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• W2079892401 abstract "Article24 May 2011Open Access Adaptation by stochastic switching of a monostable genetic circuit in Escherichia coli Saburo Tsuru Saburo Tsuru Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Nao Yasuda Nao Yasuda Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan Search for more papers by this author Yoshie Murakami Yoshie Murakami Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Junya Ushioda Junya Ushioda Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Akiko Kashiwagi Akiko Kashiwagi Faculty of Agriculture and Life Science, Hirosaki University, Aomori, Japan Search for more papers by this author Shingo Suzuki Shingo Suzuki Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Kotaro Mori Kotaro Mori Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan Search for more papers by this author Bei-Wen Ying Bei-Wen Ying Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Tetsuya Yomo Corresponding Author Tetsuya Yomo Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan Exploratory Research for Advanced Technology (ERATO), Japan Science and Technology Agency (JST), Osaka, Japan Search for more papers by this author Saburo Tsuru Saburo Tsuru Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Nao Yasuda Nao Yasuda Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan Search for more papers by this author Yoshie Murakami Yoshie Murakami Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Junya Ushioda Junya Ushioda Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Akiko Kashiwagi Akiko Kashiwagi Faculty of Agriculture and Life Science, Hirosaki University, Aomori, Japan Search for more papers by this author Shingo Suzuki Shingo Suzuki Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Kotaro Mori Kotaro Mori Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan Search for more papers by this author Bei-Wen Ying Bei-Wen Ying Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Search for more papers by this author Tetsuya Yomo Corresponding Author Tetsuya Yomo Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan Exploratory Research for Advanced Technology (ERATO), Japan Science and Technology Agency (JST), Osaka, Japan Search for more papers by this author Author Information Saburo Tsuru1, Nao Yasuda2, Yoshie Murakami1, Junya Ushioda1, Akiko Kashiwagi3, Shingo Suzuki1, Kotaro Mori2, Bei-Wen Ying1 and Tetsuya Yomo 1,2,4 1Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Osaka, Japan 2Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan 3Faculty of Agriculture and Life Science, Hirosaki University, Aomori, Japan 4Exploratory Research for Advanced Technology (ERATO), Japan Science and Technology Agency (JST), Osaka, Japan *Corresponding author. Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan. Tel.: +81 6 6879 4171; Fax: +81 6 6879 7433; E-mail: [email protected] Molecular Systems Biology (2011)7:493https://doi.org/10.1038/msb.2011.24 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Stochastic switching is considered as a cost-saving strategy for adaptation to environmental challenges. We show here that stochastic switching of a monostable circuit can mediate the adaptation of the engineered OSU12-hisC Escherichia coli strain to histidine starvation. In this strain, the hisC gene was deleted from the His operon and placed under the control of a monostable foreign promoter. In response to histidine depletion, the OSU12-hisC population shifted to a higher HisC expression level, which is beneficial under starving conditions but is not favoured by the monostable circuit. The population shift was accompanied by growth recovery and was reversible upon histidine addition. A weak directionality in stochastic switching of hisC was observed in growing microcolonies under histidine-free conditions. Directionality and fate decision were in part dependent on the initial cellular status. Finally, microarray analysis indicated that OSU12-hisC reorganized its transcriptome to reach the appropriate physiological state upon starvation. These findings suggest that bacteria do not necessarily need to evolve signalling mechanisms to control gene expression appropriately, even for essential genes. Synopsis The fundamental mechanisms underlying adaptations can be divided into responsive switching and stochastic switching (Kussell and Leibler, 2005). Responsive switching is generally considered as resulting from evolved regulatory units, such as operons and regulons, which enable immediate adaptation (Jacob and Monod, 1961). However, as cells are subject to a wide range of both genetic and environmental perturbations that damage the