circuit, acting as an perturbant to determine whether and to what extent the cellular machinery is taxed by a synthetic circuit (and vice versa).
The CFP/YFP expression plasmids were constructed using MIT’s registry of standard biological parts (http://parts.mit.edu) and DNA isolation, purification and ligation methods as described above. The host plasmid pSB4A3 (MIT’s registry of standard biological parts) was used for construction of the monocistronic (J13065) and the bicistronic constructs (J13004). pSB4A3 bears a pSC101 origin of replication (∼5 copy) and an ampicillin resistance marker. 25 μg mL−1ampicillin was used to maintain all pSB4A3-based plasmids. For noise assays, J13065 and J13004 were cloned into pASKIBA3 and expressed under the control of TetR.
To build the monocistronic construct (J13065), a DNA sequence bearing the Ptet promoter (R0040), a strong RBS (B0034), an ECFP coding sequence (E0020) and a strong transcriptional terminator (B0015) was cloned into pSB4A3 using the restriction enzymes EcoRI and PstI. A second strong transcriptional terminator (B0015) was cloned downstream of the first. Finally an EYFP expression cassette carrying the Ptet promoter, strong RBS (B0034), EYFP gene (E0030) and transcription terminator (B0015) were cloned downstream of the tandem transcription terminators using standard biobrick assembly methods (http://parts.mit.edu). The ECFP/EYFP monocistronic expression construct (J13065) was cloned into the pASKIBA3 plasmid using the restriction sites PflMI and PstI. The primers jt_pflmI_bb_prefix_F: 5′-CGTAGACTAGACCACCATCGAATGGGAATTCGCGGCCGCTTCTAG-3′ and BBa_G00101: 5′- ATTACCGCCTTTGAGTGAGC-3′ were used to add an upstream PflMI site to the beginning of J13065 while maintaining the downstream PstI site from pSB4A3. The amplified DNA was digested and cloned into pASKIBA3 using methods described above.
The bicistronic CFP/YFP expression construct, J13004 carries the same promoter, RBS, coding sequences and transcriptional terminator as J13065 (Fig. 3a). It was constructed by cloning a DNA segment containing R0040, B0034 and E0020 upstream of a segment containing B0034, E0030 and B0015 as above. J13004 was then moved to pASKIBA3 as above.
Cell populations were gated away from other objects by a polygonal gate based on the dominant forward scatter/side scatter profile. Objects with fluorescence values at the lower limit of detection of the cytometer were discarded. The first and last 0.2 s of the data were then removed to eliminate cytometer flow rate variability.40
Footnotes |
† Electronic supplementary information (ESI) available: Supplementary Table 1 and details on reporter independence. See DOI: 10.1039/b801245h |
‡ Current address: Department of Pharmaceutical Chemistry, University of California – San Francisco, San Francisco, California 94158 |
This journal is © The Royal Society of Chemistry 2008 |