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Deconvolution of gene expression noise into spatial dynamics of transcription factor-promoter interplay Angel Goñi-Moreno, Ilaria Benedetti, Juhyun Kim, and Victor de Lorenzo ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.6b00397 • Publication Date (Web): 29 Mar 2017 Downloaded from http://pubs.acs.org on March 30, 2017

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Deconvolution of gene expression noise into spatial dynamics of transcription factorpromoter interplay

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by

5 Ángel Goñi-Moreno1,$, Ilaria Benedetti1, Juhyun Kim1, Víctor de Lorenzo1,*

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Systems Biology Program, Centro Nacional de Biotecnología, Cantoblanco-Madrid, Spain.

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Keywords:

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Transcriptional noise, bet hedging, Pseudomonas putida, intracellular heterogeneity, TOL plasmid

Running title:

Intracellular microgranularity and intrinsic noise

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*Correspondence to:

Víctor de Lorenzo ([email protected])

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Centro Nacional de Biotecnología (CNB-CSIC)

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Campus de Cantoblanco, 28049 Madrid (Spain)

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Tel: (+34 91) 585 45 73; Fax: (+34 91) 585 45 06

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______________________________________________________________________________

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$

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United Kingdom

Present address: School of Computing Science, Newcastle University, Newcastle upon Tyne,

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Abstract

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Gene expression noise is not only the mere consequence of stochasticity, but also a signal that

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reflects the upstream physical dynamics of the cognate molecular machinery. Soil bacteria facing

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recalcitrant pollutants exploit noise of catabolic promoters to deploy beneficial phenotypes such

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as metabolic bet-hedging and/or division of biochemical labour. Although the role of upstream

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promoter-regulator interplay in the origin of this noise is little understood, its specifications are

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probably ciphered in flow cytometry data patterns. We studied Pm promoter activity of the

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environmental bacterium Pseudomonas putida and its cognate regulator XylS by following

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expression of Pm-gfp fusions in single cells. Using mathematical modelling and computational

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simulations, we determined the kinetic properties of the system and used them as a baseline code

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to interpret promoter activity in terms of upstream regulator dynamics. Transcriptional noise was

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predicted to depend on the intracellular physical distance between regulator source (where XylS

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is produced) and the target promoter. Experiments with engineered bacteria in which this distance

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is minimised or enlarged confirmed the predicted effects of source/target proximity on noise

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patterns. This approach allowed deconvolution of cytometry data into mechanistic information on

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gene expression flow. It also provided a basis for selecting programmable noise levels in

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synthetic regulatory circuits.

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Introduction

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Information processing inside bacterial cells in response to physicochemical stimuli requires

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regulatory cascades to propagate input/output signals effectively. This process typically involves

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several steps in which transcription factors (TF) interact with promoters to trigger gene

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expression responses. These interactions occur stochastically rather than deterministically1-5,

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leading to specific and variable noisy signals. The customary view considers this effect the

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necessary result of random fluctuations of regulatory elements present in short supply in

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individual cells6. The dynamic properties of promoter activation have a determining influence on

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expression noise range and intensity7-9.

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Single-cell technologies10-12 has helped to clarify various mechanisms behind noise generation. 2



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A major noise source in virtually every prokaryotic promoter is the so-called bursting effect13, 14,

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a pulse-like activity that results largely from discontinuous topological changes in DNA caused

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by RNA polymerase progression through DNA15, 16. Different in vivo noise generators can be

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measured by fluorescence distribution in single cells, which can be followed by cytometry17.

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Cell cytometry profiles bear embedded information on the mechanistic origin of the gene

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expression noise, but it remains unclear how these data can be retraced to the physical TF-

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promoter dynamics that produces this fluorescence distribution.

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The Gram-negative soil bacterium Pseudomonas putida mt-2 provides an excellent model for

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responding to these questions. This microorganism can thrive in sites polluted with aromatic

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chemicals18 such as m-xylene (m-xyl), because of a complex metabolic and regulatory network

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encoded in its single-copy TOL plasmid pWW019 (Figure 1A). The noise from each of the four

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promoters seems to be precisely controlled, giving rise to metabolic diversification20. This allows

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a fraction of the cells in a population to explore new nutritional landscapes without risking

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communal collapse20, 21.

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The noise of the TOL network Pu and Ps promoters is explained by the small number of

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molecules of their regulatory protein XylR22; however, that of Pm, which controls the lower

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operon of the TOL pathway in response to 3-methylbenzoate23 (3MBz) is puzzling. This

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promoter can be activated by two separate mechanisms, [i] low intracellular concentration of its

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regulator, XylS, bound to its effector, 3MBz, or [ii] m-xyl-induced XylS overproduction with no

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3MBz involvement (Figure 1A). When these conditions coincide (i.e., high XylS levels and

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presence of 3MBz), Pm activity is very high24, 25. Therefore, Pm function is that of an OR logic

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gate (Figure 1B) where either input, 3MBz or m-xyl, can trigger its activity. The puzzling feature

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of the Pm regulatory node is that the Pm noise pattern varies greatly depending on the induction

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mode. We analysed whether cell cytometry data for transcriptional Pm-gfp fusions could be

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decoded into information on the physical dynamics of promoter activation, including clues to

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XylS/Pm regulatory node spatial arrangement.

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A combination of modelling and wet experiments shows these noise regimes can be changed by 3


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regulator numbers and the spatial arrangement of regulatory components. These predictions were

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validated in cells engineered to minimise the distance between the XylS source site and Pm

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location.

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Results

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Two distinct noise regimes define Pm output. The activity of the TOL plasmid promoter Pm

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(Figure 1) can be induced by exposing P. putida mt-2 to one of two inputs. The first input, 3MBz,

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activates the XylS molecules leaked from a non-active Ps promoter. The second, m-xyl, is

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converted metabolically inside cells by the upper TOL pathway enzymes to 3MBz. In addition,

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the latter scenario leads to XylS overproduction since m-xyl triggers the activity of the Ps

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promoter via the XylR regulator. Therefore, the input m-xyl leads to a higher concentration of

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activated XylS molecules (XylSa) than 3MBz (Figure 1C). Although the Pm/XylS node of the

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TOL network is often abstracted as a binary switch with only ON/OFF states, the unique noisy

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nature of Pm output invalidates this view and highlights the role of signal variability26.

