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Optogenetic downregulation of protein levels with an ultrasensitive switch Sophia Hasenjäger, Jonathan Trauth, Sebastian Hepp, Juri Goenrich, Lars-Oliver Essen, and Christof Taxis ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.8b00471 • Publication Date (Web): 08 Apr 2019 Downloaded from http://pubs.acs.org on April 9, 2019

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ACS Synthetic Biology

Optogenetic downregulation of protein levels with an ultrasensitive switch

Sophia Hasenjäger1*, Jonathan Trauth1,2*, Sebastian Hepp2, Juri Goenrich1, Lars-Oliver Essen2 and Christof Taxis1# *these authors contributed equally

1Department

of Biology/Genetics

Philipps-University Marburg Karl-vom-Frisch-Str. 8 35032 Marburg Germany

2Department

of Chemistry/Biochemistry

Philipps-University Marburg Hans-Meerwein-Str. 4 35032 Marburg Germany

#send

correspondence to: [email protected]

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Abstract Optogenetic control of protein activity is a versatile technique to gain control over cellular processes, e.g. for biomedical and biotechnological applications. Among other techniques, the regulation of protein abundance by controlling either transcription or protein stability found common use as this controls the activity of any type of target protein. Here, we report modules of an improved variant of the photosensitive degron module and a light-sensitive transcription factor, which we compared to doxycycline-dependent transcriptional control. Given their modularity the combined control of synthesis and stability of a given target protein resulted in the synergistic down regulation of its abundance by light. This combined module exhibits very high switching ratios, profound downregulation of protein abundance at low light-fluxes as well as fast protein depletion kinetics. Overall, this synergistic optogenetic multistep control (SOMCo) module is easy to implement and results in a regulation of protein abundance superior to each individual component.

Keywords Synthetic biology; optogenetics; targeted protein degradation; transcription control; synergistic regulation


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Efficient modules for external control of cellular events and activities are increasingly demanded by many areas of biomedical research and biotechnology. Light has almost ideal characteristics as an inducer due to the high precision by which light can be spatially and temporally applied in terms of its quantity (flux) and quality (wavelength distribution). Accordingly, this led to a recent advent of a diverse set of optogenetic tools that can be used to control protein biosynthesis, stability, localization, protein-protein interactions, and activity.1,2 The impact of channelrhodopsin in neurology is a showcase for the importance of optogenetic tools.3 Tight regulation of protein activity by optogenetic modules holds similar potential in diverse areas of biomedical research as demonstrated by few examples like manipulation of cell motility4, the actin cytoskeleton5, cell polarity6, development7, characterization of gene functions8 or for metabolic engineering approaches.9 Constructs have been engineered to control gene expression by light in diverse organisms1,2, showing also that combinatorial optogenetic control of gene expression is feasible10. Recently, implementation of a light-regulated network demonstrated inverted gene expression control that was inhibited by light.9 Although high dark/light switching ratios have been established by controlling gene expression, most examples were based on further amplification steps or embedding into a cellular network.11 An inherent problem of many optogenetic constructs is a basic level of dark activity (or off-state activity) that diminishes achievable switching ratios. This problem is not easily circumvented because the light-dependent switching of often-used photoreceptors like the LOV2 domains from plant phototropins involves mostly a shift of the off-on state equilibria by several kcal/mol. In the case of LOV2 domains, the unfolding of the C-terminal Jα helix is the prominent structural change of the photoreceptor and the driving force for light-activation. The mechanistic features of such photoreceptors with their inherent dark activities hence limit the possible solution space for optogenetic implementations.11 Nevertheless, improvements of natural photoreceptors have been achieved by optimizing the optogenetic tool itself or by manipulating the photocycle of the photoreceptor.12–16 These are time- and work-consuming approaches that have to be undertaken for each optogenetic tool itself, as not all mutational changes are transferable to all constructs based on the same photoreceptor. As mentioned above, activation of a downstream network or connection to an amplification step provide further routes for optimization.11 A so far relatively untapped resource for optimization of optogenetic constructs are theoretical models of networks that result in hypersensitive switches due to feedback control, cooperativity or other kinds of interactions.17 Some implementations of these networks have been achieved with regulatory gene circuits.18 In principle, targeted proteolysis offers another level of regulation that can be used for regulatory networks.19 First implementations have been attempted for mammalian cell cultures, however resulting in only moderate switching ratios.20,21 Optogenetic regulation of 3 ACS Paragon Plus Environment


