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Regulation underlying hierarchical and simultaneous utilization of carbon substrates by flux sensors in Escherichia coli

Abstract

Many microorganisms exhibit nutrient preferences, exemplified by the ‘hierarchical’ consumption of certain carbon substrates. Here, we systematically investigate under which physiological conditions hierarchical substrate utilization occurs and its mechanisms of implementation. We show utilization hierarchy of Escherichia coli to be ordered by the carbon-uptake flux rather than the identity of the substrates. A detailed study of glycerol uptake finds that it is fully suppressed if the uptake flux of another glycolytic substrate exceeds a threshold, which is set to the influx obtained when grown on glycerol alone. Below this threshold, limited glycerol uptake is ‘supplemented’ such that the total carbon uptake is maintained at the threshold. This behaviour results from total-flux feedback mediated by cAMP–Crp signalling but also requires inhibition by the regulator fructose 1,6-bisphosphate, which senses the upper-glycolytic flux and ensures that glycerol uptake defers to other glycolytic substrates but not to gluconeogenic ones. A quantitative model reproduces all of the observed utilization patterns, including those of key mutants. The proposed mechanism relies on the differential regulation of uptake enzymes and requires a specific operon organization. This organization is found to be conserved across related species for several uptake systems, suggesting the deployment of similar mechanisms for hierarchical substrate utilization by a spectrum of microorganisms.

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Fig. 1: Diauxie and the titratable LacY or PtsG strains.
Fig. 2: Uptake fluxes reveal hierarchical and simultaneous utilization regimes.
Fig. 3: Glycerol uptake involves derepression of glycerol catabolic genes.
Fig. 4: Glycerol uptake is regulated by two flux sensors, FBP and cAMP–Crp.
Fig. 5: Analysis, modelling and predictions.

Data availability

The datasets corresponding to all figures (including the Extended Data and Supplementary figures) are available online as Source Data.

Code availability

Numerical analyses of the mathematical model were carried out using Wolfram Mathematica 11.3, gnuplot and R (v.3.5.1). A Mathematica notebook that reproduces the central modelling results has been shared at https://doi.org/10.5281/zenodo.3462129. Other code will be shared on reasonable request.

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Acknowledgements

We are grateful to U. Sauer for his generous support and encouragement; T. Egli, L. Gerosa, J. Silverman and the members of the Hwa lab for their helpful discussions; K. Applebee for providing the glpKG184T strain; and L. Chao and C. U. Rang for helping with the acquisition of the single-cell GFP images. This research is supported by the National Institutes of Health (grant no. R01GM095903 to T.H.). R.H. was supported by the Dutch Research Council (grant no. VENI 680–47–419). T.H. additionally acknowledges the hospitality of the Institute for Theoretical Studies at ETH, where some of this work was carried out.

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Contributions

H.O. and T.H. designed the experiments. H.O. performed most of the experiments. K.K. performed the mass spectrometry experiment and its analysis. R.H. performed the quantitative modelling and theoretical analysis. H.O., R.H. and T.H. analysed the data and wrote the paper.

Corresponding authors

Correspondence to Hiroyuki Okano or Rutger Hermsen.

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Extended data

Extended Data Fig. 1 Examples of diauxic (‘double growth’) curves.

a–d. Diauxic growth curves1 on glucose and one additional carbon substrate, for E. coli strains NCM3722 (closed circles) and MG1655 (open circles). A diauxic curve consists of two exponential growth phases separated by a lag phase during which the culture hardly grows. (For comparison, all data series are shifted horizontally such that the lag begins at ~100 min.) The duration of the lag phase varies between strains and substrate pairs, from a few minutes (NCM3722 in a) to over an hour (NCM3722 in d). e, Hierarchical growth on glucose plus glycerol. The same data as Fig. 1a are now plotted against OD600. The glucose concentration (blue diamonds) initially decreases linearly (black solid lines are linear fits): during balanced exponential growth, producing a unit of cell mass consumes a fixed amount of substrate. After glucose runs out, glycerol (orange triangles) is consumed, again linearly. f, Similar to e, but with lactose (green diamonds) instead of glucose, using the same data as Fig. 1b. Here, the transition to glycerol utilization is more gradual than e. Lactose uptake slows down before lactose is used up, likely due to the large Michaelis constant of the lactose permease50: KM = 0.1 to 1 mM. The reduction in lactose uptake relieves the inhibition of glycerol uptake before lactose is fully depleted, resulting in a smooth transition. In contrast, the KM of the glucose transporter PtsG51,52 is 3–10 μM; therefore, cells do not sense that glucose is running out until the glucose concentration is very low (e and Fig. 1a). g, As Fig. 1b, except that a higher initial lactose concentration was used (3 mM instead of 0.7 mM) to fully inhibit glycerol uptake. To observe the transition to glycerol utilization before the culture reaches high OD600, the culture was diluted, at OD600 = 0.5, four-fold in fresh medium containing glycerol (orange diamonds before dilution, pale-orange diamonds after) but no lactose (green triangles before dilution, pale-green triangles after). h, Same data as g, except that lactose and glycerol concentrations are plotted against OD600, revealing straight lines similar to e and f.

