A stationary phase-specific bacterial green light sensor for enhancing metabolite production

Domain: chemistry
Published: 2025-12-24
Editorial outcome: minor_revision

Abstract
Genetically-encoded sensors are used to control protein and metabolite production in bacterial fermentations. However, these sensors are generally optimized for exponential growth rather than stationary phase where production occurs. Here, we find that our previously engineered E. coli green light sensor CcaSR, which functions robustly in exponential phase, fails in stationary phase due to spontaneous loss of an engineered chromophore biosynthetic pathway and accumulation of CcaS and CcaR. We optimize the genetic context and expression determinants of each component, resulting in a stable system named CcaSR that imposes little metabolic burden, exhibits low leakiness and an 80-fold green light response, and functions exclusively in stationary phase. We combine CcaSR -driven enzyme expression with varied static and periodic illumination patterns to achieve high titers of the industrially-relevant phenylpropanoid p-Coumaric acid and demonstrate that these optimizations scale to benchtop bioreactor conditions. Finally, we use CcaSR to optimize the expression level of a co-transcribed multi-enzyme metabolic pathway encoding production of plant-derived betaxanthin family pigments. Stationary phase-optimized bacterial sensors should enhance fermentation productivity by enabling rapid interrogation of the impact of enzyme expression level and induction dynamics.

Introduction
Metabolic engineering has several benefits over traditional catalysis for chemical production. Here, microbes are genetically engineered to express heterologous enzymes that convert low-cost, renewable substrates into high-value therapeutics, fuels, and industrial precursors at near-ambient temperatures and pressures. For example, researchers have expressed pathways with up to 30 heterologous enzymes in optimized microbial hosts to produce complex metabolites such as the cancer drug vinblastine or the cannabinoid derivative cannabigerolic acid. In another example, microbes have been engineered to convert low-value CO2 streams into industrially-relevant quantities of acetone and isopropanol, creating sustainable carbon-negative production processes.

Microbial chemical production traditionally occurs in fermentations characterized by two distinct phases. The first is an exponential growth phase where cells consume nutrients and divide rapidly to reach high total biomass. The second is a production phase where nutrients become limited, growth halts (i.e., cells reach stationary phase), and metabolic flux is diverted toward the desired product. The capacity of microbes to produce heterologous molecules is higher in stationary phase than exponential phase because product synthesis does not compete with biomass accumulation. This elevated production capacity combines with high total biomass to make stationary phase the condition under which the greatest quantities of molecules can be produced.

Heterologous enzyme expression is often controlled by inducible promoter systems. These systems generally comprise a transcription factor protein that is modulated by a chemical inducer such as isopropyl β-D-1-thiogalactopyranoside (IPTG) or L-arabinose, and a target promoter that is regulated by the transcription factor. Inducible promoter systems can reduce unwanted exponential phase enzyme expression and enable control over the timing and level of enzyme expression to increase product titer and yield.

Despite their routine use in metabolic engineering, inducible promoter systems are typically optimized to function in exponential phase. However, stable proteins such as transcription factors tend to accumulate in stationary phase and elevated concentrations of transcriptional repressors (e.g., LacI, AraC) can lead to lower levels of target enzyme expression even in the presence of saturating inducer. Conversely, increases in transcriptional activator abundance can lead to high leaky target gene expression that can result in unwanted toxicity. Thus, optimizing expression of transcription factors or their accessory proteins in exponential phase could result in sub-optimal enzyme induction in the production phase of fermentations. Therefore, there is a need to optimize inducible promoter systems to perform robustly in stationary phase.

Optogenetics is emerging as an alternative to chemical induction in metabolic engineering. Here, light is used as the inducer, and genetically encoded photoreceptor proteins typically regulate transcription of target protein expression. Due to the quantitative and temporal precision with which it can be applied, light can be used to optimize metabolic pathway activity in dimensions that are difficult to access with chemical induction systems. For example, researchers have optimized the timing of light-driven enzyme induction around the exponential to stationary phase transition and the duty cycle of a fixed period light pulse to yield increases in titer of mevalonate in E. coli and isobutanol in S. cerevisiae, respectively. However, the full capabilities of optogenetic tools have yet to be leveraged for precision control of metabolic enzyme activity. For example, complex induction strategies involving varying light intensity and the period and duty cycle of a light pulse have never been utilized to optimize a metabolic pathway.

The two-component regulatory system CcaSR (Fig. 1a) is among the most widely used bacterial optogenetic tools. CcaSR offers several advantages, including photoreversibility, which enables more rapid switching between different target protein expression levels, and non-toxic visible light control wavelengths. Here, the sensor histidine kinase CcaS covalently binds the chromophore phycocyanobilin (PCB) to form a holoprotein that is activated by green and deactivated by red light. PCB is synthesized via the oxidation and subsequent reduction of heme via the enzymes heme oxygenase 1 (encoded by ho1) and ferredoxin-dependent phycocyanobilin reductase (encoded by pcyA). In the active form, holo-CcaS (hereafter CcaS) phosphorylates the response regulator protein CcaR, which binds to the PcpcG2 target promoter to activate transcription. Through a series of optimizations, including modulating the expression levels of an engineered ho1-pcyA operon as well as ccaS and ccaR, and truncating poorly-characterized elements from PcpcG2, we engineered an exponential phase-optimized system named CcaSRv3.0 that exhibits low leakiness in red light and several hundred-fold activation by green.

Here, we set out to use CcaSRv3.0 to optimize metabolite production via heterologous enzyme expression in stationary phase. However, we find that this sensor is leaky in red light, resulting in negligible activation by green in these conditions. We discover that PCB biosynthesis is lost in stationary phase due to the metabolic burden of the plasmid-borne ho1-pcyA operon. We overcome this issue by relocating ho1-pcyA to the chromosome and increasing the expression level to offset the resulting decrease in PCB biosynthesis. Furthermore, we find that CcaS and CcaR accumulate to levels that compromise the light response in stationary phase and re-optimize expression of both proteins to restore proper behavior. We combine the resulting CcaSR system with the tyrosine ammonia lyase gene (tal) and optimized steady state and dynamic light signals to optimize production of p-coumaric acid and demonstrate that these optimizations scale to a 25 mL bioreactor. Finally, we demonstrate that CcaSR can be used to optimize the steady-state expression level of larger metabolic pathways, using the betaxanthin family of plant-derived pigments as a model.

Results
CcaSRv3.0 is encoded on two multi-copy plasmids (Fig. 1a, Supplementary Fig. 1a). The first (pNO286-3) contains a ~9 copy p15a origin of replication, and encodes the J23108 and J23106 constitutive promoters driving expression of ho1-pcyA and ccaS, respectively. The second (pSR58.6) contains an ~18 copy ColE1 origin and the J23100 promoter driving constitutive ccaR expression. Finally, pSR58.6 encodes a superfolder green fluorescent protein (sfgfp) reporter gene under control of the optimized CcaR target promoter PcpcG2-172.

To characterize the performance of CcaSR v3.0 in stationary phase (Fig. 1b), we initially grew dilute cultures of the E. coli K-12 strain BW29655 transformed with pNO286-3 and pSR58.6 aerobically with vigorous shaking at 37 °C in darkness. We chose BW29655 as the host strain because we previously used it for exponential phase characterization of CcaSR v3.0. In these conditions, BW29655 CcaSR v3.0 grows exponentially between 4 and 10 h after dilution and enters stationary phase at approximately 12 h (Supplementary Fig. 1b). While BW29655 cultures lacking the CcaSR v3.0 plasmids reach an optical density at 600 nm (OD600) of 2.90 ± 0.1, the co-transformed cultures plateau at only OD600 = 1.23 ± 0.01 (Supplementary Fig. 1b). Thus, CcaSR v3.0 substantially reduces biomass accumulation of BW29655, likely due to the fact that it consumes cellular heme pools and utilizes protein expression resources.

Next, we grew cultures of BW29655 CcaSR v3.0 as before, but under saturating red or green light (Supplementary Fig. 1c). We sampled these cultures at exponential (t = 8 h, OD600 = 0.1) and stationary (t = 24 h, OD600 = 1.2) phases and used flow cytometry to quantify their respective sfGFP fluorescence levels. In exponential phase, CcaSR v3.0-driven sfGFP fluorescence increases 310-fold from the low level of 1040 ± 16 Molecules of Equivalent Fluorescein (MEFL) in red light (BW29655 autofluorescence = 223 ± 3 MEFL) to the high level 330,400 ± 8700 MEFL in green (Fig. 1c, Supplementary Fig. 1d), consistent with our previous measurements of CcaSR v3.0 performance. On the other hand, most red light-treated bacteria produce high levels of sfGFP in stationary phase, with green light resulting in little additional induction (Fig. 1c, Supplementary Fig. 1e). At 48 h, these trends remain unchanged (Supplementary Fig. 1f), confirming that behavior at 24 h is reflective of later stationary phase behavior.

Our flow cytometry measurements also revealed high cell-to-cell variability in CcaSR v3.0 output in stationary phase. While the single-cell distribution of sfGFP fluorescence exhibits a typical log-normal distribution in exponential phase, it becomes bi-modal in stationary phase, with the higher sfGFP-expressing mode bearing a long tail toward low expression (Fig. 1c, Supplementary Fig. 1e, f). Furthermore, there is a sub-population of low sfGFP-expressing cells that is not light responsive (Supplementary Fig. 1d–f). These effects occur in both defined M9 media and complex LB and 2x YT media (Supplementary Fig. 1g–j), suggesting they are not due to media composition. At high levels, PCB biosynthesis is toxic to E. coli and can result in mutations to the ho1-pcyA operon. Thus, we hypothesized that these effects could be due to a mutation(s) disabling PCB biosynthesis in stationary phase.

To examine this possibility, we grew dilute cultures of BW29655 transformed with only pNO286-3 (Fig. 2a) to exponential or stationary phase and utilized diagnostic PCR to confirm the presence of the ho1-pcyA cassette in both phases (Supplementary Fig. 2). As expected, we observed a robust ho1-pcyA cassette PCR product in exponential phase. However, this product was almost completely absent in stationary phase (Fig. 2b, Supplementary Fig. 2c). Whole-plasmid sequencing revealed that the transcriptional terminator encoded at the end of the ccaS expression cassette had recombined with a terminator of the same sequence encoded at the end of the ho1-pcyA cassette, resulting in deletion of the latter (Supplementary Fig. 2a, Supplementary Data 1). Such deletions are known to occur in plasmids encoding multiple copies of the same terminator. To confirm that PCB is not produced in stationary phase, we expressed a mutant of the Synechocystis PCC6803 phytochrome protein Cph1 (Y176H) that exhibits far-red fluorescence when bound to PCB (Supplementary Fig. 3). We observed strong far-red fluorescence in exponential phase, but very weak far-red fluorescence in stationary (Fig. 2c), consistent with mutation-driven elimination of PCB biosynthesis in the latter. We conclude that the exponential phase-optimized ho1-pcyA cassette from CcaSR v3.0 is unstable in stationary phase, leading to a loss in PCB biosynthesis and a compromised light response in most bacterial cells.

