Cell-Free Optogenetic Gene Expression System - ACS Synthetic Biology

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Cell-free optogenetic gene expression system Premkumar Jayaraman, Jing Wui Yeoh, Sudhaghar Jayaraman, Ai Ying Teh, Jing Yun Zhang, and Chueh Loo Poh ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.7b00422 • Publication Date (Web): 29 Mar 2018 Downloaded from http://pubs.acs.org on March 30, 2018

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ACS Synthetic Biology

Cell-free optogenetic gene expression system Premkumar Jayaraman1,2,3, Jing Wui Yeoh1,2, Sudhaghar Jayaraman1,2, Ai Ying Teh1,2, Jingyun Zhang1,2 and Chueh Loo Poh1,2* 1 Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 2 NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 3 Present address: Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore. * To whom correspondence should be addressed. Email: [email protected]

Optogenetic tools provide a new and efficient way to dynamically program gene expression with unmatched spatiotemporal precision. To date, its vast potential remains untapped in the field of cell-free synthetic biology, largely due to the lack of simple and efficient light-switchable systems. Here, to bridge the gap between cell-free systems and optogenetics, we studied our previously engineered one component-based blue lightinducible Escherichia coli promoter in a cell-free environment through experimental characterization and mathematical modelling. We achieved >10-fold dynamic expression and demonstrated rapid and reversible activation of target gene to generate oscillatory response. Deterministic model developed was able to recapitulate the system behaviour and helped to provide quantitative insights to optimize dynamic response. This in vitro optogenetic approach could be a powerful new high-throughput screening technology for rapid prototyping of complex biological networks in both space and time without the need for chemical induction. Cell-free systems have enabled a flexible breadboard platform for synthetic biologists to engineer biological functions in vitro, overcoming the daunting complexity of manipulating living cells1-3. Cell-free systems coupled with simpler mathematical modelling offer rapid iterative ‘design-build-test’ cycles, advancing our ability towards precision engineering3. To date, researchers have routinely performed synthetic parts prototyping4-7, tested biosensing genetic circuits for therapeutic applications8-10, screened enzyme cascades11-14 and synthesized biomolecules in cell-free conditions, making its way into a transformative technology15. Perhaps, one of the key challenge in the context of cell-free biology is the inability to program spatiotemporal control of gene expression in a simpler way16, 17. This could expedite our ability to understand how genetic networks and metabolic pathways regulate spatial organization of enzymes18-20 and temporal changes in gene expression21, 22 outside a living cell. In general, simple tools that can carry out programmable dynamic control in cell-free is necessary to achieve this goal13, 23, 24. Concomitantly, several optogenetic systems using photosensitive proteins have been developed to dynamically control gene expression in E. coli using light25-27. However, most of these systems are based on membrane-bound photoreversible two-component signal transduction systems (TCSs) and require multiple protein components. Consequently, it remains challenging to apply these systems in a cell-free environment. Recently, Kamiya et al. tethered either 2,6dimethylazobenzene-4′-carboxylic acid (DM-Azo) or 2,6-dimethyl-4- (methylthio) azobenzene-4′-carobxylic acid (S-DM-Azo) units to the T7 promoter region and demonstrated photoswitching of gene expression using UV and visible light in a cell-free system28. Briefly, synthesized template DNA with DM-Azo/S-DM-Azo inserted into the

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RNA polymerase recognition region of T7 promoter trans-isomerized when kept in dark. This terminates transcription by blocking RNAP binding to T7 promoter. Upon exposure to UV/visible light, the photoresponsive T7 promoter adopts cis-isomerization which then facilitates RNAP binding and initiates transcription. However, the sample preparation procedure was laborious and heating to above the Tm of the template was required to induce photoisomerization29, thus limiting its practical applications. In addition, this entirely in vitro approach would not work in living cells. Thus, it would not be desirable when transitioning between in vitro and in vivo prototyping. Lastly, the use of chemicals could complicate the downstream purification process in bioproduction. Previously, we have developed a simple, compact blue light-inducible E. coli promoter system based on EL222 protein30. EL222 is a blue light-switchable DNA-binding protein and it belongs to a family of LOV (Light-Oxygen-Voltage) domain proteins which change form and bind to DNA when exposed to light31. We hypothesized that the EL222-based promoter would be functional in a cell-free environment due to its simplicity. Here, to test our hypothesis and to bridge the gap between cell-free systems and optogenetics, we aimed to study the EL222-based blue light-inducible promoter30 in the cell-free environment to control protein levels in a dynamic manner. We explored the conditions to achieve the desired behaviour of light-inducible promoter in the cell-free environment. The general architecture of the cell-free based optogenetic gene expression system is illustrated schematically in Fig. 1. Our cell-free expression reaction mixture consists of E. coli S30 in vitro system, plasmid DNA encoding the blue light-inducible promoter regulating red fluorescence protein (RFP) and purified EL222 protein. The reaction mixture transferred into a 384-well plate can be induced spatially and temporally using a blue light projector housed in an incubator (Supplementary Fig. 1). In parallel to the experimental characterization, we exploited in silico simulation approach based on deterministic ordinary differential equations (ODEs) to gain quantitative understanding into the dynamics of blue-light inducible gene expression in cellfree conditions. Beyond that, we used the model to predict a blue light ON-OFF illumination pattern which could allow us to generate an oscillatory RFP expression waveform and to provide quantitative insights to optimize the temporal behaviour. The schematic diagram of our simple EL222-based blue light-inducible plasmid system (pBLind) is shown in Fig. 2a. Briefly, when exposed to blue light, EL222 dimerizes and binds DNA. This initiates transcription, presumably by recruiting RNA polymerases to the promoter region (PBLind). In contrast, EL222 is inactive in the dark and thus unable to bind DNA. To test this behaviour in cell-free environment, we exposed the reaction mixture which contains 0.5 µM of purified EL222 and pBLind plasmid under blue light illuminations. As a control, we placed the same mixture in the dark. As expected, upon blue light exposure >10fold increase in gene expression was observed, compared to that of the reaction mixture kept in dark (Fig. 2b, c). Our model shows that mRNA accumulation caused by EL222 dimerization on pBLind promoter was rapid upon exposure to blue light and reached saturation at 200 min (Supplementary Fig. 2a). Further, the fluorescence protein expression level in dark with 0.5 µM of EL222 proteins is 2-fold higher than the condition which lacks EL222 proteins (Fig.2c). This suggests the possibility of the presence of some background dimerization of EL222 and promoter activation, similar to what we have observed in our earlier cell-based system30. In contrast to the cell-based system (Supplementary Fig. 3), the synthesized RFP in the cell-free system kept accumulating in a linear manner with little to no degradation observed. This is consistent with other cell-free studies which showed a low proteolysis activity due to the protease deficiency in a constant volume32, as opposed to the high protein dilution caused by cell division and volume expansion in cell-based system.