protein stability has been achieved in yeast and higher eukaryotes with LOV2 domains from Arabidopsis thaliana (AtLOV2) or Avena sativa (AsLOV2) phototropins using different degradation signals (degrons).22 In all these implementations, the exposure of the degron by light has been achieved by light-induced unfolding of the Jα helix of the LOV2 domain, which results in degron-driven proteolysis.23–26 Here we generated an improved optogenetic module to regulate protein stability as well as a photosensitive transcription factor to inhibit gene expression with light. We provide evidence that combined control of protein biosynthesis and protein stability with blue-light confers a significant advance in terms of the achievable switching ratio resulting in near complete depletion of the target protein. Analysis of the dose-response curve of this synergistic optogenetic multistep control (SOMCo) module demonstrates tight regulation and an ultrasensitive response, which is generally applicable to light-control of protein abundance.

Results Engineering of a third generation photosensitive degron module First optogenetic control of protein stability has been achieved by combining the AtLOV2 domain with a synthetic degron (cODC1), which resulted in the photosensitive degron (psd) module.23 Variants obtained by site-directed or random mutagenesis showed that the photoreceptor domain has a profound influence on the performance of the psd module.25 We reasoned that naturally evolved LOV2 domains or domains optimized for different optogenetic constructs might provide a resource that could result in psd modules with superior characteristics. To test different LOV2 domains we used a construct, which produces the LOV2-cODC1 protein fused to the red fluorescent protein (RFP) mCherry as primary reporter and a reference GFP (Figure 1A). The GFP domain, the mCherry, and the psd variant were hence expressed from the same ORF using a viral P2A sequence that leads to ribosomal skipping at the boundary between GFP and mCherry.27 This GFP-P2A-RFP-psd construct facilitated fluorescence-based discrimination between existing psd module variants using simple ratiometric fluorescence measurements of cells grown in darkness or exposed to bluelight (Supplementary Figures S1, S2, S3). Compared to previous measurements23,25, we obtained a lowered switching ratio. However, the resulting ranking of the variants and the relation between half-life and RFP/GFP ratio measurements remained unaffected (Figure S3B). With ratiometric measurements based on the GFP-P2A-RFP-psd construct and half-life measurements using the translation inhibitor cycloheximide25, we characterized several natural 4 ACS Paragon Plus Environment

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photoreceptors of the phototropin LOV2 family (Aegilops tauschii, Avena sativa, Chlamydomonas reinhardtii; Cucumis melo, Gossypium rainmondii, Physcomitrella patens PHOTA1, PHOTB1, PHOTA2, PHOTB2, Zea mays). With the exception of the weakly expressed A. tauschii and C. reinhardtii LOV2 domains, the photoreceptors showed robust switching behavior in the framework of the psd module similar to the original AtLOV2. However, no substantial improvement compared to the original implementation was observed (Figure 1B, Figure S4 and data not shown). Furthermore, we tested the AsLOV2 variant containing the mutations that are present in the iLID construct (corresponding to amino acids M398 to A543 of AsNPH1), which was optimized for optogenetic control of protein-protein interaction.16 Interestingly, this variant failed to show a huge switching ratio due to its relatively high abundance in blue-light exposed yeast cells (Figure 1B). During cloning of the AsLOViLID domain into the psd module, we obtained clones that lacked an alanine codon in the Jα helix (A146, numbering starting at the beginning of the AsLOV2iLID construct, see Supplementary Figure S5A; sequence …QIDEAAKELP…, missing alanine in bold). Surprisingly, this AsLOV2iLID variant resulted in a psd module with a dark/light ratio of more than 40, a switching ratio four-times higher than previously tested constructs (Figure 1B). Shortening of the Jα helix by removing corresponding amino acids from AsLOV2 or AtLOV2K92R AsLOV2 or V153 of AtLOV2K92R

E132A

E155G)

