Different levels of catabolite repression optimize growth in stable and variable environments.

Organisms respond to environmental changes by adapting the expression of key genes. However, such transcriptional reprogramming requires time and energy, and may also leave the organism ill-adapted when the original environment returns. Here, we study the dynamics of transcriptional reprogramming and fitness in the model eukaryote Saccharomyces cerevisiae in response to changing carbon environments. Population and single-cell analyses reveal that some wild yeast strains rapidly and uniformly adapt gene expression and growth to changing carbon sources, whereas other strains respond more slowly, resulting in long periods of slow growth (the so-called "lag phase") and large differences between individual cells within the population. We exploit this natural heterogeneity to evolve a set of mutants that demonstrate how the frequency and duration of changes in carbon source can favor different carbon catabolite repression strategies. At one end of this spectrum are "specialist" strategies that display high rates of growth in stable environments, with more stringent catabolite repression and slower transcriptional reprogramming. The other mutants display less stringent catabolite repression, resulting in leaky expression of genes that are not required for growth in glucose. This "generalist" strategy reduces fitness in glucose, but allows faster transcriptional reprogramming and shorter lag phases when the cells need to shift to alternative carbon sources. Whole-genome sequencing of these mutants reveals that mutations in key regulatory genes such as HXK2 and STD1 adjust the regulation and transcriptional noise of metabolic genes, with some mutations leading to alternative gene regulatory strategies that allow "stochastic sensing" of the environment. Together, our study unmasks how variable and stable environments favor distinct strategies of transcriptional reprogramming and growth.

Promoter knock-in: a novel rational method for the fine tuning of genes.

BACKGROUND: Metabolic engineering aims at channeling the metabolic fluxes towards a desired compound. An important strategy to achieve this is the modification of the expression level of specific genes. Several methods for the modification or the replacement of promoters have been proposed, but most of them involve time-consuming screening steps. We describe here a novel optimized method for the insertion of constitutive promoters (referred to as "promoter knock-in") whose strength can be compared with the native promoter by applying a promoter strength predictive (PSP) model. RESULTS: Our method was successfully applied to fine tune the ppc gene of Escherichia coli. While developing the promoter knock-in methodology, we showed the importance of conserving the natural leader region containing the ribosome binding site (RBS) of the gene of interest and of eliminating upstream regulatory elements (transcription factor binding sites). The gene expression was down regulated instead of up regulated when the natural RBS was not conserved and when the upstream regulatory elements were eliminated. Next, three different promoter knock-ins were created for the ppc gene selecting three different artificial promoters. The measured constitutive expression of the ppc gene in these knock-ins reflected the relative strength of the different promoters as predicted by the PSP model. The applicability of our PSP model and promoter knock-in methodology was further demonstrated by showing that the constitutivity and the relative levels of expression were independent of the genetic background (comparing wild-type and mutant E. coli strains). No differences were observed during scaling up from shake flask to bioreactor-scale, confirming that the obtained expression was independent of environmental conditions. CONCLUSION: We are proposing a novel methodology for obtaining appropriate levels of expression of genes of interest, based on the prediction of the relative strength of selected synthetic promoters combined with an optimized promoter knock-in strategy. The obtained expression levels are independent of the genetic background and scale conditions. The method constitutes therefore a valuable addition to the genetic toolbox for the metabolic engineering of E. coli.