Engineering an E. coli Near-Infrared Light Sensor - ACS Synthetic

Subscriber access provided by UNIVERSITY OF TOLEDO LIBRARIES

Article

Engineering an E. coli near-infrared light sensor Nicholas Ting Xun Ong, Evan J. Olson, and Jeffrey J. Tabor ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.7b00289 • Publication Date (Web): 01 Nov 2017 Downloaded from http://pubs.acs.org on November 3, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

ACS Synthetic Biology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Synthetic Biology

Engineering an E. coli near-infrared light sensor Nicholas T. Ong1, Evan J. Olson1, and Jeffrey J. Tabor1,2 1

Department of Bioengineering, 2Department of Biosciences, Rice University, 6100 Main Street, Houston, Texas 77005, USA

ACS Paragon Plus Environment

1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 34

ABSTRACT Optogenetics is a technology wherein researchers combine light and geneticallyengineered photoreceptors to control biological processes with unrivaled precision. Near-infrared (NIR) wavelengths (> 700 nm) are desirable optogenetic inputs due to their low phototoxicity and spectral isolation from most photoproteins. The bacteriophytochrome photoreceptor 1 (BphP1), found in several purple photosynthetic bacteria, senses NIR light and activates transcription of photosystem promoters by binding to and inhibiting the transcriptional repressor PpsR2. Here, we examine the response of a library of output promoters to increasing levels of Rhodopseudomonas palustris PpsR2 expression, and identify that of Bradyrhizobium sp. BTAi1 crtE as the most strongly repressed in E. coli. Next, we optimize R. palustris bphP1 and ppsR2 expression in a strain engineered to produce the required chromophore biliverdin IXα in order to demonstrate NIR-activated transcription. Unlike a previously engineered bacterial NIR photoreceptor, our system does not require production of a second messenger, and exhibits rapid response dynamics. It is also the most red-shifted bacterial optogenetic tool yet reported by approximately 50 nm. Accordingly, our BphP1-PpsR2 system has numerous applications in bacterial optogenetics.

Keywords: Synthetic biology, optogenetics, near-infrared light, bacteriophytochrome


2

Page 3 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


In previous work, we and others have engineered bacterial photoreceptors that regulate transcription in response to ultraviolet (maximal wavelength (λmax) = 382 – 405 nm) 1, blue (λmax = 450 nm)

2-4

, green (λmax = 535 nm)

infrared (NIR; λmax = 712 nm)

10

5-8

, red (λmax = 650 nm)

engineer a bacterial edge detector 13

and short wavelength near-

light. Several of these photoreceptors have been used to

spatially pattern gene expression across macroscopic

expression

6, 9

12

5, 9, 11

and microscopic

2

cell populations,

, induce permanent genetic memory via recombinase

, program tailor-made expression dynamics of one

1, 14

and two independent

proteins, place protein expression under in silico feedback control

16

15

, and reveal novel

input/output dynamics of a widely-used synthetic genetic circuit 14. Bacteriophytochromes (BphPs) are the most red-shifted biological photoreceptors known. BphPs contain an N-terminal photosensory core domain (PCD) and C-terminal effector domain that regulates the biological response. The PCD is composed of a Per/Arnt/Sim (PAS), cGMP phosphodiesterase/adenyl cyclase/FhlA (GAF), phytochrome (PHY) motif with the chromophore biliverdin IXα (BV) covalently bound near the N-terminus 17. Canonical BphPs adopt an inactive ground (i.e. dark-adapted) conformation with maximal absorption between 690 nm and 710 nm 18, 19

. This ground state is referred to as Pr, for Phytochrome red absorbing state, by literature

convention. Upon exposure to red or short wavelength NIR light, these BphPs switch to the biologically active Pfr (for Phytochrome far-red absorbing state, by convention) conformation, with maximal absorption between 750 nm and 760 nm. Canonical BphPs revert from Pfr to Pr in milliseconds after exposure to NIR 21

20

or in minutes to hours by thermal reversion in the dark

17,

. A sub-family of BphPs, referred to as “bathy” variants, exhibit an inverted photocycle,

switching between inactive Pfr ground and active Pr states, respectively


22

. The dark reversion

3


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 34

process is also inverted: bathy BphPs revert from Pr to Pfr on a timescale of minutes to hours 23, 24

. Ryu and Gomelsky recently utilized a canonical BphP to engineer the most red-shifted

bacterial optogenetic tool reported to date

10

. In particular, these authors fused the PCD of the

Rhodobacter sphaeroides BphG1 (Pr λmax = 712 nm; Pfr λmax = 756 nm) to the diguanylate cyclase effector domain of Synechocystis PCC6803 Slr1143 in E. coli engineered to produce BV. When activated by red or short wavelength NIR, the BphG1-Slr1143 chimera (named BphS) synthesizes cyclic di-GMP (c-di-GMP), a bacterial second messenger. MrkH, a Klebsiella pneumoniae transcription factor, binds this c-di-GMP, resulting in transcription from the PmrkA output promoter with a reported 40-fold dynamic range (ratio of reporter gene levels in the activated versus inactive state). Despite its utility, BphS-MrkH has several limitations. First, heterologous production of c-di-GMP has been shown to alter the expression of approximately 200 E. coli genes, including those involved in the central processes of sugar metabolism and cell-wall modification GMP also controls biofilm formation

26

, cell division

27

25

. c-di-

, the production of extracellular curli

protein fibers 28, polynucleotide phosphorylase activity 29, and flagellar rotation bias 30. Second, bacterial

genomes

encode

many

poorly

characterized

c-di-GMP

synthases

and

phosphodiesterases (e.g. E. coli encodes 12 and 13, respectively) that could cross-activate or cross-repress the PmrkA output promoter in unwanted or unknown ways

31

. Third, BphS-MrkH

requires 1.5 h after exposure to activating light to produce an observable gene expression response and 6 h to reach half-maximal activation (t1/2). Most model bacterial strains double in 0.3 - 1 h in laboratory conditions, and many regulatory processes occur on these timescales as well

32

. Thus, these slow dynamics make BphS-MrkH a poor choice for characterizing the


4

Page 5 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


dynamics of many pathways

33

. Finally, BphS is likely to be spectrally incompatible with red

light responsive photoreceptors, as well as red-shifted fluorescent proteins. For example, Ryu and Gomelsky activated BphS-MrkH with 660 nm light emitting diodes (LEDs)

