Evaluation of Photosynthetic Efficiency in Photorespiratory Mutants by Chlorophyll Fluorescence Analysis

Joseph Qian, Department of Biological Sciences, Louisiana State University.
Nicholas Ferrari, Department of Biological Sciences, Louisiana State University.
Richard Garcia, Department of Biological Sciences, Louisiana State University.
Mary Beth Rollins, Department of Plant Pathology and Crop Physiology, Louisiana State University AgCenter.
Paul F. South, Department of Biological Sciences, Louisiana State University; Department of Plant Pathology and Crop Physiology, Louisiana State University AgCenter; pfsouth326@gmail.com.

Abstract

Photosynthesis and photorespiration represent the largest carbon fluxes in plant primary metabolism and are necessary for plant survival. Many of the enzymes and genes important for photosynthesis and photorespiration have been well studied for decades, but some aspects of these biochemical pathways and their crosstalk with several subcellular processes are not yet fully understood. Much of the work that has identified the genes and proteins important in plant metabolism has been conducted under highly controlled environments that may not best represent how photosynthesis and photorespiration function under natural and farming environments. Considering that abiotic stress results in impaired photosynthetic efficiency, the development of a high-throughput screen that can monitor both abiotic stress and its impact on photosynthesis is necessary. Therefore, we have developed a relatively fast method to screen for abiotic stress-induced changes to photosynthetic efficiency that can identify uncharacterized genes with roles in photorespiration using chlorophyll fluorescence analysis and low CO2 screening. This paper describes a method to study changes in photosynthetic efficiency in transferred DNA (T-DNA) knockout mutants in Arabidopsis thaliana. The same method can be used for screening ethyl methanesulfonate (EMS)-induced mutants or suppressor screening. Utilizing this method can identify gene candidates for further study in plant primary metabolism and abiotic stress responses. Data from this method can provide insight into gene function that may not be recognized until exposure to increased stress environments.