| Project/Area Number |
18K19319
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 44:Biology at cellular to organismal levels, and related fields
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| Research Institution | Iwate University |
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Principal Investigator |
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| Project Period (FY) |
2018-06-29 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2019: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2018: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 低温馴化 / 植物 / 冬季感知 / 温度ノイズ / 季節情報 / 機械学習 / 重回帰分析 |
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| Outline of Final Research Achievements |
Studies on the molecular mechanisms of cold acclimation in plants have been carried out in simple artificial environments, so the mechanisms of seasonal sensing in the field remain completely unknown. In this study, changes in freezing tolerance in the field and weather data were combined and analyzed by machine learning. As a result, it was suggested that in field cold acclimation, plants may mainly use temperature change, not photoperiod, for the control of cold acclimation. RNA-seq analysis revealed the possibility of a characteristic control of acclimation in each temperature range. On the other hand, an experiment using growth chamber which simulated the average temperature change from autumn to winter suggested that the control of cold acclimation by day length through phytochrome occurred only in the mid-winter after December.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では、野外データと気象データを組み合わせ、また、機械学習も導入することにより、植物は温度に対し鋭敏に対応し、温度域により低温馴化制御のメカニズムが異なることが明らかに出来た。野外実験では気象データを含む膨大なデータが生じるが、機械学習を用いることにより生理学的データと結びつけられ、生理変化をもたらす野外環境の因子を特定できる可能性を示せたことは、本研究の大きな成果であった。また、これまで日長や光質の季節変化も植物の冬季感知に用いられている可能性が指摘されていたが、野外実験や野外を再現した人工気象器を用いた実験により、これらの可能性が低いことが示せたことも学術的な意義が高いと考える。
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