10

, which

strongly repress the widely used E. coli green light sensor CcaSR and red light sensor Cph8OmpR (λmax = 650 nm)

15

. Additionally, imaging of the commonly used infrared fluorescent

protein IFP1.4 (excitation maximum = 684 nm) 34 would likely cross-activate BphS-MrkH. Rhodopseudomonas palustris CGA009 BphP1 is a bathy BphP with a more red-shifted ground state absorption spectrum (λmax = 760 nm) than any previously reported bacterial optogenetic tool 35. BphP1 possesses a C-terminal HOS (2-helix output sensor) effector domain that is inactive in the Pfr state, but binds and inactivates the transcriptional repressor PpsR2 in the Pr conformation

35

. PpsR2 binds to TGT-N12-ACA operator sites that often overlap the -35/-10

regions of σ70-type promoters that drive the expression of genes involved in photosynthesis 36, 37. To engineer BphP1-PpsR2 to function as an optogenetic tool, we first developed a strong E. coli promoter that is highly repressed by PpsR2 expression. Next, we optimized BphP1 and PpsR2 expression and BV production to achieve the desired light response. Finally, we performed kinetic and spectral experiments to demonstrate that our BphP1-PpsR2 system responds to changes in light input in minutes, and exhibits the most red-shifted action spectrum of any bacterial optogenetic tool.

RESULTS AND DISCUSSION Engineering PpsR2-repressible E. coli promoters To engineer a BphP1-PpsR2 output promoter in E. coli, we considered the intergenic regions upstream of bchC, crtD, and crtE. We chose these promoters because they have been


5


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

validated to be directly bound by PpsR2 in DNA footprinting experiments

Page 6 of 34

38

. We examined

sequences from both Rps. palustris and the closely related Bradyrhizobium BTAi1 which has a homologous BphP1-PpsR2 pair 39. Using the BPROM webtool 40, we identified a single putative E. coli promoter with one PpsR2 operator overlapping the -35 site and a second overlapping the 10 site in all six intergenic regions (Figure S1). BTAi1 bchC contains a third operator upstream of the -35, and Rps. palustris crtD and crtE each possess one additional semi-conserved operator (TGA-N12-ACA) near the transcriptional start site (+1). Each +1 site is separated from the start codon of the downstream ORF by 30 – 50 bp 5’ untranslated regions (UTRs) (Figure S1). Next, we redesigned each of these promoter regions to function reliably in E. coli. First, to reduce the possibility of unwanted regulation by alternative pathways, we truncated all DNA greater than 10 bp upstream of the -35 or most distal PpsR2 operator (Figure S1). Additionally, we deleted all DNA downstream of each terminal promoter element (i.e. +1, or third operator site) to eliminate any possible regulatory impact from the 5’ UTRs. We named the resulting minimized promoters PRp_bchC, PRp_crtD, PRp_crtE, PBr_bchC, PBr_crtD, and PBr_crtE to indicate the organism and gene from which they are derived. Because the third operator sites of PBr_bchC, PRp_crtD, and PRp_crtE do not overlap predicted E. coli -35/-10 elements, and may thus contribute less to transcriptional repression in our experimental system, we also designed versions of each wherein these operators are deleted (PRp_crtD2, PRp_crtE2, and PBr_bchC2) (Figure S1). Finally, we designed a synthetic PpsR2-repressible E. coli promoter by replacing the cI operator sites in the strong bacteriophage λ promoter PL

41

with PpsR2 operator sites. This promoter, which we

named PL_ppsR2, is based on the design of the strongly repressible engineered tetracycline repressor (TetR)-repressed promoter PLTetO-1 42 (Figure S2).


6

Page 7 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


To characterize the resulting library (Figure 1a), we next constructed a series of plasmids (pNO1-10) wherein we encoded superfolder green fluorescent protein (sfgfp)

43

downstream of

each candidate promoter along with a computationally designed synthetic E. coli ribosome binding site (sRBS) 44, 45 (Figure 1b, Figure S3). Then, we transformed each plasmid into E. coli and measured the resulting sfGFP expression levels by flow cytometry (Methods). Though PRp_crtD and PRp_crtD2 are very weak, the remaining eight promoters are clearly functional, driving total cellular fluorescence levels between 523 ± 17 and 6,900 ± 230 (PBr_crtE) molecules of equivalent fluorescein (MEFL) compared to the E. coli autofluorescence level of 165 ± 10 MEFL (Figure 1c). PBr_bchC2 and PRp_crtE2 are weaker than their parental promoters, demonstrating that the region containing the third operator enhances overall transcription rate (Figure 1c). Next, we characterized the extent to which each promoter is repressed by PpsR2 expression. To this end, we constructed plasmid pNO15, wherein Rps. palustris ppsR2 is transcribed under inducible control of isopropyl β-D-1-thiogalactopyranoside (IPTG) (Figure S3). Then, we co-transformed pNO15 pairwise with each member of the pNO1-10 set, grew the resulting strains in the presence of variable levels of IPTG, and measured sfGFP levels as before. As expected, transcription from each of the eight functional promoters decreases with PpsR2 induction (Figure 1c). PBr_crtE (encoded on pNO4) again exhibits the greatest response, with a 76 ± 20-fold dynamic range (Figure 1c). Accordingly, we selected PBr_crtE as the BphP1-PpsR2 output promoter for subsequent experiments.