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The P. putida mt-2-Pm, strain derives from the natural P. putida mt-2 isolate bearing the TOL

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plasmid pWW0, but was engineered to bear a transcriptional Pm-gfp fusion in its chromosome

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(Table 1 and Fig. 2A). This strain has its sole XylS source in the pWW0-encoded gene, which is

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expressed through the TOL plasmid Ps promoter (see Figure 1 and Methods), and the XylS target,

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the encoded Pm-gfp, in the chromosome. Because of this arrangement, the DNA region that

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encodes and supplies XylS transcriptional regulators is not adjacent to the target promoter, from

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which it is physically separated in trans. Differences in Pm expression regimes depend on

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whether the XylS/Pm regulatory node is induced with m-xyl or 3MBz (Figure 2A), as shown by

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flow cytometry results of promoter activity (Figure 2B, C). Induction with m-xyl (XylS

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overproduction and intracellular 3MBz production) leads to a situation in which the output signal

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noise range allows a null overlap between ON (uninduced) and OFF (induced) states. In contrast,

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induction with exogenous 3MBz (low XylS) left the OFF state unaffected while producing an ON

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state in which the noise regime consisted of a broad, plateau-like distribution from the lowest to

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the highest intensity value. The output ON signals are patently different in both cases, suggesting 4



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that they originate in a different type of TF-promoter interplay beyond randomness. We measured

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the noise produced by leaky XylS expression (no Ps activity, Figure 2C) and that of the full XylS

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production (Ps activity, Figure 2B). The former scenario, while very noisy, does not depend of

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upstream nodes. The latter does depend on upstream nodes, but does not generate noisy patterns.

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Altogether, we can assume that upstream Pm nodes are not a source of noise in our setup.

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Therefore, we focus our study in Pm-gfp kinetics and TF-promoter dynamics.

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Noise deconvolution and rate optimisation. The regulatory node formed by the pair XylS-Pm

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can be modelled according to the kinetic rates in Figure 3A. The regulator in its active form,

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XylSa, binds Pm (k1, molecules-1hour-1) to fire its activity and unbinds it (k-1, hour-1) back to the

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default silent state. When bound, mRNA molecules are transcribed (k2, hour-1) from the

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downstream gene, gfp, which produce proteins through translation (k3, hour-1). Even when the

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regulator is not bound, there is some leakage of basal Pm transcription (k6, hour-1). To complete

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the model, we included degradation rates for mRNA (k4, hour-1) and GFP (k5, hour-1).

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Our goal was to find those values for the rates that allow Pm to produce different noise regimes

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depending on the inducer used. To this end, we considered a training vector θ with the rates

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mainly responsible for Pm dynamics, defined as follows: θ = (k1, k-1, k2, k3, [XylSa]), where [x]

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(molecules) denotes number of molecule x. Basal activity (k6 = 15) and degradations (k4 = 10, k5

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= 2) were specified within standard ranges for mathematical analysis27-31 (see Methods). To

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identify the set of values that best simulated the experimental output, we defined two fitness

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parameters (f) based on the ON state produced by 3MBz (Figure 2C), wide-range signal (f1) and

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plateau-like surface (f2) (Figure 3B). An optimisation process (see Methods) yielded vector θf =

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(0.004, 1.5, 900, 80, 200). Strong expression kinetics (transcription + translation) are needed to

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produce high signal intensity (requirement for f1), while low binding/unbinding rates guarantees

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affinity instability and thus helps generate the final plateau-shaped distribution (f2). These values

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produce the broadest range signal possible while assuring flatness which resembles the ON state

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induced by 3MBz (Figure 2B). To test whether the noise regime observed in the ON state during

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m-xyl induction could be reproduced, we carried out stochastic simulations (see Methods) in

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which the number of XylSa molecules was increased while vector θf rates were untouched; this 5


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concentration was fixed at [XylSa] = 3000 molecules, which reproduced the m-xyl-induced ON

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state (simulations in Figure 3C). The balanced relationship of the two quantities, 200 and 3000,is

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based on qualitative observations32. Although some rates may have unusual values e.g. low

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binding, it is important to take into account that these are the output of the optimisation process.

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Therefore, they are information-rich from a mathematical standpoint since they indicate where

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the model needs further analysis. During the present study, more realistic rates33,

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investigated (while maintaining system output) under more complex scenarios than mere time-

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based approaches.

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will be

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We observed that Pm activity or its in-silico counterpart θf, is very specific; in other words,

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changes in certain rates can cause incorrect promoter function. As an example, the stability of the

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system at variable binding and unbinding rates is shown in Figure 3D. The vertically aligned

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graphs in the figure shared the value for k-1, while that for k1 was increased to 250% its original

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value (top graph) or decreased to 40% (bottom). The same ratios were applied to the changes in

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k-1 in the horizontal simulations, for which k1 was constant (left, right). When these key rates

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were altered, the ideal behaviour (centre, maximum differential variability of DV =11.7) was no

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longer maintained.

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A stress analysis on the model tested its robustness to rate variation. GFP degradation rate (k5)

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was gradually decreased from 2.0 to 0.7 to render the protein more stable; affinity rates (k1 and k-1)

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were increased (up to 0.64 and 24.0, respectively) to analyse different TF-to-promoter behaviour

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(Supplementary Text S1). In all cases an adjustment of XylSa numbers for high and low induction

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(maintaining the 3000-to-200 ratio previously optimised) was sufficient to restore system

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function. Additionally, we performed a multi-agent simulation (Supplementary Figure S1) to

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include [i] XylSa entry and degradation rates, [ii] molecule dilution upon cell division and [iii]

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extrinsic noise (see Methods). A decrease in the unbinding rate was enough to reproduce the

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noise patterns under this new scenario. That indicates that dilution is not a decisive source of

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noise. Moreover, both Ps and Pm double their copy number (from one to two) just before

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division, so potential effects due to dilution are mitigated by a synchronous increase/decrease in

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both regulators and promoters. Taken together, the above information, plus a more general 6



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simulation of the full TOL network (Supplementary Text S2) suggested that for a given possible

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set of rates, as θf, the model for Pm expression depends crucially on the number of TF molecules

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to produce the observed behaviour. We now analyse these considerations in detail.