Source Data

Extended Data Fig. 2 Growth-rate crossover patterns for various combinations of substrates.

a–d. The growth rate of the titratable LacY strain (Supplementary Figure 1a) grown on lactose and a second substrate (glycerol, maltose, xylose or fructose) as a function of 3MBA concentration. Each plot shows the measured growth rate on lactose only (green diamonds), the growth rate on the second substrate only (orange or red triangles), and the growth rate in the presence of both (pink circles). e–h. As a–d, but for the titratable PtsG strain (Supplementary Figure 1b) and glucose (blue diamonds) instead of lactose. i, Similar results for the titratable GlpK strain (Supplementary Figure 1c) growing on glycerol (orange triangles), mannose (red diamonds), or both (pink circles). In all cases (a–i), if the 3MBA concentration is reduced sufficiently, the growth rate on the preferred carbon substrate eventually becomes smaller than the growth rate on the non-preferred one. Yet, the growth rate in the presence of both substrates never drops below the growth rate on the non-preferred one, indicating that the uptake of the non-preferred substrate is induced. In almost all cases, the crossover regime is rather narrow. The glucose-fructose hierarchy is an apparent exception.

Source data

Extended Data Fig. 3 The diauxic lag disappears in the supplementation regime.

a, Diauxic growth curves (+) of the titratable PtsG strain (NQ1243) in medium with 1.7 mM glucose and saturating glycerol. Each curve is for a different 3MBA concentration (indicated in the figure) and horizontally shifted for convenience. The data shown is from a single series of experiments. b, Lag times were determined for each diauxic growth curve; the method is illustrated here using the condition [3MBA] = 800 µM as an example. We fitted exponential curves through the two growth phases (green lines) and a horizontal line through the lag phase (horizontal black line). The lag time (indicated in grey) is heuristically defined as the horizontal distance between the intersections of the green lines with the horizontal one. In this case, a lag time of 46 min is found. c, Lag times of the growth curves versus [3MBA]. The diauxic lag time vanishes precipitously when [3MBA] is tuned below 100 μM, where glycerol and glucose are co-utilized.

Source data

Extended Data Fig. 4 How differential regulation by cAMP–Crp can affect G3P concentration.

As demonstrated in Fig. 3b,c using the ΔglpR and ΔglpR glpK22 background, cAMP–Crp signalling affects the expression of the glpK and glpD genes differently: with increasing growth rate (decreasing cAMP–Crp transcriptional activation), glpK expression vanishes whereas glpD expression maintains a significant basal level. The figure illustrates how this differential regulation explains the marked growth-rate dependence of the G3P concentration observed in the ΔglpR glpK22 background (Fig. 4d). G3P is the product of GlpK and the substrate of GlpD. The synthesis of G3P should therefore be proportional with the abundance of GlpK, while its turnover increases with both GlpD abundance and substrate concentration [G3P]. Flux balance then implies that [G3P] increases with the ratio of GlpK (purple dashed line; sketch based on Fig. 3b) to GlpD abundance (purple solid line; sketch based on Fig. 3c). This ratio reduces with increasing growth rate, so that the G3P concentration (solid orange line; sketch) reduces as well. In strains without the ΔglpR mutation the same mechanism should act, but with an additional layer of amplification: Because both glpK and glpD expression are repressed by GlpR, an increase in its inducer G3P due to differential regulation has little effect until it is of the order of the Michaelis constant KM (horizontal dotted line) associated with the induction of GlpR, on which glpK and glpD expression are induced and glycerol uptake is turned on.

Extended Data Fig. 5 The total-flux feedback model.

a, Several key observations can be explained by a highly simplified regulatory scheme in which glycerol uptake is inhibited by a signal that reflects the total carbon-uptake flux—a total-flux sensor. In this scheme, glycerol and lactose uptake jG and jL both contribute to the total carbon- uptake flux jtot. This total flux is sensed by a total-flux sensor, which represses glycerol uptake, but only if jtot exceeds a threshold that is set to coincide with the carbon flux obtained on glycerol alone. Thus, glycerol uptake is suppressed by any substrate that supplies a larger carbon-uptake flux than glycerol can provide, but not by substrates that produce a smaller flux. b, This figure demonstrates graphically how total-flux feedback determines the uptake of glycerol. Because jG = jtotjL, the steady-state value of jG obtained for a given jL can be found by plotting both jG (jtot) (red solid curve) and jtotjL (green dashed lines, for various values of jL) as a function of jtot and determining their intersection (blue triangles). We assume that jG responds sensitively to jtot, with a threshold jth (red arrow) set slightly above the flux on glycerol alone, jG,0. Thus, it is seen that glycerol uptake is inhibited if jL > jth (the hierarchical utilization regime, intersection c). Yet, if jL < jth, glycerol uptake is adjusted such that jtotjthjG,0 (the supplementation regime; intersection a and b). cAMP–Crp signalling can function as a total-flux sensor because transcriptional activation by cAMP–Crp is a decreasing function of jtot (Fig. 4c)16. It transmits information on jtot to the glycerol uptake through differential regulation of glpK and glpD expression (Fig. 3b,c).

