Abstract

Introduction

Recently, a great deal of attention has been focused on fluctuations in biochemical processes within the cell. The magnitude of fluctuations in protein abundance among the isogenic bacterial cells have been shown to be quite large, and these fluctuations are not only because of the external noise from the environment but also because of the stochasticity in intracellular reaction processes (Oosawa, 1975; Hasty et al, 2000; Ueda et al, 2001; Elowitz et al, 2002; Ozbudak et al, 2002; Kaern et al, 2004; Pedraza and Oudenaarden, 2005; Rosenfeld et al, 2005). The relevance of phenotypic fluctuations has recently been examined in relation to biological responses (Arkin et al, 1998; Korobkova et al, 2004; Paulsson, 2004), adaptation (Balaban et al, 2004; Kashiwagi et al, 2006; Acar et al, 2008), differentiation (Kaneko and Yomo, 1999; Maamar et al, 2007; Suel et al, 2007) and evolution (Sato et al, 2003), along with mutational robustness (Wagner, 2000; Kaneko, 2007).

By contrast, the fluctuation could be an obstacle to the maintenance of a state with higher function. Under a fixed set of environmental conditions, fluctuation around an optimal state could reduce fitness. Hence, how the magnitude of phenotypic fluctuation changes through evolution is an important issue that must be examined experimentally. In fact, it was recently shown in our laboratory that the magnitude of phenotypic fluctuation is positively correlated with the rate of evolution under a fixed set of environmental conditions (Sato et al, 2003; Kaneko and Furusawa, 2006). In directed evolution experiments, in order to enhance a given function, both the fluctuation and the rate of evolution decrease over the course of evolution. Our earlier study indicated that phenotypic fluctuation is reduced when the fitness is increased with generations (Sato et al, 2003). This decrease in fluctuation through evolution is natural, as it would be advantageous to reduce fluctuations around the fittest state to maintain optimal function (Landry et al, 2007; Lehner, 2008). As the decrease in fluctuation reduces the potential for the changing parameters of the system to be tuned for further optimisation of the function, it may be natural that the potential or the rate of evolution would also decrease. However, as mentioned above, extant cells still show relatively large fluctuations derived from external noise and/or stochasticity in intracellular reaction processes, and evolution does not necessarily cease.

These observations raise the question of how selection affects phenotypic fluctuations. In nature, the environment is not fixed but fluctuates over time. To cope with such fluctuating external environmental conditions, a certain level of phenotypic fluctuation is advantageous as it can produce various phenotypes, each of which is fit for the stochastically appearing environments (Thattai and Oudenaarden, 2004; Kussell and Leibler, 2005; Acar et al, 2008). This ‘bet-hedging' strategy can explain why extant cells maintain a large degree of phenotypic fluctuation. However, this may not be the only mechanism for maintaining fluctuations. Even under a fixed set of environmental conditions, amplification of isogenic phenotypic fluctuations may be possible through evolution. To address these points, an evolutionary experiment was carried out to enhance the fluorescence of GFP protein expressed in Escherichia coli following essentially the same procedure as described earlier (Sato et al, 2003). The main difference between the present and earlier experiments is that individual cells with phenotypic diversity because of both mutation and fluctuation were selected, rather than selection based on the average phenotype of a range of cells with fluctuating phenotypes for each individual mutant genotype. In contrast to the decrease in fluctuation observed earlier (Sato et al, 2003), some mutants with a large degree of phenotypic fluctuation emerged during the process of evolution in this study. Indeed such amplification of fluctuation was shown to be possible under tight selection of individual cells.

Results and discussion

In our earlier study (Ito et al, 2004), the gene for green fluorescent protein (GFP) fused with an artificial random 149-amino-acid polypeptide (RP) RP3-34H was used as the starting material. The variant with the highest fluorescence intensity (FI) in each generation was selected from a mutant library, where part of the artificial random-polypeptide gene was mutated randomly. This iterative process of mutation and selection was repeated over 10 generations. In the evolutionary experiment, the variant was selected on the basis of the average fluorescence intensity of 10⁹ cells.

