Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge

Abstract

Cells adjust their transcriptional state to accommodate environmental and genetic perturbations. An open question is to what extent transcriptional response to perturbations has been specifically selected along evolution. To test the possibility that transcriptional reprogramming does not need to be 'pre-designed' to lead to an adaptive metabolic state on physiological timescales, we confronted yeast cells with a novel challenge they had not previously encountered. We rewired the genome by recruiting an essential gene, HIS3, from the histidine biosynthesis pathway to a foreign regulatory system, the GAL network responsible for galactose utilization. Switching medium to glucose in a chemostat caused repression of the essential gene and presented the cells with a severe challenge to which they adapted over approximately 10 generations. Using genome-wide expression arrays, we show here that a global transcriptional reprogramming (>1200 genes) underlies the adaptation. A large fraction of the responding genes is nonreproducible in repeated experiments. These results show that a nonspecific transcriptional response reflecting the natural plasticity of the regulatory network supports adaptation of cells to novel challenges.

Figure 1

Adaptive population dynamics and associated global transcriptional response. Population dynamics as measured by the cell density in the chemostat (blue line) for (A) no 3AT and (B) 40 mM 3AT. The population in each experiment exhibited four phases (I–IV) of dynamics as depicted. Expression arrays were measured at eight time points along the course of the population adaptation following the medium switch from galactose to glucose (at t=0). A SOM clustering method (see Materials and methods and Supplementary Figure S1) led to two dominant global clusters: induced (red; a—543, b—701 genes) and repressed (green; a—692, b—998 genes). Note the symmetry between induced and repressed clusters (correlation coefficient between the two clusters’ mean expression profiles, a: −0.92, b: −0.98). The error bars present the standard deviation of expression values among genes belonging to each cluster. Note the logarithmic scale. The generation time equals chemostat dilution time × ln2∼5 h.

Figure 2

The genome-wide transcription pattern. The raw transcription levels at eight time points for the two experiments, (left) no 3AT, (right) 40 mM 3AT, in a color code: red—induced, green—repressed. There are a total of 4148 genes that passed all filters (see Materials and methods). The medium switch from galactose to glucose is marked and the numbers above the columns are the measurement points as shown in Figure 1. Note the differences between the patterns of expression for the two experiments (rows correspond to the same gene in both experiments).

Figure 3

Environmental pressure leads to highly correlated transcriptional response. (A) Color-coded figure of transcriptional response for the eight time points in the two experiments, with no 3AT (top) and with 40 mM 3AT (bottom). The genes were ordered in each experiment according to the clusters presented in Figure 1. The significant increase in coherency of the response with the increase of environmental pressure by 3AT is apparent in the image. Expression profiles of genes belonging to glycolysis (B), histidine (C) and purine (D) pathways. Note the emergence of highly correlated patterns of transcription owing to the environmental pressure in the lower panel. A given functional module simultaneously contains correlated and anti-correlated trajectories.

Figure 4

The correlations of transcriptional response. A Pearson correlation coefficient was computed between all pairs of genes in both experiments, with and without 3AT. The figure shows the distributions of correlation coefficient between all possible pairs, for no 3AT (black) and 40 mM 3AT (red).

Figure 5

Expression profiles of a protein complex. Expression profiles of all genes belonging to the NER protein complex as defined in MIPS (http://mips.gsf.de) superimposed on the population density curves (blue lines), for (A) no-3AT and (B) 40 mM 3AT experiments. Note the significant increase in order among the transcriptional response of the genes in the 3AT experiment compared to the no-3AT experiment. Note also that genes belonging to the same protein complex exhibited anti-correlated expression patterns.

Figure 6

Correlation coefficient between GAL1 and all other genes. The Pearson correlation coefficient was computed between the transcriptional dynamic profiles of GAL1 and all other genes for the two experiments, with and without 3AT. The correlation coefficient value of each pair in the 3AT experiment was plotted against the correlation coefficient value in the no-3AT experiment. Only pairs with an absolute Pearson correlation above 0.8 in at least one of the experiments were plotted for clarity. There were 339 genes in the 3AT experiment and 109 genes in the no-3AT experiment with absolute correlations above 0.9. Note that genes highly correlated in one experiment may exhibit low and even negative correlation in the other experiment. The GAL1 gene served as an example here, but similar results were obtained for many other genes belonging to different functional modules.

Figure 7

Steady-state transcriptional pattern is well predicted from the transient response. The steady-state expression level (average of the two measurement points in glucose steady state) versus the transient expression level (4th time point in Figure 1B) for the experiment with 40 mM 3AT. The Pearson correlation between the transient response and steady state is 0.76. The slope deviation from the reference dashed black line represents the overshoot or undershoot in transient expression levels compared to the steady state.