The logic layout of the TOL network of Pseudomonas putida pWW0 plasmid stems from a metabolic amplifier motif (MAM) that optimizes biodegradation of m-xylene
© Silva-Rocha et al; licensee BioMed Central Ltd. 2011
Received: 9 July 2011
Accepted: 11 November 2011
Published: 11 November 2011
The genetic network of the TOL plasmid pWW0 of the soil bacterium Pseudomonas putida mt-2 for catabolism of m-xylene is an archetypal model for environmental biodegradation of aromatic pollutants. Although nearly every metabolic and transcriptional component of this regulatory system is known to an extraordinary molecular detail, the complexity of its architecture is still perplexing. To gain an insight into the inner layout of this network a logic model of the TOL system was implemented, simulated and experimentally validated. This analysis made sense of the specific regulatory topology out on the basis of an unprecedented network motif around which the entire genetic circuit for m-xylene catabolism gravitates.
The most salient feature of the whole TOL regulatory network is the control exerted by two distinct but still intertwined regulators (XylR and XylS) on expression of two separated catabolic operons (upper and lower) for catabolism of m-xylene. Following model reduction, a minimal modular circuit composed by five basic variables appeared to suffice for fully describing the operation of the entire system. In silico simulation of the effect of various perturbations were compared with experimental data in which specific portions of the network were activated with selected inducers: m-xylene, o-xylene, 3-methylbenzylalcohol and 3-methylbenzoate. The results accredited the ability of the model to faithfully describe network dynamics. This analysis revealed that the entire regulatory structure of the TOL system enables the action an unprecedented metabolic amplifier motif (MAM). This motif synchronizes expression of the upper and lower portions of a very long metabolic system when cells face the head pathway substrate, m-xylene.
Logic modeling of the TOL circuit accounted for the intricate regulatory topology of this otherwise simple metabolic device. The found MAM appears to ensure a simultaneous expression of the upper and lower segments of the m-xylene catabolic route that would be difficult to bring about with a standard substrate-responsive single promoter. Furthermore, it is plausible that the MAM helps to avoid biochemical conflicts between competing plasmid-encoded and chromosomally-encoded pathways in this bacterium.
KeywordsRegulatory networks logic gates TOL network logicome
Prokaryotic regulatory networks are organized in a hierarchical way, on top of which a few transcriptional factors (TF) may coordinate the expression of hundreds of genes of different functional categories (including other downstream TFs), thus linking extracellular conditions to distinct physiological states . It is generally accepted that cell-wide regulatory and metabolic circuits acquire an optimum of performance by connecting a large number of discrete network motifs  that, once merged, endow cells with a remarkable ability to deal with changing physicochemical and nutritional scenarios. . In environmental bacteria, such a regulatory optimum is often unsettled following the knock-in of new functions through horizontal gene transfer (HGT), typically by conjugative plasmids . This is because the new encoded traits must find a suitable functional and physical site in the recipient cells to secure their establishment in the new host , a process that is not devoid of regulatory, metabolic and structural problems . Many conjugative plasmids of bacteria thriving in sites polluted by recalcitrant chemicals (e.g. compounds released by urban and industrial activity) determine autonomous catabolic systems for biodegradation of such unusual carbon sources . These mobile elements quickly spread through the microbial population of the site upon occurrence of a suitable environmental pressure [8–10]. This creates a natural scenario of network perturbation, as the enzymes and the regulators encoded by both the indigenous genome and the acquired plasmids can interfere with each other. Yet, the literature contains numerous cases of bacteria whose native metabolic complement has been stably expanded to degrade recalcitrant and xenobiotic compounds because of naturally gained catabolic plasmids [11–14]. In these instances, one can safely assume that network implantation conflicts caused by HGT have been ultimately solved. Moreover, the structure of such successful regulatory circuits is likely to bear both the problem and the solution somehow encrypted in their topology and their dynamics.