To engineer E. coli to reliably produce PCB in stationary phase, we first integrated the ho1-pcyA cassette from CcaSR v3.0 into the ileY locus of the BW29655 chromosome (Methods). Chromosomal integration of toxic heterologous pathways is known to reduce mutation rate and metabolic burden. We used diagnostic PCR and DNA sequencing to confirm that this chromosomal ho1-pcyA cassette is stable in both exponential and stationary phases (Fig. 2a, b, Supplementary Fig. 2c). Cph1(Y176H) fluorescence confirmed that chromosomal encoding of the exponential phase-optimized ho1-pcyA cassette restores stationary phase PCB biosynthesis, albeit to only 13% of the level produced from pNO286-3 in exponential (Fig. 2b, c).

To increase stationary phase PCB levels, we replaced the medium-strength J23108 promoter driving ho1-pcyA transcription with the stronger J23104 variant (Fig. 2a). This J23104-driven ho1-pcyA cassette is also stable in the chromosome (Fig. 2b, Supplementary Figs. 2c and 4) and increases chromosomally-generated PCB levels to 30% of those generated by pNO286-3 in exponential (Fig. 2c). The maximum OD600 reached by BW29655 cultures is not reduced by either chromosomal ho1-pcyA cassette (p > 0.05, paired, 2-tailed t-test) (Fig. 2d). Thus, chromosomal integration of ho1-pcyA enables stable and non-toxic PCB production in stationary phase. We denote the BW29655 strain carrying the chromosome-optimized (i.e., J23104-driven) ho1-pcyA cassette as JTL1.

To examine whether this chromosome-optimized ho1-pcyA cassette enables CcaSR function in stationary phase, we co-transformed a variant of pNO286-3 lacking ho1-pcyA (pJTL321) with pSR58.6 into JTL1. Although this CcaSR system design produces a 180-fold increase in sfGFP fluorescence in response to green light in exponential phase, this response is reduced to 4.3-fold in stationary phase due to leaky sfGFP expression in red light (Fig. 3b). As CcaSR leakiness and green light response are strongly dependent upon CcaS and CcaR expression level, we hypothesized that the poor light response of this strain is due to accumulation of CcaS and CcaR in stationary phase.

To examine CcaS and CcaR levels in stationary phase, we translationally fused sfGFP to CcaS in the pNO286-3 backbone and to CcaR in a modified version of pSR58.6 wherein sfGFP is deleted downstream of PcpcG2-172. Consistent with our hypothesis, we found that both proteins accumulate to higher abundance in stationary phase (Supplementary Fig. 5). Thus, we moved the ccaS expression cassette onto a lower (~3) copy pSC101-origin plasmid, resulting in pJTL269 (Supplementary Fig. 5c). We found that this modification reduces stationary phase CcaS levels to those generated by CcaSR v3.0 in exponential phase (Supplementary Fig. 5d). As a result, we performed no further optimizations of ccaS expression.

As two-component systems are highly sensitive to response regulator expression levels, we constructed a small library of five plasmids wherein the strength of the ccaR RBS varies over several orders of magnitude (Fig. 3c). We used an sfGFP tag to confirm that these RBS variants result in a range of CcaR levels in the cells (Fig. 3c, Supplementary Fig. 6). Next, we individually co-transformed the untagged library plasmids with pJTL269 and a newly-constructed plasmid named pJTL256 wherein the PcpcG2-172-sfgfp reporter module is encoded on a p15A backbone. We found that the weakest ccaR RBS (stationary phase CcaR-sfGFP expression of 2350 ± 270 MEFL) results in low PcpcG2-172 activity in both red and green light (Fig. 3d). The second weakest RBS (encoded on a plasmid named pJTL257.2, stationary phase CcaR-sfGFP expression of 3150 ± 250 MEFL) results in a statistically significant (p = 3.3 × 10−5, Student’s two-tailed t-test) increase in CcaR-sfGFP expression compared to the weakest (Fig. 3d) as well as the desired properties of low leaky expression in red light and strong activation by green (Fig. 3d). Finally, the three strongest RBSs result in high levels of PcpcG2-172 activity in both red and green light, consistent with previous results in exponential phase. Interestingly, the optimal CcaR expression level is the same in stationary and exponential phases (Fig. 3c, Supplementary Fig. 5e, f), suggesting that absolute CcaR level is a critical parameter for light response across growth phases. Due to its strong light response, we named the JTL1 strain containing pJTL256, pJTL269, and pJTL257.2 CcaSRstat. Notably, CcaSRstat exhibits no growth defect compared to BW29655 (Fig. 3e), suggesting that the genetic components will be stable and that the strain can accumulate high levels of biomass. Consistent with exponential phase CcaSR systems, CcaSRstat exhibits an intensity-dependent green light response (Fig. 3f). It also exhibits first-order activation kinetics with a time constant (τ) of 16.5 h (Fig. 3g). Though these kinetics are substantially slower than those in exponential phase they are consistent with previous studies of inducible protein expression systems in stationary phase and are likely sufficient for the days-long timescales of fermentations.

During our characterization experiments, we noticed that CcaSRstat does not respond to green light until cultures reach stationary phase (Fig. 3h, Supplementary Fig. 7). We theorized that this specificity is due to insufficient CcaS and/or CcaR levels in exponential phase. Indeed, we constructed a CcaSR variant with stronger ccaS expression that responds to light in both exponential and stationary phases (Supplementary Fig. 8). The stationary phase specificity of CcaSRstat may be beneficial for metabolic engineering applications, as green light can be applied during growth and production phases, but not result in unwanted expression in the former. Alternatively, the variant that functions in exponential and stationary could enable enzyme induction slightly earlier in the fermentation process, combined with longer-term induction during production phase.

Next, we aimed to understand how CcaSRstat operates in other E. coli strains. To this end, we tested CcaSRstat in the human probiotic E. coli Nissle 1917 (EcN). As EcN does not share an identical ileY locus with K12, we targeted the torT and yhbC loci for integration of the optimized chromosomal ho1-pcyA cassette (Supplementary Fig. 9a). We then transformed the ccaS expression plasmid (pJTL269), ccaR expression plasmid (pJTL257.2), and sfgfp output plasmid (pJTL256) all into both backgrounds to create EcNtorT::CcaSRstat and EcNyhbC::CcaSRstat, respectively (Supplementary Fig. 9a). Both strains exhibit large green light responses in stationary phase (Supplementary Figs. 9b, d, f) and are inactive in exponential phase (Supplementary Fig. 9c, e). These results show that CcaSRstat can function in diverse E. coli strain backgrounds.

To demonstrate the utility of CcaSRstat for metabolic engineering, we next utilized it to optimize production of the phenylpropanoid p-coumaric (p-CA) acid in E. coli. Phenylpropanoids are a large class of mostly plant-derived secondary metabolites produced from L-tyrosine and L-phenylalanine. These compounds have strong antioxidant properties and applications in the medical and cosmetic industries. The production of p-CA is the first committed step towards phenylpropanoid metabolism. The most efficient pathway for engineered microbial p-CA biosynthesis is expression of a heterologous tyrosine ammonia lyase (tal) gene, which encodes an enzyme (TAL) that converts L-tyrosine into p-CA in one step (Fig. 4a). However, TAL-mediated p-CA production is toxic in E. coli and results in growth defects and lower total fermentation biomass. Due to the stringent and precise control of gene expression it enables, we hypothesized that CcaSRstat could be used to achieve high p-CA titers with minimal toxicity.

To explore this hypothesis, we used CcaSRstat to express Streptomyces sp. NRRL F-4489 tal, creating a strain named CcaSRstat:tal. We chose this gene variant because the TAL enzyme it encodes produces p-CA with high selectivity. First, we confirmed that we can control TAL abundance with green light by fusing sfGFP to the TAL C-terminus (Fig. 4b) and treating the corresponding cultures with green light intensities between 0.1 µmol m−2 s−1, which does not activate CcaSRstat, and 100 µmol m−2 s−1, which results in full activation (Fig. 3f) for 72 h in 30 °C batch conditions (Fig. 4a). We found that 7 µmol m−2 s−1 green light results in the highest levels of TAL-sfGFP, and that higher intensities result in unreliable expression (Fig. 4c), likely due to toxicity. Deletion of CcaSRstat abolishes light-dependent TAL-sfGFP accumulation (Fig. 4d). We repeated this experiment with a strain expressing untagged tal (Fig. 4e) and measured the resulting p-CA titers via HPLC. p-CA titers increase with green light intensity up to 7 µmol m−2 s−1, where they reach a high of 71.3 ± 2.8 mg/L (Fig. 4f). Above this intensity, titers become increasingly variable, consistent with toxicity from TAL overexpression (Fig. 4c, f). Bacteria lacking CcaSRstat produce no detectable p-CA in either light condition (Fig. 4g). These data confirm that CcaSRstat can be used to optimize the expression level of a single-enzyme metabolic pathway in stationary phase.

We next sought to use CcaSRstat to optimize production of a metabolite produced by a multi-enzyme pathway. Betaxanthins are a class of plant-derived pigments noted for their bright orange color as well as antioxidant and anti-inflammatory properties. These compounds are produced via a spontaneous Schiff base condensation between betalamic acid and an amino acid or amine (Supplementary Fig. 10a). In E. coli, betalamic acid can be produced from L-tyrosine in two enzymatic steps via 4-hydroxyphenylacetate 3-monooxygenase (hpaB) from E. coli W, along with the flavin reductase HpaC, which provides FADH to HpaB, and M. jalapa 4,5-Dopa dioxygenase (mjDoda), followed by a spontaneous rearrangement to yield the final product. Thus, we constructed a synthetic operon resulting in co-transcription of these three genes under control of CcaSRstat (Supplementary Fig. 10a). Using LC Q-TOF MS analysis, we identified betalamic acid, dopaxanthin, and portulacaxanthin II as products of our fermentation under green light only (Supplementary Figs. 10b–d and 11–13). Since betaxanthins are intrinsically fluorescent, we used bulk culture fluorescence to assay for betaxanthin levels in higher throughput plate-based assays. Using this method, we found that betaxanthin levels increase with green light intensity up to the maximum of 100 µmol m−2 s−1 tested in our experiments (Supplementary Fig. 15). Consistent with the function of CcaSRstat, we found that betaxanthins are primarily produced in stationary phase (Supplementary Fig. 16). These experiments demonstrate that CcaSRstat can be combined with static green light signals of variable intensity to optimize the production of metabolites derived from co-transcribed multi-enzyme pathways.