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Next, we performed dosage response of PBLind promoter under varying purified EL222 concentrations. We observed that the RFP expression level increases with increasing EL222 concentrations with a maximum response at 0.5 µM of EL222, followed by a subsequent decrease in expression level at higher concentrations (Fig. 2d, e and Supplementary Figs. 2b, c). This result aligns with previous study which showed that EL222 effectively bound to DNA at a concentration of 0.25–0.75 µM31. We speculate that higher EL222 concentrations may have an effect on cell-free mixture composition and molecular crowding might have also impacted the DNA binding33. In parallel, we sought to examine the effect of constitutive expression of EL222 on the PBLind promoter. This could mitigate the need to exogenously introduce purified EL222 protein. For this, we constructed a genetic circuit (pEBLind), where the EL222 expression module under a very strong (J23100) constitutive promoter was added to the pBLind plasmid (Fig. 3a). Intriguingly, the fluorescence expression level of pEBLindv1 plasmid (J23100) in the presence of blue light illumination is ≥3-fold, lower than the pBLind plasmid supplemented with purified EL222 (Fig. 2c, 3c). By comparing the time-course expression response of pBLind and pEBLindv1, we found that the RFP expression increased linearly over time when supplied with purified EL222 at 0.5 µM with pBLind plasmid whereas a gradually slowdown trend with weaker expression profile was observed when EL222 was constitutively expressed in pEBLind (Fig. 2b, 3b). We hypothesized that the slow-down trend could be attributed to the inhibitory response imposed by high concentration of EL222 being expressed, as observed in the dose-response curve (Fig. 2d, e). This is supported by our insilico simulation, which was able to capture the slow-down trend when the estimated synthesized EL222 concentration in the model was more than 0.5 µM (Supplementary Fig. 4a, b). The model for pEBLind predicts a rapid increase in mRNA abundance upon blue light exposure, similar to what as observed previously for the pBLind plasmid. This rapid increase was then followed by a gradual reduction in mRNA level upon reaching ~200 min, due to the inhibitory effect (Supplementary Fig. 4c). However, while the model was able to reproduce the slow-down trend after incorporating the inhibitory effect, considering the inhibitory response alone was insufficient to reproduce the low RFP expression level observed in our experiment. Based on earlier studies34, we postulate that the circuitry with two genetic modules (pEBLind) might compete for the finite shared pool of cellular resources such as RNA polymerase (RNAP) and ribosomes as opposed to the construct with only one module (pBLind). The notion of competing for ribosomes was excluded in view of the use of same RBS strength and the similar length of base pairs for both modules. This is corroborated by model simulations in which the model failed to properly correlate to the experimental data when considering ribosomes as the limited pool with equal distribution between the two modules (data not shown). Alternatively, our model showed a good agreement with the experimental data under the assumption of unbalanced distribution of limited RNAP pool (due to the different promoter used to drive EL222 and RFP expression) and the inhibitory effect from EL222 proteins (Fig. 3c). Considering the same protein synthesis rate and mRNA degradation rate for the two modules, our model infers that the proportion of the RNAP distribution between the two modules in pEBLind would correspond to the ratio of the mRNA synthesis rate for EL222 and RFP, which is equivalent to 2.45:1 (Fig. 3d). This implies that more resources have been used to produce the EL222 instead of the RFP, resulting in a low RFP expression level. To verify this, we carried out two sets of experiment. First, we reduced the concentration of pEBLindv1 plasmid in order to lower EL222 expression and thereby RNAP saturation. However, the experiment showed that reducing the plasmid concentration reduced the output