Engineering BphP1-PpsR2 to function as an E. coli photoreceptor


7


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 34

The architecture of BphP1-PpsR2 is similar to that of bacterial two-component histidine kinase signal transduction systems (TCSs) 46. We have previously shown that TCS performance is sensitive to the expression levels of both the sensor kinase (analogous to BphP1) and response regulator (analogous to PpsR2)

1, 6

. To optimize BphP1-PpsR2 function in E. coli, we therefore

added a cassette wherein Rps. palustris bphP1 is transcribed under control of an anhydrotetracyline (aTc)-inducible promoter to pNO15, resulting in pNO17 (Figure 2a, S4). To engineer E. coli to produce BV, we also engineered plasmid pNO41, from which a heme oxygenase gene is constitutively expressed (Figure 2a, S4). We optimized the expression levels of a heme oxygenase gene on pNO41 to maximize BV production (Figure S5). To assay the function of this engineered system, we grew E. coli carrying pNO4, pNO17, and pNO41 in various concentrations of aTc and IPTG while exposing them to either 760 nm (NIR) or 660 nm (red) light (Methods). Indeed, we observe NIR activation under a wide range of BphP1 and PpsR2 expression levels, with a maximum activation of 2.35 ± 0.28-fold (Figure 2b). While convenient for preliminary system validation, the requirement for IPTG and aTc limits the compatibility of optogenetic tools with many other engineered genetic systems that utilize these inducers or their target repressor proteins 6. Therefore, we next replaced the two inducible promoters with a constitutive version driving a synthetic bphP1-ppsR2 operon (Figures 2c, S4). To re-optimize BphP1 and PpsR2 expression levels, we designed eight sRBSs with variable predicted strengths for each gene (Table S1). We then constructed 64 plasmids (pNO131-194, Figure S4) to test the 8x8 matrix of sRBS strength combinations for these two genes (Table S1). Then, we co-transformed each plasmid with pNO4 and pNO41 and characterized the NIR light response as before. While more than half of the designs do not


8

Page 9 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


appreciably respond to light, approximately one third exhibit between 1.1 to 2-fold NIR activation (Figure S6), and five yield a ≥2-fold response (Figure S6). The best performing system is encoded on pNO183 (Figure S4), and has a dynamic range of 2.47 ± 0.17-fold, similar to the best aTc and IPTG induction conditions. To be certain we had achieved the maximum dynamic range, we also tested the other seven PpsR2-repressible promoters with pNO183 and pNO41. However, these all yield smaller dynamic ranges than PBr_crtE (Figure S7). Next, we performed a series of control experiments to validate that the light response is due to BphP1-PpsR2 signaling rather than an alternative pathway. As expected, removal of the BphP1 HOS domain results in constitutively low sfGFP levels (Figure 2d). Similarly, deletion of the PpsR2 DBD results in constitutively high sfGFP (Figure 2d). Furthermore, the lack of BV abolishes the light response (Figure 2d). Dark treatment also results in intermediate sfGFP output relative to the NIR and R illumination (Figures 2d,e). These data are consistent with in vitro results demonstrating that dark-treated holo-BphP1 first adopts the Pr state and then gradually converts to Pfr

47

, which should generate an equilibrium between these two states in

actively growing dark-treated cells. Taken together, these results clearly demonstrate that we have recapitulated the function of the BphP1-PpsR2 system in E. coli.

Transfer functions The ability to use variable light intensities or wavelength combinations to tune output transcription rate enables researchers to program gene expression level and dynamics with high quantitative precision, an important feature of optogenetic tools 14, 48. Thus, we characterized the BphP1-PpsR2 transfer function, or steady-state response to increasing NIR intensity, in presence


9


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 34

of different intensities of de-activating red light. In the absence of red, sfGFP output increases between 0 and 72 µmol/m2s NIR light. The response is well-fit by a Hill function with an intensity resulting in 50% maximal response (K1/2) equal to 8.63 ± 0.54 µmol/m2s and a Hill coefficient (n) equal to 1.21 ± 0.09 (Figure 3). This Hill coefficient reveals that the system exhibits a relatively linear response over a wide range of light intensities, a desirable performance feature for tuning transcription rate. As expected, the presence of red light reduces the NIR response (Figure 3). By repeating the transfer function measurement in the presence of variable red light intensities (Figure S8), we observe that approximately 1.2 additional NIR photons are required to overcome the inhibitory effect of each red photon (Figure S9). This result suggests the system has similar sensitivity to NIR and red light, a feature that should make it easy to modulate transcriptional output in diverse experimental conditions.

Input/output dynamics Next, we characterized the step response dynamics of our engineered BphP1-PpsR2 system. Specifically, we pre-conditioned cells in saturating red light and then instantaneously switched to saturating NIR (step ON), performed the converse experiment (step OFF), and switched from saturating NIR to dark (step DARK). Then, we used our previously developed kinetic protocol based on inhibition of transcription and maturation of sfGFP (Methods) to monitor system output over time (Figure 4). We fit each data set to a simplified version of a previous ordinary differential equation model we developed to capture TCS photoreceptor response dynamics

14

(Figure S10). This

BphP1-PpsR2 model depicts a pure delay after a change in light input during which time the


10

Page 11 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


output promoter activity is constant (τ, min) followed by a change in promoter activity that obeys single exponential kinetics. For step ON, τ fits to 5.3 ± 3.0 min and the time required to reach half-maximal activation (t1/2) fits to 26.9 (-5.4/+6.0) min (Figure 4, Methods). For step OFF, we estimate τ to be 8.6 ± 2.3 min and t1/2 as 38.2 (-4.3/+4.6) min (Figure 4). Finally, for step DARK, τ fits to 24.9 ± 2.9 min and t1/2 fits to 63.5 (-6.5/+7.4) min (Figure 4). These response times are similar to the timescales of bacterial gene expression and to the dynamics of our previous green, red, and UV sensors

1, 14

. The slower response for the step DARK is also

consistent with in vitro measurements of a 50 min half-life for BphP1 dark reversion35.

Action spectra The action spectrum(a) of an optogenetic tool depicts the relationship between input wavelength(s), intensity(ies), and system output, and are crucial for selecting optimal light conditions, especially when multiplexing with other photoreceptors

15

. Therefore, we

characterized the forward (i.e. Pfr to Pr) and reverse (i.e. Pr to Pfr) action spectra of our engineered BphP1-PpsR2 system. For the forward experiment, we exposed bacteria to 0 – 20.0 µmol/m2s light from each of 23 different LEDs with emission wavelengths ranging from 369 – 954 nm

15

. As expected, wavelengths between 740 nm and 782 nm induce the strongest

responses, while those between 636 nm and 677 nm maximally deactivate the system (Figures 5a, S11). Interestingly, the 782 nm response is quantitatively similar to that of 740 nm and 755 nm (Figure S12), demonstrating robust activation by this highly red-shifted wavelength. Other wavelengths have little effect, though there is some activation in the 388 – 451 nm region at the higher intensities (Figure 5a). BphP1, like other phytochrome-family photoreceptors, has a small absorption peak in the UV-blue region

49

also known as the Soret band, which may be


11


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 34

responsible for its activation at those wavelengths. However, we are able to repress this apparent Soret band activation of BphP1-PpsR2 by applying deactivating red light (Figure S13). For the reverse action spectrum, we applied sufficient NIR to activate the system to an intermediate level and then performed the same spectral experiment (Figure 5b). As expected, the system is maximally deactivated by 636 nm – 677 nm light (Figures 5b, S11). Overall, both action spectrum measurements are highly consistent with in vitro BphP1 absorption and action spectra 35, 49.