Pulsatile induction may result in higher product titers than static induction by distributing total enzyme expression across the course of a fermentation while avoiding acute toxicity arising from high intracellular enzyme concentration at any given point in time. To explore this possibility in our system, we next grew CcaSRstat:tal under periodic administration of the optimized green light intensity of 7 µmol m−2 s−1 with seven different periods (T) varying between 0.02 and 1000 min and eight different duty cycles (dc) varying between 20 and 90% in a constant red light background (Fig. 5a, Supplementary Fig. 17). We exposed the bacteria to all combinations of these periods and duty cycles (56 total conditions replicated on 5 separated days) 6 h after back-dilution of the cultures, an induction time that we found to maximize p-CA titer (Fig. 5b, Supplementary Fig. 18). Though there is substantial variability in the data, p-CA titers clearly increase with duty cycle (Fig. 5b, Supplementary Fig. 19a). On the other hand, there is no significant effect of light period on p-CA titer (Supplementary Fig. 19b). However, we identified two pulsatile light treatment conditions (T = 10 min, dc = 70%; T = 10 min, dc = 85%) that yield significantly higher p-CA titer compared to static 7 µmol m−2 s−1 green light (one-tailed Welch’s t-test p = 0.021 and 0.039, respectively). These effects hold for green light intensities between 15 and 100 µmol m−2 s−1 but are absent at lesser intensities where p-CA titers are much lower (Supplementary Fig. 21). Overall, the best pulsatile light condition (7 µmol m−2 s−1, T = 10 min, dc = 85%) results in our highest measured titer of 113.1 ± 39.7 mg/L, a substantial increase over the maximum titer we achieved with constant light treatment (Fig. 4f).

The optimized static light signal results in lower CcaSRstat:tal culture densities than the optimized dynamic light signal (Supplementary Fig. 20a). This effect does not occur in non-engineered E. coli (Supplementary Fig. 20a), indicating that it arises due to TAL expression differences. However, using CcaSRstat:tal-sfgfp, we find that TAL expression levels are identical in static and pulsatile light conditions over the large majority of a 3-day Light Plate Apparatus (LPA) fermentation (Supplementary Fig. 20b). These data indicate that dynamic TAL induction is less toxic to E. coli than static TAL induction at the high levels needed for optimal p-CA titers. Pulsatile light may reduce the toxicity of TAL expression by periodically allowing cells to regenerate L-tyrosine pools or liberating RNA polymerases and ribosomes to produce housekeeping or other gene products important for bacterial physiology. Ultimately, the reduced toxicity of pulsatile TAL induction results in more biomass for p-CA production.

Next, we applied similar period/duty cycle optimizations at the optimal static light intensity of 100 µmol m−2 s−1 (Supplementary Fig. 15) to further optimize our engineered betaxanthin pathway. However, pulse optimization failed to increase betaxanthins beyond levels achieved with static light treatment at the same intensity (Supplementary Fig. 22). These data suggest that multi-step biosynthetic pathways may require independent optimization of the induction dynamics of different enzymes.

We next examined how our batch fermentation results translate to a bioreactor format. To this end, we compared our optimized static and dynamic green light signals, which were conducted at 0.5 mL culture volume (Methods), to 25 mL commercial reactors capable of exposing growing cultures to programmed light signals (Methods) (Fig. 5c). We first developed a methodology based on live bacteria and p-CA measurements to calibrate the reactors to emit an intensity of green light intensity operationally equivalent to the optimized 7 µmol m−2 s−1 level from our 0.5 mL system (“Methods,” Supplementary Fig. 23). Next, we measured the effect of the optimal static and periodic light signals on p-CA titer in these reactors. For the static signal, we found that p-CA titers increase for 4 days and then saturate at 34.8 mg/L (Fig. 5d). Strikingly, p-CA titers increase for 8 days in response to the dynamic signal and reach a higher final titer of 87.8 mg/L (Fig. 5d). The dynamic signal results in faster bacterial growth and higher culture carrying capacity (OD600 = 1.5 versus 1.0) than the static signal (Supplementary Fig. 24), which explains the higher titer, at least in part. Carrying capacity decreases from OD600 = 2.1 in our batch experiments (Supplementary Fig. 20a) to OD600 = 1.4 in our reactor conditions (Supplementary Fig. 24), which may explain the lower p-CA titer in the latter. These data demonstrate that metabolic pathway optimizations using CcaSRstat can scale from batch to bioreactor settings.

Methods
DNA cloning
The ccaS, ccaR, and ho1-pcyA cassettes, PcpcG2-172, and the sfgfp gene were amplified from previous E. coli plasmids. The hpaB and hpaC genes were synthesized by Twist Biosciences based on the E. coli W sequence. The M. jalapa mjDoda gene was synthesized by Twist using the sequence codon-optimized for E. coli by Hou et al. The cph1(Y176H) gene was amplified from a B. subtilis PCB biosensor integration module. Streptomyces sp. NRRL F-4489 tal was codon-optimized for E. coli and synthesized (Twist Biosciences). The RBS BCD7, which functions relatively consistently independent of genetic context, was constructed via oligonucleotide annealing and cloning into pJTL256. The RBS BCD25k was amplified from a previous E. coli plasmid. The RBS calculator (version 2020) was used to predict ccaR RBS strengths (Supplementary Fig. 6b). Golden Gate assembly was utilized for all plasmid construction. Clonetegration was utilized for chromosomal integration of the ho1-pcyA cassettes. We confirmed integration into the ileY locus through PCR amplification with primers binding directly outside of ileY and performing Sanger sequencing on the PCR product.

Strain preparation
All plasmids were chemically transformed into their respective background strain (Supplementary Data 2). Experimental strains were streaked from -80 °C 25% v/v glycerol stocks onto Petri dishes with Luria Broth (LB) + 1.2% w/v agar. A single colony was isolated from the plate, transferred to 3 mL of M9 media (see Media Preparation) with appropriate antibiotics in a 14 mL culture tube, and incubated in a shaking incubator at 37 °C and 250 RPM. Once the cultures reached OD600 ~ 0.05–0.3, tubes were removed from the incubator. 1400 µL of culture was mixed with 600 µL of 50% v/v glycerol. OD600 of the culture-glycerol was then measured; following, 0.1 mL of the mixture was aliquoted into 0.2 mL PCR tubes and stored at -80 °C. These aliquots were used for downstream experiments.

Media preparation
All experiments utilized defined minimal M9 media. The defined M9 media consisted of 5x M9 salts (Teknova M1902), 2 mM MgSO4, 0.1 mM CaCl2, and 0.2% casamino acids. For phenylpropanoid and betaxanthin fermentation experiments, 200 mg/L L-tyrosine was added to the media along with 5 g/L glucose. No further L-tyrosine could be added to the media without resulting in precipitation. For all other experiments, the media was supplemented with only 5 g/L glucose. All M9 media was filtered through a 0.2 µm filter and stored at room temperature. At the start of an experiment, appropriate antibiotics (100 µg/mL kanamycin, 50 µg/mL spectinomycin, 50 µg/mL ampicillin, and 34 µg/mL chloramphenicol) were added along with 1 mM 5-aminolevulinic acid (Thermo 103925000), a precursor for phycocyanobilin (PCB) biosynthesis. For betaxanthin fermentations, L-ascorbic acid was added at a final concentration of 10 mM as an antioxidant to prevent spontaneous oxidation of L-DOPA.

Non-bioreactor experiments
All non-bioreactor 24-well optogenetic experiments were performed in four LPAs. Each LPA was outfitted with a green 520 nm LED and a red 660 nm LED in each well. LED calibration was performed as previously described. Briefly, a 15.2 cm diameter integrating sphere (StellarNet IS6) attached to a spectrometer (StellarNet UVN-SR-25LT16) was used to measure photon flux of each LED with a constant programmed intensity, and this flux was used to calculate adjusted dot correction values for each LPA in order to achieve maximal intensities of 100 µmol m−2 s−1 for both green and red LEDs. For basic static and dynamic light experiments, a custom Python script was used to randomize sample locations across the plate and calculate LPA grayscale values (GSint), resulting in desired light intensities. GSint values were then input into IRIS v1.0.0 and the program.lpf files for each LPA were downloaded. For the pulse function optimization, a custom Python script was created to generate the pulse patterns and save the calibrated GSint patterns into a program.lpf file to be used by the LPA.

On the day of an experiment, frozen 0.1 mL glycerol aliquots were removed from a -80 °C freezer and thawed at room temperature. For non-metabolite production experiments, aliquots were diluted to OD600 = 10−5 in the appropriate M9 media. For phenylpropanoid fermentations, we found that a starting OD600 ≥ 10−3 results in slightly higher p-CA production (Supplementary Fig. 18a). Thus, all fermentations began at an OD600 of 10−3. Media was then aliquoted into 24-well plates with 500 µL culture per well. For experiments not involving light treatment, clear 24-well plates (Fisher Scientific, 07-200-84) were utilized. For optogenetics experiments, 24-well dark-walled, clear-bottomed plates (ArcticWhite AWLS303008) were used. Plates loaded with inoculated media were sealed with adhesive foil (VWR 60941-126) and mounted onto LPAs in the shaking incubator at 250 RPM. Unless otherwise specified, all wells were supplied with 10 µmol m−2 s−1 red light in addition to the specified green light signal. Saturating green light was set to 10 µmol m−2 s−1 for Figs. 1c, 3b, d, h and 4d, g, Supplementary Figs. 1c–j, 7b–f, 8b, c, 9b–f and 100 µmol m−2 s−1 for Supplementary Figs. 10b–d and 11–13. In other optogenetic experiments, light intensities are indicated in the figure or figure caption.

For exponential phase characterization experiments, bacteria were grown for 8 h at 37 °C to OD600 = 0.05–0.5. For stationary phase characterization experiments, bacteria were grown until growth stagnated (9–14 h depending on the strain) followed by an additional 12 h in stationary phase (21–26 h total) at 37 °C. For fermentations and TAL-sfGFP expression characterization experiments, bacteria were grown at 30 °C for 72 h (p-CA) or 24 h (betaxanthins) in the noted light conditions. After the experiments were complete, the plates were removed from the incubator and immediately placed on an ice slurry for appropriate processing and analysis.

Plate reader absorbance and fluorescence detection
To assay growth dynamics, 0.1 mL glycerol aliquots were thawed at room temperature and diluted to OD600 = 10−5 in M9 media. 200 µL of inoculated medium was then pipetted into one well in a black-walled, clear-bottomed 96-well plate (VWR 82050-748) and sealed with adhesive foil. The plate was then inserted into a Biotek Synergy H1 Microplate Reader. Utilizing the associated Biotek software (Gen5 v3.08), the plate reader was programmed to shake for 12–24 h at 37 °C, stopping every 15 min. to quantify absorbance at 600 nm (A600). The A600 value of a media-only sample was then subtracted from the resulting absorbance data and converted to OD600 utilizing the linear relationship:
OD600 = 2.2 × A600,platereader,media subtracted

For Cph1(Y176H) and betaxanthin fluorescence measurements, cell cultures were diluted 1:10 in PBS, and 200 µL of each sample was pipetted into black, clear-bottomed 96-well plates. A program was created to measure both A600 and fluorescence signal arising from excitation at 644 nm and emission at 672 nm (the peak excitation/emission of fluorescent Cph1(Y176H)) or excitation of 485 nm and an emission of 515 nm (for detection of betaxanthin pigments). For each sample, fluorescence was divided by OD600 to reflect per-cell values.