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System sensitivity to alterations in the number of transcription factor. Simulations of the Pm

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response to gradual changes in regulator numbers showed our estimated XylSa figures for both

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ON states (200 and 3000 for 3MBz and m-xyl, respectively) were optimal for maximising

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differences between the two expression noise regimes (under θf). The simulated Pm transfer

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function indicated the signal range and mean value at a given XylSa concentration (Figure 4A).

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Unlike other reported promoter transfer functions35-37, by which transcriptional activity produces

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similar noise (error bars in graphs) regardless of regulator concentration, here the central section

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of the curve shows a wider noise profile than the remainder. Indeed, the noise ranges reach

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maximum and minimum levels at ~200 and 3000 XylSa molecules, respectively. Such wider

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noise at intermediate induction has been observed38, 39 for gradients of one single input. However,

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the noise patterns of the Pm promoter are controlled by two distinct inputs, yet one regulator,

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which makes it dynamically unique. The simulation in Figure 4B shows the system tested in

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continuous function in which the inducer is changed sequentially. Pm activity suggests a trinary

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(rather than binary) signal, with three states: one OFF and two ON, each state with a distinct

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shape that unequivocally recalls its input. There is thus a direct correlation between the time

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intervals of the bursting effect and the max-min distance (amplitude) of the resulting gene

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expression levels. Furthermore, such a signal could be of potential use for multi-valued genetic

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logic circuits beyond the mere binary (0/1) abstraction40, 41.

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Two further analyses that link regulator dynamics with output noise are shown in Figure 4C.

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They must be interpreted in terms of the pulsing transcriptional bursts that frame the prokaryotic

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promoter activity. Two measurements, cumulative pulse duration and signal amplitude, were

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monitored in 24-h simulations of the system at several XylSa numbers (Figure 4C). Cumulative

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pulse refers to the core of the bursting effect, the total time that Pm is in its active state when

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XylSa is bound to it. Total XylSa residence time on Pm increased with the number of regulator

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molecules, which indicated that the ON state produced during m-xyl induction corresponds to 7


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large numbers of XylSa molecules. Signal amplitude, the difference between the highest and

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lowest output values of a single simulation run, decreased except in the interval in which the

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number of XylSa molecules was in the range ∈ [0, ≃150], when the distance between the

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uppermost/lowermost signal increases. The cognate inflexion point can thus be explained as the

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number of XylSa molecules that brings about the ON state during 3MBz induction. The fact that

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such inflection point occurs at low regulator levels (direct consequence of the rate values used)

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matches our intuition since the inducer 3MBz will activate the only XylS molecules leaked from

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a silent Ps promoter.

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Influence of intracellular regulator-promoter proximity on transcriptional output. Based

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on these findings, we asked whether the low-noise regime produced by m-xyl induction would be

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generated if 3MBz were the only input. When we interrogated the model with this question, the

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answer was positive when and only when 3MBz co-occurs with large numbers (3000) of the

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regulator XylSa. Although this condition would appear impossible to achieve since m-xyl, and

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not 3MBz, is needed to stimulate XylSa production (see Figure 1), we must consider the possible

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spatial effects of XylSa molecules. In the initial zero-dimensional model, it is assumed that each

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regulator is able to bind its target promoter at a given fixed rate (thus only time-based), as if it

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were a pure chemical reaction. When measuring living cells (as in Figure 2), one must consider

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that due to imperfect diffusion caused by molecular crowding and non-homogenous

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microviscosity42, 43, not all regulators are equally effective in reaching and binding cognate target

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DNA sequences. A specific regulator will not interact with its promoter if they cannot meet, since

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its access to the target promoter will be limited by the ease of diffusion towards the physical Pm

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location.

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To examine this possibility, we simulated protein trajectories33,

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Brownian motion43, 46, 47. Figures 5A and B record the trajectories of regulator molecules (XylSa)

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being expressed from what we term the source region: the physical Ps promoter location from

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which xylS is expressed. Given the coupling of transcription/translation processes in prokaryotic

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gene expression48-50, it is safe to assume that the TF protein is produced in close proximity to the

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Ps-xylS promoter gene pair (Figure 1). To trigger transcription, XylSa must migrate to a 8


44, 45

inside a cell, following


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physically separate target site where Pm is located; Figure 5A and B illustrate two possible

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scenarios, which diverge only in the number of proteins stemming from the source region. If

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regulator numbers are low, there are necessarily empty locations within the cell that XylSa may

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not encounter easily. Should Pm be located in one such regulator-empty sector, a productive

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contact is physically impossible, which leads to an OFF promoter state.

238 239

When this situation is scaled up to several thousand bacteria, each individual target region could

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accommodate a different number of regulators ranging from all to none, leading to pronounced

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cell-to-cell variability. In contrast, when many regulatory proteins originate in the source region,

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there are few empty areas in the intracellular space and the variability range narrows. Protein

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distribution at any given time does not necessarily match trajectory distribution (Figure 5A),

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meaning that the trails of the regulators are more space-dependent than their spread. As a result,

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the apparent binding rate, k1, in reality combines promoter-TF affinity proper (the ability of the

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two molecular partners to interact physically) with the probability that the regulator is located

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near the promoter (availability). In our initial time-based kinetic model, it was not possible to

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obtain both expression noises with usual parameter values, for instance, k1=0.5 and k-1=50. Using

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such parameters in a simulation with 200 TFs (corresponding to 3MBz induction) returned a

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unimodal distribution (Figure 5B, right) that was far from the noisy pattern observed

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experimentally (Figure 2C). The initial optimisation process returned a binding value of k1 =

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0.004, unrealistic yet necessary to reproduce the observations. That was because all TFs were

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available to bind at any time i.e. the number of XylSa was fixed for a single simulation run.

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However, by taking a spatial-based approach, where the number of TFs was variable during the

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stochastic simulation, we restored system function even with k1=0.5 and k-1=50 (Figure 5B, right).

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To that end, the spatial Brownian-motion simulation updated the Gillespie algorithm with the

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number of TFs that crossed a specific target region at given time-points (see Methods). This

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generated a crucial fluctuation in the availability of TFs that could bind Pm. The source-target

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distance becomes then a decisive parameter.