Conclusions Our engineered BphP1-PpsR2 system is the most red-shifted bacterial optogenetic tool yet reported. Due to its unique action spectrum and rapid response dynamics, BphP1-PpsR2 should be able to be combined with existing UV 1, blue

2-4

, and green sensors

6

to achieve

independent control of the expression dynamics of multiple genes in the same cell. Such multiplexed optogenetic programming of gene expression dynamics promises to be a powerful new method for revealing new aspects of how information flows through gene networks

15

.

Additionally, BphP1-PpsR2 should be able to be controlled while imaging most commonly used fluorescent protein reporters

50

, including infrared fluorescent proteins

51

, a benefit that will

facilitate single cell time lapse experiments. The major limitation to our system is its relatively low (~2.5-fold) dynamic range. However, we previously engineered an E. coli green light sensor with only 2-fold dynamic range 5

that was subsequently used to reveal novel input/output dynamics of a synthetic transcriptional

inverter circuit 14, modulate growth rate via programmed expression of a methionine biosynthetic gene 52, and demonstrate that synthetic genetic circuits can adaptively filter noise 53 . Thus, even


12

Page 13 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


without further optimization, we believe that BphP1-PpsR2 can be used for a range of optogenetic applications. Nonetheless, the utility of BphP1-PpsR2 should be able to be improved by increasing its dynamic range in future work. Because the PBr_crtE output promoter has a relatively large dynamic range of nearly 80-fold (Figure 1c), we hypothesize that this small response is due to intrinsically small changes in the BphP1 Pr/Pfr ratio in response to NIR versus red light. On the other hand, BphP1-PpsR2 has recently been combined with TetR DNA binding and VP16 transcriptional activation domains along with a minimal CMV output promoter containing seven tetO operator sites to achieve to ~40-fold NIR activation in mammalian cells 49. Thus, we believe that the dynamic range of our system could be improved by adding bacterial signal-amplifying genetic modules. Available E. coli transcriptional inverters

2, 54

, RNA-based transcription

regulators 55, 56, viral protease systems 57, and recombinases that activate strong output promoters 58

have been shown to amplify transcriptional responses by one to three orders of magnitude.

Alternatively, directed evolution could be performed on BphP1 to increase its photoswitchability 59, 60

. Other NIR photoreceptors could also be explored. Lagarias and co-workers have recently

discovered

cyanobacteriochrome

sensor

histidine

kinases

(CBCR

SKs)

that

bind

phycocyanobilin (PCB) instead of BV, yet absorb strongly in the NIR spectrum up to λmax = 740 nm in the ground state

61

. The major challenge is that these photoreceptors lack known partner

response regulators and/or output promoters that are needed to control gene expression. However, domain swapping 9 could potentially be used to replace the PCD of our related green light-sensing CBCR SK CcaS 6 with that from one of these NIR sensors. Ideally, the >100-fold dynamic range and rapid response dynamics of our CcaS-based system would be preserved,


13


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 34

resulting in a new NIR sensor that retains the benefits of BphP1-PpsR2 while finding additional utility due to the larger response.

METHODS Plasmids, Strains and Media All plasmids were constructed using Golden Gate

62

or Gibson

63

assembly. The genes

encoding PpsR2 and BphP1 (rpa1536 and rpa1537 respectively) were amplified from Rps. palustris CGA009 genomic DNA (ATCC), and the frameshift mutation in rpa1537 was fixed via insertion of a guanine residue at position 430 64. pEO100c 14 and pSR34 6 were used as template vectors for building plasmids pNO1-10 and pNO41, respectively. To build pNO15, we replaced sfgfp downstream of the Ptac promoter downstream of the PLTetO-1 promoter

65

42

on pSR11-2

1

with ppsR2. We replaced the mcherry

on pNO15 with the corrected bphP1 gene to build

pNO17. All synthetic ribosome binding sites (sRBSs) were designed using DeNovoDNA software

44, 45

. To build the ppsR2-bphP1 bicistronic operon plasmids pNO131-194, we

computationally designed a set of eight sRBSs with predicted strengths varying over ~57-fold (Table S1) each for ppsR2 and bphP1, then assembled the 64 possible combinations of sRBS strengths for the 2 genes using pSR58.6

6

as a template vector. The apFAB30 constitutive

promoter 66 controls operon expression on pNO131-194. Cloning was carried out using NEB 10β cells (New England Biolabs, catalog no. C3019H). Primers were ordered from Integrated DNA Technologies, Inc., IA, while DNA sequencing services from Genewiz, NJ or Lone Star Labs, TX were used. All experiments were performed in E. coli BW29655 (BW28357 ∆(envZompR)520(::FRT)) 67. LB media was used to culture transformed bacteria at 37ºC, 250 rpm, with


14

Page 15 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


appropriate antibiotics added (50 µg/mL ampicillin, 34 µg/mL chloramphenicol, and/or 100 µg/mL spectinomycin). All experiments were conducted using M9 media (1× M9 salts, 0.4 % w/v glucose, 0.2 % w/v cas-amino acids, 2 mM MgSO4, 100 µM CaCl2) supplemented with appropriate antibiotics listed earlier. LB cultures of transformed bacteria were grown to exponential phase (OD600 < 0.3), then diluted 1:1000 in M9 media and allowed to grow to exponential phase. By keeping the cells growing in exponential phase instead of reaching stationary phase, we minimize any potential physiological changes to the cells. The OD600 of each M9 culture was recorded prior to the addition of filter-sterilized glycerol to a final concentration of 30 % (v/v) then transferred into PCR tubes in 100 µL aliquots and frozen at -80 ºC. All experiments described here were started from such individual aliquots of culture frozen at exponential phase. In addition to being convenient, we have previously determined that this protocol reduces day-to-day variability in the outputs of engineered genetic systems 1, 15, 68-70.