Here, with mutagenesis of the selected clone at the 10th generation, HLG10-6, as the starting material, selection was done on the basis of individual cells, where the fluctuation could influence the clones that evolve. GFP, FI and forward scattering (FS) of the cells were measured by flow cytometry (FCM) for selection at the individual cell level (Figure 1A). The intensity of FS is generally regarded as proportional to the size of the cell (Bouvier et al, 2001). Thus, the GFP fluorescence density was estimated by FI/FS, which was used as an index of selection. Specifically, the selected genes were amplified from 20 000 cells (0.2% of total cells measured) in region R1 in Figure 1A, reinserted into fresh vector, and reintroduced into fresh E. coli cells to exclude unexpected mutations in other regions of the vector. After repeating the selection and amplification (FCM selection cycle) several times, the selected genes were mutated in the next generation. This evolutionary process was carried out for three generations. In this selection procedure, cells that happened to possess a higher value of FI/FS because of phenotypic fluctuations could be selected. Thus, the genes or clones causing larger fluctuations in FI/FS, as well as those with a higher average value, may evolve.

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Figure 1

Evolutionary experiment. (A) GFP fluorescence intensity (FI) and forward scattering (FS) of individual cells measured by FCM (those of HLG10-6 are also shown). Cells in the R1 region (top 0.2% of the total in fluorescence density (FI/FS)) were sorted for the next selection cycle. (B) The selection threshold value, log(FI/FS) at the 99.8th percentile in the cell population, was measured. Open squares indicate values at each round of selection. Values of 12 clones picked at random from the last cycle of each generation are plotted (some clones had the same value). Blue and magenta bars indicate narrow and broad mutants, respectively. The selection threshold values in clone analysis (blue and magenta bars) were slightly lower than those in population analysis (open squares). This decrease was probably because of the differences in culture conditions. (C) Phylogenetic tree of the selected clones, 12 clones sampled from each generation (as in B) and 11 clones in our earlier experiment (Ito et al, 2004). Nodes of the 12 clones sampled from each of the first, second and third generations are indicated in orange, green and light blue, respectively. Broad mutants are encircled in red. Nodes of the clone selected at each generation, RP3-34H, first to ninth, and HLG10-6, of our earlier experiment, are indicated in red, grey and black, respectively. The evolutionary experiment was started from the clone of the 10th generation (black). Scale bar: five nucleotide substitutions.

Figure 1B and C show an outline of the evolutionary process used in this study. The selection threshold value, defined as log(FI/FS) at 99.8 percentile among the cells in a population, increased during the evolutionary experiment. Twelve clones were picked at random from the last selection cycle of each generation. The cell distribution of FI/FS was measured (Figure 2 and Supplementary Figure S1) and the RP gene for each clone was sequenced (Supplementary Figure S2). The selection threshold values for these randomly picked clones also increased with the generations (Figure 1B). Sequence analysis of these clones revealed a phylogenetic tree with diverged branches. These observations indicated that the evolutionary experiment succeeded in increasing the selection threshold value as designed but retained some diversity in the evolved population.

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Figure 2

Phenotypic distributions of broad and narrow mutants. (A–C) Histograms of log(fluorescence intensity (FI)/forward scattering (FS)) measured by FCM for each of the population of HLG10-6 (A), examples of broad mutants HLG2nd-5 (B) and narrow mutants HLG2nd-2 (C). The histograms (A–C) show the results of the re-cloning experiments. The peak position of HLG10-6 is indicated with the vertical line as a reference. (D) Peak value versus full-width at half-maximum. These values were obtained from the histogram of log(FI/FS) for each clone represented in Figure 1C. Colours and symbols are as in Figure 1C. Broad mutants are encircled by a red broken line.

Surprisingly, two distinct groups were found among the selected clones constituting the phylogeny: one with a large fluctuation and another with a small fluctuation (Figure 2D and Supplementary Figure S1), which are referred to as ‘broad mutants' and ‘narrow mutants', respectively.

To examine whether the distinct phenotypes with respect to fluctuation are reproducible, the RP gene was recloned from each of the variants in each round into a fresh vector carrying the GFP gene and its promoter sequence. The cell distributions of FI/FS before and after re-cloning were almost identical (Figure 2A–C and Supplementary Figure S1). These re-cloning experiments showed that the two distinct levels of fluctuation were reproducible phenotypes controlled by the genotype.