PCR and sequencing of ho1-pcyA expression cassettes
To analyze ho1-pcyA expression cassettes, 50 µL of culture was transferred into a 0.2 mL microtube and boiled at 98 °C for 10 min. Boiled cultures were added to a PCR mixture containing primers PJL01 and PJL02 (Supplementary Data 1) to amplify the expression cassette. For plasmid-based ho1-pcyA strains, the remaining 450 µL of culture was plasmid-purified and submitted to Plasmidsaurus Inc. for whole-plasmid sequencing.

Bioreactor experiments
Four 25 mL reactors were purchased from Chi.Bio (Berlin, Germany) and assembled according to the manufacturer’s instructions. Custom Python programs were written to modulate light levels dynamically and implemented into the ChiBioOS app.py (ChiBio Operating System v1.0). To calibrate the optimal 7 µmol m−2 s−1 of green light to the arbitrary intensity units of the Chi.Bio reactor, we measured TAL-sfGFP levels in the LPA at 7 µmol m−2 s−1 of green light after a 72 h, 30 °C fermentation (Supplementary Fig. 23b). We then grew CcaSRstat with the TAL-sfGFP output in the Chi.Bio reactor with varying light conditions, matching the LPA fermentation conditions (72 h, 30 °C) to determine the arbitrary unit green light intensity that results in similar TAL-sfGFP levels to match optimal LPA values. Fortuitously, the lowest testable intensity in the Chi.Bio reactor (0.003 au) results in a value of TAL-GFP of 59,730 ± 1575, which closely matches optimal TAL-GFP expression in the LPAs (65,500 ± 2800 MEFL). Higher intensities of green light in the Chi.Bio reactor resulted in lower TAL expression (Supplementary Fig. 23c), likely due to both phototoxicity and TAL toxicity, a phenomenon we also observed in LPAs at high intensities.

To begin the experiment, 0.1 mL glycerol aliquots of the specified strains were thawed at room temperature. To generate enough cells for the seed culture, the aliquots were diluted 1:100 in 3 mL of M9 media supplemented with 200 mg/L L-tyrosine and grown for 3–4 h until an OD600 = 0.05–0.2 was reached. This culture was then diluted to OD600 = 10−3 in 15 mL M9 media with 200 mg/L L-tyrosine and transferred to a 25 mL glass vial (Fisher 14-955-320) outfitted with a Chi.Bio-specific opaque lid. Vials were then fastened inside the reactors, which were set to an internal temperature of 30 °C with a stirring rate of 0.7, and each optogenetic program was started. Starting at t = 6 h (start of the pulse induction), reactors were sampled every 24 h. To do so, programs were briefly paused, and 500 µL of media from each reactor chamber was removed. At the first five time points, a bolus 1 mL M9 with 200 g/L glucose and 200 mg/L L-tyrosine was also added to offset total volume loss from sampling.

Flow cytometry measurements of sfGFP fluorescence
Culture samples were diluted to an OD600 ~ 0.01 in ice-cold PBS supplemented with 1 mg/mL of chloramphenicol to halt translation. Diluted samples were then transferred to a 37 °C water bath and incubated for 15 min to allow sfGFP maturation. Samples were then removed and placed back in an ice slurry for flow cytometry analysis. Cytometry was performed with a Beckman Coulter Cytoflex S using a 488 nm excitation laser and a 525/40 nm emission filter. 20,000 events per sample were collected. In addition, 10,000 events of a suspension of calibration beads (Spherotech RCP-30-5A) were collected at the end of flow cytometry data collection.

The resulting .fcs files were analyzed utilizing the FlowCal software package v1.3, following the processing workflow in Supplementary Fig. 25. Samples were density-gated by forward scatter/side scatter, retaining 50% of the total events and removing unwanted debris. Calibration bead samples were similarly density-gated to retain solely 30% of the total events. We then used bead data to convert all sfGFP data from arbitrary units into MEFL values as previously described. Specifically, E. coli autofluorescence was subtracted from total sample fluorescence utilizing the following equation:
MEFLSample,AFsubtracted = MEFLSample,raw − μAF
where MEFLSample,AFsubtracted represents the reported, background-subtracted MEFL value of the sample, MEFLSample,raw is the raw MEFL value of the sample data, and μAF is the arithmetic mean of the MEFL value of n = 3 biological replicates of the autofluorescence control. For all CcaSRv3.0-sfGFP samples, BW29655 was used as the autofluorescence control. For all others samples, the control was JTL1. Autofluorescence values are not subtracted from any plotted histograms. Rather, histograms of autofluorescent bacteria are shown alongside those of engineered fluorescent bacteria for reference. Histograms are represented with kernel smoothing, and raw bin data can be found in the source data file.

HPLC detection of phenylpropanoids
For HPLC analysis of p-CA, culture samples were prepared in 1.5 mL conical tubes and centrifuged for 2 min at 10,000 × g. Supernatant was then filtered through a 0.2 µm polyethersulfone (PES) filter (VWR, 76479-024) and stored at -20 °C before HPLC analysis. Filtered samples were then diluted 1:10 in 100% methanol and pipetted into glass chromatography vials. Liquid chromatography was then performed with a Shimadzu HPLC column oven (Shimadzu CTO-20A) with a C18 column (5 µM, 50 × 3 mm) equipped with an autosampler (Shimadzu SIL-20ACHT), quaternary solvent delivery unit (Shimadzu LC-20AD), and UV-Vis detector (Shimadzu SPD-M20A). To detect p-CA, an isocratic method was developed based on past literature. The protocol utilized 65% solvent A (1.3% v/v glacial acetic acid) and 35% solvent B (100% methanol) with a rinse solution of 100% methanol. 10 µL of sample was injected and run for 12 min isocratically with a flow rate of 0.2 mL min−1 at 28 °C. A p-CA standard (VWR IC10257610) was used to create a calibration curve. LabSolutions v. 5.9.7 software was utilized to analyze HPLC data. For every sample, the corresponding peak at 306 nm with a retention time of ~6 min. was utilized to calculate p-CA peak area. A p-CA calibration curve was then utilized to calculate the sample titer.

LC Q-TOF MS detection of betaxanthins
Culture samples were centrifuged for 5 min. at 5000 × g and the supernatant was transferred to a clean, pre-labeled microcentrifuge tube and stored at -20 °C. Betalamic acid, dopaxanthin, and portulacaxanthin II were then identified and qualitatively analyzed using an Agilent 6545XT Q-TOF mass spectrometer (Agilent, US) equipped with a Dual AJS ESI source, operating in positive ion mode and scanning a mass range of m/z 100–1200. Chromatographic separation was carried out using an Agilent 1290 Infinity II binary pump at a flow rate of 0.400 mL/min. The LC gradient started at 98% solvent A (0.1% formic acid in water) and 2% solvent B (0.1% formic acid in acetonitrile) from 0 to 2 min, ramped linearly to 50% B between 2 and 12 min, then increased to 98% B from 12 to 15 min, followed by a 2-min hold at 98% B. A post-time of 3 min was applied for re-equilibration. The column compartment was maintained at 35 °C, and the autosampler was set for an injection volume of 10.0 µL. For mass spectrometry, a capillary voltage of 4 kV with a nozzle voltage of 500 V was used. The drying gas flow rate was set to 11 L/min at a temperature of 325 °C, and the sheath gas flow rate was set to 12 L/min at a temperature of 275 °C. Traces were collected and analyzed using an Agilent MassHunter Workstation v10.1 for LC/Q-TOF and MassHunter Qualitative Analysis software v10.0, respectively. The engineered bacteria exposed to green light displayed new chromatogram peaks at retention times of 4.83, 5.08, and 5.354 min (Supplementary Figs. 11–13) that are absent from control samples, including engineered bacteria exposed to red light alone. The corresponding MS spectra indicate m/z ratios of 391.1132, 212.0548 and 375.1192 ([M+H]+) suggesting the peaks belong to dopaxanthin (expected ([M+H]+) m/z = 391.1136), betalamic acid (expected ([M+H]+) m/z = 212.0553), and portulacaxanthin II (expected ([M+H]+) m/z = 375.1187). The difference between the experimental m/z and expected m/z is 1.08 ppm, 2.3 ppm, and 1.27 ppm, respectively, which are all within the instrument’s margin of error.

Model fitting
Green light intensity transfer functions were fitted to the Hill function:
y = ymin + (ymax − ymin) × xn / (xn + K1/2n)
where y is sfGFP fluorescence, TAL-sfGFP fluorescence, or p-CA titer. ymin and ymax represent the minimum and maximum y values in the response to no green light and saturating green light, respectively. x represents the green light level in units of µmol m−2 s−1. K1/2 is the intensity resulting in half-activation. n is the Hill coefficient.

For modeling activation kinetics, we utilized a first-order transfer function with delay, following the format:
y = K × (1 − e−t/τ) if t > 0
(ymin) if t ≤ 0
Here, K indicates the gain, or difference between the on and off state of the system, t is time elapsed since t = 0 h or the start of green light induction in stationary phase, and τ is the time constant. To aid in the fitting of our activation kinetics, we specified the gain of the system based on the saturating stationary phase activation signal after 24 h (7660 ± 1510 MEFL). All Hill functions and first-order transfer functions were fit to their respective data sets utilizing the LmFit 1.0.1 python package, which utilizes the Levenberg-Marquardt algorithm to perform non-linear least-squares minimization. Pearson’s correlation coefficient R and R2 were calculated utilizing Scipy 1.7.3.

To generate Bode diagrams, we utilized the bode function of Scipy 1.7.3. Inputting the fitted parameters of the first-order transfer function for CcaSR stationary phase activation, we generated the corresponding Bode reported magnitude and phase data, which were then plotted utilizing Matplotlib 3.5.3 on Python.