Optical Hardware All optogenetic experiments were performed in six 24-well Light Plate Apparatus (LPA) devices

70

mounted in a shaking incubator operating at 37 ºC 250 rpm, allowing for precise

optical control with up to two LED inputs per culture. LEDs were calibrated using a spectrophotometer (StellarNet UVN-SR-25 LT16) following our previous method 15. Replicates of each experimental data point were randomized to different well positions where possible to minimize systematic errors from well-specific effects such as uneven LED calibration. Unless otherwise stated, NIR and R illumination used were set at 72 µmol/m2s for 760 nm, and 40 µmol /m2s for 660 nm.


15


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 34

Experimental protocols All experiments were performed according to our previous protocol

15

, which we

summarize here. Frozen aliquots of engineered cells were thawed at the bench and inoculated at low densities (OD600 = 2 x 10-5 – 1 x 10-4) into fresh media. Inoculation densities were picked on the basis that cell cultures would reach steady state and remain in exponential phase (OD600 < 0.3) by the end of the experiment, thus ensuring the cells undergo minimal physiological changes during the experiment. Chemical inducers anhydrotetracycline (aTc) (Clontech cat. no. 631310) and isopropyl β-D-1-thiogalacto-pyranoside (IPTG) (VWR cat. no. 14213-263) were added as needed. 500 µL of each inoculated M9 culture was then added into a well of a clear-bottomed black-walled 24-well plate (ArcticWhite AWLS-303008) that was then sealed with adhesive foil (VWR 60941-126). Culture plates were mounted onto pre-configured LPA devices running the selected light exposure program 70. For dynamics experiments, cells were pre-conditioned for 4 h under selected light conditions then switched to the final light conditions using a previously described staggered-start protocol

14

over the next 4 h. The staggered-start protocol allows ease

of capturing light dynamics data with precise control of time-points, while eliminating the need for repeated samplings of a continuous culture. Assays were run for 5 h (PpsR2-promoter screen) or 8 h (optogenetic experiments). At the end of each experiment, the plates were removed from the incubator and placed in an ice-water slurry for 15 min to inhibit further cell growth. 100 µL cell sample aliquots were transferred from each well into pre-chilled flow cytometry tubes holding 1 mL PBS + 500 µg/mL rifampicin and these were placed in a 37ºC water bath for 1 h to allow for maturation of translated sfGFP while inhibiting new sfGFP transcription. They were placed back in ice-water for 15 min then measured for sfGFP fluorescence using flow cytometry.


16

Page 17 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Flow Cytometry and Data Analysis Single cell fluorescence was measured using a BD FACScan flow cytometer as previously described

14

. Calibration bead samples (Spherotech RCP-30-5A) in PBS were

measured with each day’s experiments. Following data acquisition, raw flow cytometry files were processed using FlowCal

69

. Cell populations were gated by density and the arbitrary

fluorescence units were converted by FlowCal into standardized MEFL values based on the calibration bead samples. The individual arithmetic means of all gated events were calculated and reported as the population mean of each sample.

Model Fitting ∙

All Hill function (deactivating form: = − ; activating form: = + /

∙

/

) and dynamics model (Figure S10) fits were performed using a non-linear least-squares

curve-fitting algorithm in Prism v7.0a for Mac OSX, GraphPad Software, Inc., La Jolla, CA. Reported t1/2 values were calculated by addition of the respective fit values of the delay parameter τ and half-life of the exponential parameter k. Because the four function parameters including k and τ (Figure S10) were fit locally to each step response curve, k and τ fit values have symmetric uncertainties (Table S4). However, t1/2 fit values inherit asymmetrical uncertainties from logarithmic calculation of k half-life uncertainties. Model function parameters were fit locally to each dataset of n = 3 replicates per data point unless otherwise stated.

ABBREVIATIONS


17


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 34

NIR, near-infrared; BphPs, bacteriophytochrome photoreceptors; PCD, photosensory core domain; BV, biliverdin IXα; MEFL, molecules of equivalent fluorescein; TCS, twocomponent system; CBCR, cyanobacteriochrome; SK, sensor kinase.

AUTHOR INFORMATION Corresponding Author Current Address and Contact *

Jeffrey J. Tabor 6100 Main Street, Rice University, Houston, TX 77005 [email protected] +1 (713) 348-8316

Author Contributions N.T.O., E.J.O., and J.J.T conceived of the study and designed the experiments. N.T.O. performed the experiments and analyzed the data. N.T.O. and J.J.T wrote the manuscript. All authors discussed the project and commented on the manuscript.

Conflict of Interest The authors declare no competing financial interests.

ACKNOWLEDGEMENTS We thank Karl Gerhardt and other Tabor Lab members for assistance in configuring and calibrating optogenetic hardware. We also thank the Joel Moake lab for generous sharing of their flow cytometer. This work was supported by the Office of Naval Research (MURI


18

Page 19 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


N000141310074) and a National Science Scholarship provided by the Agency for Science, Technology and Research (A*STAR), Singapore. Plasmids are available from Addgene.

SUPPORTING INFORMATION 14 Figures and 6 Tables supporting main figures, results and discussion (PDF; Ong_2017_SI.pdf).