We have previously formalized the regulatory network of the TOL system as a digital circuit by converting all known molecular interactions into binary logic operations . In this work, we have further exploited such a Boolean approach for decoding the underlying reason for the complex genetic circuit that controls m-xylene metabolism in this plasmid. To this end, we have [i] minimized the TOL logicome by removing non-critical connections, [ii] translated the resulting logic network into a set of piecewise-linear differential equations  amenable to a dynamic modeling, [iii] performed simulations on the extant circuit along with counterparts lacking distinct interactions and [iv] matched in silico predictions to in vivo assays. As shown below, a minimized logicome model is composed of only five variables that not only faithfully described the behavior of the TOL system but revealed that the entire network architecture frames the action of an unprecedented regulatory device that accounts of the entire topology of the system.
Results and Discussion
Minimization and streamlining of the catabolic TOL network
The organization TOL regulatory and metabolic circuit of P. putida mt-2 for biodegradation of m-xylene is shown in Figure 1. The two pathways/operons encoded in the self-transmissible plasmid pWW0 present in this strain are coordinately expressed in response to the aromatic compounds which can be used by this bacterium as a sole carbon source if no other more palatable growth substrate is available . Degradation of m-xylene takes place through two series of biotransformations. First, the upper pathway encodes enzymes for the conversion of m-xylene into m-toluate (i.e. 3-methylbenzoate, 3 MBz), which are expressed from the Pu upon activation by the regulatory protein XylR in response to the aromatic substrate (; Figure 1). Second, the meta (also called lower) pathway encodes activities for the ensuing metabolism of 3 MBz into intermediates of the TCA cycle. This second operon of the system is activated by another plasmid-encoded regulator, XylS. This factor has the ability to trigger transcription at the cognate promoter Pm either by itself (provided that there is enough concentration of the protein) or in combination with 3 MBz, in which case much lower levels of XylS are required to the same end [23, 24]. Apart of these plasmid-encoded regulatory components, a number of host factors (such as σ70, σ54, σ38, σ32, IHF and HU) and global regulators (Crc, PtsN, TurA, PprA, ppGpp; ) mediate a fine tuning of the system to a large number of environmental signals. Under the same physiological conditions, these default connections to the growth status of the host remain unaffected and they can be basically ignored. In particular, the action of the Crc factor that inhibits XylR translation when cells grow in a rich medium  can be suppressed experimentally by culturing cells in a synthetic mineral medium devoid of amino acids and other repressive substrates [27, 28].
Structure of the minimalist logic circuit that governs the TOL system
Equations and threshold inequalities used to simulate the TOL network
PL equations for the TOL model
dupper/dt = k 0 upper * s + (XylR, θ XylR ) * s + (m xyl , θ mxyl ) - g upper * upper
Upper pathway expression
dXylS/dt = k 0 XylS + k 1 XylS * s + (XylR, θ XylR ) * s + (m xyl , θ mxyl ) - g XylS * XylS
dmeta/dt = k 0 meta * s + (XylS, θ 2 XylSh ) + k 1 meta * s + (XylS, θ 1 XylSi ) * s + (upper, θ upper ) * s + (m xyl , θ mxyl )
- g meta * meta
Meta pathway expression
dXylR/dt = k 0 XylR - g XylR * XylR
zero upper <θ upper < k 0 upper /g upper < max upper
Parameter inequalities for equation 1
zero XylS < θ 1 XylSi < k 0 XylS /g XylS < θ 2 XylSh < (k 0 XylS + k 1 XylS )/g XylS < max XylS
Parameter inequalities for equation 2
zero meta < θ meta < k 0 meta /g meta < k 1 meta /g meta < (k 0 meta + k 1 meta )/g meta < max meta
Parameter inequalities for equation 3
zero XylR <θ XylR < k 0 XylR /g XylR < max XylR
Parameter inequalities for equation 4
Alternative parameter inequalities
zero XylS < θ 1 XylSi < k 0 XylS /g XylS < (k 0 XylS + k 1 XylS )/g XylS < θ 2 XylSh < max XylS
No XylS hyper-expression condition (for eq. 2)
zero upper < k 0 upper /g upper <θ upper < max upper
No XylSa condition (for eq.1)
Coarse description of TOL network dynamics
In order to simulate the activation of the TOL network in response to m-xylene, equations 1-4 (see Table 1) where implemented in the Genetic Network Analyzer software (GNA; ), as described in the Methods section. Also, m-xylene was placed as an input variable , meaning that [i] no PL equation is specified in the model associated to this component, and [ii] its concentration is not allowed to change during the simulations. As a pre-requisite to perform model simulation, parameter inequalities (Table 1) where defined for all variables in the system as described previously . This approach allows setting the thresholds of the interaction processes, a fundamental attribute when a component of the system has more than one target or synthesis rate (which is indeed the case in TOL).