E132A E155G

(A146 of

resulted in non-functional psd modules

(Supplementary Figure S5B). The low RFP/GFP ratio in these mutants was due to proteasomal degradation (Supplementary Figure S5C), which indicates constitutive cODC degron activation in these variants. We performed microscopy analyses with the AsLOV2iLID A146Δ-psd construct and found uniform RFP fluorescence similar to the original AtLOV2-psd construct (Figure 1C and Supplementary Figure S2). Next, we measured the half-life of the AsLOV2iLID

A146Δ-psd

construct using

immunoblotting, which demonstrated increased degradation in darkness and blue-light (44±6 min and 5±0.2 min) compared to AtLOV2-psd (123±21 min and 20±1min) and AtLOV2K92R E132A E155G-psd

(102±41 min and 12±0.4 min; refer to Figure 1D and ref.25). We repeated this

translational

shut-off

experiment

measuring

RFP

fluorescence

by

flow

cytometry

Supplementary Figure S6). This resulted in similar half-life in cells exposed to blue-light (6.1±0.2 min) and slightly higher stability in cells kept in darkness (96±8 min) compared to the results obtained by immunoblotting. Accordingly, the AsLOV2iLID A146Δ-psd construct shows the fastest degradation rate of any so far characterized psd module and excels due to its high switching ratio and relatively high stability in darkness. To exclude plasmid-loss effects during the characterization of the AsLOV2iLID A146Δ-psd construct, we integrated the construct into the genome. This confirmed the very low in vivo abundance of a target protein at restrictive conditions that was achievable with the AsLOV2iLID A146Δ-psd construct (Supplementary Figure 5 ACS Paragon Plus Environment


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S7A, B). Next, we measured the abundance of RFP- AsLOV2iLID A146Δ-psd by immunoblotting in darkness and blue-light using promoters of different strength. As expected, usage of the very weak CYC1 promoter resulted in signals just above background in darkness and undetectable in cells exposed to blue-light (Supplementary Figure S7C). Transcriptional control by the very strong TEF1 and TDH3 promoters resulted in RFP- AsLOV2iLID A146Δ-psd abundance clearly detectable in cells kept in darkness as well as in blue-light exposed cells. However, a profound switching between dark and light conditions was observable (Supplementary Figure S7C). The switching ratio was reduced compared to the ADH1 promoter constructs, as we obtained a ratio of about 5 for the TDH3 and the TEF1 promoters and 29 for the ADH1 promoter (Supplementary Figure 7C). Previously, the comparison between TDH3 and ADH1 promoters using the tagRFP-AtLOV2-psd module showed much smaller differences in switching ratio in fluorescence measurements.23 Similarly, comparing the ADH1, TEF1, and TDH3 promoter by quantification of RFP fluorescence also showed small differences in the switching ratios (data not shown). Next, the influence of the synthetic cODC1 degron and ubiquitin-dependent degradation were tested for the AsLOV2iLID A146Δ construct using a mutation that inactivates the cODC1 degron as well as a temperature-sensitive uba1 mutant strain.25,28 Although we did not observe an effect of decreased ubiquitin-dependent degradation on proteolysis of AsLOV2iLID A146Δ-psd, residual differences in AsLOV2iLID

A146Δ-psdCA

abundance between darkness and blue-light

exposed cells were observable (Supplementary Figure S8A). These differences decreased in uba1ts cells, arguing for a minor contribution of ubiquitin-dependent degradation during the light-induced proteasomal proteolysis of AsLOV2iLID

A146Δ-psdCA.

