| Project/Area Number |
20K04751
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 22060:Environmental systems for civil engineering-related
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| Research Institution | Ehime University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
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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| Project Period (FY) |
2020-04-01 – 2021-03-31
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| Project Status |
Discontinued (Fiscal Year 2020)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | Environmental / Engineering / Evolution / Adaptation / Freshwater biology / adaptation / freshwater / climatic change / genetics / proteomics |
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| Outline of Research at the Start |
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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| Outline of Annual Research Achievements |
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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