260 261

Upon the inclusion of spatial dynamics in time-based kinetics, the distinction between the

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absolute number of TFs and the trajectory points becomes unavoidable. Since a given TF would 9


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bind/unbind more than once51, it seems coherent to talk about TF crossings in a particular region

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when referring to the availability parameter described above. That value would be obviously

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higher than the actual number of TF molecules present in the cell. In our study, we match time-

266

based absolute TF numbers (Figure 3C) with spatial-based trajectory points (Figure 5B).

267 268

Physical proximity between Pm and XylS decreases transcriptional noise.

In the

269

experimental setting that showed the differences in Pm noise (Figure 2), the promoter and its

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regulator were placed at distant locations within the cell. This was done by placing the Ps

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promoter for xylS expression and the reporter Pm-gfp fusion in different replicons, the P. putida

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mt-2-Pm TOL plasmid and the chromosome, respectively (see Methods). A key interpretation of

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the simulations is that physical proximity between genomic sites bearing the Ps-xylS and Pm-gfp

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DNA segments would result in better Pm occupation at lower XylS concentrations, and thus in

275

reduced GFP expression noise.

276 277

To test this prediction, we positioned the Ps-xylS and Pm-gfp sequences within the frame of a

278

mini-Tn7 transposon vector (see Methods), which was delivered to the single attTn7 site of P.

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putida KT2440 (identical to P. putida mt-2 without the TOL plasmid) to generate a strain termed

280

P. putida KT-BGS (Table 1). In these engineered bacteria, the two components of the regulatory

281

device (Ps-xylS/Pm-gfp) were designed to be adjacent, in monocopy and at a fixed chromosomal

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site, with an artificially minimised distance between TF source and promoter target regions

283

(Figure 6A).

284 285

We then carried out flow cytometry measurements of 3MBz induction in the P. putida KT-BGS

286

strain as for the reference P. putida mt-2-Pm strain (Figure 6A, top). For the sake of comparison,

287

Figure 6A (bottom) reproduces the information for 3MBz-induced P. putida mt-2-Pm (from

288

Figure 2C). The proximity of Ps-xylS to Pm-gfp in P. putida KT-BGS results in a 3MBz

289

response that delivers a much narrower noise regime at high GFP intensity values.

290

fluorescent signals of 3MBz-induced P. putida KT-BGS (in which xylS expression is low but

291

spatially proximal to Pm, Figure 6A) were indistinguishable from those of m-xyl-induced P.

292

putida mt-2-Pm (in which XylS expression is high but distant from the Pm target promoter). 10


The


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According to simulations, for the P. putida KT-BGS strain, Ps-xylS proximity to Pm-gfp yields a

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larger number of regulators in the local molecular environment of Pm, which are thus available

295

for binding. The uninduced performance is null in both strains (Supplementary Figure S2)

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pointing out that XylS molecules alone do not trigger Pm activity. It has been reported that a high

297

concentration of non-active XylS molecules would, in principle, induce Pm52. However, that

298

scenario is not applicable to our study. Not even in KT-BGS where XylS molecules are more

299

abundant in the proximity of Pm.

300 301

To ensure that the modifications in P. putida KT-BGS did not distort the physical structure of the

302

bacteria53, we compared the size and complexity of individual cells to those of the P. putida mt-

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2-Pm counterpart, in which Ps-xylS and Pm-gfp are separated. The two strains were virtually

304

indistinguishable, with no important differences in physical quality (Supplementary Figure S3).

305

We compared Ps promoter activity in mt-2-Pm and KT-BGS strains by quantitative PCR, which

306

showed similar levels of XylS molecules in response to 3MBz, (Figure 6B); growth curves

307

validated strain comparability (Supplementary Figure S4). Visualisation of the TOL plasmid

308

(Figure 6C, see Methods) allowed us to confirm its single-copy nature, thus comparable to the

309

single copy Ps insertion in the chromosome.

310 311

It has been recently suggested that molecular crowding effects inside bacteria lead to a non-

312

homogenous intracellular space54. This spatial heterogeneity adds complexity to the abstract cell

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compartment of Figure 5 and would cause an uneven diffusion of molecules. To analyse the

314

simulation effects resulting from local environments with different diffusion specifications (see

315

Methods), we included regions where the Brownian motion was slowed down (Supplementary

316

Figure S5). The presence of low mobility regions facilitated TF accumulation and help regulator

317

numbers increase in highly-condensed areas. This scenario matched the physical architecture of P.

318

putida KT-BGS, in which the target region was inserted into the chromosome, presumably a

319

crowded environment with restricted mobility.

320 321

The noise-dependence of promoter-to-regulator distance is likely to increase if the TF is very

322

unstable, as appears to be the case for XylS55. These data identify a function within the intricate 11


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323

architecture of the regulatory network that governs biodegradation of m-xylene in Pseudomonas.

324

Future work on this line would focus on re-arranging network components across different

325

genome locations and/or vary their copy number to establish universal spatial-dependent design

326

rules. Moreover, it is worth studying in detail the complex motility of molecules inside crowded

327

regions of the cell56 in order to develop more accurate spatial predictions.

328 329

Discussion

330 331

Intracellular signals are transmitted according to specific dynamics of the components involved

332

in their transfer. These communications are therefore endowed with precise information, whose

333

decodification promises valuable insights into cellular kinetic and structural properties9. Signal

334

variability, commonly referred to as gene expression noise4, 5, constitutes the fingerprint of such

335

transmission, and thus the target data to be interpreted. In the case documented here, the

336

expression signals produced initially by the Pm promoter activity in P. putida19, 22, 23 lead to

337

highly specific, stable noise patterns depending on the stimulus to which the cells were exposed.