These observations raise the question about how the broad-mutant genes with lower average FI survived through the evolution experiment. In this evolution experiment, the fitness of selection consisted mainly of two parameters: FI/FS in FCM and the cellular growth rate in cell culture followed by FCM selection. Under the conditions used in this evolution experiment, however, the growth rate differences did not significantly affect the total fitness and relative survival rate (as shown in Supplementary information). Therefore, the evolution was considered to have proceeded in a single selective environment where phenotypic selection was carried out with respect to FI/FS. Even the selection threshold values for the broad mutants, selected in each generation, were almost the same as those of narrow mutants (Figure 1B), indicating that the former had the same number of cells as the latter above the threshold. Although the broad mutants had lower average fluorescence than the narrow mutants, larger distribution of the broad mutants could result in a similar survival probability. Moreover, the broad mutants appeared on different branches in the phylogenetic tree (Figure 1C), indicating that the emergence of a gene showing large phenotypic fluctuation would occur several times in the course of evolution.

These findings raise the question about how such a large fluctuation was produced in the broad mutants. To determine the possible origin of the fluctuation, the plasmid copy number and global noise level (Pedraza and Oudenaarden, 2005) in each cell were estimated by introducing the red fluorescent protein (RFP) gene into the plasmids with an independent promoter to that for GFP expression (Supplementary Figure S4A). HLG10-6 and one of the broad mutants showed narrow and broad distributions, respectively, in FI/(RFP fluorescence intensity (RFP FI)) (Figure 3A and B, Supplementary Figure S4B and C), and the same trend in FI/FS (Supplementary Figure S4D and E). These observations indicated that the broad mutant had a wide distribution of GFP fusion-protein expression level.

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Figure 3

Differences between broad and narrow mutants. (A–E) Fluctuations in GFP gene copy number, transcript and protein levels in broad and narrow mutants. Fluorescence intensity (FI) and red fluorescence intensity of individual cells of the broad mutant HLG3rd-1 (A) and HLG10-6 (B), in which the RFP gene was introduced in the plasmid, indicated by density plots. The colour gradient from grey to light grey represents an increase in cell density. The two-dimensional fluorescence distribution of diagonal direction on which GFP and RFP are equal, corresponds to ‘coordinated variety', whereas the horizontal direction corresponds to ‘broadness'. The cells in each region indicated by magenta (in A) and blue squares (in B) were sorted for the quantification of plasmid copy number and amounts of GFP mRNA by FCM. (C) Fluorescence comparison of the broad mutant (magenta) and HLG10-6 (blue), with each sorting region described in (A) and (B). The RFP fluorescence was determined by normalization for leakage of GFP fluorescence. (D, E) Quantification of GFP mRNA and plasmid copy number are shown as GFP fluorescence intensity versus GFP mRNA (D) and GFP mRNA versus plasmid copy number (E). The colour code is the same as in (C). Bars indicate s.d. from three or four experiments. (F–I) Time-course analysis of FI and FS for the broad mutant HLG3rd-1 (F, G) and HLG10-6 (H, I). Histograms of log FS (F, H) and log FI (G, I) at 0.5 h (black), 1 h (magenta), 1.5 h (green), 2 h (blue), 2.5 h (light blue) and 3 h (red) after IPTG induction, are shown.