RFP expression level without any significant improvement in the fold change, as compared to the dark and light state (Fig. 3e). Prior model simulations have been performed to examine the effect of reducing the constitutive promoter strengths of EL222 production (refer Supplementary Figure 5). Our model suggests that weaker promoter strength of 4 to 5x relative to J23100 would give the highest RFP expression level. Consequently, we constructed pEBLindv2 plasmid by replacing J23100 with ~4x weaker constitutive promoter (J23106) to drive the EL222 expression (Fig. 3f). As predicted from our model, a 4.5-fold increase in fluorescence was observed when exposed to blue light compared to the control reaction kept in the dark (Fig. 3g, h). When utilizing a ~4x weaker constitutive promoter for EL222 expressions, our model analysis demonstrates a shift of RNAP distribution towards RFP module from EL222 module with a proportion of 1:5.1, which corresponds to the ratio of the mRNA synthesis rate for EL222 and RFP (Fig. 3i). Overall, we have demonstrated that pEBLindv2 or supplementing pBLind with pre-translated purified EL222 increases the expression level of the PBLind promoter in the cell-free environment. Finally, we sought to study the reversibility and dynamics of the light controllable promoter system in the cell-free environment using a model-driven approach. To this end, the pBLind plasmid with 0.5 µM of EL222 in the cell-free mixture was exposed to different light patterns. The system was first exposed to a 3 h-ON-5.5 h-OFF pattern (Fig. 4a, b) to determine the EL222 deactivation (‘OFF’ state) kinetics for our existing model structure. As there are many possible illumination patterns, the model was then used to suggest a threecycle (1.5 h-ON-4.5 h-OFF-4 h-ON) and four-cycle (i.e., 2 h-OFF-2 h-ON-5 h-OFF-2 h-ON) illumination pattern to achieve an optimized oscillatory waveform (Supplementary Fig. 6). Independent experiments in which the system was exposed to the model suggested patterns were then performed (Fig. 4c - f). From both the experiments, we observed an increase in RFP after an initial delay in gene expression (~ 60 min) in the first 2 h ‘ON’ period (Fig. 4d, f). After which, this activated RFP expression slowed down in the subsequent 5 h ‘OFF’ period (Fig. 4d, f). In the succeeding 2 h ‘ON’ period, there was significant increase in RFP expression for three-cycle (Fig. 4c, d) while, for the four-cycle study we observed only slight increase in expression followed by saturation (Fig. 4e, f and Supplementary Fig. 7a, b). Presumably, this might be due to depleted resources caused by macromolecular crowding effect33. While there is a difference between the model predicted RFP value and the value from the experiment (possibly due to the variation in the cell-free extracts), the model predicted the trend of the oscillatory behavior well in both the experiments. A grid search of the best fitted parameter values (i.e., mRNA synthesis rate and EL222 deactivation rate) was then performed in an attempt to provide better quantitative information for the observed temporal performance (refer Supplementary Fig. 7c). After which, the model was able to reproduce the system oscillatory behavior more accurately (Fig. 4e, f). To improve the oscillatory behavior in the cell-free system, we simulated the responses under varying protein degradation rates. Our model suggests a protein degradation rate of more than 0.005 min-1 is needed to repress the RFP expression in the existing cell-free system for a smooth temporal control (Fig. 4e). For better control of the gene expression dynamics, it is important to strike a balance between the source (mRNA and protein synthesis) and the sink (mRNA and protein degradation) processes. To improve the sink system in cell-free expression while retaining a good RFP expression level, a modified pulse gain formulation was used to evaluate the system performance under a variation of mRNA and RFP degradation rate (Fig. 4g). The pulse gain was calculated as the ratio of the difference between the peak and the final RFP value, to the final RFP value. The RFP expression level was considered by further multiplying the pulse gain with the peak value to better quantify