338 339

Using mathematical modelling and computational analysis, we deconvoluted the flow cytometry

340

data for each setting to describe the kinetics that could reproduce that behaviour. As a result, the

341

kinetic values that fit the experimental observations highlight the importance of the bursting-

342

specific rates, binding and unbinding13-16, where each of these values influences the final

343

promoter activity distinctly. We pinpointed how the dynamics of Pm-regulator interplay

344

determines gene expression by including spatial effects, in particular protein distribution, within a

345

cell. Our model, validated by the experiments shown above, indicates that the physical distance

346

between the regulator source and the target promoter is translated into specific noise patterns that

347

change radically depending on promoter-TF proximity. This is due to the fact that regulators, or

348

rather their trajectories (Figure 5), are not distributed homogeneously57 and TF are thus more

349

likely to meet the promoters they regulate if located near the source58. This concept was

350

hypothesized by Ten Wolde to explain the frequent genomic association of TF and target

351

promoters as an evolutionary remedy to an excess of noise59, 60. In contrast, our analyses raise

352

questions as to whether gene expression noise caused by a non-homogeneous intracellular matrix 12



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Page 14 of 32

353

is an adaptive trait that endows regulatory networks with specific properties. We show that

354

changing the spatial positioning of components, Pm noise patterns can be altered, which opens

355

the opportunity to use one expression profile or another depending on needs. As one Pm activity

356

regime is much more variable than the other, it might well have been co-opted evolutionarily to

357

create phenotypic heterogeneity within a population to increase its metabolic or else fitness61, 62.

358

The other regime, full-expression and low variability could be used, for example for decreasing

359

phenotypic diversity of a clonal population of productive cells63.

360 361

Our data also offer a new challenge for engineering non-native regulatory circuits or, more

362

generally, synthetic genetic implants in the genomic and biochemical chassis of a bacterial cell36,

363

41, 64

364

in the spatial frame of a cell for optimal performance, a question that is rarely considered in

365

contemporary synthetic biology and which deserves more attention. Finally, our results add a new

366

perspective to the much-debated generality of transcription/translation coupling in prokaryotes48-

367

50

368

generated near the genes that encode them or whether they must migrate to other cell sites.

. Each gene sequence and each protein (including TF) might need a specific physical address

, as the noise regime of promoters is certainly influenced by whether their cognate TF are

369 370

Materials and Methods

371 372

Bacterial strains, growth conditions and genetic constructs. Bacterial strains and plasmids

373

used are listed in Table 1. Escherichia coli cells were grown at 37ºC in LB medium and used as

374

hosts for cloning procedures. Pseudomonas putida cells were incubated at 30ºC in M9 minimal

375

medium supplemented with 2 mM MgSO4 and 20 mM citrate as sole carbon source65. When

376

needed, gentamycin (Gm; 10 µg mL-1), kanamycin (Km; 50 µg mL-1), ampicillin (Ap; 150 µg

377

mL-1) and chloramphenicol (Cm; 30 µg mL-1) were added to growth media. Reporter strain P.

378

putida mt-2-Pm is the original TOL plasmid pWW0-containing P. putida mt-2, which was

379

inserted in the single attTn7 site of its genome with a Pm-gfp transcriptional fusion. The Pm

380

promoter sequence was amplified from plasmid pSEVA22866 as a 122 base pair (bp) PacI/AvrII

381

fragment with primers 5’TTAATTAAGGTTTGATAGGGATAAGTCC3’ and 5’CCTAGGT

382

CTGTTGCATAAAGCCTAA3’, and cloned into the mini-Tn7 promoter-calibrating vector pBG 13


Page 15 of 32

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383

(Zoebel et al., 2015). The organization of this vector (Supplementary Figure S6A) is such that

384

inserting promoter-bearing PacI/AvrII originates a standardised translation/transcription fusion

385

that minimises any effect of the non-translated 5’ end of the reporter transcript in the final GFP

386

readout. Cloning the Pm promoter in pBG generated mini-Tn7 delivery vector pBG-Pm

387

(Supplementary Figure S6B). This construct was then mobilised to pWW0-containing P. putida

388

mt-2 strain by tetra-parental mating67. Finally, GmR exconjugants were verified for insertion of

389

the hybrid mini-Tn7 transposon (bearing the Pm-gfp fusion) in a specific orientation at the attTn7

390

site by amplifying the genomic region of interest with diagnostic PCR using primer pairs 5-Pput-

391

glmS

392

ACACCC3’

393

5’CACAGCATAACTGGACTGATTTC3’. One of these clones yielding DNA products of 400

394

and 200 bp68, 69, was designated as P. putida mt-2-Pm and used for the experiments discussed

395

above. To obtain an entirely equivalent P. putida strain with a physically rearranged XylS/Pm

396

regulatory node, a 1088 bp DNA segment containing the array of regulatory parts xylS ← Ps -

397

Pm → was excised from plasmid pSEVA22866 as a PacI/AvrII fragment and cloned in the

398

corresponding sites of the pBG vector (Supplementary Figure S6C). The resulting construct

399

(pBGS) was mobilised to the genome of the pWW0-less strain P. putida KT2440, and GmR

400

exconjugants were tested for insertion of the mini-Tn7 transposon (with the Pm-gfp fusion

401

adjacent to the xylS gene) in the same genomic site and orientation as before. One of these clones,

402

termed P. putida KT-BGS, was chosen to test the effects of XylS/Pm proximity. This genetic

403

strategy allowed a faithful comparison between the expression noise produced by the Pm-gfp

404

fusion borne by either P. putida mt-2-Pm (Ps → xylS and Pm in non-adjacent, separate replicons)

405

or P. putida KT-BGS (Ps → xylS and Pm in close genomic proximity).

UP

5’AGTCAGAGTTACGGAATTGTAGG3’/3-Tn7L and

5-PpuglmS

DOWN

(5’ATTAGCTTACGACGCT

5’TTACGTGGCCGTGCTAAAGGG3’/3-Tn7R

406 407

Single cell analysis by flow cytometry. Single-cell experiments were performed with a Gallios

408

(Beckam Coulter) flow cytometer. GFP was excited at 488 nm, and the fluorescence signal

409

recovered with a 525(40) BP filter. Strains grown overnight were diluted 1/100 and allowed to

410

grow at 30ºC in pre-filtered M9 citrate medium and incubated (3-4 h). After pre-incubation, cells

411

in the late exponential phase (OD600nm = 0.4) were treated with the inducer 3MBz (1 mM);

412

cultures were incubated with aeration (30ºC). At 3 h post-induction, an aliquot of each sample 14



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413

Page 16 of 32

was analysed by flow cytometry; 20,000 events were analysed for each sample.

