Biology subjects

Tanabata, T.

Publications and source records attributed to Tanabata, T..

4 recordsLinked to original sources

bioRxiv2025

Multi-omics profiling with indoor-unmanned phenotyping reveals drought adaptation through constitutive ABF1 expression in wild rice

Improving drought resistance is crucial for stable crop production under climate change. Identifying the mechanisms of drought resistance using diverse genetic resources, including crop wild relatives, would be beneficial for molecular breeding. Here, we developed an indoor, unmanned phenotyping platform that can noninvasively and automatically collect temporal data on plant responses to drought stress. Using this system, we analyzed the phenotypic, transcriptomic, and environmental data of four cultivated rice varieties and five wild relatives. Multi-omics analysis revealed that one wild rice species exhibited drought adaptation through the constitutive expression of ABSCISIC ACID RESPONSIVE ELEMENT-BINDING FACTOR 1 (ABF1), which encodes a transcription factor that regulates drought resistance, before drought stress. Drought testing of introgression lines of cultivated rice with constitutive ABF1 expression revealed higher drought tolerance than in cultivars without a growth penalty. Our findings suggest that constitutive ABF1 expression contributes to drought adaptation in both cultivated and wild rice.

plant biology↗

bioRxiv2024

MMM and CLCFM: A 3D point cloud reconstruction method based on photogrammetry for all-around images taken by rotating an individual plant

This research aims to develop a novel technique to acquire a large amount of high-density, high-precision 3D point cloud data for plant phenotyping using photogrammetry technology. The complexity of plant structures, characterized by overlapping thin parts such as leaves and stems, makes it difficult to reconstruct accurate 3D point clouds. One challenge in this regard is occlusion, where points in the 3D point cloud cannot be obtained due to overlapping parts, preventing accurate point capture. Another is the generation of erroneous points in non-existent locations due to image-matching errors along object outlines. To overcome these challenges, we propose a 3D point cloud reconstruction method, CLCFM3 (Closed-Loop Coarse-to-Fine method with Multi-Masked Matching). This method repeatedly executes a process that generates point clouds locally to suppress occlusion (multi-matching) and a process that removes noise points using a mask image (masked matching). Furthermore, we propose the Closed-Loop Coarse-to-Fine Method (CLCFM) to improve the accuracy of structure from motion, which is essential for implementing the proposed point cloud reconstruction method. CLCFM solves loop-closure by performing coarse-to-fine camera position estimation. By facilitating the acquisition of high-density, high-precision 3D data on a large number of plant bodies, as is necessary for research activities, this approach is expected to enable comparative analysis of visible phenotypes in the growth process of a wide range of plant species based on 3D information.

plant biology↗

bioRxiv2024

Field-crop transcriptome models are enhanced by measurements in systematically controlled environments

Plants in the field respond to seasonal and diel changes in various environmental factors such as irradiance and temperature. We previously developed a statistical model that predicts rice gene expression from the meteorological data and identified the environmental factors regulating each gene. However, since irradiance and temperature (the two most critical environmental factors) are correlated in the field, it remains difficult to distinguish their roles in gene expression regulation. Here, we show that transcriptome dynamics in the field are predominantly regulated by irradiance, by the modelling involving diurnal transcriptome data from the 73 controlled conditions where irradiance and temperature were independently varied. The models prediction performance is substantially high when trained using field and controlled conditions data. Our results highlight the utility of a systematic sampling approach under controlled environments to understand the mechanism of plant environmental response and to improve transcriptome prediction under field environments.

plant biology↗

bioRxiv2021

Artificial mimicry of seasonal transcriptome dynamics in Arabidopsis thaliana reveals short- and long-term responses to environmental conditions

Plants must respond to various seasonally changing environmental stimuli. In a previous study, seasonally oscillating genes were identified by a massive time-series transcriptome analysis for the wild population of Arabidopsis halleri ssp. gemmifera, a sister species to Arabidopsis thaliana. It was not clear how environmental stimuli shaped the seasonal expression pattern of these seasonally oscillating genes. In this study we show that responses in different time-scales contributed to the formation of seasonal expression patterns for several genes. To analyze the seasonally oscillating genes, we established an experimental system to mimic seasonal expression trends using A. thaliana and a "smart growth chamber mini," a hand-made low-cost small chamber. Arabidopsis thaliana plants were cultured under conditions that mimicked the average monthly temperatures and daylengths under different day-scale incubation. In total, the seasonal trends of 1627 seasonally oscillating genes were mimicked, and they showed varying temporal responses (constant, transient, and incremental) to environmental stimuli. Our results suggest that plants perceive and integrate information regarding environmental stimuli in the field by combining seasonally oscillating genes with different temporal responsiveness.

plant biology↗