specificity or efficiency of regulatory systems (Carroll, 2005; Crombach and Hogeweg, 2008), the limited number of regulatory units that can evolve and remain functional may not be sufficient to completely protect cell populations from the danger of extinction. Whether and how cells are able to survive external perturbations, when the corresponding regulatory units are absent or have been genetically disrupted, is an open question of great importance. Recent studies showed the stochastic switching provided cells a huge potential for sustenance under severe conditions via a so-called ‘bet-hedging’ strategy. The experimental evidence was generally based on a bistable genetic structure that fixed stochastically appearing fit state thus limiting further random switching (Kussell and Leibler, 2005; Acar et al, 2008). In contrast to bistable gene expression, monostable gene expression is much more common (Newman et al, 2006) and does not rely on a specific complex genetic architecture. Since a monostable structure has no fixation effect, the fit cells that would appear stochastically tend to return to the original steady state (i.e., unfit state). To achieve a population shift from a maladaptive state (but stable) to an adaptive state (but unstable), a significant increase in fitness (i.e., growth rate) of the fit cells is necessary. Otherwise, the random switching will mask occasionally occurring adaptive transitions and lead to an unchanged population at the stable but maladaptive state. Whether adaptation can be achieved by stochastic switching based on a monostable structure is however an open issue. To address this question, we applied an engineered E. coli strain, OSU12-hisC, carrying a foreign gene circuit encompassing a physiologically functional gene, hisC, replaced from its native chromosomal locus (Figure 1A). Consequently, hisC in OSU12-hisC is no longer responsive to the native regulation (His operon) that senses histidine depletion. Instead, the foreign gene circuit provided a monostable structure for hisC's stochastic switching. The green fluorescent protein (gfpuv5) was co-expressed with hisC for the quantitative evaluation of HisC in single cells. The upstream regulation of TetR, whose expression level was reported by the red fluorescent protein (dsred.T4), was introduced to achieve the inducible GFP (HisC) level. The full induction of TetR by IPTG was applied to avoid any possible upstream noise that caused by the abundance of endogenous LacI. Microscopic observation revealed that the OSU12-hisC cells showed stronger green fluorescence after histidine depletion (Figure 1B), which suggested an increased expression level of hisC. Population analysis using flow cytometry showed that the distributions of both GFP concentration and GFP bias (GFP/RFP ratio) in OSU12-hisC shifted towards a higher level in histidine-free conditions (Figure 1C and D), whereas, the depletion caused only a slight change in distributions of OSU11, a control strain carrying both the same engineered genetic circuit and an intact His operon, including the hisC gene in its native context. Repeated experiments revealed that the increases in both GFP concentration (∼2.1 folds) and GFP bias (∼1.5 folds) due to histidine depletion were highly significant (P<0.005, N=6) in OSU12-hisC. In particular, the increased GFP bias strongly suggested that the change in gene expression occurred specifically in the rewired hisC (i.e., GFP) but not in all genes (e.g., RFP). Furthermore, both the growth recovery accompanied population shift and the stress relaxation triggered restoration were clearly observed. It strongly indicated that the adaptation was mediated by stochastic switching of hisC under the monostable control. Analysis on microcolonies’ formation (Figure 4A) showed stochastic behaviour and directionality in individual cells. Variation in cellular GFP level was clearly observed in individual cells. Stochastic switching of hisC was verified according to the random changes in GFP bias along with the cell division under histidine-rich conditions (Figure 4B). On the other hand, the microcolonies formed under the histidine-free conditions tended to the higher level of GFP bias were observed (Figure 4B). The directional tendency favoured the high GFP (HisC) level was evidently detected in the first 2 h after histidine depletion, which resulted in a population shift (Figure 4C). In contrast, the distributions of microcolonies grown in histidine-rich conditions kept steady, due to the randomized directions of stochastic switching (Figure 4C). Further analysis showed that the stochastic fluctuations in the initial state had an important role not only in fate