414 415

RNA purification and real-time q-PCR. P. putida strains KT-BGS and mt-2-Pm were grown

416

overnight (30ºC) in citrate-supplemented M9 with in aerated flasks. Cultures were diluted 1/100

417

in the same medium, alone or with 1 mM 3MBz and grown to exponential phase (OD600 = 0.3-

418

0.5). A 3 ml aliquot of each sample was treated with 300 µl ice-top solution (5% water-saturated

419

phenol in ethanol) and centrifuged; pellets were frozen in liquid nitrogen and stored at -80ºC.

420

RNA was extracted using the miRNeasy kit (Qiagen) with some modifications to optimise

421

isolation of high quality RNA from P. putida. The quantity of total RNA was determined in a

422

Nanovue Plus spectrophotometer (GE Healthcare Life Sciences), and RNA integrity analysed by

423

agarose gel electrophoresis. The absence of DNA was confirmed using primers for rpoN

424

(5’TCGACCCGGAGCTGGATA

425

(5’CGAGTTGCTGGAGATTGTGT and 3’TCGTTAAATTGCCCTCAGTG). Real-time RT-

426

PCR was performed using total RNA preparations from three independent cultures (three

427

biological replicates). The process was monitored by q-PCR in an ABI pRISM 7900HT Fast

428

RealTime PCR system (Applied Biosystems). To calculate the relative amount of xylS transcript,

429

we used the ∆∆Ct method70, which is designed to compare levels of a given RNA in two

430

conditions (induced and uninduced cells). The primers used for xylS were (xylS 141: 5’TAAT

431

CCAGGCGAGATTACCC and 3’AACCAGTATGTCGGTACGCA; xylS 108: 5’CGAGTT

432

GCTGGAGATTGTGT and 3’TCGTTAAATTGCCCTCAGTG; rpoN: 5’TCGACCCGGAG

433

CTGGATA and 3’CGGCTCGAACTGCTGGAT). Results were normalised relative to for the

434

rpoN gene, the expression of which remains constant throughout the growth curve.

and

3’CGGCTCGAACTGCTGGAT)

and

xylS

435 436

Kinetic reactions, simulation and optimisation. The kinetic reactions that describe the model

437

depicted in Figure 3 are:

15


Page 17 of 32

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438 439

where Pma and Pm are the promoter with and without bound XylSa, respectively, XylSa denotes

440

the regulator in its active form, mRNA is the output of the transcription process and GFP is the

441

final green fluorescent protein. The description of the rates is as follows: k1 is the binding rate of

442

XylSa to Pm (molecules-1 hour-1), k-1, the unbinding rate of XylSa from the promoter (hour-1), k2

443

and k3, the transcription and translation rates (hour-1), k4 and k5 are the degradation rates of mRNA

444

and GFP (hour-1) and k6, the basal transcription of the promoter, which is Pm activity with no

445

regulator bound (hour-1). Stochastic simulations were performed using the Gillespie algorithm71.

446

To obtain cytometry-like graphs (Figure 3C), the values Gillespie’s algorithm returns must be

447

converted into a time-course array in which time intervals are fixed, τa, and are small enough to

448

have cells (each time point) that correctly represent (in terms of frequency) all possible molecular

449

levels72. Here we used τa = 0.01 h (Supplementary Figure S7). The values in Figure 3C where

450

optimised within the ranges k1 ∈[0.001 - 1.2], k-1 ∈[0.2 - 80], k2 ∈[100 - 1000], k3 ∈[10 - 120]

451

and XylSa ∈ [20 – 3000], were each combination was weighted following the fitness parameters

452

width and flatness (Figure 3B). Each combination of rates was assigned a fitness value. The

453

width of the output distribution was measured as the difference between maximum and minim

454

protein values. The flatness parameter corresponds to the minimum probability that represents

455

any given protein value of a given run. Such value would be 0 in a one-peak Gaussian-like

456

distribution while higher for a flat surface. Therefore, the goal (for 3MBz induction) was to

457

maximize width while at the same time maximizing flatness. Five runs per combination of rates

458

assisted the process to discard outlier values (not representative of the final distribution).

459

Different degradation rates were tested, as well as affinity values, in the stress analysis of

460

Supplementary Text S1 to test system robustness. The full TOL network was formalized,

461

including reactions and rates, and simulated in Supplementary Text S2.

462 463

The multi-agent simulation of Supplementary Figure S2 was performed using our software

464

DiSCUS72, 73. XylSa entry rate (k7 = 400 molecules hour-1) and degradation (k8 = 2 hour-1) were

465

added to reactions 1-6. Extrinsic noise was simulated by changing the rates vector after division

466

to reflect fluctuations in environmental conditions. Every new rate was the result of a Gaussian

467

distribution where the mean was the previous rate value and the standard deviation a 20% of the 16



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Page 18 of 32

468

original rate. Dilution was simulated by dividing by two the number of molecules in each

469

daughter cell compared with the mother.

470 471

Differential variability (DV), the relation between the variance of the noise under two different

472

conditions74, was used to measure the simulations of Figure 3D. This value was defined as f =

473

σ12/ σ22 where σ12 and σ22 are the variances of the signal at low induction (3MBz) and high

474

induction (m-xyl), respectively.

475 476

Spatial protein trajectories. For the spatial simulation of regulators shown in Figures 5 we

477

implemented a two-dimensional Brownian motion instance, written as an iteration scheme as

478

follows:

479

480 481

where t identifies the last time event, dt is the time step (dt = T⁄N with T the total time per

482

iteration and N the number of steps, 15.0 and 1.0, respectively, in this case) and δ the so-called

483

Wiener process parameter (here, 0.25). Each protein ran for 400 iterations. Time parameters were

484

dimensionless and the simulated cell area was based on a 60 x 20 2D lattice. The target region

485

monitored to obtain the graphs of Figure 5B (right) was a 10 x 5 lattice situated on a pole (source

486

region at the middle). The numbers of trajectory points (not TF numbers, but TF crossings) in the

487

target region for the ‘variable TF’ simulation were obtained during 16 spatial simulations: 147,

488

87, 47, 128, 103, 71, 12, 26, 91, 35, 47, 0, 214, 21, 29, 152. These numbers updated the TF

489

variable within a single Gillespie simulation to generate the wide-rage plateau-like signal. To

490

simulate the low mobility areas of the heterogeneous spatial distribution (Figure S5) the time step

491

was set to 1.0 in such areas in contrast with the 15.0 of the normal mobility regions.

