Remote Sensing of Endogenous Pigmentation by Inducible Synthetic Circuits in Grasses

Plant synthetic biology holds great promise for engineering plants to meet future demands. Genetic circuits are being designed, built and tested in plants to demonstrate the proof of concept. However, developing these components in monocots, which the world relies on for grain, lags behind dicot models, such as Arabidopsis thaliana and Nicotiana benthamiana. Here, we show the successful adaptation of a ligand-inducible sensor to activate an endogenous anthocyanin pathway in the C4 monocot model Setaria viridis. We identify two transcription factors that can be expressed as a single transcript that are sufficient to induce endogenous anthocyanin production in S. viridis protoplasts and whole plants in a constitutive or ligand-inducible manner. We also test multiple ligands to overcome physical barriers to ligand uptake, identifying triamcinolone acetonide (TA) as a highly potent inducer of this system. Using hyperspectral imaging and a discriminative target characterisation method in a near-remote configuration, we can non-destructively detect anthocyanin production in leaves in response to ligands. This work demonstrates the use of inducible expression systems in monocots to manipulate endogenous pigmentation production for remote detection. Applying inducible anthocyanin production coupled with sensitive detection algorithms could enable crop plants to report on the status of field contamination or detect undesirable chemicals impacting agriculture, ushering in an era of agriculture-based sensor systems.

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Publication Details

Journal
Plant Biotechnology Journal
Published
2025-12-05
DOI
https://doi.org/10.1111/pbi.70480
Primary Topic
Plant Gene Expression Analysis
Type
article
Field-Weighted Citation Impact
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article

Remote Sensing of Endogenous Pigmentation by Inducible Synthetic Circuits in Grasses

Malia Gehan, Hudanyun Sheng, Lucia Acosta‐Gamboa, Dong‐Yeon Lee et al.
Plant Biotechnology Journal
Plant Gene Expression Analysis
article

Remote Sensing of Endogenous Pigmentation by Inducible Synthetic Circuits in Grasses

Malia Gehan, Hudanyun Sheng, Lucia Acosta‐Gamboa, Dong‐Yeon Lee, Samuel Kenney, Sunita Pathak, Santiago Calderon, Austin Morgan, Nathan Swyers, Alina Zare, Susan Meerdink, Dmitri A. Nusinow, Luke Saleh, Caroline Kuzio
article en

Abstract

Plant synthetic biology holds great promise for engineering plants to meet future demands. Genetic circuits are being designed, built and tested in plants to demonstrate the proof of concept. However, developing these components in monocots, which the world relies on for grain, lags behind dicot models, such as Arabidopsis thaliana and Nicotiana benthamiana. Here, we show the successful adaptation of a ligand-inducible sensor to activate an endogenous anthocyanin pathway in the C4 monocot model Setaria viridis. We identify two transcription factors that can be expressed as a single transcript that are sufficient to induce endogenous anthocyanin production in S. viridis protoplasts and whole plants in a constitutive or ligand-inducible manner. We also test multiple ligands to overcome physical barriers to ligand uptake, identifying triamcinolone acetonide (TA) as a highly potent inducer of this system. Using hyperspectral imaging and a discriminative target characterisation method in a near-remote configuration, we can non-destructively detect anthocyanin production in leaves in response to ligands. This work demonstrates the use of inducible expression systems in monocots to manipulate endogenous pigmentation production for remote detection. Applying inducible anthocyanin production coupled with sensitive detection algorithms could enable crop plants to report on the status of field contamination or detect undesirable chemicals impacting agriculture, ushering in an era of agriculture-based sensor systems.

Plant Biotechnology JournalVol. 24(4)
University of Iowa (US), Donald Danforth Plant Science Center (US), University of Florida (US)
Defense Advanced Research Projects Agency, Advanced Research Projects Agency
Openalex Percentile: Top 65%
Plant Gene Expression Analysis
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