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the system performance. The modified pulse gain analysis suggests a mRNA degradation rate within 0.01 to 0.04 min-1 and a RFP degradation rate in the range of 0.015 to 0.04 min-1 could be used to obtain the optimal oscillatory performance with high protein expressions (Fig. 4g). Further, to accelerate the repression of RFP during ‘OFF’ phase, EL222 deactivation rate could be another salient factor apart from increasing RFP degradation rate (Fig. 4h). The modelling results show that an EL222 deactivation rate higher than 0.02 min-1 coupled with a RFP degradation rate of 0.02 min-1 achieved the highest pulse gain, which gave the desired oscillatory behaviors with good expression levels. As a proof of concept, we showed that it is possible to achieve dynamic control of target gene expression using a protein-based blue light-inducible system in vitro. Taken together, our simple and rapid blue light-inducible in vitro molecular tool offers numerous benefits compared to the current cell-free light controllable system28. For example, gene regulation could be carried out without having to rely on expensive and unstable chemical cues35 the system is compatible for parallel prototyping in both cell30 and cell-free systems and userdefined expression profiles could be tailored with ease by varying EL222 levels, light pulse illumination and/or intensity. This flexibility in spatiotemporal control could be very useful in a number of applications including high-throughput screening for biomolecules, enzyme cascading, decipherment of the complex web of genetic networks and perhaps most excitingly, in vitro bio-manufacturing of therapeutics in remote, low resource locations15. Methods Plasmid construction and purification All plasmids used in this study were constructed using Gibson assembly36 with NEBuilder HIFI DNA assembly master mix (New England Biolabs) and following manufacturer’s protocol. Sequencing service was provided by Axil Scientific Pte Ltd (1st BASE, Singapore). All plasmid design and sequencing analysis were performed using Benchling web-based sequence designer (Benchling, San Francisco, CA, USA). pBbE8k (JBEI Part ID: JPUB_000036, colE1 ori, Kanr) and pBbE6k (JBEI Part ID: JPUB_000054, colE1 ori, Kanr), supplied by Addgene37 (Cambridge, Massachusetts, USA) were used as the backbone in this study. BBa_B0034 (rbs34) was used as the default ribosome binding site for all the encoded genes except for pEL222-His (encodes rbsD: default RBS in pBbE6k). Double terminator BBa_B0015 was used to terminate gene transcription in all cases38. Primers and gene fragments (gblocks) were supplied by Integrated DNA Technologies, IDT (Coralville, Iowa, USA). Plasmid pBLind from our earlier work (pBbE8k-PBlind-v1-rbs34-RFP)30 was used as a backbone to clone BBa_J23108 promoter with rbs34 to drive EL222 gene, resulting in pEBLindv1 (J23100-rbs34-EL222-B0015-PBlind-v1-rbs34-RFP-B0015) and pEBLindv2 (J23106-rbs34-EL222-B0015-PBlind-v1-rbs34-RFP-B0015). For the purification of EL222 protein, we constructed pEL222-His using pBbE6k plasmid as the backbone. pEL222-His (pBbE6k-pLlacO-1-rbsD-EL222-His-B0015). The sequences of all the coding sequence, promoters and RBS used in this study are provided in Supplementary Table 1. All protocols for transformations, polymerase chain reaction (PCR) and DNA manipulation used in this work were followed using Sambrook or the manufacturer’s manual and were optimized as needed39. Plasmid DNA purification was carried out using Qiagen Plasmid Midi Prep Kit (Cat No./ID: 12143) from overnight grown E. coli Top10 cells transformed with respective plasmid in 100 ml Luria-Bertani (LB) media with 50 µg/ml kanamycin at 37°C with shaking at 225 rpm. The plasmid maps of genetic constructs used in this study, with annotations of the promoters, RBSs, genes, and terminators are given in the Supplementary Fig. 8.



EL222 purification E. coli TOP10 transformed with pEL222-His was exponentially grown to an OD600 of 0.6 in 2x400 ml LB media with 50 µg/ml kanamycin at 37°C shaking at 225 rpm. The expression of EL222-His was induced using isopropyl β-D-1-thiogalactopyranoside (IPTG) at a final concentration of 1 mM and cultured for 4 h at 37°C. Subsequently, cells were harvested by centrifugation at 4000 x g for 20 min. The cell pellet was frozen and stored overnight at 20°C. QIAexpress Ni-NTA Fast Start kit was used for the purification of EL222-His protein. Briefly, cell pellet was thawed and re-suspended in 10 ml native lysis buffer supplemented with 1 mg/ml lysozyme and 25 U/ml benzonase nuclease. The cell lysate was centrifuged at 12,000 x g for 30 min at 4°C to pellet the cellular debris. The resulting supernatant was applied to a Ni-NTA column (Qiagen) that had been equilibrated with the storage buffer. The column was washed with wash buffer (2x-4 ml) and EL222-His was eluted using elution buffer. Since the estimated molecular weight of EL222-His is approximately 25 kDa, the eluate was added to Amicon Ultra-15 Centrifugal Filter Unit, Millipore and the purified protein was concentrated. The buffer was then exchanged into 1xPBS using a 10-kDa molecular mass cut-off membrane. The resulting retentate was collected and EL222-His concentration was determined using NanoDrop spectrophotometer (Thermo Fisher Scientific) at 280 nm. The purified protein was then analysed using sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) (Supplementary Fig. 9) and was stored at -20°C for subsequent use. Illumination setup Plate containing cell-free reactions was illuminated using our custom-built blue light projector setup (~12 W/m2) as shown earlier30. Briefly, the structural design incorporates a modified car door projection light and a 3D printed holder. The white LED inside the original projection light was replaced by Cree® XLamp® XP-E LED (royal blue) with dominating wavelength of 460nm. The LED was powered by 1W constant current driver module at 350mA, stepped down from 5V 1A power adapter. The holder positioned the projector to get an uniform illumination on the surface of the plate. For the dark condition, plates were wrapped in black cloth covering all edges. Cell-free characterization experiments The Promega E. coli S30 extract system for circular DNA (Cat. no: L1020) was used for all the cell-free characterization experiments. Reactions were setup according to manufacturer’s recommendations, except that the final reaction volume was reduced to 25 µl containing 0.5 µg of plasmid DNA. For consistency, a master mix containing complete amino acid mixture, S30 premix without amino acids and S30 extract was prepared for each experimental run. Reactions were set up in Corning Low Volume 384-well microplate (CLS3544) and sealed with a breathable membrane (Breathe-Easy®, Diversified Biotech, USA). Samples were incubated at 30°C without shaking in an incubator under illumination setup or dark conditions between each cycle of measurements. Time-series fluorescence (RFP: excitation 540 nm, emission 600 nm) was measured for up to 9 h for the promoter characterization studies and 11 h for the temporal expression study, at 60 min intervals (shaken 10 s before each measurement) using BioTek Synergy 2 plate reader with the optics position set at bottom with 100% sensitivity. For every characterization run, the reactions containing only


