Measurements of Gene Expression at Steady State Improve the

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Measurements in the steady state of gene expression improve the predictability of part assembly Haoqian M. Zhang, Shuobing Chen, Handuo Shi, Weiyue Ji, Yeqing Zong, Qi Ouyang, and Chunbo Lou ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.5b00156 • Publication Date (Web): 14 Dec 2015 Downloaded from http://pubs.acs.org on December 15, 2015

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ACS Synthetic Biology

Measurements in the steady state of gene expression improve the predictability of part assembly Haoqian M. Zhang1║, Shuobing Chen1║, Handuo Shi1, Weiyue Ji1, Yeqing Zong2, Qi Ouyang1,3* and Chunbo Lou2* 1

Centre for Quantitative Biology and Peking-Tsinghua Joint Centre for Life Sciences, Peking University, Beijing, 100871, China; 2 CAS Key Laboratory of Microbial Physiological and Metabolic Engineering, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China; 3 The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China.

Abstract Mathematical modeling of genetic circuits generally assumes that the gene expression is in a steady state when measurements are performed. However, the conventional methods of measurement do not necessarily guarantee this assumption. In this study, we reveal a bi-plateau mode of gene expression at single-cell level in bacterial batch cultures. The first plateau proves to be dynamically active where the gene expression is at the steady state; the second plateau, however, is dynamically inactive. We further demonstrated that the predictability of assembled genetic circuits in the first plateau, the steady state, is much higher than that in the second plateau where conventional measurements were often performed. Considering the nature of the steady state, our method of measurement promises to directly capture the intrinsic property of biological parts/circuits regardless of circuit-host or circuit-environment interactions.

Keywords Steady state, Predictable assembly, Gene expression, Flow cytometry, Genetic circuits, Batch culture.

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Predictable assembly of biological parts is fundamental to the development of synthetic biology 1. “Predictable” means that the characterized parameters for individual parts is sufficient to predict the outcome of their combinations 2. Therefore, the quality of measurements during part characterization and circuit test is crucial for the predictability. In previous synthetic biology studies, the quantitative measurements were obtained roughly following three methodologies: (i) enzyme activity-based chromogenic, spectrophotometric or fluorimetric reactions, such as Miller assay 3, (ii) fluorescent protein accumulation, namely, fluorescence recording after a growth duration 4, and (iii) protein production rate calculated with the time-course data of gene expression 5. In general, the accompanying mathematical modeling assumes that the parameters of biological parts are measured in the steady state. However, the conventional methodologies do not necessarily meets this assumption. In non-steady states, key parameters such as the transcription and translation rates would vary with time 6; moreover, different measures of gene expression level, such as protein concentration or transcription rate, exhibit different tendencies varying with growth 6a. Therefore, we reason that measurements at real steady states would be crucial to endow the assembly of biological parts with higher predictability. We examined the time-course data of gene expression in bacterial batch cultures using flow cytometry (Figure 1a). Unlike microplate reader, the flow cytometry records fluorescence in single-cell level. This allows accurate measurements when the cell density is very low. Surprisingly, when an inducible promoter (pTAC) controlling yellow fluorescent protein (yfp) was subjected to the time-course measurement, a bi-plateau mode of gene expression was observed (Figure 1b): in the lag to the early log phase of cell growth, the expression level of YFP rapidly increases and plateaus (Plateau 1); once cell growth enters the mid-log phase, YFP level exhibits a secondary increase followed by another plateau (Plateau 2). We next exhaustively tested the combinations of two promoters, two RBSs, and three host strains (Figure 1c). As expected, the bi-plateau YFP expression was observed in every combination (Figure 1d). Notably, when the promoter J23100 was used, the expression level of YFP was constant immediately after the dilution (Figure 1d, lower panel); this is in consistent with its constitutive nature. To test whether the bi-plateau YFP expression arises from the carbon source conversion (Figure 1a, Steps 2-3), M9 medium was replaced by LB medium accordingly. Our experiments indicated that the bi-plateau mode still existed, despite the duration of Plateau 1 was significantly shortened (Supporting Information Figure S1). We then substituted yfp with superfolder green fluorescent protein (sfgfp) to exclude the possibility that the identity of reporter gene contributes to the bi-plateau mode. Results showed the bi-plateau expression remained regardless of reporter identity or its combination with RBS/promoter identity (Supporting Information Figure S2). These experiments altogether demonstrated that the bi-plateau mode of gene expression is general. The mechanism underlying the bi-plateau mode, to our knowledge, is unknown. We


