Molecular Bonsai: Elucidating the design principles for engineering plant organ size

Abstract

Enhancements to crop morphology, such as the semi-dwarfing that helped drive the green revolution, are often driven by changes in gene expression. These are challenging to translate across species, which slows the rate of crop improvement. Synthetic transcription factors (SynTFs) offer a rapid alternative to generate targeted alterations to gene expression. However, the complexity of developmental pathways makes it unclear how to best apply them to predictably engineer morphology. In this work, we explore whether mathematical modeling can guide SynTF-based gene expression modulation to help elucidate the design principles of engineering organ size. We targeted genes in the phytohormone, gibberellin (GA), signaling pathway, which is a central regulator of cell expansion. We demonstrate that modulation of GA signaling gene expression can generate consistent dwarfing across tissues and environments in Arabidopsis thaliana, and that the degree of dwarfing is dependent on the strength of regulation, as predicted by modeling. We further validate the models predictive power by demonstrating its capacity to predict the qualitative impacts of different regulatory architectures for engineering organ size. Additionally, we develop expression parameterized models to quantitatively predict organ size and elucidate how temperature will affect growth. Finally, we show that these insights can be generalized for engineering organ size in tomato (Solanum lycopersicum). This work creates a framework for predictable engineering of an agriculturally important trait across tissues and plant species. It also serves as a proof-of-concept for how mathematical models can guide SynTF-based alterations in gene expression to enable bottom-up design of plant phenotypes. Significance StatementWhile traditional breeding approaches have identified mutations that enhance crop performance via targeted gene expression changes, these are not easily translated across varieties and species. Synthetic transcription factors (SynTFs) offer an avenue to generate such changes de novo, but the optimal regulatory architectures necessary to generate desired phenotypes remain unclear. We demonstrate how mathematical models can be used to guide SynTF deployment and elucidate the design principles for engineering organ size, an agriculturally important trait, via modulation of gibberellin signaling. In addition to revealing regulatory architectures that can consistently increase or decrease organ size across a range of tissues, environments, and plant species, this work demonstrates how model-guided SynTF-based modulation of gene dosage can be used to predictably engineer plants.