Statistics and reproducibility
To determine statistical significance between strains/conditions/etc, one/two-tailed Student’s t-tests were deployed. Information on the p-values of each t-test is clearly stated in the manuscript. Chi.Bioreactor experiments were performed in duplicate due to long timescales and low throughput of experimentation. All other experiments contain at least n = 3 biological replicates. In the case of light intensity/pulsing optimizations, the light conditions were randomized, and the scientist performing the experiment was blinded to the ordering of the light conditions per plate.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript by Lazar et al. optimized a genetically-encoded light biosensor system for practical applications. In\nsummary, the authors addressed the malfunction issue of the previously developed CcaSR v3.0 biosensor system during\nthe stationary phase. This malfunction was attributed to the loss of the chromophore phycocyanobilin synthesis cassette.\nThe problem was successfully solved by integrating the cassette into the chromosome, consequently enabling the biosensor\nsystem to function effectively during the stationary phase. Expression optimization of the histidine kinase CcaS and\ntranscriptional regulator CcaR further enhanced its performance. The biosensor system was then employed for\nphenylpropanoid production and showcased improved titers through dynamic light induction. Although the strategies of\nchromosomal integration and expression optimization are conventional, this study stands out for its systematic and logical\napproach, bolstered by concrete validations and well-founded reasoning. The researchers also conducted a detailed\ninvestigation into the light duty cycle and period settings. However, the following comments needs to be addressed.\n1. The manuscript lacks key words, which are essential for categorizing the content and aiding discoverability.\n2. The manuscript introduces a novel aspect by optimizing a biosensor that functions in the stationary phase. However, the\ndistinction between biosensors operating in the exponential growth phase and those in the stationary phase is not\nadequately explained. Additionally, the advantages of the biosensor system CcaSR remain unclear.\n3. The engineered light-triggered biosensor system's reliance on the stationary phase, while suited for production, raises\nconcerns about high cell density impacting light penetration and target gene regulation.\n4. Control Experiments and Dynamic Regulation: Despite optimizing the CcaSR biosensor system and its application in\ndynamic regulation of p-coumaric acid and bidesmethoxycurcumin in E. coli, the manuscript lacks control experiments to\nsubstantiate the advantages of dynamic regulation. The logic requires further elucidation to underscore the strength of\ndynamic regulation.\n5. The hypothesis suggesting that pulsatile induction leads to higher product titers is intriguing. However, the manuscript\nneeds to delve into potential reasons for this phenomenon, particularly in the context of the observed increase in p-coumaric\nacid.\n6. Lines 23-24. How can an optogenetic biosensor be applied in the gastrointestinal tract? How can the activation of green\nand red lights be achieved?\n7. Lines 118-120. No such data about OD600 value was shown in Fig. S1B. The data points should be correctly labeled.\n8. In Figure S1, the figure caption was needed for Fig. S1C.\n9. Lines 166-168, and in Figure 2b and Figure S2C, a control group (wildtype E.coli) should be added to the chromosomal\nho1-pcyA cassette verification and used to prove the correct loci of the integration.\n10. Line 184, it's important to explicitly state the purpose of such optimization. Clarification is needed on why there was a\nneed to reduce the levels of CcaS and CcaR. It is recommended that Figure 3 includes a direct comparison between the\nsystem before and after optimization to illustrate the achieved results and effects.\n11. Lines 184-189. How can we tell that “both proteins accumulate to higher abundance in stationary phase” from Figure\nS4? And why did the authors want to reduce the CcaS levels? A solid analysis should be done here.\n12. Lines 228-229. The authors should prove the statement that “TAL expression is toxic in E. coli”. Citation 41 demonstrated\nthat the high titer of p-coumaric acid was toxic but did not contain the statement in this manuscript.\n13. In both Figure 3 and Figure 4, please add a control group without the implementation of the biosensor system.\nTo enhance the manuscript's clarity and impact, I recommend addressing these points in detail. Providing clearer\nexplanations for the differentiation of biosensors in distinct growth phases, outlining the merits of the CcaSR system,\ndiscussing strategies to mitigate the high cell density impact, incorporating control experiments for dynamic regulation, and\nunraveling the reasons behind increased product titers due to pulsatile induction would significantly enrich the overall\nquality of the study.", "reviewer_2": "The manuscript titled \"A Green Light Sensor for Enhancing Metabolite Production in Bacteria During Stationary Phase\"\ndescribes the development and optimization of a CcaSRstat system that allows optogenetic control for metabolic\nengineering during stationary phase. The authors worked to improve the genetic context and expression determinants of the\nCcaSR light-switchable two-component system, resulting in a stable CcaSRstat system that operates exclusively during\nstationary phase, with minimal metabolic burden and low leakiness. They also integrated the ho1-pcyA cassette into the\nchromosome to reduce metabolic load and optimized CcaS and CcaR expression levels during stationary phase. The\nmanuscript presents the practical application of CcaSRstat in optimizing the synthesis of two phenylpropanoids in E. coli,\nachieving substantial titers of these industrially relevant compounds through CcaSRstat-mediated enzyme expression with\nvarious illumination patterns. Furthermore, these optimized conditions were successfully translated to benchtop bioreactors.\nWhile the manuscript is well-organized and informative, several aspects of the system's development and application\nrequire further discussion and revision.\nMajor comments:\n1. Lines 72-78: It would be beneficial for the authors to provide a schematic diagram that visually explains how the two-\ncomponent system CcaSR functions in regulating bacterial gene expression through the use of light.\n2. Fig. 3B: The expression levels of CcaR using RBS1 and RBS2 exhibited only a slight difference. The authors should\npresent a statistical analysis for this data and discuss the significant variation in the light response (Fig. 3C) resulting from\nthis minor difference in CcaR expression levels. Simply stating that the \"absolute CcaR level is a critical parameter for light\nresponse, regardless of the growth phase\" might give the impression of a less versatile system.\n3. Lines 211-217: The authors presented evidence that insufficient expression of CcaS and/or CcaR led to the characteristic\nof stationary phase-specificity in CcaSRstat. However, this feature significantly limits the range of achievable induction\ncontrol using CcaSRstat. Induction control would then be restricted to tuning the expression levels of enzymes by adjusting\nlight intensity and duration, without the capability to determine the initiation time of enzyme expression through a light switch.\nThe authors should further optimize CcaSRstat by identifying the optimal expression levels of ho1-pcyA, CcaS, and CcaR,\nenabling CcaSRstat to function effectively in both exponential and stationary growth phases.\n4. Fig. 4C: In the main text, the authors stated that titers became highly variable above the intensity of 7 μmol m-2 s-1.\nHowever, these data points were not shown in the figure. Please clarify.\n5. Fig. S8: CcaSRstat regulates the expression of target enzymes under the specific promoter PcpcG2-172. This\ncharacteristic of CcaSRstat renders it more suitable for controlling the expression of individual pathway components. In the\nexperimental design depicted in Fig. S8, the authors applied CcaSRstat to optimize a multi-enzyme pathway by expressing\na single operon encoding four genes under the control of PcpcG2-172. However, the system treated all four genes as a\ncollective unit and was unable to modulate the expression levels of these pathway components independently. As a result, the\noutcomes of the optimization do not ensure optimal production and thus cannot effectively support the appropriate\nutilization of CcaSRstat in optimizing multi-enzyme metabolic pathways. The study's section might give readers the wrong\nimpression that CcaSRstat can adjust the levels of several pathway components at the same time for optimization purposes.\nThe authors should revise the main text to clarify this issue.\n6. Lines 259-276: Using static light signals, 7 μmol m-2 s-1 demonstrated the highest levels of TAL-sfGFP expression (Fig.\n4B-C). The authors speculated that levels of expression beyond 7 μmol m-2 s-1 might become unreliable due to potential\ntoxicity. In the subsequent experiments using dynamic light signals (Fig. 5B), the authors hypothesized that “pulsatile\ninduction may result in higher product titers than static induction by distributing total enzyme expression across the course of\na fermentation while avoiding acute toxicity arising from high intracellular enzyme concentration at any given point in time”.\nHowever, the authors directly employed the intensity of 7 μmol m-2 s-1 in the optimization using dynamic light signals, which\noverlooked the interplay between light intensity and pulsatile induction for reducing toxicity and increasing product titer. The\nauthors should incorporate varying light intensities into their optimization strategy under dynamic light conditions, ensuring a\nmore comprehensive control of target enzyme expression.\n7. Lines 260-262: Dynamic light induction was shown to enhance p-coumaric acid production further. It was also indicated in\nthe manuscript that in many cases, pulsatile induction may result in a higher product titre than static induction (line 260-262).\nHowever, the application of this dynamic light induction was not shown in the production of bidesmethoxycurcumin.\nFurthermore, the abstract stated that high titres of “two industrially-relevant phenylpropanoids” were achieved by combining\nCcaSRstat-driven enzyme expression with varied static and periodic illumination patterns. The authors should present data\nthat illustrate the effects of dynamic illumination patterns on bidesmethoxycurcumin production.\nMinor comments:\n1. Lines 51-52: The authors stated “Despite their routine use in metabolic engineering, nearly all inducible promoter systems\nare optimized to function in exponential phase.” To support this claim, it would be helpful if the authors could provide\nexamples along with references.\n2. Lines 108-109: The phrase “the J23106 and J23108 constitutive promoters driving expression of ho1-pcyA and ccaS,\nrespectively” seems to indicate that the J23106 promoter controlled ho1-pcyA expression and the J23108 promoter\ncontrolled ccaS expression. However, the authors mentioned in Line 171 that the J23108 promoter was responsible for\ndriving the expression of ho1-pcyA. It would be helpful if the authors could clarify the precise promoters utilized for\nexpressing ho1-pcyA and ccaS and adjust the labeling of the promoter's name in Fig. 1A.\n3. Line 186: In the phrase, “we moved the corresponding expression cassette from onto a lower (~3) copy pSC101-origin\nplasmid.”, the authors might have missed a word (the name of the plasmid replication origin) between ‘from’ and ‘onto’.\n4. Lines 186-187: To improve the logical flow, the authors should explain their decision to propose the reduction of CcaS\nexpression as part of the optimization process.\n5. Lines 186-193: Please elaborate on why two different methods were employed for optimizing the expression levels of\nCcaS and CcaR.\n6. Line 263: Define “CcaSRstat-tal”.\n7. Lines 309-321: The authors discussed several design features of the stationary phase-optimized CcaSRstat system in this\nparagraph. However, the key points of most stated features seem to be the same as “releasing metabolic burden or toxicity”.\nPlease revise this part of the content to be more concise or incorporate more diverse aspects for discussion.