decision (i.e., whether to grow) but also in the directionality of the stochastic switch. Microarray analysis showed the adaptation of OSU12-hisC was resulted from the enhanced expression of the structural genes within the native His operon, along with the transcriptional reorganization of a large number of genes. In summary, in contrast to bistable structures, the monostable structure used here did not fix the phenotype but allowed the cells to decide where to go. Taken together, the findings suggest that bacteria do not necessarily need to evolve signalling mechanisms to control gene expression appropriately, even for essential genes. Introduction The fundamental mechanisms underlying adaptations can be divided into responsive switching and stochastic switching (Kussell and Leibler, 2005). Responsive switching is generally considered as resulting from evolved regulatory units, such as operons and regulons, which enable immediate adaptation (Jacob and Monod, 1961; Henkin and Yanofsky, 2002; Gama-Castro et al, 2008). However, as cells are subject to a wide range of both genetic and environmental perturbations that damage the specificity or efficiency of regulatory systems (Carroll, 2005; Crombach and Hogeweg, 2008), the limited number of regulatory units that can evolve and remain functional may not be sufficient to completely protect cell populations from the danger of extinction. Whether and how cells are able to survive external perturbations, when the corresponding regulatory units are absent or have been genetically disrupted, is an open question of great importance. The studies on persistence, competence and sustenance proposed a complementary mechanism relying on stochastic switching (Balaban et al, 2004; Kussell and Leibler, 2005; Suel et al, 2007; Acar et al, 2008). Through a so-called ‘bet-hedging’ strategy, stochastic switching generates population diversity without the need for a specific responsive regulatory system (Slatkin, 1974). Noise in gene expression causes cell-to-cell variation and provides a chance for survival of the cells that occasionally are in the fit state (Avery, 2006). As stochastic switching requires neither an evolved regulatory unit nor the maintenance of any signal transduction machinery, it is considered as a universal cost-saving strategy for adaptation. Such stochastic switching-mediated survival strategy was greatly supported by the studies employing cells whose default sensing system was deficient or missing (Kashiwagi et al, 2006; Stolovicki et al, 2006; Suzuki et al, 2006; Stern et al, 2007; Isalan et al, 2008). So far, the experimental evidence on stochastic switching was generally based on a bistable genetic structure (Kussell and Leibler, 2005; Kashiwagi et al, 2006; Stolovicki et al, 2006; Stern et al, 2007; Acar et al, 2008). Bistability allows the cells to stay at a stable state, either fit or unfit, so that the stochastically appearing fit state can be stabilized without further random switching to the unfit state (Supplementary Figure S1; Kussell and Leibler, 2005; Acar et al, 2008). Owing to the fixation effect of bistability, the stochastic switching-induced population transition was intensively characterized (Kussell and Leibler, 2005; Acar et al, 2008). Nevertheless, the universality of stochastic switching as an adaptation strategy remains open, as bistability is known to be a special case in native genetic structures. In contrast to bistable gene expression, monostable gene expression is much more common (Newman et al, 2006) and does not rely on a specific complex genetic architecture. Stochastic switching based on a monostable genetic structure has, however, not yet been identified as an accessible strategy for adaptation. Since a monostable structure has no fixation effect, the fit cells that appear stochastically tend to return to the original steady state (i.e., unfit state) (Supplementary Figure S1). To achieve a population shift from a maladaptive state (but stable) to an adaptive state (but unstable), a significant increase in fitness (i.e., growth rate) of the fit cells is necessary. Otherwise, the random switching will mask occasionally occurring adaptive transitions and lead to an unchanged population at the stable but maladaptive state. Therefore, in contrast to a bistable structure, the final adaptive state in a monostable system is not determined a priori by the genetic architecture, but is determined by the cellular response. Whether adaptation can be achieved by stochastic switching based on a monostable structure is an intriguing issue. To address this question, we used an engineered Escherichia coli strain carrying a monostable genetic circuit