XylR is the master regulator of a synchronized single-input module (SIM)
In order to examine the consequences of having XylR as the upstream regulator in the SIMXylR motif we simulated the response Pu and Ps under various θ XylR parameter values (Table 1). To this end we just varied the value of the θ XylR parameter in equations for Pu and Ps. The same value for the two promoters means synchronization (i.e. XylR is equally capable to activate Pu and Ps; Figure 4b) whereas setting different θ XylR parameters for each promoter results in a temporal order of activation (not shown). But what is the actual state of affairs in the TOL system in vivo? To answer this question we analyzed experimentally the activation kinetics of Pu and Ps. For this, we cloned these promoters upstream of a promoter-less luxCDABE operon placed in a low-copy broad-host range plasmid (Figure 4c). The resulting transcriptional fusions were introduced into a wild type P. putida mt-2 strain to faithfully monitor the dynamics of the TOL system. The system was induced with 3-methylbenzyl alcohol (3MBA) as a proxy of m-xylene. 3MBA is the first intermediate of the biodegradation route and it is equally able to trigger the TOL system . Furthermore, its much higher solubility (in contrast to the volatile m-xylene) makes 3MBA more suitable for induction experiments in liquid media . As shown in Figure 4d, both cloned promoters were efficiently induced upon 3MBA exposure. In order to quantify the response of Pu and Ps to 3MBA, overnight grown cells were diluted in fresh M9 medium supplemented with either 3MBA as the sole carbon source or with 3MBA plus succinate. The luminescent signals of the strains were quantified along the growth curve and normalized respect to the respective optical density at 600 nm. The very small offsetting between Pu and Ps observed in the medium with both succinate and 3MBA (Figure 4e) disappeared altogether in the culture where 3MB was employed as sole C-source (Figure 4f). The behavior of both promoters is thus virtually identical under the conditions tested (absolute values were also comparable, not shown). The lack of significant differences in the timing or overall kinetics of Pu and Ps activation indicated that the SIMXylR motif of the TOL network operates in a synchronous way for triggering expression of the upper pathway and the xylS gene. Finally, we could observe that Pm activity reached its maximum activity with a noticeable delay in respect to Pu and Ps (Figure 4f), as anticipated with the results of the simulation of Figure 3b. This delay is expected because Pm functionality does require more steps (production of XylS, formation of 3 MBz) than the instant trigger of Pu and Ps by effector-activated XylR (XylRa).
