Scholarlink

Background <p>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.</p> Results <p>We show that transcriptome dynamics in the field are predominantly regulated by irradiance, by the modeling involving diel transcriptome data from the 73 controlled conditions where irradiance and temperature are independently varied. The model’s prediction performance is substantially high when trained using field and controlled conditions data.</p> Conclusions <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

查看全文
  • Yoichi Hashida,
  • Daisuke Kyogoku,
  • Suguru E. Tanaka,
  • Naoya Mori,
  • Takanari Tanabata,
  • Hiroyuki Watanabe,
  • Atsushi J. Nagano

摘要

Background

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.

Results

We show that transcriptome dynamics in the field are predominantly regulated by irradiance, by the modeling involving diel transcriptome data from the 73 controlled conditions where irradiance and temperature are independently varied. The model’s prediction performance is substantially high when trained using field and controlled conditions data.

Conclusions

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.