19


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 34

REFERENCES [1] Ramakrishnan, P., and Tabor, J. J. (2016) Repurposing Synechocystis PCC6803 UirS-UirR as a UV-Violet/Green Photoreversible Transcriptional Regulatory Tool in E. coli, ACS Synth Biol 5, 733-740. [2] Ohlendorf, R., Vidavski, R. R., Eldar, A., Moffat, K., and Moglich, A. (2012) From dusk till dawn: one-plasmid systems for light-regulated gene expression, J Mol Biol 416, 534-542. [3] Chen, X., Liu, R., Ma, Z., Xu, X., Zhang, H., Xu, J., Ouyang, Q., and Yang, Y. (2016) An extraordinary stringent and sensitive light-switchable gene expression system for bacterial cells, Cell Res 26, 854-857. [4] Jayaraman, P., Devarajan, K., Chua, T. K., Zhang, H. Z., Gunawan, E., and Poh, C. L. (2016) Blue light-mediated transcriptional activation and repression of gene expression in bacteria, Nucleic Acids Research 44, 6994-7005. [5] Tabor, J. J., Levskaya, A., and Voigt, C. A. (2011) Multichromatic control of gene expression in Escherichia coli, J Mol Biol 405, 315-324. [6] Schmidl, S. R., Sheth, R. U., Wu, A., and Tabor, J. J. (2014) Refactoring and optimization of light-switchable Escherichia coli two-component systems, ACS Synth Biol 3, 820-831. [7] Abe, K., Miyake, K., Nakamura, M., Kojima, K., Ferri, S., Ikebukuro, K., and Sode, K. (2014) Engineering of a green-light inducible gene expression system in Synechocystis sp. PCC6803, Microb Biotechnol 7, 177-183. [8] Sugie, Y., Hori, M., Oka, S., Ohtsuka, H., and Aiba, H. (2016) Reconstruction of a chromatic response system in Escherichia coli, J Gen Appl Microbiol 62, 140-143. [9] 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., and Voigt, C. A. (2005) Synthetic biology: engineering Escherichia coli to see light, Nature 438, 441-442. [10] Ryu, M. H., and Gomelsky, M. (2014) Near-infrared light responsive synthetic c-di-GMP module for optogenetic applications, ACS Synth Biol 3, 802-810. [11] Fernandez-Rodriguez, J., Moser, F., Song, M., and Voigt, C. A. (2017) Engineering RGB color vision into Escherichia coli, Nature Chemical Biology 13, 706-708. [12] Tabor, J. J., Salis, H. M., Simpson, Z. B., Chevalier, A. A., Levskaya, A., Marcotte, E. M., Voigt, C. A., and Ellington, A. D. (2009) A synthetic genetic edge detection program, Cell 137, 1272-1281.


20

Page 21 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


[13] Farzadfard, F., and Lu, T. K. (2014) Genomically encoded analog memory with precise in vivo DNA writing in living cell populations, Science 346, 1256272. [14] Olson, E. J., Hartsough, L. A., Landry, B. P., Shroff, R., and Tabor, J. J. (2014) Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals, Nat Methods 11, 449-455. [15] Olson, E. J., Tzouanas, C. N., and Tabor, J. J. (2017) A photoconversion model for full spectral programming and multiplexing of optogenetic systems, Mol Syst Biol 13, 926. [16] Milias-Argeitis, A., Rullan, M., Aoki, S. K., Buchmann, P., and Khammash, M. (2016) Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth, Nat Commun 7, 12546. [17] Lamparter, T., Michael, N., Mittmann, F., and Esteban, B. (2002) Phytochrome from Agrobacterium tumefaciens has unusual spectral properties and reveals an N-terminal chromophore attachment site, Proceedings of the National Academy of Sciences of the United States of America 99, 11628-11633. [18] Bhoo, S. H., Davis, S. J., Walker, J., Karniol, B., and Vierstra, R. D. (2001) Bacteriophytochromes are photochromic histidine kinases using a biliverdin chromophore, Nature 414, 776-779. [19] Lamparter, T., Carrascal, M., Michael, N., Martinez, E., Rottwinkel, G., and Abian, J. (2004) The biliverdin chromophore binds covalently to a conserved cysteine residue in the Nterminus of Agrobacterium phytochrome Agp1, Biochemistry 43, 3659-3669. [20] Borucki, B., von Stetten, D., Seibeck, S., Lamparter, T., Michael, N., Mroginski, M. A., Otto, H., Murgida, D. H., Heyn, M. P., and Hildebrandt, P. (2005) Light-induced proton release of phytochrome is coupled to the transient deprotonation of the tetrapyrrole chromophore, J Biol Chem 280, 34358-34364. [21] Burgie, E. S., Wang, T., Bussell, A. N., Walker, J. M., Li, H., and Vierstra, R. D. (2014) Crystallographic and electron microscopic analyses of a bacterial phytochrome reveal local and global rearrangements during photoconversion, J Biol Chem 289, 24573-24587. [22] Karniol, B., and Vierstra, R. D. (2003) The pair of bacteriophytochromes from Agrobacterium tumefaciens are histidine kinases with opposing photobiological properties, Proc Natl Acad Sci U S A 100, 2807-2812. [23] Rottwinkel, G., Oberpichler, I., and Lamparter, T. (2010) Bathy phytochromes in rhizobial soil bacteria, J Bacteriol 192, 5124-5133.


21


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 34

[24] Yang, X., Kuk, J., and Moffat, K. (2008) Crystal structure of Pseudomonas aeruginosa bacteriophytochrome: Photoconversion and signal transduction, Proceedings of the National Academy of Sciences of the United States of America 105, 14715-14720. [25] Mendez-Ortiz, M. M., Hyodo, M., Hayakawa, Y., and Membrillo-Hernandez, J. (2006) Genome-wide transcriptional profile of Escherichia coli in response to high levels of the second messenger 3',5'-cyclic diguanylic acid, J Biol Chem 281, 8090-8099. [26] Boehm, A., Steiner, S., Zaehringer, F., Casanova, A., Hamburger, F., Ritz, D., Keck, W., Ackermann, M., Schirmer, T., and Jenal, U. (2009) Second messenger signalling governs Escherichia coli biofilm induction upon ribosomal stress, Molecular Microbiology 72, 15001516. [27] Kim, H. K., and Harshey, R. M. (2016) A Diguanylate Cyclase Acts as a Cell Division Inhibitor in a Two-Step Response to Reductive and Envelope Stresses, Mbio 7. [28] Weber, H., Pesavento, C., Possling, A., Tischendorf, G., and Hengge, R. (2006) Cyclic-diGMP-mediated signalling within the sigma(S) network of Escherichia coli, Molecular Microbiology 62, 1014-1034. [29] Tuckerman, J. R., Gonzalez, G., and Gilles-Gonzalez, M. A. (2011) Cyclic di-GMP Activation of Polynucleotide Phosphorylase Signal-Dependent RNA Processing, Journal of Molecular Biology 407, 633-639. [30] Fang, X., and Gomelsky, M. (2010) A post-translational, c-di-GMP-dependent mechanism regulating flagellar motility, Molecular Microbiology 76, 1295-1305. [31] Povolotsky, T. L., and Hengge, R. (2016) Genome-Based Comparison of Cyclic Di-GMP Signaling in Pathogenic and Commensal Escherichia coli Strains, Journal of Bacteriology 198, 111-126. [32] Olson, E. J., and Tabor, J. J. (2012) Post-translational tools expand the scope of synthetic biology, Current Opinion in Chemical Biology 16, 300-306. [33] Castillo-Hair, S. M., Igoshin, O. A., and Tabor, J. J. (2015) How to train your microbe: methods for dynamically characterizing gene networks, Curr Opin Microbiol 24, 113-123. [34] Shu, X., Royant, A., Lin, M. Z., Aguilera, T. A., Lev-Ram, V., Steinbach, P. A., and Tsien, R. Y. (2009) Mammalian expression of infrared fluorescent proteins engineered from a bacterial phytochrome, Science 324, 804-807. [35] Bellini, D., and Papiz, M. Z. (2012) Structure of a bacteriophytochrome and light-stimulated protomer swapping with a gene repressor, Structure 20, 1436-1446.