492 493

All computational simulations were written in Python. The source code is available in

494

Supplementary File S1.

495

17


Page 19 of 32

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496

Visualization of the TOL plasmid (Figure 6C). The plasmid was labelled using the fluorescent

497

operator repressor system tetO-TetR-EYFP75 to investigate its localization within the cell

498

P.putida mt-2. Tandemly repeated tetO sequences were introduced into specific locus of the TOL

499

plasmid. Given TetR fused EYFP chimera, the labelled DNA was visualized using epifluorescent

500

microscopy. Cells were grown on succinate amended agarose pad.

501 502

ASSOCIATED CONTENT

503 504

This material is available free of charge via the Internet at http://pubs.acs.org:

505

Supplementary Text S1. Stress analysis document. Effects of rate variation on circuit

506

performance.

507

Supplementary Figure S1. System variances due to the inclusion of dilution dynamics and

508

extrinsic noise in the model.

509

Supplementary Figure S2. Side-by-side comparison of Pm activity in strains mt-2-Pm and KT-

510

BGS induced with 3MBz.

511

Supplementary Figure S3. Comparison of cell complexity between mt-2-Pm and KT-BGS

512

strains.

513

Supplementary Figure S4. Growth curves for P. putida KT-BGS and mt-2-Pm strains.

514

Supplementary Figure S5. Local TF density affected by heterogeneous diffusion areas within

515

the cell.

516

Supplementary Figure S6. Organisation of genetic constructs for inspection of transcriptional

517

noise of the Pm promoter using GFP cytometry.

518

Supplementary Figure S7. Time-course conversion from the Gillespie output to a frequency-

519

constant line.

520

Supplementary Text S2. Full TOL simulation

521

Supplementary File S1. Compressed file containing all Python source code used in this study.

522 523

COMPETING INTERESTS 18



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524

Page 20 of 32

The authors declare that there are no competing interests.

525 526

AUTHORS' CONTRIBUTIONS

527

AGM and VDL conceived the whole study and wrote the article. IB and JK carried out the

528

experimental parts of the work. All the authors contributed to the discussion of the research and

529

interpretation of the data.

530 531

ACKNOWLEDGEMENTS

532

This work was funded by the CAMBIOS Project of the Spanish Ministry of Economy and

533

Competitiveness RTC-2014-1777-3 (MINECO), HELIOS Project of the Spanish Ministry of

534

Economy and Competitiveness BIO 2015-66960-C3-2-R (MINECO/FEDER). ARISYS (ERC-

535

2012-ADG-322797), EmPowerPutida (EU-H2020-BIOTEC-2014-2015-6335536) and FUTURE

536

(704410-H2020-MSCA-IF-15) Contracts of the European Union.

537 538

References

539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558

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759

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Figures

760 761 762 763 764

765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784

Figure 1. Structure of the TOL network borne by plasmid pWW0of Pseudomonas putida mt-2 with Pm promoter as output.

A. As shown, m-xylene is first converted to 3-methylbenzoate (3MBz) through the action of the enzymes encoded by the upper TOL pathway; this compound is further processed by the activity of the lower, and the resulting intermediates are metabolised in the tricarboxylic acid (TCA) cycle. XylR and XylS are transcriptional regulators, whereas Pu, Pm, Ps and Pr are promoters. The master regulatory gene xylR controls expression of both the upper pathway and the second transcription factor, XylS, which is encoded in a location adjacent to the end of the lower operon.. This regulatory architecture has a decisive role in Pm activation dynamics, as the levels of its cognate activator (XylS) vary depending on the inducer. In one case, 3MBz activates those XylS molecules found in low numbers in the cell due to leaky Ps promoter expression. This results in the active form of the protein, termed XylSa, which is able to bind and activate Pm. In the second case, m-xylene (m-xyl) causes both XylS overexpression (due to Ps activation by XylR) and intracellular production of metabolic 3MBz (due to Pu-driven activity of the upper pathway operon)... B. Abstracted Pm activation as an OR logic gate. Pm activity will be triggered by using either 3MBz or m-xyl as the inducer. C. m-xyl leads to a higher concentration of XylSa (XylS overexpression and intracellular generation of 3MBz) than externally added 3MBz (use of only leaked XylS moecules). This difference is the key feature for decoding Pm output

25


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785 786 787

788 789 790 791 792 793 794 795 796 797 798 799 800 801 802


Figure 2. Variable noise patterns depending on input signal in P. putida mt-2-Pm strain

A. In these experiments, XylS molecules are produced by the TOL plasmid borne by the P. putida mt-2-Pm, whereas the target Pm-gfp reporter fusion is inserted in the chromosome (see Methods); that is, the source of the TF and its target promoter are non-adjacent and encoded in separate mono-copy replicons (i.e., TOL plasmid and chromosome). B. In cells alone or in the presence of m-xyl (m-xylene), the Pm promoter activity recorded (based on green fluorescent protein intensity) can be abstracted as a binary switch with a 0 or OFF state and a 1 or ON state. Flow cytometry results show this behaviour, where the noise range allows a null overlap between 0 and 1 (termed 1a to differentiate it from the following). C. Using 3MBz as the inducer again provokes switch-like behaviour in Pm, with 0/OFF and 1/ON states. As the cytometry results show, the noise range is much wider here, from maximum expression to the minimum (ON state thus called 1b).

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805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823

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Figure 3. Rate optimisation according to output-state fitness and binding sensitivity

A. The Pm promoter studied here and the rates involved in the model. XylSa in its active form is the activator of the inducible promoter. The Pma complex refers to the promoter with the bound regulator. Rates k1 and k-1 correspond to binding and unbinding events, respectively. Transcription, k2, translation, k3, degradation rates, k4 and k5; the basal transcription rate is represented by k6. B. The two fitness parameters (conditions) used in the optimisation process to select values for the rates: wide-rage signal going from basal to full expression, and plateaushaped distribution with roughly the same number of cells representing each value in between. C. Simulated signals under the same rate values but different TF numbers: 200 (3MBz case, up) and 3000 (m-xyl case, bottom) with flow cytometry signals shadowed for comparison. D. Several time-course simulations are shown in which the Pm promoter is exposed to three concentrations of its regulator, XylSa: 10 (null induction, thus basal, yellow line), 200 (low induction, green line) and 3000 molecules (high induction, purple line). Centre; the graph corresponds to the rates established in Figure 3C, with k1 = 0.004 and k-1 = 1.5. Top, k1 increased to 250%. Bottom, k1 reduced to 40% its original value. Left, k-1 at 250%. Right, k-1 at 40%.