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speculated that the Plateau 1 is a dynamically active steady state where the production and the degradation/growth-dependent dilution are at a balance whereas in Plateau 2 the cells have little capacity to produce proteins. For proof, we delayed the time of inducer addition (Figure 1a, Step 3) to examine whether the gene expression is dynamically active in Plateau 1 but inactive in Plateau 2 (Figure 2a). Reassuringly, before the mid-log phase of cell growth (Supporting Information Figure S3), the induction caused gene expression in a similar mode, indicating a strong and robust expression capacity; cells induced later then, however, were nearly non-inducible, implying their expression capacity is severely declined (Figure 2a). Then the measurements of activities of biological parts in the two plateaus were compared (Figure 2b-c). Considering of general interest, 26 natural RBSs were gleaned from the genome of E.coli K12, forming an RBS library together with 1 synthetic RBS; the promoter library was built by collecting 23 constitutive promoters from different sources (Supporting Information Table S1-2). Results showed that the fold change in GFP fluorescence exhibited a significant variance between two plateaus across both RBS and promoter libraries (Figure 2b-c), indicating that there is a systematic difference between the measured part activities in the two plateaus. Next, we assembled two RBSs (yagE and prpB) with different constitutive promoters and measured the resultant GFP fluorescence in two plateaus, respectively, (Figure 3a, left). It should be noted that the generally recognized intrinsic interference between promoters and RBSs have been eliminated by inserting a ribozyme-based insulator between them7. If the measurements allow ideally predictable part assembly, the translational activity ratio of two RBSs (yagE/prpB, indicated by fold difference in fluorescence) would be a constant regardless of promoters; otherwise, the higher the variation of ratio is, the less predicable the part assembly would be. As expected, the variation of translation activity ratio in Plateau 2 (CV=0.39) is much higher than in Plateau 1 (CV=0.24; Figure 3a, right), indicating that the part assembly measured in Plateau 1 is more predictable. Further, the predictability of building transcriptional circuits was estimated for each plateau (Figure 3b). We first measured the dose-responses of two NOT Gates and one Buffer Gate using pTAC promoter and characterized pTAC promoter per se as well (input, inducer concentration; output, YFP fluorescence; Supporting Information Figure S4). The transfer functions of three gates were then reported with inputs and outputs in the same unit (YFP fluorescence; see calculation process in Supplementary Methods). If measurements are in high quality, the transfer functions should be an intrinsic property of the gates; as a result, when the input promoter is replaced by pSal (Figure 3b), the gate dose-responses should be ideally predictable by combining the gate transfer functions with the dose-response of pSal (Supporting Information Figure S5). As expected, results showed that the predicted dose-responses using measurements obtained in Plateau 1 is in a striking precision to the experimental data; predictions based on Plateau 2, however, are much more



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error-prone (Figure 3c-e). One problem for measuring in the steady state is the limited time widow of Plateau 1. Normally Plateau 1 lasts from 0 to 6.5 hours after the 700-fold dilution (Figure 1a-c). However, for those circuits with dynamic functions requiring larger timescale, such as multi-layered logic circuits, a steady state for long duration is needed. This problem could be solved by diluting cell cultures every 6 hours, thus to long-time maintain the Plateau 1; for example, the measurements of pTAC-sfgfp after each dilution are highly constant (Supporting Information Figure S6). In summary, a bi-plateau mode of gene expression in single-cell level was revealed and the first plateau proved to allow the measurements of biological parts in high quality. It is notable that the bi-plateau expression is widespread across different part combinations, growth conditions and bacterial strains. Therefore, measurements in the steady state of gene expression are available regardless of either the circuit components or the context of a living cell. This allows the insulation of effects of host-circuit and environment-circuit interactions from the intrinsic property of biological parts/circuits, thus to enable a better understanding of how parts/circuits work and fail.

