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Optogenetic control of gene expression in plants in the presence of ambient white light

Abstract

Optogenetics is the genetic approach for controlling cellular processes with light. It provides spatiotemporal, quantitative and reversible control over biological signaling and metabolic processes, overcoming limitations of chemically inducible systems. However, optogenetics lags in plant research because ambient light required for growth leads to undesired system activation. We solved this issue by developing plant usable light-switch elements (PULSE), an optogenetic tool for reversibly controlling gene expression in plants under ambient light. PULSE combines a blue-light-regulated repressor with a red-light-inducible switch. Gene expression is only activated under red light and remains inactive under white light or in darkness. Supported by a quantitative mathematical model, we characterized PULSE in protoplasts and achieved high induction rates, and we combined it with CRISPR–Cas9-based technologies to target synthetic signaling and developmental pathways. We applied PULSE to control immune responses in plant leaves and generated Arabidopsis transgenic plants. PULSE opens broad experimental avenues in plant research and biotechnology.

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Fig. 1: PULSE is an optogenetic system for the control of gene expression in plants grown under standard light–dark cycles.
Fig. 2: Characterization of BOff in Arabidopsis protoplasts.
Fig. 3: Characterization of PULSE in Arabidopsis protoplasts.
Fig. 4: PULSE-controlled expression of a Cas9-derived gene activator (dCas9–TV) and an Arabidopsis transcription factor (LFY) in Arabidopsis protoplasts.
Fig. 5: Implementation and characterization of PULSE in N. benthamiana leaves.
Fig. 6: In planta optogenetic heterologous induction of immunity and conditional subcellular targeting of receptors, and PULSE functionality in Arabidopsis transgenic lines.

Data availability

Raw and associated data generated with plate-reader-, RT-qPCR- and microscope-specific software that support the findings of this study are available from the corresponding author upon request. The plasmids used in all experiments are available at AddGene, and the plasmid maps at the public repository JBEI-ICE (https://public-registry.jbei.org/folders/577). Source data are provided with this paper.

Code availability

The numerical integration, fitting process and identifiability analysis with the profile likelihood method were performed in MATLAB using the freely available Data2Dynamics software. Details relative to the equations used can be found in the Supplementary Note. Source data are provided with this paper.

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Acknowledgements

This study was supported in part by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (CEPLAS—EXC-1028 project no. 194465578 to R.S. and M.D.Z., EXC-2048/1—project no. 390686111 to R.S. and M.D.Z., CIBSS – EXC-2189—project no. 390939984 to T.O., J.T. and W.W., and BIOSS – EXC-294 to J.T. and W.W.), the iGRAD Plant (IRTG 1525 to R.O.F., J.S., R.S. and M.D.Z.), and the Collaborative Research Centers SFB1208 (project no. 267205415; project A13 to M.D.Z.) and SFB924 (INST 95/1126-2; project B4 to T.O.), the European Commission – Research Executive Agency (H2020 Future and Emerging Technologies FET-Open project no. 801041 CyGenTig to M.D.Z.). J.B.M. is supported by a fellowship from the Eastern Academic Research Consortium. We thank D. Orzaez (Polytechnic University of Valencia) and K. Gardner (City University of New York) for kindly providing the GoldenBraid and EL222 plasmids, respectively, T. Brumbarova (University of Düsseldorf) for aid with quantitative reverse-transcription PCR experiments, R. Wurm and M. Gerads (University of Düsseldorf) for technical assistance, and J. Schmidt (Technical Workshop Biology, University of Freiburg) for designing and constructing the light boxes used in this work. We are indebted to J. Casal (University of Buenos Aires), D. Nusinow (Danforth Center), S. Romero, H. Beyer and U. Urquiza (University of Düsseldorf) for careful reading and their suggestions to improve the manuscript.

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R.O.F., N.B.A., L.-A.K., J.B.M. and S.M.B. designed and cloned the constructs. S.M.B. performed preliminary tests and R.O.F. conducted all Arabidopsis protoplasts experiments. F.-G.W. and R.E. developed the mathematical model. R.O.F., N.B.A., J.S. and L.-A.K. contributed to the establishment of PULSE in planta. N.B.A. conducted the conditional targeting and immunity induction in planta. R.O.F. and G.G. generated the transgenic Arabidopsis PULSE lines and performed the experiments. R.O.F., N.B.A., T.O., R.S. and M.D.Z. designed the experiments. J.T., W.W., T.O., R.S. and M.D.Z. supervised the research. T.O., R.S. and M.D.Z. analyzed the data and discussed results. M.D.Z. planned and directed the project. R.O.F. and M.D.Z. designed the system and wrote the initial manuscript with input from all authors. All authors contributed to editing and read the final version of the manuscript.

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Correspondence to Matias D. Zurbriggen.

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Extended data

Extended Data Fig. 1 Model-based functional characterization, and prediction and validation of PULSE function.

a,b, Quantitative characterization of On-Off PULSE kinetics and reversibility. PULSE-driven FLuc expression assays in Arabidopsis protoplasts. (a) FLuc/RLuc ratios for protein expression kinetics, n = 6 protoplast samples. (b) Normalized starting quantity (SQ) of FLuc transcript to the SQ geometric mean of EF and TIP41L transcripts (internal normalization controls), n = 2 technical replicates for each transcript (b). Protoplasts were transformed and kept in the dark, 12 h for protein (a) and 16 h for mRNA (b) determination, followed by illumination with either 10 µmol m−2 s−1 of red or blue light, or kept in darkness. Arrows indicate the time point where the samples were split into different illumination conditions, for example, red to dark, red to blue (On-Off), red to blue to red (On-Off-On). The curves are the fits to the ODE-based model. The shaded areas represent the error bands as calculated in 95% confidence intervals with a constant Gaussian error model using the profile likelihood method. c, Model aided prediction of PULSE-controlled protein expression levels as a function of red light intensities and illumination times. The calibrated model yields estimated FLuc/RLuc expression ranges (heatmap). d, Experimental validation of the model predictions of the operating range of PULSE. Selected model simulated expression levels at different red light intensities and illumination times as indicated in (c) were experimentally tested and the resulting FLuc/RLuc ratios (means and 2xSEM are plotted, n = 6 protoplast samples for each condition, black circles) were compared to the predicted values (grey squares). RLU = Relative Luminescence Units. Source data

Supplementary information

Supplementary Information

Supplementary Note, Figs. 1–11 and Tables 1–3.

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Supplementary Dataset

Statistical Source Data Supplementary Fig. 1.

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Source Data Fig. 3

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Source Data Fig. 4

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Source Data Fig. 5

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Source Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 1

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Ochoa-Fernandez, R., Abel, N.B., Wieland, FG. et al. Optogenetic control of gene expression in plants in the presence of ambient white light. Nat Methods 17, 717–725 (2020). https://doi.org/10.1038/s41592-020-0868-y

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