Biology subjects

Hashida, Y.

Publications and source records attributed to Hashida, Y..

5 recordsLinked to original sources

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↗

bioRxiv2024

Simulation study of factors affecting the accuracy of transcriptome models under complex environments

Characterization of molecular responses in real and complex field environments is essential for understanding the environmental response of plants. Field transcriptomics, i.e. modelling large amounts of transcriptomic and meteorological data, is the most comprehensive method of studying gene expression dynamics in complex environments. However, it is not clear what factors influence the accuracy of field transcriptome models. In this study, a novel simulation system was developed. Using the system, we performed a large-scale simulation to reveal the factors affecting the accuracy of the models. We found that the factors that had the greatest impact on the accuracy are, in order of importance, the expression pattern of the gene, the number of samples in the training data, the diurnal coverage of the training data, and the temperature coverage of the training data. Validation using actually measured transcriptome data showed similar results to the simulations. Our simulation system and the analysis results will be helpful for developing efficient sampling strategies for training data and for generating simulated data for benchmarking new modelling methods. It will also be valuable to dissect the relative importance of various factors behind transcriptome dynamics in the real environment. Key messageNovel simulation system revealed how prediction accuracy of field transcriptome was affected by number and diversity of training data

plant biology↗

bioRxiv2021

Fillable and Unfillable Gaps in Rice Transcriptome under Field and Controlled Environments

The differences between plants grown in field and controlled environments have long been recognised; however, few studies have addressed the underlying molecular mechanisms. Here, we show fillable and unfillable gaps in the transcriptomes of rice grown in field and controlled environments by utilising SmartGC, a high-performance growth chamber that reproduces the fluctuating irradiance, temperature, and humidity of field environments. Rice transcriptome dynamics in SmartGC mimicked those in the field, particularly during the morning and evening; those in conventional growth chamber conditions did not. Further analysis revealed that fluctuation of irradiance affects transcriptome dynamics in the morning and evening, while fluctuation of temperature only affects transcriptome dynamics in the morning. We found upregulation of genes related to biotic and abiotic stress, whose expression was affected by environmental factors that cannot be mimicked by SmartGC. Our results accelerate the understanding of plant responses to field environments for both field and laboratory studies.

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↗

bioRxiv2018

Prediction of environmental response in field-grown rice using expression-dynamics-QTL

How genetic variations affect gene expression dynamics of field-grown plants remains unclear. Using statistical analysis of large-scale time-series RNA-sequencing of field-grown rice from chromosome segment substitution lines (CSSLs), we identified 1675 expression dynamics quantitative trait loci (edQTLs) leading to polymorphisms in expression dynamics under field conditions. Based on the edQTL and environmental information, we successfully predicted gene expression under environments different from training environments, and in rice cultivars with more complex genotypes than the CSSLs. Overall, edQTL identification helped understanding the genetic architecture of expression dynamics under field conditions, which is difficult to assess with laboratory experiments1.The prediction of expression based on edQTL and environmental information will contribute to crop breeding by increasing the accuracy of trait prediction under diverse conditions.

bioinformatics↗