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the master mix without the plasmid DNA were assayed to determine background fluorescence. Model development and parameterization An in silico deterministic modeling approach has been adopted to represent the essential aspects of the light-inducible gene expression in the cell-free system. Our intent is to use mathematical model to appropriately capture the fundamental dynamics of the system of interest and to identify quantitative details which could aid in optimizing future experimental design. The kinetics of the underlying transcription and translation in the cell-free expression system were described by a set of ODE equations. The system of ODEs for pBLind and pEBLind constructs were integrated by using a variable-step continuous numerical solver, while a constant-step numerical solver was employed to better capture the fluctuations observed in the ON-OFF temporal control study. The model parameters were estimated by implementing the optimization algorithm to minimize the sum-squared residuals between experimental measurements and model simulations. All simulations were performed in Matlab R2016b (Mathworks, MA). The system of ODEs that details the transcription and translation kinetics of the blue lightinducible gene expression upon supplemented with different concentrations of purified EL222 proteins is given in Equation 2-3. An additional phenomenological equation was used to describe the initial delay caused by the EL222 activation, eliminating the intricate underlying activity with little supporting information.

[222] = [222]

[]

[!"###]$%& '

= + [!"###] [01]

$%&

'

$%&

(( ))

'

(') '( '( [!"###]'( (')

= 01 [,-./]

− + [,-./]

1, 9: = 1 [!"###]' 2ℎ454 = 6 1.75

' , 9: = 0 ' [!"###] $%&

(1) (2) (3)

(4)

where [222] and [222] represent the concentration of activated and supplied purified EL222 proteins, respectively and is a coefficient assigned to represent the switchable blue light illumination, in which 1 refers to the ‘ON’ state and 0 denotes the ‘OFF’ state. The parameter ? is the time delay due to the blue light-inducible activation of EL222 proteins and @ is the corresponding slope factor. The and are the basal and the concentration-dependent mRNA synthesis rates, 01 is the rbs-dependent synthesis rate for the fluorescent proteins RFP, and + is the mRNA degradation or inactivation rate. The concentration-dependent activation and inhibition of mRNA synthesis was modelled by Hill equation where parameters @ , @ABC , D, DA refer to the half-maximal EL222 activation and inhibition concentration, and the respective hill coefficients, respectively. accounts for the presence of some background activities in the dark which is sensitive to the available EL222 concentrations in the mixture. Next, the entire model framework and parameters were also utilized to describe the behavior of pEBLind plasmid in cell-free system that comprises two modules: (i) constitutive



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production of EL222 proteins; and (ii) blue-light inducible expression of fluorescent proteins. Two additional ODEs were incorporated to elaborate the constitutive production of EL222 proteins with strong promoter and mid strength RBS as given in Equation 5-6. []EFGGG

= !"### − + [,-./]!"###

[!"###]

= !"### [,-./]!"###

(5) (6)

where !"### is the promoter-dependent mRNA synthesis rate for EL222 proteins, + is the respective mRNA degradation rate, and !"### refers to the ribosome-binding site (RBS)-dependent EL222 proteins synthesis rate. Built on our current model, the mathematical formulation was further extended to describe the dynamics of the 2 h-OFF-2 h-ON-5 h-OFF-2 h-ON light temporal behavior of the blue light inducible gene expression in the cell-free environment. An additional EL222 deactivation term was introduced in Equation 7 as exponential decay to capture the ‘OFF’ state responses, while Equation 1 was modified to cover the subsequent time delay of EL222 activation in the following cycles and the generalized form is given in Equation 8. In this model, two key parameters (mRNA synthesis rate and EL222 deactivation rate) were reoptimized to reproduce the experimental data. The root-mean-square deviation (RMSD) comparing the fitting performance of different combinations of the two parameters value was computed and depicted as a contour error plot shown in Supplementary Fig. 6c. The optimized parameters value could be obtained from the combination with the lowest RMSD. The discrepancy between the current fitted parameters and previous estimated values arises from the experimental variability which is not considered in our model development. [!"###]$%&

[222] = [222]

(JK' )

= −HI [222]

[222] − [222]

(JK' )

(7) (

((K' ) ))

(8)

where HI is the deactivation rate for EL222 proteins, ?LB is the ‘ON’ time for blue light (JK' ) in the second oscillation, and [222] is the activated EL222 concentration one time-step before the second ‘ON’ phase. Root-mean-square deviation (RMSD) RMSD, a relative error metric was utilized to measure the differences between the experimental observed behaviors and the model predicted responses for better parameters estimation. These calculations were computed over the data sample at various time points and serve to aggregate the magnitudes of residuals into a single performance score, which is often used to locate the best fitted parameter set with the lowest RMSD. G