As expected, a profound

reduction of AsLOViLID A146Δ-psd degradation was observed in a yeast strain with mutations affecting proteasomal proteolysis (Supplementary Figure S8B). In contrast to this, impairing vacuolar degradation did not have a considerable impact on blue light-induced AsLOViLID A146Δpsd degradation (Supplementary Figure S8C). In conclusion, the main degradation pathway for AsLOViLID A146Δ-psd is ubiquitin-independent and carried out by the proteasome with some ubiquitin-dependent proteolysis may take place in case the cODC1 degron is inactivated. Finally, we compared the depletion efficiency of AtLOV2-psd, AtLOV2K92R E132A E155G-psd, and AsLOV2iLID

A146Δ-psd

by kinetic measurements. Again, the latter showed the fastest drop in

protein abundance after blue-light exposure with a protein depletion half time of 33 min (Figure 1E). Similarly, measuring the appearance of fluorescence after transfer of yeast cells from blue-light into darkness revealed that the most rapid increase was observed with the AsLOV2iLID A146Δ construct (Supplementary Figure S9). Due to the superior performance of the AsLOV2iLID A146Δ-containing module compared to the two previous psd generations, we refer to it as psd3. 6 ACS Paragon Plus Environment

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The efficiency and performance of the psd3 module was tested in vivo by fusing it to essential proteins involved in proteasomal degradation. We obtained robust growth in darkness and loss of viability of blue-light exposed yeast cells for Cdc48 and Npl4 (Figure 2A). Immunoblotting confirmed that in both cases a considerable switch in protein abundance is observable (Figure 2B). Cell cycle analysis of these mutant strains revealed an increase of large budded cells in both cases (Figure 2C), which is very pronounced for Cdc48-psd3 and matches the expected phenotype.23,29 We further validated the applicability of the psd3 module regarding control of protein abundance by fusing it to dominant-negative variants of the cyclin-dependent kinase regulators Clb2 and Sic1 (Figure 2D). These variants have previously been used to characterize the original psd module.23 Similarly, we found robust growth of construct-bearing cells exposed to blue-light and a strong decrease of cell proliferation in darkness (Figure 2E). Using immunoblotting, the Clb2 and Sic1 variants were almost undetectable in cells exposed to blue-light but accumulated in cells in darkness (Figure 2F). Not surprisingly, cell cycle analysis showed an increase of large budded cells for the Clb2 variant in darkness and appearance of many G1/S arrested cells for the Sic1 variant (Figure 2G). Overall, our in vivo analysis of the psd3 module confirmed the utility of the module to control protein stability. Combining chemical control of protein biosynthesis with light control of stability We reasoned that we could obtain improved control over protein abundance by combining the psd3 module with chemical control of transcription. For the latter, we used the tet-off system30,31 with three modifications: We replaced the core promoter in the tetO7 construct with the GAL1 core promoter, we used the ADH1 promoter to control the expression of the tetracycline transactivator (tTA) and, finally, fused the psd3 module to the end of tTA. This expression module was used to regulate the expression of psd3 fused to the RFP mCherry (Figure 3A). Quantitative immunoblotting experiments demonstrated high mCherry abundance in yeast cells grown in darkness, reduced fluorescence in the presence of blue-light and near background fluorescence in cells treated with doxycycline or exposed to blue-light and treated with doxycycline (Figure 3B). The mCherry-psd levels dropped to roughly 50 % in blue-light exposed cells, to 300, demonstrating the highly efficient regulation that is achievable by combining expression control and protein stability by a single physical inducer (Figure 4F). Both transcription factor modules tRAL and ZVPd were present in cells exposed to darkness; as expected, ZVPd levels were greatly reduced in cells exposed to blue-light. Compared to the ADH1 promoter, the production of RFP-psd3 was reduced to about 50 % (flow cytometry) and 36 % (immunoblotting) when controlled by the psTF (Supplementary Figure S13A, B). The combination of the two optogenetic tools implemented a synergistic optogenetic multistep control (SOMCo). In contrast to the psd module and the psTF, light regulated two synergistic steps in SOMCo (Figure 5A). We followed the kinetics after blue-light induced shutdown by transcriptional control alone, induced protein degradation alone and both combined (Figure 5B). We observed slow decline of RFP fluorescence in case of the first construct and faster decline in case of the latter two. SOMCo resulted in considerably lower RFP levels in darkness and background fluorescence after 60 min, whereas for protein degradation alone achieved fluorescence levels still above background after 90 min. Next, we measured dose-response curves for the three regulation strategies, i. e. psd3, psTF, and SOMCo (Figure 5C). This revealed that the psd3 module dependent constructs reacted significantly to light fluxes below 1 µmol m-2 s-1., whereas the module that was controlled by psTF alone required a light flux of 4-5 µmol m-2 s-1 to show reduced abundance. Light fluxes of 5-10 µmol m-2 s-1 appeared to be sufficient for full activation of all three constructs. Steady state fluorescence with psTF control was just below 30 % of dark-state levels, degradation by psd3 control already below 10 %, and SOMCo below 5 %. This shows the high efficiency that can be achieved with synergistic multistep regulation. Fitting of the experimental data to the Hill function resulted in Hill coefficients of 1.1 for the psd module, 3.8 for the photosensitive transcription factor and 1.6 for the SOMCo module (Figure 5D). This indicates an ultrasensitive response by the psTF module and the SOMCo module. In the latter, the combination of light-induced and non-cooperative protein degradation with ultrasensitive transcriptional regulation leads to almost no leakage in the off-state (Figure 5C, D).