The results above were very informative because -to the best of our knowledge- synchronous SIM motifs have not been reported before in genetic networks. The role of SIMXylR for the TOL circuit dynamics is therefore likely to be crucial. If upper were expressed earlier than xylS, 3 MBz production would occur also earlier than maximal expression of meta (i.e. Pm activation by hyper-expressed XylSh would be delayed) and it would thus result in a transient accumulation of 3 MBz. In contrast, if xylS were activated before upper, expression of meta would start earlier and cells would have the degradation machinery for 3 MBz in place before the compound could actually materialize from m-xylene biodegradation. Interestingly, proteins TurA and PprA have been recently demonstrated to interfere with XylR binding to the Pu promoter but not to Ps [39, 40]. Such interference, which is factually equivalent to decreasing the affinity of XylR for Pu, would favor the second scenario (i.e. meta expressed before 3 MBz appears), thereby suggesting that these proteins have a role to set a temporal order in activation of the TOL operons. Alas, the signals that trigger TurA and PprA activities are unknown [39, 40].
Expression of the metaoperon reflects the combination of two separate activation loops
The outcome of these in vivo experiments is that the Pm induction levels derived from each of the two forms of XylS are similar when acting separately but they become synergistic by >4-fold when working together (Figure 7d). This is mechanistically easy to explain, because overproduced XylSh can be converted to XylSa by exposure to 3 MBz. The XylSh loop thus ensures [i] that expression of the meta pathway is well underway before 3 MBz is formed through the action of the upper TOL pathway and [ii] that the lower route is boosted very significantly by 3 MBz. These in vivo results not only match the findings stemming from model simulations discussed above but also suggest that the rationale of the regulatory architecture of the TOL network is to maintain a good level of all products of the two operons at all times following exposure to m-xylene and thus avoid any transient accumulation of 3 MBz by first anticipating its production from m-xylene (the XylSh loop) and then by amplifying expression of the 3 MBz-degrading genes (i.e. the lower pathway) as soon as 3 MBz is formed. This regulatory device could have evolved to solve a metabolic conflict between the enzymatic modules encoded in the TOL plasmid and the indigenous metabolic network of the host, as argued below.
Mathematical modeling with piecewise-linear differential equations
This inequality indicates that when component i is produced at rate k i and degraded with a rate constant g i , so that its concentration converges towards the level k i /g i , it exceeds the threshold θ i 1 , but not θ i 2 . This allows an estimate of the concentration of the components in reference to their threshold values, even in absence of quantitative information on the parameters of the system. The inequalities for the TOL model were set as shown in Table 1.
It is possible to follow the dynamics of the regulatory network by computing a temporal progression of so-called qualitative states, each consisting of the levels of the concentration variables with respect to their thresholds. In each qualitative state the trend of the concentration variables (increasing/decreasing/steady) determines the possible transitions to successor states. The resulting directed graph of qualitative states and transitions between qualitative states is called a state transition graph (for a more detailed description, see [21, 46]. Note that the PL equations above and the associated transition graph describe the temporal order of signal propagation in the network when the first input signal is present and the system moves toward a steady state (where the concentrations of the components stop to change). The actual time interval during which the system remains in a state before reaching the next is not contained in these qualitative models. However, the representation reliably predicts the temporal ordering of states, and thus the consecutive changes in the levels of each of the components of the network.
Using the biological assumptions for known regulatory interactions (Figure 1) and the resulting logic operations (Figure 2c), we defined four PL equations describing XylR, XylS, upper and meta production (Table 1). The sole input for the system was m-xylene, which was defined as an input variable i.e. one having a constant concentration along the simulations, . For implementation of in silico mutations, we changed threshold inequalities as follows. In one case Pm activation was simulated in the absence of the XylSh loop by setting the parameter θ 2 XylSh to be higher than the maximal concentration reachable upon Ps activation (No XylS hyper-expression condition, Table 1). Similarly, simulation of the Pm activation event in a scenario lacking XylSa, we set the upper pathway not to produce enough concentrations of 3 MBz for creating XylSa (No XylSa condition, Table 1). Consideration of different threshold inequalities in the TOL model allowed us to simulate the specific conditions as discussed in the Results section.
