8 research outputs found

    Circadian rhythms are associated with variation in photosystem II function and photoprotective mechanisms

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    The circadian clock regulates many aspects of leaf gas supply and biochemical demand for CO2 20 , and is hypothesized to improve plant performance. Yet the extent to which the clock may regulate the efficiency of photosystem II (PSII) and photoprotective mechanisms such as heat dissipation remains largely unexplored. Based on measurements of chlorophyll a fluorescence, we estimated the maximum efficiency of photosystem II in light (Fv'/Fm') and heat dissipation by non-photochemical quenching (NPQ). We further dissected total NPQ into its main components, qE (pH-dependent quenching), qT (state-transition quenching) and qI (quenching related to photoinhibition), in clock mutant genotypes of Arabidopsis thaliana, the cognate wild-type genotypes, and a panel of recombinant inbred lines (RILs) expressing quantitative variation in clock period. Compared to mutants with altered clock function, we observed that wild-type genotypes with clock period lengths of approximately 24 hr had both higher levels of Fv'/Fm ', indicative of improved PSII function, and reduced NPQ, suggestive of lower stress on PSII light harvesting complexes. In the RILs, genetic variances were significant for Fv'/Fm' and all three components of NPQ, with qE explaining the greatest proportion of NPQ. Bivariate tests of association and structural equation models of hierarchical trait relationships showed that quantitative clock variation was empirically associated with Fv'/Fm' and NPQ, with qE mediating the relationship with gas exchange. The results demonstrate significant segregating variation for all photoprotective components, and suggest the adaptive significance of the clock may partly derive from its regulation of the light reactions of photosynthesis and of photoprotective mechanisms. Key words: Arabidopsis thaliana, circadian rhythms, chlorophyll a fluorescence, maximum efficiency of PSII, non-photochemical quenchin

    Quantifying physiological trait variation with automated hyperspectral imaging in rice

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    Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R2 = 0.797 and RMSEP = 0.264 for N; R2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies

    Circadian Rhythms and Redox State in Plants: Till Stress Do Us Part

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    A growing body of evidence demonstrates a significant relationship between cellular redox state and circadian rhythms. Each day these two vital components of plant biology influence one another, dictating the pace for metabolism and physiology. Diverse environmental stressors can disrupt this condition and, although plant scientists have made significant progress in re-constructing functional networks of plant stress responses, stress impacts on the clock-redox crosstalk is poorly understood. Inter-connected phenomena such as redox state and metabolism, internal and external environments, cellular homeostasis and rhythms can impede predictive understanding of coordinated regulation of plant stress response. The integration of circadian clock effects into predictive network models is likely to increase final yield and better predict plant responses to stress. To achieve such integrated understanding, it is necessary to consider the internal clock not only as a gatekeeper of environmental responses but also as a target of stress syndromes. Using chlorophyll fluorescence as a reliable and high-throughput probe of stress coupled to functional genomics and metabolomics will provide insights on the crosstalk across a wide range of stress severity and duration, including potential insights into oxidative stress response and signaling. We suggest the efficiency of photosystem II in light conditions (Fv′/Fm′) to be the most dynamic of the fluorescence variables and therefore the most reliable parameter to follow the stress response from early sensing to mortality
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