(9)

-MNO = P ∑BAJ(RA − SA )# B

where D is the number of data points, RA corresponds to the observed experimental data at a specific time point, and SA is the respective predicted value at the same time point. Data analysis


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Data shown represent the averages of at least three replicates. Fluorescence (AU) at a specific time for a reaction sample was calculated by subtracting the background fluorescence from each of the technical triplicate readings. The rate of fluorescence synthesis (min-1) of any sample at time t, was calculated by taking the difference of background subtracted fluorescence from two time points and dividing the result by the time interval δt. The molar concentrations of the fluorescent protein RFP were estimated from the standard calibration curve of fluorescence level (AU) against protein concentrations (ng/µL) with a gradient of 18.814 and an approximated molecular weight of 30 kDa. The fold change of RFP expression (dark/BL) was calculated by dividing RFP expression in dark/BL (with plasmid) with RFP expression in dark/BL (without plasmid). Author Contributions P.J. and C.L.P. conceived the project. P.J. designed and performed the experiments and analysed the data. S.J., A.Y.T. and J.Z. assisted in the experiments. J.W.Y. constructed the kinetic model and analysed the data. C.L.P. advised on the experiments and data analysis. P.J., J.W.Y. and C.L.P. wrote the manuscript. All authors commented and approved on the manuscript. Acknowledgements This work was supported by an NUS Startup Grant and MOE AcRF Tier 1. Key Words Optogenetics, cell-free synthetic biology, spatiotemporal control and EL222 References 1. Hodgman, C. E.; Jewett, M. C., Cell-Free Synthetic Biology: Thinking Outside the Cell. Metabolic Engineering 2012, 14 (3), 261-269. 2. Lu, Y., Cell-free synthetic biology: Engineering in an open world. Synthetic and Systems Biotechnology 2017, 2 (1), 23-27. 3. Moore, S. J.; MacDonald, J. T.; Freemont, P. S., Cell-free synthetic biology for in vitro prototype engineering. Biochemical Society Transactions 2017, 45 (3), 785-791. 4. Chappell, J.; Jensen, K.; Freemont, P. S., Validation of an entirely in vitro approach for rapid prototyping of DNA regulatory elements for synthetic biology. Nucleic Acids Research 2013, 41 (5), 3471-3481. 5. Sun, Z. Z.; Yeung, E.; Hayes, C. A.; Noireaux, V.; Murray, R. M., Linear DNA for Rapid Prototyping of Synthetic Biological Circuits in an Escherichia coli Based TX-TL Cell-Free System. ACS Synthetic Biology 2014, 3 (6), 387-397. 6. Hu, C. Y.; Varner, J. D.; Lucks, J. B., Generating Effective Models and Parameters for RNA Genetic Circuits. ACS Synthetic Biology 2015, 4 (8), 914-926. 7. Niederholtmeyer, H.; Sun, Z. Z.; Hori, Y.; Yeung, E.; Verpoorte, A.; Murray, R. M.; Maerkl, S. J., Rapid cell-free forward engineering of novel genetic ring oscillators. Elife 2015, 4, e09771. 8. Wen, K. Y.; Cameron, L.; Chappell, J.; Jensen, K.; Bell, D. J.; Kelwick, R.; Kopniczky, M.; Davies, J. C.; Filloux, A.; Freemont, P. S., A Cell-Free Biosensor for Detecting Quorum Sensing Molecules in P. aeruginosa-Infected Respiratory Samples. ACS Synthetic Biology 2017. 9. Pardee, K.; Green, Alexander A.; Ferrante, T.; Cameron, D. E.; DaleyKeyser, A.; Yin, P.; Collins, James J., Paper-Based Synthetic Gene Networks. Cell 2014, 159 (4), 940-954.