22

Page 23 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


[36] Gomelsky, M., and Kaplan, S. (1995) Genetic-Evidence That Ppsr from RhodobacterSphaeroides-2.4.1 Functions as a Repressor of Puc and Bchf Expression, Journal of Bacteriology 177, 1634-1637. [37] Ponnampalam, S. N., and Bauer, C. E. (1997) DNA binding characteristics of CrtJ - A redox-responding repressor of bacteriochlorophyll, carotenoid, and light harvesting-II gene expression in Rhodobacter capsulatus, Journal of Biological Chemistry 272, 18391-18396. [38] Jaubert, M., Zappa, S., Fardoux, J., Adriano, J. M., Hannibal, L., Elsen, S., Lavergne, J., Vermeglio, A., Giraud, E., and Pignol, D. (2004) Light and redox control of photosynthesis gene expression in Bradyrhizobium: dual roles of two PpsR, J Biol Chem 279, 44407-44416. [39] Giraud, E., and Vermeglio, A. (2008) Bacteriophytochromes in anoxygenic photosynthetic bacteria, Photosynth Res 97, 141-153. [40] Solovyev, V., and Salamov, A. (2011) Automatic Annotation of Microbial Genomes and Metagenomic Sequences, In Metagenomics and its Applications in Agriculture, Biomedicine and Environmental Studies (Li, R. W., Ed.), pp 61-78, Nova Science Publishers. [41] Kincade, J. M., and deHaseth, P. L. (1991) Bacteriophage lambda promoters pL and pR: sequence determinants of in vivo activity and of sensitivity to the DNA gyrase inhibitor, coumermycin, Gene 97, 7-12. [42] Lutz, R., and Bujard, H. (1997) Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements, Nucleic Acids Research 25, 1203-1210. [43] Pedelacq, J. D., Cabantous, S., Tran, T., Terwilliger, T. C., and Waldo, G. S. (2006) Engineering and characterization of a superfolder green fluorescent protein, Nature Biotechnology 24, 79-88. [44] Salis, H. M., Mirsky, E. A., and Voigt, C. A. (2009) Automated design of synthetic ribosome binding sites to control protein expression, Nat Biotechnol 27, 946-950. [45] Borujeni, A. E., Channarasappa, A. S., and Salis, H. M. (2014) Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites, Nucleic Acids Research 42, 2646-2659. [46] Gao, R., and Stock, A. M. (2009) Biological Insights from Structures of Two-Component Proteins, Annu Rev Microbiol 63, 133-154.


23


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 34

[47] Evans, K., Fordham-Skelton, A. P., Mistry, H., Reynolds, C. D., Lawless, A. M., and Papiz, M. Z. (2005) A bacteriophytochrome regulates the synthesis of LH4 complexes in Rhodopseudomonas palustris, Photosynth Res 85, 169-180. [48] Olson, E. J., and Tabor, J. J. (2014) Optogenetic characterization methods overcome key challenges in synthetic and systems biology, Nat Chem Biol 10, 502-511. [49] Kaberniuk, A. A., Shemetov, A. A., and Verkhusha, V. V. (2016) A bacterial phytochromebased optogenetic system controllable with near-infrared light, Nat Methods 13, 591-597. [50] Shaner, N. C., Steinbach, P. A., and Tsien, R. Y. (2005) A guide to choosing fluorescent proteins, Nat Methods 2, 905-909. [51] Shcherbakova, D. M., Baloban, M., and Verkhusha, V. V. (2015) Near-infrared fluorescent proteins engineered from bacterial phytochromes, Curr Opin Chem Biol 27, 52-63. [52] Davidson, E. A., Basu, A. S., and Bayer, T. S. (2013) Programming microbes using pulse width modulation of optical signals, J Mol Biol 425, 4161-4166. [53] Zechner, C., Seelig, G., Rullan, M., and Khammash, M. (2016) Molecular circuits for dynamic noise filtering, Proceedings of the National Academy of Sciences of the United States of America 113, 4729-4734. [54] Karig, D., and Weiss, R. (2005) Signal-amplifying genetic circuit enables in vivo observation of weak promoter activation in the Rhl quorum sensing system, Biotechnol Bioeng 89, 709-718. [55] Westbrook, A. M., and Lucks, J. B. (2017) Achieving large dynamic range control of gene expression with a compact RNA transcription-translation regulator, Nucleic Acids Research 45, 5614-5624. [56] Liu, C. C., Qi, L., Lucks, J. B., Segall-Shapiro, T. H., Wang, D., Mutalik, V. K., and Arkin, A. P. (2012) An adaptor from translational to transcriptional control enables predictable assembly of complex regulation, Nat Methods 9, 1088-1094. [57] Fernandez-Rodriguez, J., and Voigt, C. A. (2016) Post-translational control of genetic circuits using Potyvirus proteases, Nucleic Acids Research 44, 6493-6502. [58] Yang, L., Nielsen, A. A. K., Fernandez-Rodriguez, J., McClune, C. J., Laub, M. T., Lu, T. K., and Voigt, C. A. (2014) Permanent genetic memory with > 1-byte capacity, Nature Methods 11, 1261-1266.