Discussion Control of protein abundance and protein activity by regulation of transcription or protein stability are powerful techniques with broad applicability. Here, we generated an improved photosensitive degron module by testing several LOV2 domains in the context of the psd 9 ACS Paragon Plus Environment


module. The psd3 module showed a significantly higher dark/light switching factor than any previous implementation of a light-controlled protein degradation tool. This feature includes a very low half-life in cells when exposed to blue-light, but relatively stable fusion proteins in darkness. Furthermore, we developed the psTF expression module that is sensitive to bluelight due to the light-sensitive interaction between AsLOV2 and the synthetic peptide Zdk1. Optogenetic tools often suffer from a certain activity in the off state, a characteristic that is linked to the reaction mechanism of natural photoreceptors. This behavior influences dark/light switching ratios and might limit successful applications. Possible solutions to this problem are introduction of amplification steps or the embedding in a network, which resemble somehow natural light-responsive circuits.33–37 However, such implementations cannot always be achieved for all applications by straightforward means. Here, we demonstrate that a simple linear network consisting of two light regulated steps with moderate switching factors results in a dramatically improved overall behavior. Similar combinatorial approaches should be feasible for other optogenetic constructs as well, e.g. adding optogenetic transcriptional control to light-regulated localization or light-controlled enzyme activity. Our approach has similarities to the usage of several potyvirus proteases creating a posttranslational network of feedback regulation to control the output range of a genetic circuit in E. coli cells.19 Both implementations tackle the same problem, optimizing the off-state activity of a given output module or protein activity. In both cases, combination of proteolysis with gene expression results in minimized off-state activity and effective amplification of the overall ratio. Simulations have predicted the impact of proteolysis on the behavior of gene expression dynamics38; however, few investigations have systematically characterized the combination of controllable proteolysis and gene expression using a single inducer. Most published examples feature unregulated degradation with degrons like the SsrA tag in bacteria or the ODC degron in eukaryotic cells combined with regulation of transcription.39,40 Other published studies use regulated degradation to influence activity of a transcription factor.9,41 However, these tools might lack the amplification that we observe by synergistic multistep regulation. Dual regulation of protein biosynthesis and stability is useful for cases, in which a very fast switch in protein abundance has to be achieved as well as for applications that depend on very low protein levels in the off state. We demonstrated the potential of the psd3 module combined with transcriptional control to achieve dual regulation of protein abundance. Near background levels of target proteins were reached with the psd3 module with both, the established tet-off system as well as with the novel photosensitive transcription factor. With the psd3/psTF combination, we also achieved very fast downregulation of protein levels. In summary, in case the kinetics of protein inactivation matter and very low target protein levels have to be reached, the synergistic optogenetic multistep control is the method of choice for the application. 10 ACS Paragon Plus Environment

