Biology subjects

Yokota Hirai, M.

Publications and source records attributed to Yokota Hirai, M..

3 recordsLinked to original sources

bioRxiv2025

NPF4.6-mediated ABA transport modulates stomatal responses to fluctuating light

Stomatal responses to fluctuating light (FL) strongly influence photosynthetic efficiency and biomass accumulation in seed plants. The abscisic acid (ABA) transporter NPF4.6 (also known as NRT1.2 or AIT1) mediates hormone import into guard cells, but its physiological roles in leaves remain unclear. Here, we examined the functions of NPF4.6 in Arabidopsis thaliana by characterizing two independent loss-of-function mutants (npf4.6) under both steady and FL conditions. Compared with wild type, npf4.6 showed a 21-43% reduction in the half-time of stomatal opening and a 26-32% increase in the mean net CO2 assimilation rate under simulated field light conditions, resulting in a 41-48% increase in shoot biomass. In contrast, no marked differences in photosynthetic efficiency were observed between the wild type and npf4.6 under drought stress, suggesting that the role of NPF4.6 is more pronounced under well-watered, dynamic-light conditions. Overall, these findings demonstrate that NPF4.6 contributes to the fine-tuning of stomatal kinetics under variable light environments, and highlight hormone transport as a promising target for improving photosynthesis and growth in the field.

plant biology↗

bioRxiv2024

High-throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models

High throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole genome sequences under two irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of five conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

plant biology↗

bioRxiv2021

Characterization of γ-Glutamyl Peptidases and γ-Glutamyl Cyclotransferases for Glutathione Degradation in Arabidopsis

O_LIOrganic sulfur is stored as glutathione (GSH) in plants. In Arabidopsis, {gamma}-glutamyl cyclotransferases (GGCT2;1, GGCT2;2, and GGCT2;3) degrade cytosolic GSH, but they do not fully explain the rapid GSH turnover. Here, we demonstrate that {gamma}-glutamyl peptidases, GGP1 and GGP3, play a substantial role in degrading GSH in the cytosol. C_LIO_LIWe conducted yeast complementation assay and activity assay of recombinant proteins to identify the novel GSH degradation enzymes. The expression patterns were investigated by RT-qPCR. GSH concentrations in the mutants were also analyzed. C_LIO_LIGGP1 complemented the yeast phenotype. Recombinant GGP1 and GGP3 showed reasonable Km values considering cytosolic GSH concentration, and their activity was comparable to that of GGCTs. The GGP1 transcript was highly abundant in mature organs such as rosette leaves. The expression of GGCT2;1 was conspicuously enhanced under sulfur deficiency. GSH concentration was higher in ggp1 knockout mutants regardless of nutritional conditions; the concentration was higher in ggct2;1 knockout mutants under sulfur-deficient conditions. C_LIO_LIWe propose a model wherein cytosolic GSH is degraded fundamentally by GGP1. The degradation is accelerated by GGCT2;1 under sulfur deficiency. Given the energy cost throughout the reactions, GGPs could render a more efficient route for GSH degradation than GGCTs. C_LI

plant biology↗