10. Pardee, K.; Green, Alexander A.; Takahashi, Melissa K.; Braff, D.; Lambert, G.; Lee, Jeong W.; Ferrante, T.; Ma, D.; Donghia, N.; Fan, M.; Daringer, Nichole M.; Bosch, I.; Dudley, Dawn M.; O’Connor, David H.; Gehrke, L.; Collins, James J., Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components. Cell 2016, 165 (5), 1255-1266. 11. You, C.; Zhang, Y. H. P., Annexation of a High-Activity Enzyme in a Synthetic ThreeEnzyme Complex Greatly Decreases the Degree of Substrate Channeling. ACS Synthetic Biology 2014, 3 (6), 380-386. 12. Dudley, Q. M.; Anderson, K. C.; Jewett, M. C., Cell-Free Mixing of Escherichia coli Crude Extracts to Prototype and Rationally Engineer High-Titer Mevalonate Synthesis. ACS Synthetic Biology 2016, 5 (12), 1578-1588. 13. Dudley, Q. M.; Karim, A. S.; Jewett, M. C., Cell-free metabolic engineering: Biomanufacturing beyond the cell. Biotechnology journal 2015, 10 (1), 69-82. 14. Bujara, M.; Schümperli, M.; Billerbeck, S.; Heinemann, M.; Panke, S., Exploiting cellfree systems: Implementation and debugging of a system of biotransformations. Biotechnology and Bioengineering 2010, 106 (3), 376-389. 15. Pardee, K.; Slomovic, S.; Nguyen, P. Q.; Lee, J. W.; Donghia, N.; Burrill, D.; Ferrante, T.; McSorley, F. R.; Furuta, Y.; Vernet, A.; Lewandowski, M.; Boddy, C. N.; Joshi, N. S.; Collins, J. J., Portable, On-Demand Biomolecular Manufacturing. Cell 2016, 167 (1), 248-259 e12. 16. Karzbrun, E.; Tayar, A. M.; Noireaux, V.; Bar-Ziv, R. H., Programmable on-chip DNA compartments as artificial cells. Science 2014, 345 (6198), 829. 17. Niederholtmeyer, H.; Stepanova, V.; Maerkl, S. J., Implementation of cell-free biological networks at steady state. Proceedings of the National Academy of Sciences 2013, 110 (40), 15985-15990. 18. Agapakis, C. M.; Boyle, P. M.; Silver, P. A., Natural strategies for the spatial optimization of metabolism in synthetic biology. Nat Chem Biol 2012, 8 (6), 527-535. 19. Quin, M. B.; Wallin, K. K.; Zhang, G.; Schmidt-Dannert, C., Spatial organization of multi-enzyme biocatalytic cascades. Organic & Biomolecular Chemistry 2017, 15 (20), 42604271. 20. Zieske, K.; Schwille, P., Reconstitution of self-organizing protein gradients as spatial cues in cell-free systems. Elife 2014, 3, e03949. 21. Yosef, N.; Regev, A., Impulse control: Temporal dynamics in gene transcription. Cell 2011, 144 (6), 886-896. 22. Zaslaver, A.; Mayo, A. E.; Rosenberg, R.; Bashkin, P.; Sberro, H.; Tsalyuk, M.; Surette, M. G.; Alon, U., Just-in-time transcription program in metabolic pathways. Nature Genetics 2004, 36, 486. 23. Schmidt-Dannert, C.; Lopez-Gallego, F., A roadmap for biocatalysis – functional and spatial orchestration of enzyme cascades. Microbial Biotechnology 2016, 9 (5), 601-609. 24. Brockman, I. M.; Prather, K. L. J., Dynamic metabolic engineering: New strategies for developing responsive cell factories. Biotechnology journal 2015, 10 (9), 1360-1369. 25. Han, T.; Chen, Q.; Liu, H., Engineered Photoactivatable Genetic Switches Based on the Bacterium Phage T7 RNA Polymerase. ACS Synthetic Biology 2017, 6 (2), 357-366. 26. Levskaya, A.; Chevalier, A. A.; Tabor, J. J.; Simpson, Z. B.; Lavery, L. A.; Levy, M.; Davidson, E. A.; Scouras, A.; Ellington, A. D.; Marcotte, E. M.; Voigt, C. A., Synthetic biology: Engineering Escherichia coli to see light. Nature 2005, 438 (7067), 441-442. 27. Ohlendorf, R.; Vidavski, R. R.; Eldar, A.; Moffat, K.; Möglich, A., From Dusk till Dawn: One-Plasmid Systems for Light-Regulated Gene Expression. Journal of Molecular Biology 2012, 416 (4), 534-542.


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28. Kamiya, Y.; Takagi, T.; Ooi, H.; Ito, H.; Liang, X.; Asanuma, H., Synthetic Gene Involving Azobenzene-Tethered T7 Promoter for the Photocontrol of Gene Expression by Visible Light. ACS Synthetic Biology 2015, 4 (4), 365-370. 29. Lubbe, A. S.; Szymanski, W.; Feringa, B. L., Recent developments in reversible photoregulation of oligonucleotide structure and function. Chemical Society Reviews 2017, 46 (4), 1052-1079. 30. Jayaraman, P.; Devarajan, K.; Chua, T. K.; Zhang, H.; Gunawan, E.; Poh, C. L., Blue light-mediated transcriptional activation and repression of gene expression in bacteria. Nucleic Acids Research 2016, 44 (14), 6994-7005. 31. Rivera-Cancel, G.; Motta-Mena, L. B.; Gardner, K. H., Identification of natural and artificial DNA substrates for the light-activated LOV-HTH transcription factor EL222. Biochemistry 2012, 51 (50), 10024-10034. 32. Shin, J.; Noireaux, V., Study of messenger RNA inactivation and protein degradation in an Escherichia coli cell-free expression system. J Biol Eng 2010, 4, 9. 33. Spruijt, E.; Sokolova, E.; Huck, W. T. S., Complexity of molecular crowding in cell-free enzymatic reaction networks. Nat Nano 2014, 9 (6), 406-407. 34. Karig, D. K.; Iyer, S.; Simpson, M. L.; Doktycz, M. J., Expression optimization and synthetic gene networks in cell-free systems. Nucleic Acids Research 2012, 40 (8), 37633774. 35. Mendelsohn, A. R., An enlightened genetic switch. Nat Biotech 2002, 20 (10), 985987. 36. Gibson, D. G.; Young, L.; Chuang, R.-Y.; Venter, J. C.; Hutchison, C. A.; Smith, H. O., Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat Meth 2009, 6 (5), 343-345. 37. Lee, T. S.; Krupa, R. A.; Zhang, F.; Hajimorad, M.; Holtz, W. J.; Prasad, N.; Lee, S. K.; Keasling, J. D., BglBrick vectors and datasheets: A synthetic biology platform for gene expression. Journal of Biological Engineering 2011, 5 (1), 12. 38. Guzman, L. M.; Belin, D.; Carson, M. J.; Beckwith, J., Tight regulation, modulation, and high-level expression by vectors containing the arabinose PBAD promoter. Journal of Bacteriology 1995, 177 (14), 4121-30. 39. Sambrook J, R. D. W., Molecular cloning: a laboratory manual. Cold Spring Harbor Laboratory: 2001; Vol. 1.