\n8. Lines 357-358: The authors stated “Future work to optimise such orthogonal light sensors for stationary phase could\nenable optogenetics to be used for modular optimization of multi-step metabolic pathways.” They should elaborate on the\nsteps required to optimise orthogonal light sensors for the stationary phase.", "reviewer_3": "In this study, the authors have developed a stable light-regulated CcaSRstat system that imposes little metabolic burden,\nexhibits low leakiness and 80-fold dynamic range, and functions exclusively in the stationary phase. In addition, the stable\nsystem was further utilized for chemical production. However, before publication, several issues need to be carefully\naddressed.\nQ1: The introduction, specifically lines 82-103, contains excessive wording and can be condensed by eliminating redundant\ndescriptions.\nQ2: In lines 113-121, the authors have observed that CcaSR v3.0 negatively affects strain growth under normal conditions\n(dark conditions). However, the authors must provide evidence regarding whether this negative effect also occurs under light\nconditions.\nQ3: In lines 138-144, the authors suggest that population heterogeneity in the stationary phase may be attributable to a\nmutation disabling PCB biosynthesis. Nonetheless, it should be noted that using different media for culturing strains can\nalso result in population heterogeneity. Therefore, it is recommended that the authors exclude the influence of culture\nmedium as a contributing factor.\nQ4: In lines 145-149, the authors have employed diagnostic PCR to confirm the presence of the ho1-pcyA cassette in both\nphases. It would strengthen the conclusion if the authors were to conduct protein-level experiments, such as SDS-PAGE, to\nvalidate their findings.\nQ5: Although the authors have successfully optimized the CcaSRstat system for their purposes, it remains unclear whether\nthe system is universally applicable to other types of E. coli. It would be beneficial if the authors could investigate and\ndiscuss the system's suitability for other strains of E. coli.\nQ6: In the section where the authors optimize phenylpropanoid production with static light signals, they utilize the CcaSRstat\nsystem to produce p-CA and BDMC. However, no control experiments are conducted to compare the titers and OD600\nlevels when the CcaSRstat system is not used. Therefore, it is recommended that the authors include such control\nexperiments to provide a baseline for comparison.\nQ7: The authors focus on optimizing dynamic light induction for p-CA production in the sections related to dynamic light\nsignals. However, they do not conduct related experiments on BDMC. This raises doubts regarding whether the CcaSRstat\nsystem is effective for multienzyme metabolic pathways under dynamic light signal conditions. It is suggested that the\nauthors address this discrepancy and provide an explanation.\nQ8: In lines 331-338, the authors should highlight specific examples detailing the advantages of the CcaSRstat system over\nother photogenetic systems. This would further emphasize the unique features and benefits of their proposed system."}, "author_response": "Thank you for this comment. We have added key words to the beginning of the manuscript.\n2. The manuscript introduces a novel aspect by optimizing a biosensor that functions in the\nstationary phase. However, the distinction between biosensors operating in the exponential\ngrowth phase and those in the stationary phase is not adequately explained. Additionally, the\nadvantages of the biosensor system CcaSR remain unclear.\nWe thank the reviewer for noticing these issues. We have included a description of how\naccumulation of transcriptional activators or repressors can alter expression of metabolic\npathways during stationary phase in an unwanted fashion, and how the expression levels of such\ntranscription factors may need to be re-optimized for stationary phase to achieve robust\ninducibility (lines 57-65).\nWe have also added descriptions of the advantages of the CcaSR system (lines 79-81) and\nCcaSR specifically (lines 382-384) to the introduction and discussion, respectively.\nstat\n3. The engineered light-triggered biosensor system's reliance on the stationary phase, while\nsuited for production, raises concerns about high cell density impacting light penetration and\ntarget gene regulation.\nWe thank the reviewer for pointing out the challenge of light penetration in high density\nfermentations. Previous work has achieved light penetration for target gene regulation on the 2 L\nscale up to OD ~ 50 (Zhao et al., Nature 2018; doi: 10.1038/nature26141). Similarly, we\n600\nobserve no issues in our 25 mL reactor experiments (Figure 5D). That said, we agree that light\npenetration is an unsolved issue at larger scales and acknowledge these limitations in our\ndiscussion. In line with this point, we state that CcaSR is most valuable as a metabolic\nstat\npathway prototyping tool, allowing metabolic engineers to explore new multi-dimensional\npathway optimizations (induction timing, intensity, frequency, duty cycle) more quickly (lines\n393-400). Future studies of light penetration in high density large fermentations and the\nengineering of new photobioreactor systems will be critical in understanding the full impact of\noptogenetic induction schemes for large scale fermentations.\n4. Control Experiments and Dynamic Regulation: Despite optimizing the CcaSR biosensor\nsystem and its application in dynamic regulation of p-coumaric acid and bidesmethoxycurcumin\nin E. coli, the manuscript lacks control experiments to substantiate the advantages of dynamic\nregulation. The logic requires further elucidation to underscore the strength of dynamic\nregulation.\nWe thank the reviewer for this comment. To optimize p-coumaric acid titer, we sequentially\noptimize static light intensity, induction timing, and dynamic light period and duty cycle. This\nthree-step process reveals that optimized dynamic light delivery results in higher titers than\noptimized static light delivery. In particular, we first find that the optimal intensity of static green\nlight is 7 µmol m-2 s-1, resulting in a titer of 71.3 ±2.8 mg/L (Fig 4F, reproduced below).\nNext, we grow bacteria in deactivating red light for 0-12 h and switch to the 7 µmol m-2 s-1 static\ngreen light at 0, 2, 4, 6, 8, or 12 h into the culture growth. The results demonstrate that a 6 h\ninduction time (OD ~ 0.56 ± 0.02), increases p-CA titer to a new optimum of 81.7 ± 5.5 mg/L\n(Fig. S18B-D, reproduced below)\nNext, we optimize the period and duty cycle of a periodic pulse of 7 µmol m-2 s-1 green light\napplied at a 6 h induction time. This third level optimization further increases the titer to 113.1 ±\n39.7 mg/L at a period of 10 minutes and a duty cycle of 85% (Fig. 5B, reproduced below).\nFinally, we compare our optimized static and dynamic light treatments in scaled-up 25 mL\nChi.Bio reactors (compared to the original 0.5 mL Light Plate Apparatuses). We find that the\ndynamic light program still outperforms the static program at this larger reactor scale (final titers\nfor the pulse and static programs are 87.8 ± 12.4 mg/L and 34.8 ± 5.2 mg/L, respectively),\nproducing larger amounts of p-CA further into the fermentation (Fig. 5D, reproduced below).\nOverall, we use p-CA titer to directly compare optimized dynamic and optimized static light\ntreatment at two different fermentation scales. We believe these experiments address the\nreviewer’s concern.\n5. The hypothesis suggesting that pulsatile induction leads to higher product titers is intriguing.\nHowever, the manuscript needs to delve into potential reasons for this phenomenon, particularly\nin the context of the observed increase in p-coumaric acid.\nWe thank the reviewer for the comment. We have revised the main text (lines 308-318) and\nadded Fig. S20 (reproduced below) to discuss the reasons for the increase in p-CA production\nwith pulsatile light.\nWe find that the optimized static light signal results in lower densities of CcaSR -tal cultures\nstat\nthan the optimized dynamic light signal (Fig. S20A). Notably, this effect is absent in non-\nengineered (WT) E. coli. Together, these results indicate that static light treatment is more toxic\nto E. coli than pulsatile light treatment due to TAL expression patterns. Strikingly, using the\nstrain CcaSR -tal-sfgfp, we find that TAL-sfGFP expression levels are identical in static and\nstat\npulsatile light conditions over the large majority of a three-day LPA fermentation (Fig. S20B).\nThis result rules out TAL expression level as the culprit. Thus, we believe that pulsatile light\nreduces the toxicity of TAL expression by periodically allowing cells time to regenerate -\nL\ntyrosine and liberating gene expression machinery (RNAPs, ribosomes) to make other\nhousekeeping gene products. Ultimately, this lowered toxicity results in a higher saturating\nOD and thus more biomass to convert into the desired p-coumaric acid product.\n600\n6. Lines 23-24. How can an optogenetic biosensor be applied in the gastrointestinal tract? How\ncan the activation of green and red lights be achieved?\nPreviously, our group utilized CcaSR to induce production of a longevity-enhancing\nexopolysaccharide from engineered E. coli in the gastrointestinal tract of live C. elegans\nnematodes (Hartsough et al. eLife 2020, DOI: 10.7554/eLife.56849). These roundworms are\noptically transparent, enabling transmission of green and red light into the gut environment.\nMore recently, another group used CcaSR to induce expression of a Pb-detoxifying enzyme in E.\ncoli residing in the gastrointestinal tract of D. melanogaster fruit flies (Wang et al. ACS\nSynthetic Biology 2024, DOI: 10.1021/acssynbio.4c00409). However, these technologies do not\ncurrently translate into larger animals. The reviewer’s comment has made us realize that this\nstatement was thus a distraction in the abstract, and we have removed it. We have included a\ndiscussion of this possible work in transparent hosts in the discussion.\n7. Lines 118-120. No such data about OD600 value was shown in Fig. S1B. The data points\nshould be correctly labeled.\nWe apologize for the mistake in calling the incorrect figure and have the adjusted the text to call\nthe proper growth curve.\n8. In Figure S1, the figure caption was needed for Fig. S1D-E (an expanded version of the original Fig.\nS1C).\nWe have added a figure description for Fig. S1D-E (an expanded version of the original Fig.\nS1C).\n9. Lines 166-168, and in Figure 2b and Figure S2C, a control group (wildtype E.coli) should be\nadded to the chromosomal ho1-pcyA cassette verification and used to prove the correct loci of\nthe integration.\nWe have added a wild-type E. coli control (BW29655), which we now show in a new Fig. S4\n(reproduced below).\n10. Line 184, it's important to explicitly state the purpose of such optimization. Clarification is\nneeded on why there was a need to reduce the levels of CcaS and CcaR. It is recommended that\nFigure 3 includes a direct comparison between the system before and after optimization to\nillustrate the achieved results and effects.\nThank you for this helpful comment. We have created a CcaSR v3.0 strain with optimized\nchromosomal ho1-pcyA and characterized its light response in exponential and stationary phases\n(Fig. 3B, reproduced below). Because this system still has optimized expression of ccaS and\nccaR for exponential phase, we find that it performs optimally in exponential phase. Due to\naccumulation of the components, the dynamic range drops significantly in stationary phase, as\nthe red light response becomes exceedingly leaky. This result motivates the need to re-optimize\nexpression of ccaS and ccaR or stationary phase. We believe this new experiment improves the\nmanuscript as the reviewer indicates.\n11. Lines 184-189. How can we tell that “both proteins accumulate to higher abundance in\nstationary phase” from Figure S4? And why did the authors want to reduce the CcaS levels?\nA solid analysis should be done here.\nWe thank the reviewer for the suggestion.\nOur past work has demonstrated that CcaS expression must reside in a specific range for proper\nCcaSR signal transduction (Schmidl et al. ACS Synthetic Biology 2014, DOI:\n10.1021/sb500273n; Castillo-Hair et al. Nature Communications 2019, DOI: 10.1038/s41467-\n019-10906-6). Considering we find that stationary phase CcaS levels increase compared to their\npreviously-optimized exponential phase levels, we aimed to reduce stationary phase CcaS levels\nto be closer to that of their optimum exponential phase levels.\nWe have reformatted Fig. S5 (originally Fig. S4) and included additional analyses to the caption\nto motivate our characterization and engineering decisions. In the caption, we now provide the\nspecific mean MEFL values to provide insight into how CcaS-sfGFP/CcaR-sfGFP increase in\nfluorescence from exponential to stationary phase and now describe the need for optimizing\nCcaS levels.