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824 825 826

827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843


Figure 4. Analysis of Pm activity noise relative to regulator dynamics.

Study of simulation sets in which regulator number is the only parameter changed. A. Pm transfer function, which measures the output level resulting from different input values. Whereas the average value (red line) shows no additional relevant information, the noise produced by the signal (blue error bars, which denote max-min signal values) displays distinct behaviour according to input numbers, with wider range at middle concentrations. B. Number of GFP molecules over time while input changes (3MBz, m-xyl or none). The three logic values of the signal (‘0’, ‘1a’ and ‘1b’ in Figure 2B, C) are wide-range noise (0-20 h and 40-60 h), small-range high-level noise (20-40 h, 60-80 h) and small-range low-level noise (80 h onwards). C. 24 h simulations at different XylSa levels (from 0 to 3000 molecules) were used to measure 1) the cumulative pulse duration, which corresponds to the length of time that the Pm promoter is in the ON state (regulator bound to the DNA) and 2) signal amplitude, defined here as the the maximum difference (in molecules) between the highest and lowest values achieved during the simulation (measured in steady-state).

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Figure 5. Effects on promoter activity of its distance from regulator source

A. Left: spatial distribution of simulated proteins following Brownian movement in a cell-like compartment with high protein occupancy. Each coloured line inside the cell represents a protein trajectory from its source (in the middle of the space; labelled S) to its final position at a given time. Density of trajectory positions in each section (longitudinal and transversal) is shown in side graphs. Right: final protein position and their distribution in side graphs. B. Left: simulated spatial trajectories (colour lines) under low protein occupancy. Desnsity of trajectory positions in each section (longitudinal and transversal) is shown in side graphs. Two zoom-in regions (source S and target T) with distinct trajectory points occupation are displayed in detailed. Right: stochastic simulations of Pm-gfp activity with k1 = 0.5 and k-1 = 50 under basal, 200 TFs (blue) and variable TFs (green) conditions. The latter reproduce the noise pattern of 3MBz induction with the new rates and spatial-based constraints.

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861 862 863

864 865 866 867 868 869 870 871 872 873 874 875 876 877 878


Figure 6. Pm promoter activity influenced by its distance from regulator source

A. Left: physically rearranged XylS/Pm regulatory node engineered in P. putida KT-BGS (Table 1) to maximise proximity between source (XylS production via Ps promoter) and target (Pm). Both promoters were inserted adjacent to each other into the chromosome of strain KT2440, from which the TOL plasmid was removed (see Methods). Right: flow cytometry results with P. putida KT-BGS cells. As predicted by the model, use of 3MBz as inducer with minimal distance between source and target (top, in green) gives results similar to use of m-xyl as inducer of the reference P. putida mt-2-Pm strain (Figure 2B). Bottom, 3MBz induction in P. putida mt-2-Pm cells, where the TF source and the target promoter are not adjacent. B. Quantitative PCR to measure mRNA molecules transcribed from xylS shows similar Ps promoter activity in both strains, uninduced or 3MBz-induced. C. Visualisation of the single-copy TOL plasmid (J. Kim) At division, the TOL plasmid replicates itself using its own machinery.

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879 880

Table I. Bacterial strains and plasmids used in this study Bacterial strain or plasmid Strains Escherichia coli CC118pir HB101

Pseudomonas putida KT2440

Relevant characteristics

Reference or source

Cloning host; ∆(ara-leu) araD ∆lacX174 galE galK phoA thiE1 rpsE rpoB(Rif) agrE(Am) recA1 λpir lysogen Helper strain; F- λ- hsdS20(rB- mB-) recA13 leuB6(Am) araC14 ∆(gpt-proA)62 lacY1 galK2(Oc) xyl-5 mtl-1 thiE1 rpsL20(Smr) glnX44(AS)

(Herrero et al., 1990)76

Wild-type strain; mt2 derivative cured of the TOL plasmid pWW0

(Boyer and RoullandDussoix, 1969)77

(Nelson et al., 2002; Bagdasarian et al., 1981)78, 79

mt-2

Wild-type strain bearing pWW0 plasmid

mt-2-Pm

Gmr. P. putida KT2440 inserted in its genomic attTn7 with the hybrid mini-Tn7 delivered by plasmid pBGPm (Supplementary Figure S6) Gmr. P. putida KT2440 inserted in its genomic attTn7 with the hybrid mini-Tn7 delivered by plasmid pBGS (Supplementary Figure S6)

KT-BGS Plasmids pRK600 pTnS-1 pBG

pBG-Pm pSEVA228 pBGS

881 882

Cmr. Helper plasmid used for conjugation; oriV ColE1, RK2(mob+ tra+) Apr, oriR6K, TnSABC+D (Tn7 transposase) operon Kmr, Gmr, oriR6K, mini-Tn7 delivery vector; Tn7L and TnR bracketting a mobile DNA segment for engineering standardised BCD2-msf GFP reporter fusions (Supplementary Figure S6) Kmr, Gmr, oriR6K. pBG inserted with Pm promoter and thus bearing a standardised Pm-gfp fusion Kmr, oriRK2, xylS/Pm expression system Kmr, Gmr, oriR6K, pBG inserted with the xylS/Pm module of pSEVA228 and thus bearing the TF adjacent to the same standardised Pm-gfp fusion as in pBG-Pm

883 884 885

31


(Worsey and Williams, 1975)80 This study This study

(Kessler et al., 1994; Keen et al., 1988)67, 81 (Choi et al., 2005)82 (Zoebel et al., 2015)83

This study (Martínez-García et al., 2014)66 This study

Deconvolution of Gene Expression Noise into Spatial Dynamics of

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