PMC

Sugar system promoters show very little cross regulation. Promoter activity for six different sugar utilization promoters in the presence of only one sugar, at saturating concentration (0.2%) at mid-exponential phase. Rows represent the reporter genes and columns represent the sugar in the medium. Promoter activity of each reporter gene was normalized to the activity in its cognate sugar.

Two possible regulatory mechanisms that can implement a hierarchal decision in sugar utilization. a) Hierarchy can be obtained if CRP shows differential regulation for the different sugar systems so that the induction curves of each system as a function of CRP-cAMP activity are separated. b) Hierarchy can also be obtained by cross regulation so that systems lower in the hierarchy are directly repressed, for example by the sugar-specific transcription factors of the better sugar systems.

Differential activation by cAMP-CRP can quantitatively explain the sugar utilization hierarchy. a) Plotted is the predicted normalized promoter activity versus the measured one. The two agree well with a correlation coefficient R² = 0.95, p < 10^-10. The error bars are standard errors of 4 biological repeats (x-coordinate error bar) and 95% confidence interval of fits (y-coordinate error bar) b) measured promoter activity (same as figure ) c) predicted promoter activity from linear fits to CRP input functions of each promoter (data of Fig , predicted points removed from data used for fit).

Sugar system promoters show a linear increase with CRP-reporter activity but with different slopes that match the hierarchy. Promoter activity at mid exponential phase of each sugar system promoter in the presence of its cognate sugar and one of the five other sugars, normalized to when only its cognate sugar is present, as a function of the promoter activity of a CRP reporter normalized to its highest value. Each color represents a different sugar system promoter (lacZ light blue, araB blue, xylA brown, rhaB orange, srlA yellow, rbsD green). Inset: promoter activity at mid-exponential phase in a two sugar mixture in the presence of external cAMP at 0,0.15,0.3,0.6,1.25,2.5,5 mM. The promoters are lacZ and rbsD measured with external cAMP in lactose + ribose; araB and rhaB measured with external cAMP in arabinose + rhamnose.

A hierarchy of sugar gene expression matches the hierarchy in growth rate. Promoter activity for six different sugar utilization operons at mid exponential growth, in the presence of the cognate sugar alone or paired with each of the 5 other sugars. All sugars are at saturating concentrations (0.2%). Rows represent the promoter activity from the indicated reporter grown in the presence of its cognate sugar. Rows are ordered according to growth rate, with a sugar supporting higher growth as sole carbon source rate located in an upper row. Columns represent the second sugar in the mixture. The diagonal represents the presence of only the cognate sugar (0.2%); promoter activity values in each row were normalized to this value.

Different sugar promoters can be either simultaneously or sequentially expressed in a sugar mixture. a-e) Promoter activity of CRP reporter (black), araB (blue), and a second sugar system promoter (red) in a mixture of sub-saturating arabinose (0.005%) and saturating second sugar (0.2%). The second sugars and promoters are a) lacZ and lactose, b) xylA and xylose, c) srlA and sorbitol, d) rhaB and rhamnose, e) rbsD and ribose. Note that a, b and c show simultaneous expression of the two promoters, whereas d and e show sequential expression. Also shown are optical density OD₆₀₀ (f-j), and growth rate defined as dlog(OD)/dt panels (k-o) for the corresponding growth conditions. Drop in growth rate at late times is entry to stationary phase. Colors represent the strains as in fig a-e. Promoter activity data is normalized to its maximal value, mean day-day relative errors of growth rate and promoter activity were 8% and 9% respectively.

Simple linear programming optimality models predict that utilizing a single sugar is optimal; more complex models can allow co-utilization of both sugars. a) Simplified linear programming model: The growth rate increases with the expression of the two sugar systems, E1 and E2 – dashed contours. Given a cost constraint of total proteins (blue line), expressing only one of the two sugar systems maximizes the growth rate (red dot). As the concentration of that sugar decreases, growth rate contours shift their slope, until a point in time is reached when b) the optimal solution jumps to expressing the other sugar system exclusively (yellow dot). c) If the constraint (blue line) is convex, the constraint curve bulges outwards and co-expression of the two sugar systems can be optimal (green dot). This predicts that growth rate in co-expression exceeds the maximal growth rate expressing each system alone. d) Co-expression can also be optimal if tasks other than immediate rapid growth affect fitness, for example future growth on the poorer sugar. The green box symbolizes a potential best compromise solution.