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Fast photosynthesis measurements for phenotyping photosynthetic capacity of rice.

ABSTRACT: Background:Over the past decades, the structural and functional genomics of rice have been deeply studied, and high density of molecular genetic markers have been developed. However, the genetic variation in leaf photosynthesis, the most important trait for rice yield improvement, was rarely studied. The lack of photosynthesis phenotyping tools is one of the bottlenecks, as traditional direct photosynthesis measurements are very low-throughput, and recently developed high-throughput methods are indirect measurements. Hence, there is an urgent need for a fast, accurate and direct measurement approach. Result:We reported a fast photosynthesis measurement (FPM) method for phenotyping photosynthetic capacity of rice, which measures photosynthesis of excised tillers in environment-controlled lab conditions. The light response curves measured using FPM approach coped well with that the curves measured using traditional gas exchange approach. Importantly, the FPM technique achieved an average throughput of 5.4 light response curves per hour, which was 3 times faster than the 1.8 light response curves per hour using the traditional method. Tillers sampled at early morning had the highest photosynthesis, stomatal conductance and the lowest variability. In addition, even 12 h after sampling, there was no significant difference of photosynthesis rate between excised tillers and in situ. We finally investigated the genetic variations of photosynthetic traits across 568 F2 lines using the FPM technique and discussed the logistics of screening several hundred samples per day per instrumental unit using FPM to generate a wealth of photosynthetic phenotypic data, which might help to improve the selection power in large populations of rice with the ultimate aim of improving yield through improved photosynthesis. Conclusions:Here we developed a high-throughput method that can measure the rice leaf photosynthetic capacity approximately 10 times faster than traditional gas exchange approaches. Importantly, this method can overcome measurement errors caused by environmental heterogeneity under field conditions, and it is possible to measure 12 or more hours per day under lab conditions.

SUBMITTER: Du T

PROVIDER: S-EPMC6979334 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Fast photosynthesis measurements for phenotyping photosynthetic capacity of rice.

Du Tingting T Meng Ping P Huang Jianliang J Peng Shaobing S Xiong Dongliang D

Plant methods 20200124

<h4>Background</h4>Over the past decades, the structural and functional genomics of rice have been deeply studied, and high density of molecular genetic markers have been developed. However, the genetic variation in leaf photosynthesis, the most important trait for rice yield improvement, was rarely studied. The lack of photosynthesis phenotyping tools is one of the bottlenecks, as traditional direct photosynthesis measurements are very low-throughput, and recently developed high-throughput meth ...[more]

PMID: 31998402

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Project description:BackgroundNitrogen (N) and potassium (K) are two important mineral nutrients in regulating leaf photosynthesis. Studying the interactive effects of N and K on regulating N allocation and photosynthesis (Pn) of rice leaves will be of great significance for further increasing leaf Pn, photosynthetic N use efficiency (PNUE) and grain yield. We measured the gas exchange of rice leaves in a field experiment and tested different kinds of leaf N based on N morphology and function, and calculated the interactive effects of N and K on N allocation and the PNUE.ResultsCompared with N0 (0 kg N ha- 1) and K0 (0 kg K2O ha- 1) treatments, the Pn was increased by 17.1 and 12.2% with the supply of N and K. Compared with N0K0 (0 kg N and 0 kg K2O ha- 1), N0K120 (0 kg N and 120 kg K2O ha- 1) and N0K180 (0 kg N and 180 kg K2O ha- 1), N supply increased the absolute content of photosynthetic N (Npsn) by 15.1, 15.5 and 10.5% on average, and the storage N (Nstore) was increased by 32.7, 64.9 and 72.7% on average. The relative content of Npsn was decreased by 5.6, 12.1 and 14.5%, while that of Nstore was increased by 8.7, 27.8 and 33.8%. Supply of K promoted the transformation of Nstore to Npsn despite the leaf N content (Na) was indeed decreased. Compared with N0K0, N180K0 (180 kg N and 0 kg K2O ha- 1) and N270K0 (270 kg N and 0 kg K2O ha- 1), K supply increased the relative content of Npsn by 17.7, 8.8 and 7.3%, and decreased the relative content of Nstore by 24.2, 11.4 and 8.7% respectively.ConclusionsThis study indicated the mechanism that K supply decreased the Na but increased the Npsn content and then increased leaf Pn and PNUE from a new viewpoint of leaf N allocation. The supply of K promoted the transformation of Nstore to Npsn and increased the PNUE. The decreased Nstore mainly resulted from the decrease of non-protein N. Combined use of N and K could optimize leaf N allocation and maintain a high leaf Npsn content and PNUE.

Project description:Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (V c,max ), maximum electron transport rate (J max ) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted V c,max, J max and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for V c,max, but not for J max, and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R2 increases of 17% for V c,max . and 13% J max . Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower V c,max. The PLSR model was able to accurately predict both lower V c,max and higher leaf [N] for this genotype suggesting that the spectral based estimates of V c,max and leaf nitrogen [N] are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required.

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Fast photosynthesis measurements for phenotyping photosynthetic capacity of rice.

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Fast photosynthesis measurements for phenotyping photosynthetic capacity of rice.

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