RESEARCH: Predicting Genotypic Variation in Growth and Yield under Abiotic Stress through Biophysical Process Modeling

University Of Wyoming, Laramie WY

Investigators

Abstract

Rising demand for high quality food crops due to increasing world populations along with more likely temperature and drought stress requires further crop improvements from breeding programs. A major limitation to these programs is an understanding of how the genetic information affects the characteristics of plants that improve the amount of edible portions. Moreover, the predictive understanding is even less if the plants are placed in new, stressful environments like low rainfall or high temperature. A likely avenue of inquiry to improve breeding is to better connect how information stored in genes becomes traits of plants that combine their biology, such as photosynthetic rate or the amount of resources allocated to an edible root, with the physical world, such as the amount of water available or an excessive heat wave. These connections are currently often made in a way that requires new data collection for every new crop plant or environment such as a new soil, new temperature range or even a new improved line that shows variation in the amount of resources the plant allocates to an edible root. This continuous need for new information ultimately slows down the breeding program and the ability of plant scientists to quickly respond to the needs of society. This project will test a new approach that uses large amounts of data to calculate the probability that a particular plant characteristic will be displayed by a given plant line under various environmental conditions. Specifically, the project will measure plant performance continuously by sending electrical pulses through plants, integrating the data generated with large data sets that show which genes are active as well as the level of biologically relevant molecules that contribute to major metabolic pathways within the plants at any given time. This new approach requires high performance computing to test many times how the probability of phenotypic improvement in the crop may occur. These high performance computing approaches will become a core part of a modern, competitive workforce. In this regard, the project will provide workshops for high school teachers in the use of high performance yet open source computing tools in their classrooms. In addition, the project will develop experimental and computational modules in biological and quantitative learning for students in grades 6-12 using the highly successful Wisconsin FastPlants system (http://www.fastplants.org/). With increasing world populations, genetic advances to improve crop growth, yield and resistance to abiotic stress are a pressing need. Limiting the speed of crop improvement is a crucial knowledge gap regarding biophysical processes that modulate the relationship between the genome and phenome, hindering the ability to predict the phenotype of novel genotypes in novel environments. As a first step towards bridging this gap, a combination of high-throughput phenotyping and biophysical process modeling will incorporate allelic variation at key genes affecting plant carbon metabolism, hydraulics, and resource allocation, all of which are known to impact drought- and heat-stress resistance in plants. Variable selective pressures during crop diversification have caused extensive phenotypic variation among B. rapa crops, making it an excellent study system to both connect organ-level measures both down to the level of transcriptomic and metabolomic phenotypes and up to yield and to test predictive process models. Process models will be developed and refined using the mechanistic links that connect cell processes and ultimately whole plant physiology to regulatory intermediates such as metabolites and gene transcripts. If successful, the models developed will enable prediction of whole-plant stress-response phenotypes in heterogeneous genotypes and environments. The goals of the project are to: 1) deploy a novel high-throughput and real-time phenotyping method to measure diel physiological dynamics in eight B. rapa parental Nested Association Mapping (NAM) lines under drought- and heat-stress conditions; 2) predict yield in a Recombinant Inbred Line (RIL) population of B. rapa using a biophysical process model of carbon metabolism, hydraulics and resource allocation to test systems-level links between circadian, transcriptomic, metabolomic, and physiological QTL; and 3) test the predictive ability of the biophysical process model under heat- and drought-stress environments using the RIL population used in Aim 2. All data and resources generated in this project will be made accessible to the public through long-term open access repositories such as Project Github and the NCBI Short Read Archive.