that allows the experimental observation on stochastic switching. In this strain, the gene hisC was deleted from the His operon and placed under the control of a foreign gene circuit at another chromosomal locus, as previously described (Kashiwagi et al, 2009). The circuit showed a clear monostable behaviour, which was characterized in detail previously (Tsuru et al, 2009). The gene hisC encodes the histidinol-phosphate aminotransferase, an enzyme essential for histidine biosynthesis, and is regulated by a transcriptional attenuator in the His operon (Keller and Calvo, 1979; Gama-Castro et al, 2008). In the hisC rewired strain, the responsive switching of hisC mediated by the native transcriptional regulation of the His operon was unavailable. Only the stochastic switching of the engineered hisC monostable circuit can provide the cells a chance of survival from histidine starvation. As a result, stochastic switching-mediated adaptation was clearly observed in the hisC rewired strain at both population and microcolony levels. Furthermore, transcriptome analysis showed that such adaptation was associated with a global transcriptional reorganization. The stochastic switching of a monostable structure reported here strongly suggests that this stochastic strategy may represent a generic mechanism for adaptation in living organisms. Results The hisC rewired strain and its response to histidine depletion The hisC rewired E. coli OSU12-hisC strain has been previously described (Kashiwagi et al, 2009). The gene hisC responsible for histidine biosynthesis was deleted from the His operon and placed under the control of the extraneous promoter, PtetA in an engineered gene circuit inserted at a different chromosomal location (Figure 1A). Consequently, hisC in OSU12-hisC is no longer responsive to the native regulation (His operon) that senses histidine depletion, according to the genomic annotation around this locus. Instead, the foreign gene circuit provided a monostable structure (Supplementary Figure S1) for hisC's stochastic switching. The green fluorescent protein (GFP) (gfpuv5) was co-expressed with hisC for the quantitative evaluation of HisC in single cells. The upstream regulation of TetR, whose expression level was reported by the red fluorescent protein (RFP) (dsred.T4), was introduced to achieve the inducible GFP (HisC) level. The full induction of TetR by isopropyl β-D-1-thiogalactopyranoside (IPTG) was applied to avoid any possible upstream noise that caused by the abundance of endogenous LacI. The doxycycline-dependent growth of OSU12-hisC has been characterized previously (Kashiwagi et al, 2009). Accordingly, the full induction of both Ptrc and PtetA, that is, the addition of 100 μM IPTG and 100 nM doxycycline, was employed here to study how the stochastic switching of hisC allows the cells to grow in histidine-free conditions. Figure 1.OSU12-hisC grown in the absence of histidine. (A) Genetic construction of OSU12-hisC. The gene circuit (bold fonts with dark lines) comprising the rewired hisC was integrated into the E. coli chromosome (Kashiwagi et al, 2009). hisC located in the native His operon was disrupted and newly controlled by the repressor TetR and the chemical doxycycline. The expression levels of TetR and RFP were regulated by the endogenous LacI and the inducer IPTG. The related genes within the inherent chromosomal locations under their native regulations are indicated in grey. (B) Microscopic observation of the cells before and after histidine depletion in the presence of both inducers. The scale bar represents 10 μm. (C, D) Steady distributions of the relative cellular GFP concentration and GFP bias in the presence (broken lines) or absence (solid lines) of histidine. OSU11 refers to the control strain (see Supplementary Figure S2). The distributions of OUS12-hisC indicate the populations formed 10 or 14 h after inoculation in the presence or absence of histidine, respectively. The distributions of OUS11 indicate the populations formed 12 or 14 h after inoculation in the presence or absence of histidine, respectively. The bin-width of the distributions is 0.1 (a.u.). Green fluorescence intensity (GFP FI), red fluorescence intensity (RFP FI) and forward scattering (FSC) represent the abundances of GFP and RFP expressed in single cells and the relative cell size, respectively. GFP FI/FSC and GFP bias (GFP FI/RFP FI) indicate the relative GFP level in cells. Source data is available for this figure at www.nature.com/msbSource data for Figure 1 [msb201124-sup-0002-SourceData-S2.xls] Download figure Download PowerPoint Under the described condition, the response of OSU12-hisC to histidine depletion was