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On a conceptual level, we engineered a synergistic multistep signaling module that results in an ultrasensitive protein abundance response.17 Many natural regulatory networks contain analogous modules17; similar implementations of synthetic networks that show switch-like behavior have been created by combination of transcriptional and post-translational devices.42 Synthetic genetic modules that recreate toggle switches, oscillators or bi-stable switches rely often on intricate gene networks based on positive and negative feedback loops.43 The optogenetic synergistic multistep regulation module is regulated at three levels by blue-light, transcriptional activation and proteolysis of one transcription factor module as well as the target protein. This ensures tight regulation and high switching capacity using a relatively simple design. Thus, the SOMCo module should be applicable in numerous organisms and genetic settings for tight regulation of protein amounts. Due to the superior performance of light as an inducer in microbial settings, its usage for biotechnical purposes is an attractive future application. Methods Yeast strains, growth conditions and plasmids All Saccharomyces cerevisiae strains are derivatives of the S288C strain ESM356-1.44 Strains are listed together with their relevant genotypes in Supplementary Table S1. Standard preparations of media were used to grow cells.45 Low-fluorescence medium (LFM) was used to grow yeast cells in liquid cultures25. Cells were diluted 1:5 and spotted on solid YPD medium for serial dilution experiments. Petri dishes were incubated at 30°C in darkness or exposed to blue-light (465 nm, 30 µmol m-2 s-1). Chromosomal tagging of genes was performed with PCR products as described46; yeast strains containing genes modified with the psd module were accordingly obtained.47 The lithium acetate method was used for yeast transformations.48 Yeast cells were illuminated with blue-light using high power LED stripes (465 nm; revoART, Borsdorf, Germany), StrawHat LED clusters (6 clusters of 42 LEDs, 465 nm; revoART, Borsdorf, Germany) or RGB LEDs (5050 RGB LEDs; revoART, Borsdorf, Germany); each set equipped with a dimmer to select an appropriate light flux (0-30 μmol m−2 s−1). The light flux was checked before the experiment at the level of the yeast cells with an optometer (P2000, equipped with light-detector PD-9306-2, Gigahertz-Optik, Türkenfeld, Germany). Plasmids were constructed by standard procedures and are listed in Supplementary Table S2; details and sequences of the used vectors are available on request. Microscopy Live-cell imaging of yeast cells was performed as described49 with a Zeiss Axiovert 200M equipped with a Hamamatsu camera, DAPI, EGFP, EYFP, and rhodamine filter sets using a 63x Plan Apochromat oil lens (NA 1.4). Transmitted-light images were collected in a single 11 ACS Paragon Plus Environment


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plane, fluorescence images in a single plane or as z-stack (0.3-0.5 µm intervals) using the image acquisition software Volocity 5.03 (Perkin Elmer). The software ImageJ was used for image processing and fluorescence quantification.50 Fluorescence measurement Fluorimeter measurements were essentially performed as described.51 Briefly, cells were grown in LFM medium to logarithmic growth phase. Prior to the measurement, cells were treated with sodium azide (10 µM end concentration). 2x107 cells were transferred into a black multiwell plate to measure green and red fluorescence with a SynergyMX multi-detection reader

(BioTek).

The

fluorescence

intensities

were

background-corrected

using

measurements of wild-type cell autofluorescence. The red fluorescence signal was normalized to the green fluorescence signal for ratiometric measurements. For flow cytometer measurements, cells were grown in LFM medium to logarithmic growth phase, treated with sodium azide (10 µM end concentration), diluted 1:10 and transferred to a multi-well plate. Red fluorescence was measured with an Attune NxT (ThermoFisher) equipped with an autosampler using a yellow laser (561 nm) for excitation and a bandpass filter (620/15 nm); a blue laser (488 nm) was used to determine forward (FSC) and sideward scatter (SSC) and a bandpass filter (530/30 nm) was used for GFP detection. FSC-H versus FSC-A plots were used to gate on single events. Additionally, signals were removed by gating that were accumulating on an axis. The fluorescence intensities were background-corrected using measurements of wild-type cell autofluorescence. Mean fluorescence measurements were used to assemble the graphs. Immunoblotting, cycloheximide-chase assay, quantification, and fitting Immunoblotting experiments with samples obtained from yeast cells by alkaline lysis were performed as described.49 Cycloheximide chases were performed as described.8 Immunoblotting was performed as described.51 Antibodies detecting mCherry, Myc, Tub1, and HA epitopes were obtained by commercial suppliers. Quantification was done with the software ImageJ50 using the gel analyzer tool after background subtraction (rolling ball radius 50 pixels). The software LibreOffice Calc was used to generate graphs; error bars show the standard error of the mean (SEM) or standard deviation (as indicated in figure legends). Box plots were generated with the software Qtiplot; boxes show values ranging from 25 to 75 and whiskers indicate the full range of measurements. The median of a measurement is indicated by a horizontal line, the mean by an open square. The Hill equation fit was performed with the software Dr Fit.52 Standard settings were used to fit experimental data to the Hill equation.


