ABI Innovation: A New Framework to Analyze Plant Energy-related Phenomics Data

University Of Kentucky Research Foundation, Lexington KY

Investigators

Abstract

To increase crop productivity, photosynthetic reactions must be tightly regulated to efficiently capture light energy and to avoid photodamage. This regulation is especially critical under unpredictable fluctuations in the natural environment, which could damage the balance between light input and the capacity of assimilatory reaction to process it. New plant photosynthesis phenotyping platforms have been developed in Dr. David Kramer (co-PI)'s lab, allowing one to determine how the photosynthetic machinery is integrated into cells and is delicately balanced to provide the right amount of energy, at the right times, in the correct forms without damaging the plant. The current major step is to extract useful information from massive plant phenotyping (performance) data to generate testable hypotheses and discover unknown plant energy-related genes and processes. Specifically, the objective is to develop new software approaches for processing, modeling and visualizing sophisticated and overwhelming amount of phenomics data in plant science to forms that are interpretable computers (to classify plants into genetic and performance categories) and by humans (through advanced visualization), leading to new insights on how plants function and new targets for plant improvement. Large-scale phenotyping (phenomics) promises to bridge the gap between genomics, gene functions and traits. Specifically, to meet our growing needs for food and fuel, new bio-imaging approaches were developed to allow high-throughput, detailed plant phenotyping, with a focus on improving the efficiency of photosynthesis. Dr. Jin Chen (PI) and Dr. David Kramer (co-PI) aim to identify genes and processes that control photosynthesis efficiency in response to fluctuating environmental conditions, which are critical for understanding and improving plant energy storage and improving crop productivity. To achieve this, the research team must resolve a wide range of interacting factors that respond to environmental factors over very wide dynamic ranges of frequency, duration and intensity of conditions. Recently, the Dynamic Environmental Phenotype Imager (DEPI), a novel platform for monitoring responses of plant phenotypes under dynamic conditions has been developed in Dr. David Kramer (co-PI)'s lab. Initial data from DEPI reveals previously unseen effects attributable to genes formerly thought to have no known function. While these developments on plant phenotyping are exciting, researchers are limited by the tools to analyze fully the phenomics data. Removing that limitation is the proposed goal of this project. Dr. Jin Chen (PI) and Dr. David Kramer (co-PI) will discover, develop, and apply Plant Phenomics Data Analytics (PPDA) solutions, such that massive phenomics data is transformed into knowledge or testable hypotheses to identify important genes to improve photosynthesis efficiency under dynamic environmental conditions. PPDA will ensure high data quality, identify and visualize important genes from complex plant phenomics data, and will advance knowledge discovery in the broader community. The project is comprised of four components: Aim 1. Develop, test and apply phenomics data quality control program to identify abnormal data and distinguish whether they arise from noise, artifacts or more interesting cases of altered biological responses. Aim 2. Develop, test and apply phenomics pattern discovery algorithms to identify important energy-related genes from photosynthesis phenomics data. The research team will develop dynamic phenotype network construction and phenotype module discovery algorithms to turn sophisticated phenomics data to testable hypotheses, to discover unknown genes, and to connect biological processes. Aim 3. Develop a data visualization package for complex phenomics data display using integrative multi-dimensional visualization methods, in order to facilitate scientific discovery on energy-related genes in response to changing environmental conditions. Aim 4. Provide proof of utility by applying PPDA to rationale for testing the G protein activation state regulation on photosynthesis efficiency. The researchers will phenotype Arabidopsis thaliana a large informative set of G protein mutants under changing environmental conditions. Then they will apply PPDA to identify genes with emergent functions under subsets of the dynamic environmental conditions. They will resolve the role of G signaling in fluctuation detection. The results of the project can be found at http://www.msu.edu/~jinchen/PPDA.