RoL: FELS: EAGER: Exploring the adaptive possibilities of 'redundancy' in a plant defense hormone signaling pathway

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Among the challenges facing global agriculture is the increased frequency of crop disease outbreaks as a result of increasing global temperatures. Molecular-genetic studies of the 20th century yielded benefits for agriculture, by showing the roles that genes play in crop biology, guiding the hands of plant breeders to produce better crops for farmers. In the 21st century far more information is available about the DNA sequences of plant genomes, but this wealth of information still requires experimental analysis to understand how it can be used to proper effect in plant breeding. This project will develop new approaches to studying sets of genes from the model plant Arabidopsis thaliana, measuring the way that slight differences in individual gene functions can lead to broad effects on plant health. Specifically, this project addresses genes involved in the plant immune response to herbivores and microbial pests. Plant immune responses are directed by a small set of chemical messengers, known as plant hormones, and the genes studied in this project convert this chemical message into an immune response. Conceptual approaches and laboratory methods developed during this project may form the foundation for future work in crop plants, providing material for plant breeders to develop disease resistant crops.This project combines approaches from systems and synthetic biology to characterize functions of genetic regulatory networks composed of seemingly redundant signaling components. Specifically the Jasmonic acid (JA) receptor system of Arabidopsis thaliana will be studied. Mathematical models of JA signaling based on differential equations will be used to guide design of minimal synthetic systems in budding yeast able to convert JA inputs into transcriptional outputs according to a range of predetermined transfer functions. Using this information CRISPR-Cas9 based transcription factors will be designed to target particular subsets of the JA network in planta. Guide RNA target genes will be chosen to carry out specific changes to the emergent properties of the system, as informed by our models. All this work relies on first being able to generate experimental data to fit parameters for relevant interaction strengths, and to do this the SynAg method, recently developed in the Klavins lab, will be adapted and expanded. SynAg relies on yeast surface display and next generation sequencing to quantify entire interaction networks in a single experiment. By developing models of JA signaling and testing the predictive power of these models for the construction of new genetic circuits in yeast, or to perturb existing circuits in planta, this project aims to elucidate the emergent properties encoded in apparent redundancy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.