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Supporting Information The Supporting Information is available free of charge on the ACS Publications website. The Supplementary Information file contains Supplementary Figures S1-S13, the Supplementary Tables S1 and S2 contain the list of yeast strains and plasmids, respectively. Author Information JT: [email protected]; SoH: [email protected]; SeH: [email protected]; JT: [email protected]; LOE: [email protected]; CT: [email protected] Department of Biology/Genetics Philipps-University Marburg Karl-vom-Frisch-Str. 8 35032 Marburg Germany *send correspondence to: [email protected]

Author Contribution JG, JT, SeH, and SoH, performed experiments, LOE and CT planned the project and constructs. CT wrote the manuscript; all authors read and approved the manuscript. Conflict of Interest The authors declare no conflict of interest.

Acknowledgements The authors thank J Dohmen, E Schiebel, and W Heinemeyer for sending crucial yeast strains, A Batschauer for his generous sharing of equipment, and D Störmer for her excellent technical support. This work was supported by the German Research Foundation priority programme 1926 (SPP1926, 'Next Generation Optogenetics', TA320/7-1 and ES152/16-1) and the German Federal Ministry of Education and Research (BMBF; project “MELICOMO”, grant 031B0358A).



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Figures



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Figure 1 Characterization of an improved photosensitive degradation (psd) module. A) Scheme of the expression cassette to test psd module variants. Different LOV2 domains were benchmarked for their influence on RFP/GFP ratio in yeast cells grown in darkness or exposed to blue-light. B) Quantification of RFP/GFP ratios of different psd modules in cells grown in darkness or exposed to blue-light (lower graph). Dark/light switching ratios are shown on top (n≥6; lower graph: whiskers indicate the full range of measurements, a horizontal line indicates median, open square mean value; upper graph: error bars SEM; yeast strain ESM356-1 with pDS185, pSH5, pSH3, pDS219-1, pDS219-2). C) Fluorescence microscopy analysis of GFP-P2A-RFP-AsLOV2iLID A146Δ-psd expressing yeast cells (ESM356-1 pDS219-2) in darkness or exposed to blue-light. D) Determination of the half-life of RFP-AsLOV2iLID A146Δpsd using the translation inhibitor cycloheximide. Antibodies directed against mCherry and Tub1 were used to detect the psd construct and tubulin (loading control), respectively. Quantification of four independent measurements is shown below the immunoblot (error bars SEM). E) Protein depletion assay with AtLOV2-psd (pDS185), AtLOV2K92R (pSH5), and AsLOV2iLID

A146Δ-psd

E132A E155G-psd

(pDS219-2). RFP/GFP ratio (upper graph) and therefrom

normalized ratios (lower graph) are shown (n=4, error bars SEM; yeast strain ESM356-1 with pDS185, pSH5, pDS219-2).


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Figure 2 Application of psd3 for cell cycle control in yeast cells



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A) Dilution (1:5) growth test of Cdc48-psd3 (YSH1), Npl4-psd3 (YSH10), and control cells (ESM356-1) on YPD plates kept in darkness or exposed to blue-light. B) Immunoblot of the strains used in A. Antibodies directed against mCherry (Cdc48), myc (Npl4), and Tub1 (loading control) were used to detect the proteins. Quantifications of four independent measurements are shown (error bars SEM). C) Characterization of Cdc48-mCherry-psd3 (YSH2) depletion after blue-light exposure by fluorescence microscopy. Cells were shifted from darkness to bluelight at the beginning of the experiment. The graph shows quantification of three biological replicates (>200 cells per time point). D) Quantification of cell cycle stages in Cdc48-psd3 (YSH1), Npl4-psd3 (YSH10), and control cells (YDS28). Cells were classified in cells with no bud, small budded, large budded and cells during anaphase and telophase based on cell morphology and spindle morphology visualized with mCherry-Tub1. E) Control of the cyclindependent kinase regulators Clb2 and Sic1 by light. The cell cycle degrons in Clb2 (destruction box) and Sic1 (phosphorylation-dependent degron in the N-terminus recognized by the Skp1cullin-F-box protein complex containing Cdc4) were removed and psd3 was fused to the end of clb2Δdb and

ΔNsic1.

