A non-invasive method to predict drought survival in Arabidopsis using quantum yield under light conditions


Background: Survival rate (SR) is frequently used to compare drought tolerance among plant genotypes. While a variety of techniques for evaluating the stress status of plants under drought stress conditions have been developed, determining the critical point for the recovery irrigation to evaluate plant SR often relies directly on a qualitative inspection by the researcher or on the employment of complex and invasive techniques that invalidate the subsequent use of the tested individuals. Results: Here, we present a simple, instantaneous, and non-invasive method to estimate the survival probability of Arabidopsis thaliana plants after severe drought treatments. The quantum yield (QY), or efficiency of photosystem II, was monitored in darkness (Fv/Fm) and light (Fv’/Fm’) conditions in the last phase of the drought treatment before recovery irrigation. We found a high correlation between a plant’s Fv’/Fm’ value before recovery irrigation and its survival phenotype seven days after, allowing us to establish a threshold between alive and dead plants in a calibration stage. This correlation was maintained in the Arabidopsis accessions Col-0, Ler-0, C24, and Kondara under the same conditions. Fv’/Fm’ was then applied as a survival predictor to compare the drought tolerance of transgenic lines overexpressing the transcription factors ATAF1 and PLATZ1 with the Col-0 control. Conclusions: The results obtained in this work demonstrate that the chlorophyll a fluorescence parameter Fv’/Fm’ can be used as a survival predictor that gives a numerical estimate of the Arabidopsis drought SR before recovery irrigation. The procedure employed to get the Fv’/Fm’ measurements is fast, non-destructive, and requires inexpensive and easy-to-handle equipment. Fv’/Fm’ as a survival predictor can be used to offer an overview of the photosynthetic state of the tested plants and determine more accurately the best timing for rewatering to assess the SR, especially when the symptoms of severe dehydration between genotypes are not contrasting enough to identify a difference visually.

© 2023, The Author(s). cc-by
Chlorophyll a fluorometry, Drought, Fv’/Fm’, Handheld fluorometer, Non-invasive, Quantum yield
Rico-Cambron, T.Y., Bello-Bello, E., Martinez, O., & Herrera-Estrella, L.. 2023. A non-invasive method to predict drought survival in Arabidopsis using quantum yield under light conditions. Plant Methods, 19(1). https://doi.org/10.1186/s13007-023-01107-w