Methods Bi-plateau gene expression and cell growth. 96-well plates (Corning, flat bottom) and sealing film (Corning, BF-400-S) were used throughout the study. Bacteria harboring parts/circuits of interest were inoculated from plates to LB medium and grown overnight (8~12h, 1,000 r.p.m., 37 °C, mB100-40 Thermo Shaker, AOSHENG). 10 µl of each overnight culture was sequentially diluted into 130 µl fresh M9 medium for twice; the total dilution fold was 196. After growing the diluted cultures for ~3 hours, we diluted the exponentially growing cultures 700-fold using fresh M9 medium (supplemented with inducer if needed); the dilution process is 10 µl cell culture into 130 µl M9 medium; followed by 3 µl into 147 µl). Then cultivation continued (1,000 r.p.m., 37 °C, mB100-40 Thermo Shaker, AOSHENG); at specific time points, a 2~50 µl aliquot of each culture was transferred to a new plate containing 200 µl PBS with 2 mg/ml kanamycin pre-added to terminate protein expression. For the time course of cell growth after 700-fold dilution, OD600 was recorded using Varioskan Flash (Thermal Scientific); the time interval is 5 min.

Associated Content Supporting Information Supplementary figures, tables, and method. This material is available free of charge via the Internet at http://pubs.acs.org.


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Author Information Corresponding Author *E-mail: [email protected]; [email protected]. Author Contributions ║ These authors contributed equally to this work. Author Contributions Q.O. and C.L. conceived and supervised the project. H.M.Z., S.C., and H.S. designed the experiment. H.M.Z., S.C., H.S., W.J., and Y.Z. performed the experiment and analyzed the data. H.M.Z., Q.O. and C.L. wrote the manuscript. Notes The authors declare no competing financial interest.

Acknowledgement This study was supported by NSFC (11074009, 11434001 and 31470818), 863 “Synthetic Biology” project from MSTC (2012AA02A702), and MSTC (973 grant 2011CBA00805 and No. 2013CB734001).

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Figure Legends Figure 1. The bi-plateau mode of gene expression in bacterial single cells. (a) The experimental procedure for recording the time course of gene expression using flow cytometry. (b) A representative measurement. (c) The combinations of promoters, RBSs and host strains used for the verification of bi-plateau expression. (d) The bi-plateau expression is widespread. The transcriptional regulator lacI for pTAC promoter and error bars for the growth curves are not shown for clarity. Error bars represent s.d.; n=3. Figure 2. Gene expression in two plateaus exhibits a systematic difference. (a) The inducer addition was delayed to different time points to show that the plateau in Plateau 1 is a dynamically active steady is state while the one in Plateau 2 is dynamically inactive. (b-c) The measurements of biological parts obtained from the two plateaus are not proportional, either for promoters (b) or RBSs (c). The transcriptional regulator lacI for pTAC promoter is not shown for clarity. Error bars represent s.d.; n=3. Figure 3. Assembly of biological parts using measurements obtained in Plateau 1 is more predictable. (a) When two RBSs were combined with different promoters, their relative translational strength was robustly maintained in Plateau 1, rather than Plateau 2. (b) The dose-responses of two inducible promoters and the characterization of three circuits using pTAC were used to predict the circuit dose responses under the control of pSal. (c-e) The measurements obtained in Plateau 1, rather than Plateau 2, enabled precise predictions about the dose-response curves for either transcriptional repression (c-d) or activation (e). The transcriptional regulators for pTAC and pSal are not shown for clarity. Error bars represent s.d.; n=3.



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The bi-plateau mode of gene expression in bacterial single cells. 190x153mm (300 x 300 DPI)



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Gene expression in two plateaus exhibits a systematic difference. 190x70mm (300 x 300 DPI)


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Assembly of biological parts using measurements obtained in Plateau 1 is more predictable. 190x121mm (300 x 300 DPI)


Measurements of Gene Expression at Steady State Improve the

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