Reflecting Photosynthesis: a Tool for Gene Hunters

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By Natalia Bateman, CoETP, 5 February 2018.


Looking for genes that produce plants that need less water or nutrients, produce more seeds or leaves, or are better at resisting drought and pests is a critical global challenge. We need more productive crops to feed the growing global population while being able to adapt to the changing climate.

The problem is that discovering a useful gene in wheat, which contains 40 times more base pairs than rice and 6 times more than a human, is a mammoth task that requires hours of painstaking measurements.

In a recent study published on the Journal of Experimental Biology, a team of scientists developed a methodology that accelerates this discovery process.

“The advantage of this method is that it is non-destructive and quick, allowing the measurement of many plants to map populations and identify useful genes for improving crop production,” said lead researcher Dr Viridiana Silva-Perez from the ARC Centre of Excellence for Translational Photosynthesis and CSIRO.

“Essentially, we measured the colour of light reflected from leaves, which contains information about many leaf properties, including photosynthesis, the process by which plants transform sunlight and carbon dioxide into sugars.”

Improving photosynthesis has emerged as a major target to raise wheat yields. However, plant breeders cannot breed for photosynthesis without markers. That is where plant researchers come in.

Photosynthesis is a process that depends on many variables, and this measurement gives us a unique fingerprint of each plant’s capacity to capture the sun’s energy,” said ANU Professor John Evans, Chief Investigator at the ARC Excellence for Translational Photosynthesis and one of the authors of this study.

“This technique allows scientists the see how big the engine inside plant cells is. It permits us to ask very useful questions about what is happening inside the plant, for example, if the plant can get more carbon for the water it spends.”

It takes researchers 20 minutes per leaf to obtain part of this information using traditional methods. Now, this methodology enables 100 measurements per hour.

“In the time it takes you to take one measurement with other methods, hyperspectral reflectance allows you to predict a whole suite of leaf properties. These include the dry mass of the leaf per unit area, nitrogen and chlorophyll contents, quantity of the enzyme Rubisco and electron transport capacities. More leaf properties are being added.”

During the study, the scientists worked in wheat fields in Australia and Mexico, and compared hyperspectral reflectance to other methods, which allowed them to calibrate the technique. Since then, the technique has been used for other crops such as corn, rice and sorghum in Australia, the UK and the USA.

This work was partly funded by the Australian Research Council (ARC) Centre of Excellence for Translational Photosynthesis (CoETP), the Sustainable Modernization of Traditional Agriculture (MasAgro) initiative and CONACYT. It is a collaboration between the CoETP, the International Maize and Wheat Improvement Centre (CIMMYT), CSIRO, ANU and the Grains Research and Development Corporation (GRDC). Access to glasshouse and field experiments was supported by Grains Research & Development Corporation funding (CSP00168). SPS was supported by the United States Department of Energy contract No. DE-SC0012704 to Brookhaven National Laboratory.

Read full article here


For Interview contact:

Professor John Evans

Chief Investigator Centre of Excellence for Translational Photosynthesis

Phone: +61 2 6125 4492 m. +61 477 777 132



Media Contact:

Natalia Bateman

Communications Officer, ARC Centre of Excellence for Translational Photosynthesis

Phone: +61 02 6125 1703 m. 0401 083 380



Publication Details

Silva-Perez V, Molero G, Serbin SP, Condon AG, Reynolds, MP, Furbank RT & Evans JR, (2017) Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat. Journal of Experimental Botany (in press 4 Nov 2017) JEXBOT/2017/209023