| 研究課題/領域番号 |
20K04751
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分22060:土木環境システム関連
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| 研究機関 | 愛媛大学 |
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研究代表者 |
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| 研究分担者 |
渡辺 幸三 愛媛大学, 沿岸環境科学研究センター, 教授 (80634435)
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| 研究期間 (年度) |
2020-04-01 – 2021-03-31
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| 研究課題ステータス |
中途終了 (2020年度)
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| 配分額 *注記 |
4,290千円 (直接経費: 3,300千円、間接経費: 990千円)
2022年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2021年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2020年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
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| キーワード | Environmental / Engineering / Evolution / Adaptation / Freshwater biology / adaptation / freshwater / climatic change / genetics / proteomics |
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| 研究開始時の研究の概要 |
Evolutionary adaptation to environmental conditions can occur through genetic mutations in DNA sequences and select protein expression among organisms. Despite genetic mutations has been observed, studies combining genetics and proteomics remains poor. Here, we propose to investigate the genetic mutations associated with physiological responses of aquatic organisms to environmental conditions by gene-protein interaction. This project will improve our understanding of evolutionary interactions on multiple freshwater organisms that can be used to predict responses to future environmental change.
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| 研究実績の概要 |
The funding support partially the work for identifying the physiological and genomic basis of local adaptation using a combination of genomics techniques. We were able to complete previous work by analyzing gene expression data leading to a first-time enhanced understanding of adaptation in freshwater insect species using multi-species analysis. We conducted statistical analysis on RNA-seq data obtained applying Next-generation of sequencing technology, by a combination of R program libraries and detected gene expression among species along a latitudinal gradient. The result of the project led to detect stress responses of multiple aquatic species due to changes in environmental conditions. The physiological responses detected could be used to predict future environmental change. Notably, our study could serve as a framework for future work on integrating temporary data to further investigate gene-ecosystem models that could improve ecosystem and climate policies. We planned to continue gathering data and collecting more insects in the freshwater ecosystems to resolve the long-term temporal dynamics of gene expression profiles among these stonefly species to improve our understanding of the contributions of gene expression changes within the context of environmental changes.
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