Extended Data Fig. 6 Model predictions.

With a single parameter set, our mathematical model reproduces the main features of various measurements in addition to the flux relations of the various mutants (Fig. 5b). a, The flux relation of the titratable PtsG strain with the ΔglpR glpK22 mutations (NQ1264) is linear (Pearson correlation: \(R_{{\mathrm{adj}}}^2\) = 0.97, n = 13 experimental conditions, t = 21, d.f. = 11, 𝑝 = 3 × 10−10), as predicted by the model. The flux relation for the glpR+ glpK+ strain (NQ1243) is also plotted for comparison. b, The growth-rate crossover of the titratable LacY strain. The model predicts that, on lactose only, the growth rate decreases linearly (dashed line) on lactose only as lactose uptake (green area) is reduced. On lactose and glycerol, the growth rate initially follows the same trend (solid line), but below the threshold lactose flux of ≈ 25 glycerol uptake (orange area) is gradually induced such that the growth rate remains approximately constant. This behaviour underlies the observations in Fig. 2a,c. (The model makes identical predictions for the titratable PtsG strain.) c, The G3P pools. The model predicts that in the titratable LacY strain grown on lactose + glycerol (purple solid line), as the lactose uptake is reduced, the G3P pool remains low until the growth rate approaches that on glycerol only, ≈ 0.7/h; it then sharply increases and converges to the level obtained during growth on glycerol only (orange circle). This closely resembles the measured behaviour in Fig. 3e (purple and orange circles). The sensitive response is disrupted in the ΔglpR glpK22 double mutant (purple dotted line and orange square), in agreement with Fig. 4d (purple and orange squares). d-e, Expression from glpF and glpD promoters. If the titratable PstG strain is grown on glucose + glycerol and glucose uptake is reduced, expression levels from both glpF and glpD promoters are negligible until the growth rate approaches the growth rate on glycerol only, ~0.7/h (purple solid lines in both figures). The sudden onset of expression is completely lost in the ΔglpR mutation strain (light blue curves in both figures). This agrees with the measured expression levels in Fig. 3b,c.

Source data

Extended Data Fig. 7

a, This diagram illustrates the difference in the effect of glycolytic and gluconeogenic substrates on glycerol uptake. The uptake and catabolism of glycerol, gluconeogenic substrates and glycolytic substrates are drawn as three pathways (gray arrows) that merge at different places. In the regulation (red lines), two flux-sensors are involved: one (FBP) senses the upper-glycolytic flux jL, the other (cAMP-Crp) the total carbon flux jtot. Crucially, both flux sensors are required to fully suppress glycerol uptake; this is symbolized in the diagram using the symbol of a logical AND gate. Glycolytic substrates contribute both to the upper-glycolytic flux and the total carbon flux. A sufficiently large glycolytic flux therefore activates both the upper-glycolytic flux sensor AND the total-flux sensor, which together suppress glycerol uptake. In contrast, gluconeogenic substrates do not contribute to the upper-glycolytic flux and will not fully inhibit glycerol uptake even if they provide a large carbon flux. (Through the total-flux sensor cAMP-Crp, gluconeogenic substrates will affect glycerol uptake mildly, but both substrates remain co-utilized.) This difference between glycolytic and gluconeogenic substrates underlies the pattern in Fig. 5c. b, A pattern similar to Fig. 5c is obtained if glycerol is replaced by xylose. Shown here is the growth rate of WT cells (NCM3722) in M9 medium24 on xylose plus a second substrate plotted against growth rate with the second substrate only, for a variety of “second” substrates. The growth rate on xylose only is indicated by horizontal and vertical dotted lines. The growth rate on both substrates shows a similar dependence on the “second” substrate species as seen in Fig. 5c of the main text: If the second carbon substrate is processed at least partially by upper glycolysis (blue circles) the growth rate is approximately the larger of the two single-substrate growth rates, possibly with an exception for mannose. If on the other hand the second substrate is a gluconeogenic substrate (orange circles) the growth rate on both substrates is usually larger than either of the two single-substrate growth rates. c, Same as b, but for fucose as the “first” substrate.

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Okano, H., Hermsen, R., Kochanowski, K. et al. Regulation underlying hierarchical and simultaneous utilization of carbon substrates by flux sensors in Escherichia coli. Nat Microbiol 5, 206–215 (2020). https://doi.org/10.1038/s41564-019-0610-7

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