24

Page 25 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


[59] Esvelt, K. M., Carlson, J. C., and Liu, D. R. (2011) A system for the continuous directed evolution of biomolecules, Nature 472, 499-503. [60] Ellefson, J. W., Meyer, A. J., Hughes, R. A., Cannon, J. R., Brodbelt, J. S., and Ellington, A. D. (2014) Directed evolution of genetic parts and circuits by compartmentalized partnered replication, Nature Biotechnology 32, 97-101. [61] Rockwell, N. C., Martin, S. S., and Lagarias, J. C. (2016) Identification of Cyanobacteriochromes Detecting Far-Red Light, Biochemistry 55, 3907-3919. [62] Engler, C., Gruetzner, R., Kandzia, R., and Marillonnet, S. (2009) Golden Gate Shuffling: A One-Pot DNA Shuffling Method Based on Type IIs Restriction Enzymes, Plos One 4. [63] Gibson, D. G., Young, L., Chuang, R. Y., Venter, J. C., Hutchison, C. A., and Smith, H. O. (2009) Enzymatic assembly of DNA molecules up to several hundred kilobases, Nature Methods 6, 343-345. [64] Braatsch, S., Johnson, J. A., Noll, K., and Beatty, J. T. (2007) The O2-responsive repressor PpsR2 but not PpsR1 transduces a light signal sensed by the BphP1 phytochrome in Rhodopseudomonas palustris CGA009, FEMS Microbiol Lett 272, 60-64. [65] Deboer, H. A., Comstock, L. J., and Vasser, M. (1983) The Tac Promoter - a Functional Hybrid Derived from the Trp and Lac Promoters, Proceedings of the National Academy of Sciences of the United States of America 80, 21-25. [66] Mutalik, V. K., Guimaraes, J. C., Cambray, G., Lam, C., Christoffersen, M. J., Mai, Q. A., Tran, A. B., Paull, M., Keasling, J. D., Arkin, A. P., and Endy, D. (2013) Precise and reliable gene expression via standard transcription and translation initiation elements, Nat Methods 10, 354-360. [67] Zhou, L., Lei, X. H., Bochner, B. R., and Wanner, B. L. (2003) Phenotype microarray analysis of Escherichia coli K-12 mutants with deletions of all two-component systems, Journal of Bacteriology 185, 4956-4972. [68] Daeffler, K. N., Galley, J. D., Sheth, R. U., Ortiz-Velez, L. C., Bibb, C. O., Shroyer, N. F., Britton, R. A., and Tabor, J. J. (2017) Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation, Mol Syst Biol 13, 923. [69] Castillo-Hair, S. M., Sexton, J. T., Landry, B. P., Olson, E. J., Igoshin, O. A., and Tabor, J. J. (2016) FlowCal: A User-Friendly, Open Source Software Tool for Automatically Converting Flow Cytometry Data from Arbitrary to Calibrated Units, ACS Synth Biol 5, 774-780.


25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 34

[70] Gerhardt, K. P., Olson, E. J., Castillo-Hair, S. M., Hartsough, L. A., Landry, B. P., Ekness, F., Yokoo, R., Gomez, E. J., Ramakrishnan, P., Suh, J., Savage, D. F., and Tabor, J. J. (2016) An open-hardware platform for optogenetics and photobiology, Scientific Reports 6, 35363.


26

Page 27 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


FIGURE LEGENDS Figure 1 Engineering PpsR2-repressible E. coli promoters. (a) Annotated sequences of 10 candidate promoters. (b) Device schematic for PpsR2 induction and promoter activity measurement plasmid system. (c) Promoter transfer functions. Dashed lines indicate the maximal cellular fluorescence levels. Data points represent the total fluorescence levels of cells carrying the promoter-reporter plasmid and pNO15. Data are from experiments on three separate days. Error bars represent the standard deviations (SD) of those means. Solid lines represent the deactivating ∙

form of the Hill function, = − fit to each dataset where I is the concentration of /

IPTG (mM), b and a are the fit parameters for initial and range of sfGFP (MEFL) output values respectively, K1/2 represents the concentration of IPTG (mM) needed to reach half maximal repression of the promoters, and n is the Hill coefficient. Fit parameter values are listed in Table S2. E. coli cell autofluorescence is denoted by the gray area.

Figure 2 Engineering BphP1-PpsR2 to function as an E. coli photoreceptor (a) Device schematic for the chemically inducible BphP1-PpsR2 system. (b) Dependence of system response on BphP1 and PpsR2 expression. (c) Device schematic for the hardwired BphP1-PpsR2 system. (d) Response of hardwired system and negative controls to NIR, R, and dark. WT: wild-type. bphP1∆HOS: deletion of BphP1 HOS domain. ppsR2∆DBD: deletion of PpsR2 DNA binding domain. ∆ho1: deletion of BV production plasmid. Bar heights represent the mean of experiments performed on three separate days. Error bars represent the SD of those means. Reported sfGFP values are corrected for cell autofluorescence. Statistically significant differences are annotated as such: *,


27


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 34

p < 0.05; **, p < 0.01 (two-way ANOVA followed by Tukey multiple comparison test) (e) Representative flow cytometry histograms of the data in panel D.

Figure 3 BphP1-PpsR2 steady-state transfer functions. Markers and error bars represent the means and SD respectively of n = 3 experiments conducted on separate days. Reported sfGFP values are corrected for cell autofluorescence. Lines represent the activating form of the Hill function, = +

∙

/

fit to each dataset. Fit parameters for each dataset can be found in Table S3.

Figure 4 Step-response dynamics. Response to step changes in light from R to NIR (dark red), NIR to R (red), and NIR to dark (black). Markers and error bars represent the means and SD respectively of n = 3 experiments conducted on separate days. Reported sfGFP values are corrected for cell autofluorescence. Lines represent model (Figure S10) fits. Fit parameters are listed in Table S4.

Figure 5 BphP1-PpsR2 action spectra. (A) Forward action spectrum. LED centroid wavelength is shown. (B) Reverse action spectrum. Cells were treated with 7.82 µmol/m2s 755 nm light to activate the system to an intermediate level and simultaneously exposed to the LED centroid wavelengths shown. All values represent the mean of n = 3 experiments conducted on separate days. The 369 nm LED is incapable of reaching the brightest intensities (grey squares). Absolute sfGFP responses are shown in Figure S11. LED details are listed in Table S5.


28

Page 29 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1


29


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 34

Figure 2


30

Page 31 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3


31


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 34

Figure 4


32

Page 33 of 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 5


33


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

80x39mm (300 x 300 DPI)


Page 34 of 34

Engineering an E. coli Near-Infrared Light Sensor - ACS Synthetic

Recommend Documents