The gene variants were introduced in wild type yeast strains with

plasmids. F) Dilution (1:5) growth test of wild type cells (ESM356-1) containing an empty plasmid (pRS315; control), pDS228 (ΔNSic1-psd3), and pDS229 (Clb2Δdb-psd3) on solid synthetic medium kept in darkness or exposed to blue-light. G) Immunoblot analysis characterizing the abundance of Clb2Δdb-psd3 and

ΔNSic1-psd3.

Antibodies directed against

myc and Tub1 (loading control) were used to detect the proteins. H) Quantification of cell cycle stages in the yeast strains used in F. Experimental conditions as in D. Additionally, cells in darkness containing ΔNSic1-psd3 showed a G1/S phase arrest with elongated buds.


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Figure 3 Doxycycline-based expression control combined with light-control of protein stability A) Scheme of the tet-off system in combination with the psd module. The transcription factor tTA is fused to the psd3 module. Doxycycline addition leads to dissociation of the TF fusion protein from the DNA, blue-light induces TF degradation as well as degradation of mCherrypsd3. B) Immunoblotting analysis of yeast cells (ESM356-1) carrying plasmid pDS239 in the absence or presence of blue-light and doxycycline (50 µg/ml). Antibodies against mCherry, myc, and Tub1 were used to detect the proteins. Quantification of RFP-psd3 is shown in the upper right graph, quantification of tTA-3myc-psd3 in the lower left graph. The graph in the lower right corner shows the ratios (as indicated; n=7, error bars SEM).



Figure 4 Light-control of gene expression and protein stability A) Scheme of the light-sensitive transcription factor. It consists of the DNA-binding domain tetR fused to the photoreceptor AsLOV2 as well as the peptide Zdk1 connected to the 24 ACS Paragon Plus Environment

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transcriptional activation domain VP16 and the psd3 module. B) Fluorimeter measurements of yeast cells (ESM356-1) carrying plasmid pSH32 in the absence or presence of blue-light (n=8; whiskers indicate the full range of measurements, line indicates median, open square mean value). C) Immunoblotting analysis of the same strain as in B. Antibodies against tRFP, HA, myc, and Tub1 were used to detect the proteins. Quantification of RFP is shown in the graph together with the dark/light ratio (right side; n=4, error bars standard deviation). D) Scheme of the light-sensitive transcription factor regulating the expression of RFP-psd3. E) Fluorimeter measurements of yeast cells (ESM356-1) carrying plasmid pSH33 in the absence or presence of blue-light (n=8; left graph: whiskers indicate the full range of measurements, line indicates median, open square mean value). F) Immunoblotting analysis of the same strain as in E. Antibodies against mCherry, HA, myc, and Tub1 were used to detect the proteins. Quantification of RFP is shown in the graph together with the dark/light ratio (right side; n=4, error bars standard deviation).



Figure 5 Synergistic multistep signaling with blue-light A) Schematic drawing of the different modules controlled by light: i) photo-sensitive degron module; ii) light-sensitive transcription factor: iii) light-controlled gene expression and protein stability. B) Kinetics of target protein depletion by regulation of TF activity (tRAL ZVPd Pteto6RFP), protein stability (PADH1-RFP-psd3) or combined modules (tRAL ZVPd PtetO6-RFP-psd3). Fluorescence measurements by flow cytometry are shown (n=6; error bars SEM; yeast strains: ESM356-1 with plasmid pSH32, pSH25, or pSH33). C) Dose response curve with blue-light intensity variation between 0 and 30 µmol m-2 s-1 (n≥6; error bars: SEM). Experimental conditions as in B. D) Semi-logarithmic representation of the experimental data shown in C and their non-linear fitting by using the Hill equation (error bars: Standard deviation).


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