Next, whether the broad phenotype of the broad mutants was mediated at the level of transcription or translation was investigated. As shown in Figure 3A and B, the two-dimensional fluorescence distribution showed two types of variety, that is, coordinated variety (diagonal correlation) and broadness (horizontal width). To examine the coordinated variety and the broadness in HLG10-6 and the broad mutant, 5000 cells in each of the small regions were sorted (Figure 3A and B) and the amounts of GFP mRNA and the plasmid were determined (Figure 3D and E). The average fluorescence over the cells for each region is shown in Figure 3C. As shown in Figure 3D, there was a positive correlation between GFP fluorescence and the GFP mRNA level in both HLG10-6 and the broad mutant, indicating that the variation in GFP fluorescence between the cells of the two genotypes was attributable to the variation in mRNA abundance. The mRNA abundance was then found to be partly correlated with the plasmid copy number (Figure 3E). HLG10-6 showed a clear correlation between GFP mRNA level and plasmid copy number, whereas the data from the broad mutant constituted two separated positive correlations: one for the small regions on the right edge in the two-dimensional distribution and the other for those on the left edge. The plots of FI versus RFP FI (Figure 3C) and GFP mRNA versus plasmid copy number (Figure 3E) resembled each other. This indicated that the coordinated variety, which affected the fluctuations in both GFP and RFP fluorescence equally, could be attributed to the variation in plasmid copy number, whereas the broadness cannot be explained by the plasmid copy number. The broad-mutant cells from the right and left edges but with the same plasmid copy number showed higher and lower mRNA abundance, respectively (Figure 3E). These observations indicated that the large broadness of GFP fluorescence in the broad mutant reflects a larger fluctuation in transcription and/or degradation of the GFP mRNA.

The broadness described above was also analysed with regard to temporal changes in FS and FI (Figure 3F–I). Although the HLG10-6 clone constituted a homogeneous population with regard to the increases in FS and FI, the cells of the broad mutant were divided into two populations from 1.5 h after induction: a major population with persistence and a minor population with an increase similar to that seen in HLG10-6. The heterogeneity among the cells in FS and FI corresponds to the broadness, and can arise from the physiological heterogeneity of the cells, which may be amplified by the toxicity of fused GFP protein of the broad mutant. A detailed discussion of the temporal changes and a possible model for the increase in heterogeneity of the broad mutants at the transcriptional level is presented in the Supplementary information.

There are two strategies to leave offspring with an increased chance of survival even under a fixed set of conditions: one is to maintain a high average value in the property under selection by tightly suppressing fluctuation, and the other is to leave a larger degree of fluctuation while having a lower average. For example, in the process of selecting cells with a threshold value of log(FI/FS) larger than 0.25 in Figure 2B and C, several cells of the narrow mutant were selected, but those of the broad mutant could also be selected as the distribution was extended to a much higher value than the peak. If there are two mutants of comparable population size with the same average properties but with different levels of fluctuation, the mutant with larger fluctuation will become dominant when the selection threshold value is higher than the average value, that is, less than half of the cells survive through the selection process. The effects of phenotypic selection stringency on the fitness of the broad mutants were calculated and are discussed quantitatively in the Supplementary information. In general, the strategy of large fluctuation is preferable as the survival fraction is smaller. Note that this strategy works even under a fixed environment, in contrast to the strategy to maintain stochasticity by environmental fluctuation (Thattai and Oudenaarden, 2004; Kussell and Leibler, 2005; Acar et al, 2008). Indeed, the broad mutant was considered to have evolved in a single environment under which phenotypic selection was carried out, as the growth rate selection in our evolution experiment contributed little to the total fitness (Supplementary information).

In summary, an increase in phenotypic fluctuation was observed during an evolutionary experiment based on the individual phenotypes under a fixed set of environmental conditions, whereas the fluctuation decreased gradually in an earlier evolutionary experiment based on selection with the average phenotype of each mutant genotype. The present evolutionary experiment simulated the situation wherein organisms are faced with a severe environment allowing only a small fraction of individuals to survive. When severe selection works at the individual level as in this study or in nature, the increase in phenotypic fluctuation is a relevant evolutionary strategy (Clayton and Robertson, 1957; Hill and Zhang, 2004). As the phenotypic fluctuation is correlated with the potential of evolution (Sato et al, 2003; Kaneko, 2007), this increase results in recovery of evolutionary potential.

Organisms require a large degree of phenotypic diversity in which only a small number of phenotypes survive, especially when they encounter a severe environment. This phenotypic diversity is derived from both phenotypic fluctuation and genetic diversity. Hence, the phenotypic fluctuation may be recruited and maintained in the evolutionary history of encountering severe environments.

Materials and methods

This study was performed using the same E. coli strain DH5α(DE3), plasmid pETHLGT1 and enzymes as in our earlier study (Ito et al, 2004).

Supplementary Material

Acknowledgments

Footnotes

References