examined. Cells initially grown in the presence of 1 mM histidine were inoculated into fresh medium with the same induction but without histidine. Microscopic observation clearly revealed that the cells showed stronger green fluorescence after histidine depletion (Figure 1B), which suggested an increased expression level of hisC. Population analysis using flow cytometry was performed to detect the difference in GFP (HisC) level of the cell populations newly formed in the presence and absence of histidine. It showed that the distributions of both GFP concentration and GFP bias (GFP/RFP ratio) in OSU12-hisC shifted towards a higher level in histidine-free conditions (Figure 1C and D), whereas the depletion caused only a slight change in distributions of OSU11, a control strain carrying both the same engineered genetic circuit and an intact His operon, including the hisC gene in its native context (Supplementary Figure S2). Repeated experiments revealed that the increases in both GFP concentration (∼2.1 folds) and GFP bias (∼1.5 folds) due to histidine depletion were highly significant (P<0.005, N=6) in OSU12-hisC (Supplementary Figure S3). In particular, the increased GFP bias strongly suggested that the change in gene expression occurred specifically in the rewired hisC (i.e., GFP) but not in all genes (e.g., RFP). Population shift along with growth recovery and its relaxation The temporal changes in fluorescent intensity of OSU12-hisC in response to histidine depletion were investigated. Exponentially growing cells in histidine-supplied conditions were transferred to fresh medium in the presence or absence of histidine, and the timed sampling was subsequently performed at 2 h intervals. OSU12-hisC did grow in the absence of histidine, but the growth rate was slower than that in the presence of histidine (Figure 2A). In addition, the growth of OSU11 was approximately equivalent in both nutritional conditions, and the hisC-deleted strain, OSU11ΔhisC (genotype in Supplementary Figure S2), failed to propagate under conditions of starvation (Supplementary Figure S4). These results verified that the depletion of histidine did bring severe stress to the cells, due to the rewiring of the starvation-stringent gene, hisC. Figure 2.Temporal changes in the cell population in response to histidine depletion. (A) Growth curves of OSU12-hisC in the different nutritional conditions. Cells grown in the presence (+His) and absence (−His) of histidine was sampled at 2 h intervals. Cycles in white, black and green refer to the cell growth before histidine depletion (pre-culture), in the presence or absence of histidine after histidine depletion, respectively. Zero hour is the time point at which histidine was removed or newly supplied. (B) Temporal changes in GFP level of the cell population. The distributions of GFP concentration and GFP bias in grey and green represent the cell populations in the presence (+His) and absence (−His) of histidine, respectively. Temporal changes in the distributions are indicated for every 2 h interval as the gradation from dark to light, 0–14 h (green) and 0–12 h (grey), respectively. (C) Growth recovery after histidine depletion. The growth recovery trajectory is indicated with respect to the temporal change in distribution. The white, black and green cycles, and the time points are described in (A). GFP concentration and GFP bias are described in Figure 1. Download figure Download PowerPoint The cell population of OSU12-hisC shifted gradually to higher levels of both GFP concentration and GFP bias in histidine-free conditions, but maintained the initial level in histidine-supplied conditions (Figure 2B). The cell growth rates were evaluated according to the cell concentrations between every two measurements (sampling points). Both the growth rate and the GFP level stayed approximately constant, when OSU12-hisC grown in the presence of histidine (Figure 2C, black cycles). On the contrary, the temporal trajectory of the relation between cell growth and GFP level showed that the growth rate dropped off markedly with histidine depletion, but recovered gradually within a few hours (Figure 2C, green cycles). In the later period, the growth rate recovered accompanied by an increase in GFP (HisC) concentration. The gradual shift along with growth recovery in response to starvation was clearly identified in the independent replications (Supplementary Figure S5). In response to external perturbations, the changes in gene expression relying on a stochastic strategy were reported to be generally slower than the changes due to operon regulation (Keller and Calvo, 1979; Zaslaver et al, 2004; Yamada et al, 2010). The slow