Strains, chemicals and growth conditions
E. coli CC118 strain  was used as the host for plasmid constructions and maintenance, while P. putida mt-2  was employed for the analysis of promoter activity with reporter constructs (see below). E. coli was grown in Luria-Bertani (LB) medium at 37°C. Unless indicated otherwise, P. putida was cultured in a minimal medium M9 supplemented with MgSO4 (2.0 mM), citrate or succinate (0.2%) as the only carbon source and grown at 30°C. Plasmids were conjugally transferred from E. coli to P. putida with a tripartite mating procedure  using E. coli HB101 (RK600) as the helper strain. When required, growth media was supplemented with kanamycin (Km, 50 μg/ml) or chloramphenicol (Cm, 30 m/ml). All chemicals and substrates, including aromatic effectors (m-xylene, o-xylene, 3-methylbenzyl alcohol and 3-methyl benzoate) were purchased from Sigma-Aldrich.
Construction of reporter gene fusions
The TOL promoters Pu, Ps and Pm were separately cloned in pSEVA226, a KmR broad host range vector (RK2 origin of replication) bearing a promoterless luxCDABE  operon downstream of the multiple cloning site of pUC (Silva-Rocha et al., in preparation). To this end, each of the promoters of interest was amplified from P. putida mt-2 DNA through PCR reactions with Pfu DNA polymerase (Promega) using primer pairs PUF (5'-GCG GAA TTC TTG ATC AAA TC GA CA GG TG GT TAT G-3') and PUR (5'-GCG CGG ATC CGT CTC GTA TAG CTA GCA ACC GCC-3') for Pu, PSF (5'-GGC CGA ATT CAT TCC ATC TGC CAC TTT AG-3') and PSR (5'-CGG CCG GAT CCC GGT TCT CTT ATT TTA ATG TGG-3') for Ps, and PMF (5'-CGG CCG AAT TCG GTT TGA TAG GGA TAA GTC C-3') and PMR (5'-CGG CCG GAT CCT CTG TTG CAT AAA GCC TAA-3') for Pm. These primers introduced in each case EcoRI and BamHI sequences in equivalent sites of the 5' and 3' regions of each promoter (underlined in the primer sequence). PCR products were purified, digested with EcoRI and BamHI (NewEngland BIolabs), ligated to pSEVA226 cleaved with the same enzymes and transformed in chemically competent E. coli CC118 cells. The resulting clones were named pSEVA226-Pu, pSEVA226-Ps and pSEVA226-Pm. Sequence fidelity of the cloned promoters was verified in all cases by DNA sequencing.
Promoter activity quantification and data processing
The activity of the TOL promoters in response to inducers was examined with different procedures depending on the nature of the specific chemical tested. In case of soluble inducers (3MBA and 3 MBz), overnight grown P. putida cells harboring the reporter plasmid under examination were diluted 1:20 in fresh minimal media with the inducer of interest at a final concentration of 1 mM. 200 μl aliquots (with four replicates) of the thereby diluted cells were placed in 96-well Microplates (Optilux™, BD Falcon) and incubated in a Wallac Víctor II 1420 Multilabel Counter (Perkin Elmer) at 30°C, the optical density at 600 nm (OD600) and the bioluminescence being recorded every 30 min. Promoter activity was quantified by normalizing bioluminescence in respect to cell density (i.e. bioluminescence/OD600). For testing volatile inducers (m-xylene and o-xylene), single colonies of P. putida clones bearing the reporter plasmids indicated were patched on M9/citrate agar plates, grown overnight an exposed to saturating vapors of a 1 M inducer solution in DMSO. Non-disruptive monitoring of promoter output was carried out with a VersaDoc™ Imaging System (BioRad) and the results processed with the ImageJ software (http://rsbweb.nih.gov/ij/). In either case, graphic representations of promoter activities were generated with MATLAB software (MathWorks).
This work was defrayed by generous grants of the CONSOLIDER program of the Spanish Ministry of Science and Innovation, by the BACSIN and MICROME Contracts of the EU and by funds of the Autonomous Community of Madrid.
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