Figure 1. Characterization process of optogenetic gene expression using the cell-free system. The cell-free reaction mixture comprising transcription apparatus: RNA polymerase holoenzyme complex, translation core-machineries: ribosomes, aminoacyl tRNA synthetases, translation factors and energy mix was combined with blue light-inducible DNA and pretranslated EL222 (blue light-switchable DNA binding protein). The mixture was transferred into a 384-well plate, placed under our in-house blue light projector device. The output characterization measurement was fed directly into a kinetic model to estimate the parameters for optimal system function. The model estimated illumination pattern (time-scale) was used as input to the LED system to program dynamic gene expression.


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Figure 2. Cell-free optogenetic gene expression characterization with pre-translated purified EL222. (a) A schematic of blue-light inducible gene expression at PBLind promoter in pBLind plasmid with purified EL222. (b) Time-course RFP expression data of PBLind promoter with 0.5 µM of EL222 in the presence of blue light or kept in dark conditions. (c) Fold-change of RFP expression from PBLind promoter with and without 0.5 µM of EL222 after 9 h of incubation. (d) Time-course RFP expression data of PBLind promoter with different EL222 concentrations under blue light illuminations. (e) Transfer function of PBLind promoter under varying concentrations of EL222. All data are represented as mean ± s.d (n = 3). Solid lines represent model predictions. The statistical significance of ***P < 0.05 was calculated using ‘t’ test.



Figure 3. Cell-free optogenetic gene expression characterization with constitutive EL222 expression. (a) A schematic of blue-light inducible gene expression at PBLind promoter with constitutive EL222 expression module under J23100 promoter in pEBLindv1 plasmid. (b) Time-course RFP expression data of PBLind promoter in pEBLindv1 plasmid upon exposure to blue light or kept in dark conditions. (c) Fold-change of RFP expression from PBLind promoter in pEBLindv1 plasmid after 9 h of incubation. (d) Contour error representation of the computed RMSD (comparing model simulations against pEBLindv1 experimental data) for different combinations of the parameters value for EL222 and RFP mRNA synthesis rates. The dark region denotes the parameters combinations which are better in capturing the existing system performance with the optimal combination falls on the center point indicated by white cross symbol. (e) Fold-change of RFP expression from PBLind promoter under different concentrations of pEBLindv1 plasmid after 9 h of incubation. (f) Schematic representation of pEBLindv2 plasmid with EL222 expression under ~4x weaker constitutive promoter (J23106). (g) Time-course RFP expression data of PBLind promoter in pEBLindv2 plasmid upon exposure to blue light or kept in dark conditions. (h) Fold-change of RFP expression from PBLind promoter in pEBLindv2 plasmid after 9 h of incubation. Solid lines represent model predictions. (i) Contour error representation of the computed RMSD (comparing model simulations against pEBLindv2 experimental data). All data are represented as mean ± s.d (n = 3).


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Figure 4. Temporal control of gene expression in cell-free system. Cell-free system combined with pBLind plasmid and 0.5 µM of purified EL222 were exposed to blue light reversibly in (a) and (b) 3h-ON-5.5h-OFF cycle over a period of 8.5 h. (c) and (d) 1.5 h-ON-



4.5 h-OFF-4 h-ON cycle over a total period of 10 h. (e) and (f) 2 h-OFF-2 h-ON-5 h-OFF-2 h-ON cycle over a total period of 11 h. We also simulated the oscillatory responses under varying protein degradation rates. All data are represented as mean ± s.d. Solid lines indicate model predictions while dots represent experimental measurements. Grey areas represent dark state (‘OFF’) while the blue regions represent bright state (illumination ‘ON’). The heat maps indicate the computed value of modified pulse gain responses at different combinatorial (g) mRNA and RFP degradation rates. (h) EL222 deactivation and RFP degradation rates. The optimal ranges of combination were denoted by the red regions and the white region in (h) indicates the range of parameters’ values that generate no suppression on the RFP expression levels. Solid lines represent model predictions. All data are represented as mean ± s.d (n = 3).


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Cell-Free Optogenetic Gene Expression System - ACS Synthetic Biology

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