\n12. Lines 228-229. The authors should prove the statement that “TAL expression is toxic in E.\ncoli”. Citation 41 demonstrated that the high titer of p-coumaric acid was toxic but did not\ncontain the statement in this manuscript.\nWe apologize for this error. We have updated the text to read “However, TAL-mediated p-CA\nproduction is toxic in E. coli and results in growth defects and lower total fermentation biomass”\n(lines 251-252).\n13. In both Figure 3 and Figure 4, please add a control group without the implementation of the\nbiosensor system.\nWe thank the reviewer for this comment. We have included a ∆CcaSR control in Fig. 3D\n(reproduced below)\nIn addition, we have included a control for a strain with only the output TAL-sfGFP plasmid in\nFig. 4D (reproduced below):\nFinally, we have also included controls for p-coumaric acid production with both the wild-type\nE. coli strain and a control of a strain with only the output TAL plasmid in Fig. 4G.\nTo enhance the manuscript's clarity and impact, I recommend addressing these points in detail.\nProviding clearer explanations for the differentiation of biosensors in distinct growth phases,\noutlining the merits of the CcaSR system, discussing strategies to mitigate the high cell density\nimpact, incorporating control experiments for dynamic regulation, and unraveling the reasons\nbehind increased product titers due to pulsatile induction would significantly enrich the overall\nquality of the study.\nThank you for this helpful guidance. We believe we have addressed this comment in the edits\nabove.\nReviewer #2 (Remarks to the Author):\nThe manuscript titled \"A Green Light Sensor for Enhancing Metabolite Production in Bacteria\nDuring Stationary Phase\" describes the development and optimization of a CcaSRstat system\nthat allows optogenetic control for metabolic engineering during stationary phase. The authors\nworked to improve the genetic context and expression determinants of the CcaSR light-\nswitchable two-component system, resulting in a stable CcaSRstat system that operates\nexclusively during stationary phase, with minimal metabolic burden and low leakiness. They also\nintegrated the ho1-pcyA cassette into the chromosome to reduce metabolic load and optimized\nCcaS and CcaR expression levels during stationary phase. The manuscript presents the practical\napplication of CcaSRstat in optimizing the synthesis of two phenylpropanoids in E. coli,\nachieving substantial titers of these industrially relevant compounds through CcaSRstat-mediated\nenzyme expression with various illumination patterns. Furthermore, these optimized conditions\nwere successfully translated to benchtop bioreactors. While the manuscript is well-organized and\ninformative, several aspects of the system's development and application require further\ndiscussion and revision.\nMajor comments:\n1. Lines 72-78: It would be beneficial for the authors to provide a schematic diagram that\nvisually explains how the two-component system CcaSR functions in regulating bacterial gene\nexpression through the use of light.\nWe thank the reviewer for the suggestion. In response, we have modified Fig. 1A (reproduced\nbelow) to show how this two-component system regulates gene expression with light. We have\nmoved the simplified representation of CcaSR, focusing solely on the role of the different\ngenetic components, to Fig. S1A.\n2. Fig. 3B: The expression levels of CcaR using RBS1 and RBS2 exhibited only a slight\ndifference. The authors should present a statistical analysis for this data and discuss the\nsignificant variation in the light response (Fig. 3C) resulting from this minor difference in CcaR\nexpression levels. Simply stating that the \"absolute CcaR level is a critical parameter for light\nresponse, regardless of the growth phase\" might give the impression of a less versatile system.\nThank you for this comment. In investigating this question, we found that the ccaR-sfgfp fusion\nthat we had used to characterize ccaR expression level encoded an unintended second start codon\nat the beginning of the sfgfp gene, allowing for expression of sfGFP proteins that may have\ncontaminated our signal. To address this issue, we removed the unwanted start codon from all of\nour ccaR-sfgfp fusions and re-measured the expression of each in stationary (Fig. 3C, reproduced\nbelow) and exponential (Fig. S6B, below).\nFig. 3C\nFig. S6B\nAdditionally, we have added the results of a statistical analysis to both figures and the text\ndemonstrating a significant increase in the expression of CcaR-sfGFP with sRBS2 compared\nsRBS1 in exponential (p = 3.4 x 10-4, student’s two-tailed T-test) and stationary phase (p = 3.3 x\n10-5, student’s two-tailed T-test).\nFinally, we also fixed the sfgfp RBS problem in our measurement of ccaR expression in the\noriginal CcaSR v3.0 system (Fig. S5E).\n3. Lines 211-217: The authors presented evidence that insufficient expression of CcaS and/or\nCcaR led to the characteristic of stationary phase-specificity in CcaSRstat. However, this feature\nsignificantly limits the range of achievable induction control using CcaSRstat. Induction control\nwould then be restricted to tuning the expression levels of enzymes by adjusting light intensity\nand duration, without the capability to determine the initiation time of enzyme expression\nthrough a light switch. The authors should further optimize CcaSRstat by identifying the optimal\nexpression levels of ho1-pcyA, CcaS, and CcaR, enabling CcaSRstat to function effectively in\nboth exponential and stationary growth phases.\nThank you for this helpful comment. We now provide an alternative strain (Fig. S8, reproduced\nbelow) where we increase ccaS gene expression relative to CcaSR by placing the expression\nstat\ncassette on a higher copy plasmid (p15a). This strain enables optical control of gene expression\nin both exponential and stationary phases (Fig. S8B, C).\n4. Fig. 4C: In the main text, the authors stated that titers became highly variable above the\nintensity of 7 μmol m-2 s-1. However, these data points were not shown in the figure. Please\nclarify.\nWe apologize for not including these data points in the figure. We now include the data points\ntested for the 4 light intensities tested in the toxic regime in (Fig. 4F, below).\n5. Fig. S8: CcaSRstat regulates the expression of target enzymes under the specific promoter\nPcpcG2-172. This characteristic of CcaSRstat renders it more suitable for controlling the\nexpression of individual pathway components. In the experimental design depicted in Fig. S8,\nthe authors applied CcaSRstat to optimize a multi-enzyme pathway by expressing a single\noperon encoding four genes under the control of PcpcG2-172. However, the system treated all\nfour genes as a collective unit and was unable to modulate the expression levels of these pathway\ncomponents independently. As a result, the outcomes of the optimization do not ensure optimal\nproduction and thus cannot effectively support the appropriate utilization of CcaSRstat in\noptimizing multi-enzyme metabolic pathways. The study's section might give readers the wrong\nimpression that CcaSRstat can adjust the levels of several pathway components at the same time\nfor optimization purposes. The authors should revise the main text to clarify this issue.\nWe apologize for this confusing wording. As the reviewer notes, in our original manuscript\nsubmission we reported on the use of CcaSR to optimize the expression level of a four enzyme\nstat\npathway leading to the production of bidesmethoxycurcumin (BDMC) (formerly Fig. S8).\nDuring our revision process, we encountered difficulties in reproducibly quantifying BDMC\nlevels. As a result, we removed the BDMC pathway experiments from the manuscript and\nreplaced them with new experiments wherein we use CcaSR to optimize the production of\nstat\nplant-derived betaxanthin compounds via a three-enzyme pathway in E. coli (Figs. S10-S16). In\nthese new experiments, we are able to robustly detect the pathway products betalamic acid (Fig.\nS11), dopaxanthin (Fig. S12), and portulacaxanthin II (Fig. S13) via LC Q-TOF MS (Fig. S11-\nS13) as well as culture fluorescence (Fig. S14) enabling higher-throughput optogenetic\nexperiments. We believe that these new betaxanthin results are stronger and better demonstrate\nthe utility of our method for optimizing multi-enzyme pathways.\nThat said, the reviewer’s original comment applies to these new experiments. In particular, we\nused CcaSR to optimize the expression of three enzymes encoded in a single operon (i.e. co-\nstat\ntranscribed from the same promoter). To address the reviewer’s comment, we have replaced the\noriginal text with a new section describing betaxanthin optimization, being careful to avoid\nwording that may confuse readers into thinking that CcaSR can be independently used to\nstat\noptimize different steps of a multi-enzyme pathway (lines 269-286). In addition, we include a\nsection in the discussion about the limitations of CcaSR for multi-enzyme pathways and how\nstat\nmultiplexing light sensors with different wavelengths will be necessary to optimize each module\nin multi-enzyme pathways (lines 401-422).\n6. Lines 259-276: Using static light signals, 7 μmol m-2 s-1 demonstrated the highest levels of\nTAL-sfGFP expression (Fig. 4B-C). The authors speculated that levels of expression beyond 7\nμmol m-2 s-1 might become unreliable due to potential toxicity. In the subsequent experiments\nusing dynamic light signals (Fig. 5B), the authors hypothesized that “pulsatile induction may\nresult in higher product titers than static induction by distributing total enzyme expression across\nthe course of a fermentation while avoiding acute toxicity arising from high intracellular enzyme\nconcentration at any given point in time”. However, the authors directly employed the intensity\nof 7 μmol m-2 s-1 in the optimization using dynamic light signals, which overlooked the\ninterplay between light intensity and pulsatile induction for reducing toxicity and increasing\nproduct titer. The authors should incorporate varying light intensities into their optimization\nstrategy under dynamic light conditions, ensuring a more comprehensive control of target\nenzyme expression.\nWe thank the reviewer for this suggestion. To address this comment, we tested multiple green\nlight intensities both above and below the optimal intensity of 7 µmol m-2 s-1. We included the\nresults of this experiment in Fig. S21 (reproduced below).\nWe found that below the light intensity threshold resulting in toxicity, the optimized pulsatile\nlight program does not increase p-CA production relative to the static program, possibly due to\ninsufficient photon flux. However, for intensities in the toxic regime of TAL induction, the pulse\nprogram results in an increase in p-CA titer compared to the static program. These results\nsuggest that the pulsatile light program generally increases p-CA production in toxic regimes.\nWe describe this experiment on lines 303-304 of the main text. This result could apply to other\ntoxic metabolic enzymes as well.\n7. Lines 260-262: Dynamic light induction was shown to enhance p-coumaric acid production\nfurther. It was also indicated in the manuscript that in many cases, pulsatile induction may result\nin a higher product titre than static induction (line 260-262). However, the application of this\ndynamic light induction was not shown in the production of bisdemethoxycurcumin.\nFurthermore, the abstract stated that high titres of “two industrially-relevant phenylpropanoids”\nwere achieved by combining CcaSRstat-driven enzyme expression with varied static and\nperiodic illumination patterns. The authors should present data that illustrate the effects of\ndynamic illumination patterns on bisdemethoxycurcumin production.\nWe thank the reviewer for this comment. As mentioned above, we have replaced the BDMC\npathway optimization with betaxanthin pathway optimization experiments. In particular, we use\nstatic light treatment to optimize betaxanthin pathway productivity (Fig. S15), and show that\nbetaxanthins are primarily produced in stationary phase, consistent with CcaSR function (Fig.\nstat\nS16). We also demonstrate that dynamic optimization of this pathway fails to improve\nbetaxanthin titer over static illumination, as measured through a fluorescence assay (Fig. S22),\nlikely due to the aforementioned limitations of CcaSR in controlling only one promoter, which\nstat\nare described in the main text.\nMinor comments:\n1. Lines 51-52: The authors stated “Despite their routine use in metabolic engineering, nearly all\ninducible promoter systems are optimized to function in exponential phase.” To support this\nclaim, it would be helpful if the authors could provide examples along with references.