recovery in either GFP concentration or growth rate was therefore assumed to be dependent on the stochastic switching of hisC located in the monostable circuit. Furthermore, the population shift against the environmental transition showed reversibility. The cell population remained steady at the high GFP (HisC) level in the absence of histidine. However, once exponentially growing OSU12-hisC under starved conditions was supplied with 1 mM histidine, the population distribution returned to a low level of GFP (HisC) within 3 h (Figure 3). That is, the reduction in GFP (HisC) level of the whole population (∼106 cells/ml) took place within only 1–2 generations (i.e., 14–17 h). As described previously, in contrast to bistable structures, monostability did not fix the phenotype but allowed the cells to adapt dynamically. The relaxation to the initial steady state in response to stress release in a stochastic switching-dependent manner could only be observed only in a monostable structure. Additionally, the restoration also demonstrated that the population shift was neither caused by the mutation in genome nor due to the efficiency in GFP maturation. Figure 3.Reversibility of gene expression according to nutritional conditions. Changes in GFP concentration of the cell population were investigated, once OSU12-hisC kept growing in the absence of histidine (−His → −His), or supplied with histidine 14 h after starvation (−His → +His). The temporal changes in GFP concentration are shown as the distributions in greyscale from dark to light, representing the indicated sampling time points, respectively. GFP concentration is as described in Figure 1. Download figure Download PowerPoint Directionality in stochastic switching initiated adaptation To examine how the stochastic switching occurring in individual cells initiated adaptation, 30–50 microcolonies were characterized in the presence or absence of histidine at time points of 0, 2 and 4 h. Cells in the logarithmic growth phase were placed on glass coverslips and covered with a thin layer of agarose gel pad medium in the presence or absence of histidine, respectively. Microcolony formation from a single cell was observed under the microscope (Figure 4A). Variation in cellular GFP level was clearly observed in individual cells (Figure 4B; Supplementary Figure S6). Stochastic switching of hisC was verified according to the random changes in GFP bias along with the cell division under histidine-rich conditions. For example, one of four daughter cells showing stronger green fluorescence were born from a single cell 2 h after shifting to histidine-supplied conditions, but one of seven daughter cells showed weaker green fluorescence 4 h later (Figure 4B, first row, +His). On the other hand, the microcolonies formed under the histidine-free conditions tended to higher levels of GFP bias (Figure 4B; Supplementary Figure S6). For example, two daughter cells of higher GFP bias appeared 2 h after histidine depletion; subsequently, four daughter cells with stronger or similar GFP level were born (Figure 4B, first row, −His). The directional tendency favoured the high GFP (HisC) level, which was evidently detected in the first 2 h after histidine depletion, resulting in a population shift (Figure 4C). In contrast, the distributions of microcolonies grown in histidine-supplied conditions kept steady, due to the randomized directions of stochastic switching (Figure 4C). In addition, the restoration to lower GFP bias upon addition of histidine (i.e., stress release) was clearly observed at the microcolony level as well (Supplementary Figure S7). Such dynamic changes in microcolonies were consistent with the population dynamics (Figures 2 and 3). Figure 4.Time trace of cells at the microcolony level. (A) Snapshot of the cells grown under the microscope in the presence (upper panel) or absence (bottom panel) of histidine at 0, 2 and 4 h. The insets show higher magnification views of the microcolonies indicated by broken lines. The scale bar represents 10 μm. (B) Temporal changes in cell number and GFP bias of the microcolonies in the presence (+His) or absence (−His) of histidine. The divisions in the circle represent the numbers of cells grown in a single microcolony, and the gradation (greyscale) from light to dark represents the GFP bias. (C) Distributions of GFP bias at the single-cell level within the microcolonies grown in the presence (+His) or absence (−His) of histidine. From 30 to 50 microcolonies in each condition were used for population analysis (10 microcolonies are shown here, and the others are shown in Supplementary" @default.
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