Transcription factor levels enable metabolic diversification of single cells of environmental bacteria

Abstract

Transcriptional noise is a necessary consequence of the molecular events that drive gene expression in prokaryotes. However, some environmental microorganisms that inhabit polluted sites, for example, the m-xylene degrading soil bacterium Pseudomonas putida mt-2 seem to have co-opted evolutionarily such a noise for deploying a metabolic diversification strategy that allows a cautious exploration of new chemical landscapes. We have examined this phenomenon under the light of deterministic and stochastic models for activation of the main promoter of the master m-xylene responsive promoter of the system (Pu) by its cognate transcriptional factor (XylR). These analyses consider the role of co-factors for Pu activation and determinants of xylR mRNA translation. The model traces the onset and eventual disappearance of the bimodal distribution of Pu activity along time to the growth-phase dependent abundance of XylR itself, that is, very low in exponentially growing cells and high in stationary. This tenet was validated by examining the behaviour of a Pu-GFP fusion in a P. putida strain in which xylR expression was engineered under the control of an IPTG-inducible system. This work shows how a relatively simple regulatory scenario (for example, growth-phase dependent expression of a limiting transcription factor) originates a regime of phenotypic diversity likely to be advantageous in competitive environmental settings.

Figure 1

Organization of the TOL network of the TOL plasmid pWW0 of P. putida mt-2. The TOL pathway encoded by plasmid pWW0 consists of two different operons: the upper operon, the products of which transform m-xylene into 3-methylbenzoate, and the lower operon that produces enzymes for further metabolism of this compound into TCA cycle intermediates. XylR and XylS are the transcriptional regulators that control expression of either operon. The master regulatory gene xylR is encoded in a location adjacent to the end of the meta operon and is expressed from the Pr promoter. XylR is produced in an inactive form (Ri) that, in the presence of the pathway substrate (m-xylene) changes to an active form (Ra). Ra then activates both Pu and Ps, triggering expression of the upper pathway and XylS, respectively. At the same time, Ra acts as repressor of its transcription, thereby decreasing its own expression. The part of the network under study is highlighted in colour, the rest is faded. Operons and regulatory elements not to scale.

Figure 2

The Pr/XylR/Pu node and formalization of regulatory interactions. (a) Basic interactions between the Pu promoter, its cognate regulator XylR and the global regulators IHF and Crc whose levels are modulated by growth state. (b) Biochemical reactions involved in the upper route of the TOL pathway. We take into account transcription from the Pr and Pu promoters (with rates β_R and β_U, respectively), translation of the corresponding mRNAs (with rates ρ_R and ρ_U), and degradation of the molecular species: mRNAs (with degradation rates δ_mR and δ_mU) and proteins (with rates δ_R and δ_U). Translational repression of xylR mRNA (m_R) by Crc is described by the effective association rate constant μ. We also consider, sketched as double arrows, the activation (mediated by m-xylene) and inactivation reactions of XylR, as well as the binding/unbinding reactions of active XylR (Ra) to the Pu promoter. A detailed description of the model is provided in Supplementary Information, and the values of the reaction rates specified in Table 1.

Figure 3

Single-cell measurements of Pu-GFP activity and stochastic model simulations. (a) Cells grown overnight were diluted in fresh M9-succinate media, incubated to the mid-exponential phase and then exposed to saturating vapours of m-xylene. The fluorescence distributions were calculated from flow cytometry measurements at time intervals of 1 h. (b) Distribution of number of GFP molecules per cell as obtained from 20 000 stochastic trajectories using the model parameters in exponential phase (see Supplementary Information and Table 1). (c) Fluorescence distribution of Pu-GFP levels in P. putida Mmt-2-Pu cells arrested in stationary phase (viable but without any growth), at different time points after exposure to m-xylene vapours. (d) Distribution of number of GFP molecules per cell from ensembles of trajectories generated by the stochastic model in stationary phase conditions.

Figure 4

Deterministic and stochastic dynamics of TOL network components in different growth phases. Differences in Pu promoter activation, Pu mRNA variability and GFP induction in exponential (a, b) and stationary (c, d) phases. (a) Time evolution of active XylR (top panel), active Pu promoter (intermediate panel) and Pu mRNA (low panel). Black line is the stochastic simulation and coloured lines the deterministic results. (b) Seven stochastic trajectories mimicking GFP induction in different cells during the time course of experiments. (c, d) The same variables and GFP trajectories in stationary phase conditions.

Figure 5

Effect of IHF levels in response variability. (a) Simulation of increasing intracellular IHF levels in exponential phase to the concentration present in stationary conditions (~14 000 molecules). Distributions remain bimodal (left panel) and Pu promoter dynamics exhibits slow fluctuations (right panel). (b) Simulation of decreasing IHF levels in exponential phase to those of exponential conditions (~2000 molecules). The population distributions of GFP are still unimodal (left panel) although noise increases, due to larger fluctuations in Pu mRNA (right panel).

Figure 6

Genetic strategy to conditionally overexpress XylR in P. putida upon IPTG addition. (a) Organization of the mini-Tn7 carrying xylR under control of lacI^q/Ptrc system and assembled in vector pTn7-M to yield the mini-Tn7 [L-Gm lacI^q/Ptrc → xylR-R] transposon delivery plasmid pIB. (b) The business part of the mini tranposon is then inserted into the attTn7 site (close to the glmS gene) of recipient strain P. putida KT [Pu-GFP], which bears a chromosomally determined transcriptional Pu-GFP fusion (see Materials and methods). In the resulting strain (P. putida KT-IB1), XylR production depends on the concentration of IPTG added to the medium, while m-xylene is added to activate XylR for triggering Pu promoter activity.

Figure 7

Overexpression of XylR reduces cellular heterogeneity in exponential phase. Experimental (left) and numerical (right) distribution of GFP outputs in exponential phase conditions in Pu-GFP engineered strain P. putida KT-IB1, which is inserted with the lacI^q/Ptrc → xylR genetic construct sketched in Figure 6. Even without inducer ([IPTG]=0), the basal activity of the Ptrc promoter is ~2–3-fold than that of the native Pr promoter, blurring its bimodality but still producing very heterogeneous responses. Addition of IPTG reduces variability in exponential phase and yields only unimodal distributions.