\nWe have added references for this claim.\n2. Lines 108-109: The phrase “the J23106 and J23108 constitutive promoters driving expression\nof ho1-pcyA and ccaS, respectively” seems to indicate that the J23106 promoter controlled ho1-\npcyA expression and the J23108 promoter controlled ccaS expression. However, the authors\nmentioned in Line 171 that the J23108 promoter was responsible for driving the expression of\nho1-pcyA. It would be helpful if the authors could clarify the precise promoters utilized for\nexpressing ho1-pcyA and ccaS and adjust the labeling of the promoter's name in Fig. 1A.\nWe apologize for the error. The labels in Fig. 1A are correct, and we have updated the main text\naccordingly (lines 108-109).\n3. Line 186: In the phrase, “we moved the corresponding expression cassette from onto a lower\n(~3) copy pSC101-origin plasmid.”, the authors might have missed a word (the name of the\nplasmid replication origin) between ‘from’ and ‘onto’.\nThank you. We have fixed the grammatical error.\n4. Lines 186-187: To improve the logical flow, the authors should explain their decision to\npropose the reduction of CcaS expression as part of the optimization process.\nWe have an included text explaining the need to reduce stationary phase CcaS levels to be closer\nto optimal exponential phase (lines 195-196).\n5. Lines 186-193: Please elaborate on why two different methods were employed for optimizing\nthe expression levels of CcaS and CcaR.\nOur previous work has shown that sensor histidine kinases can be expressed over a wide range\nwhile response regulators must be expressed in a relatively narrow range for optimal two-\ncomponent system function. Thus, we performed a relatively course optimization of ccaS\nexpression levels via plasmid copy number, and used a finer RBS library-mediated optimization\nto tune ccaR. We have updated the text to reflect this point (lines 198-200).\n6. Line 263: Define “CcaSRstat-tal”.\nWe have now defined CcaSR -tal in the main text as “the strain where CcaSR controls\nstat stat\nexpression of tal”.\n7. Lines 309-321: The authors discussed several design features of the stationary phase-\noptimized CcaSRstat system in this paragraph. However, the key points of most stated features\nseem to be the same as “releasing metabolic burden or toxicity”. Please revise this part of the\ncontent to be more concise or incorporate more diverse aspects for discussion.\nThank you for this comment. We have edited this paragraph according to the reviewer’s\nsuggestion (lines 358-366).\n8. Lines 357-358: The authors stated “Future work to optimise such orthogonal light sensors for\nstationary phase could enable optogenetics to be used for modular optimization of multi-step\nmetabolic pathways.” They should elaborate on the steps required to optimise orthogonal light\nsensors for the stationary phase.\nWe have included detailed information on the steps required to optimize these orthogonal light\nsensors for stationary phase and co-expressing them in a single cell for multiplexed optogenetic\ncontrol of multi-step enzymatic pathways (lines 401-422).\nReviewer #3 (Remarks to the Author):\nIn this study, the authors have developed a stable light-regulated CcaSRstat system that imposes\nlittle metabolic burden, exhibits low leakiness and 80-fold dynamic range, and functions\nexclusively in the stationary phase. In addition, the stable system was further utilized for\nchemical production. However, before publication, several issues need to be carefully addressed.\nQ1: The introduction, specifically lines 82-103, contains excessive wording and can be\ncondensed by eliminating redundant descriptions.\nThank you for this comment. We have condensed this section in the revised manuscript (lines\n91-103)\nQ2: In lines 113-121, the authors have observed that CcaSR v3.0 negatively affects strain growth\nunder normal conditions (dark conditions). However, the authors must provide evidence\nregarding whether this negative effect also occurs under light conditions.\nWe thank the reviewer for the suggested experiment. We have measured how red/green light\naffects growth of CcaSR v3.0 and wildtype (WT) E. coli, and have included the plot in Fig. S1C\n(reproduced below). These data show that red and green light have no additional effect beyond\nthat of CcaSR v3.0 on growth.\nQ3: In lines 138-144, the authors suggest that population heterogeneity in the stationary phase\nmay be attributable to a mutation disabling PCB biosynthesis. Nonetheless, it should be noted\nthat using different media for culturing strains can also result in population heterogeneity.\nTherefore, it is recommended that the authors exclude the influence of culture medium as a\ncontributing factor.\nWe thank the reviewer for the suggestion. Our previously reported experiments were performed\nin defined M9 media. In response to this comment, we additionally tested two rich media\nformulations (LB and 2xYT) with CcaSR v3.0 in exponential and stationary phase (Fig. S1G-J,\nand below).\nAs can be seen in the figures, we still observe stationary-phase dependent heterogeneity in red\nand green light. We believe these results address the reviewer’s comment.\nWe also note that with both LB and 2xYT media formulations, the green light response of\nCcaSR v3.0 additionally becomes more heterogenous than in M9 (Fig. S1D-E, shown below for\ncomparison). This data agrees with the reviewer’s comment that media formulation can affect\nheterogeneity.\nQ4: In lines 145-149, the authors have employed diagnostic PCR to confirm the presence of the\nho1-pcyA cassette in both phases. It would strengthen the conclusion if the authors would conduct\nprotein-level experiments, such as SDS-PAGE, to validate their findings.\nWe thank the reviewer for the suggestion. We performed SDS-PAGE analysis on the whole cell\nlysate of wild-type cells, cells with plasmid-based ho1-pcyA expression, and cells with\nchromosomally-optimized ho1-pcyA expression in exponential (E) and stationary (S) phase\n(below)\nThe expected size of both the Ho1 and PcyA proteins is ~27kDa. Unfortunately, we could not\ndetect expression of these enzymes in this assay due to the fact that they are not strongly\noverexpressed, and the fact that they overlap with several cellular proteins of similar size.\nDespite this result, we believe our diagnostic PCR analysis, sequencing of the plasmid-borne\nmutations, and Cph1(Y176H) PCB biosensor experiments provide strong evidence of the loss\nand engineering of stationary phase PCB biosynthesis.\nQ5: Although the authors have successfully optimized the CcaSRstat system for their purposes, it\nremains unclear whether the system is universally applicable to other types of E. coli. It would be\nbeneficial if the authors could investigate and discuss the system's suitability for other strains of\nE. coli.\nWe thank the reviewer for this thoughtful comment. To address it, we separately integrated our\noptimized chromosomal ho1-pcyA cassette into the torT and yhbC loci of the well-studied\nprobiotic strain E. coli Nissle 1917 (EcN) (Fig. S9, reproduced below). We then transformed the\nthree CcaSR plasmids into each of these two modified EcN strains (Fig. S9A). This EcN\nstat\nCcaSR exhibits low leakiness in red light, strong gene expression activation in green (Fig.\nstat\nS9B), and specificity for stationary phase (Fig. S9C-F), recapitulating its performance in E. coli\nBW29655. As BW29655 is a laboratory-adapted strain and EcN is a non-domesticated gut-\nadapted strain, we believe CcaSR is likely to work in a wide variety of E. coli strains.\nstat\nQ6: In the section where the authors optimize phenylpropanoid production with static light\nsignals, they utilize the CcaSRstat system to produce p-CA and BDMC. However, no control\nexperiments are conducted to compare the titers and OD600 levels when the CcaSRstat system is\nnot used. Therefore, it is recommended that the authors include such control experiments to\nprovide a baseline for comparison.\nWe thank the author for suggesting the comparison between strains with and without the\nCcaSR system. For p-CA, we have added Fig. 4G to demonstrate that in a strain with the\nstat\noutput tal plasmid but lacking CcaSR , no p-CA is detectable.\nstat\nDue to concerns about the reproducibility of our BDMC data described above, we have replaced\nour BDMC pathway experiments with experiments optimizing the multi-enzyme betaxanthin\npathway (Fig. S10). We demonstrate optimization of production of betaxanthins using static light\n(Fig. S15), as measured through a culture fluorescence assay and LC Q-TOF MS (Fig. S11-\nS14). The LC Q-TOF MS data (Fig. S11-S13) show that no betaxanthins are detectable when the\nCcaSR system is absent from the strain, which we believe addresses the reviewer’s concern.\nstat\nQ7: The authors focus on optimizing dynamic light induction for p-CA production in the sections\nrelated to dynamic light signals. However, they do not conduct related experiments on BDMC.\nThis raises doubts regarding whether the CcaSRstat system is effective for multienzyme\nmetabolic pathways under dynamic light signal conditions. It is suggested that the authors\naddress this discrepancy and provide an explanation.\nThis is an important comment. As discussed above, we switched from optimizing BDMC\nproduction to optimizing betaxanthin production due to concerns about the reproducibility of the\nBDMC data. We show that optimization of the betaxanthin pathway with static light signals can\nachieve increased titer, as measured by betaxanthin fluorescence (Fig. S15). However,\noptimization of the pathway using dynamic light signals failed to identify conditions in which\nfluorescence was increased with statistical significance relative to the best static light condition\n(Fig. S22). This result highlights limitations of CcaSR in optimizing multi-enzyme pathways,\nstat\na result which we believe is shared by any single inducible promoter system. We highlight this\nlimitation in the discussion section and discuss future work that could address it (lines 407-427).\nQ8: In lines 331-338, the authors should highlight specific examples detailing the advantages of\nthe CcaSRstat system over other photogenetic systems. This would further emphasize the unique\nfeatures and benefits of their proposed system.\nWe thank the author for the suggestion. We have added descriptions of the advantages of CcaSR\nin general (lines 79-81) and CcaSR in particular (lines 382-384) to the introduction and\nstat\ndiscussion, respectively.\nPoint by point reviewer response\nReviewer #1 (Remarks to the Author):\nThe revised manuscript addressed my comments well.\nThank you for your careful evaluation of our paper.\nReviewer #2 (Remarks to the Author):\nIn the revised manuscript and rebuttal letter, the authors have carefully addressed and\nresponded to all my comments. The revisions demonstrate substantial effort and notably\nimproved the completeness and clarity of the work. Here, a few minor revisions are\nsuggested for further refinement prior to publication.\nMinor comments:\n1. Line 206 & 209: These sentences appear to describe [Fig. 3D], not [Fig. 3B].\nThank you. We have corrected this mistake.\n2. Line 213: Please also cite the figure that shows the optimal CcaR expression level in\nthe exponential phase.\nDone.\n3. Figure 4D & 4G: Please add one or two sentences in the main text to describe the\ncontent of these figures.\nThank you. We have added one sentence for each of these figure panels."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript addressed my comments well.", "reviewer_2": "In the revised manuscript and rebuttal letter, the authors have carefully addressed and responded to all my comments. The revisions demonstrate substantial effort and notably improved the completeness and clarity of the work. Here, a few minor revisions are suggested for further refinement prior to publication.\nMinor comments:\n1. Line 206 & 209: These sentences appear to describe [Fig. 3D], not [Fig. 3B].\n2. Line 213: Please also cite the figure that shows the optimal CcaR expression level in the exponential phase.\n3. Figure 4D & 4G: Please add one or two sentences in the main text to describe the content of these figures."}, "author_response": "Thank you. We have corrected this mistake.\n2. Line 213: Please also cite the figure that shows the optimal CcaR expression level in\nthe exponential phase.\nDone.\n3. Figure 4D & 4G: Please add one or two sentences in the main text to describe the\ncontent of these figures.\nThank you. We have added one sentence for each of these figure panels."}]

Decision letter