Using single-sample networks and genetic algorithms to identify radiation-responsive genes in rice affected by heavy ions of the galactic cosmic radiation with different LET values Article Swipe
Yan Zhang
,
Wei Wang
,
Meng Zhang
,
Binquan Zhang
,
Shuai Gao
,
Hao Meng
,
D. Zhou
,
Lei Zhao
,
G. Reitz
,
Yeqing Sun
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2024.1457587
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2024.1457587
Introduction Heavy ions of the galactic cosmic radiation dominate the radiation risks and biological effects for plants under spaceflight conditions. However, the biological effects and sensitive genes caused by heavy ions with different linear energy transfer (LET) values have not been thoroughly studied. Methods To comprehensively analyze the biological effects of heavy ions with different LET values on rice under spaceflight conditions, we utilized the Shijian-10 recoverable satellite (SJ-10) to transport the dehydrated rice seeds on a 12.5-day mission in a 252 km low Earth orbit (LEO), and obtained rice plants hit by individual heavy ions with LET values ranging from 18 keV/μm to 213 keV/μm. The transcriptome and methylation sequencing were conducted on above plants, and a bioinformatics pipeline based on single-sample networks (SSNs) and genetic algorithms (GA) was developed to analyze the multi-omics expression profiles in this work. Note that SSNs can depict the gene interaction patterns within a single sample. The LET regression models were constructed from both gene expression and interaction pattern perspectives respectively, and the radiation response genes that played significant roles in the models were identified. We designed a gene selection algorithm based on GA to enhance the performance of LET regression models. Results The experimental results demonstrate that all our models exhibit excellent regression performance (R2 values close to 1), which indicates that both gene expressions and interaction patterns can reflect the molecular changes caused by heavy ions with different LET values. LET-related genes (genes exhibiting strong correlation with LET values) and radiation-responsive genes were identified, primarily involved in DNA damage and repair, oxidative stress, photosynthesis, nucleic acid metabolism, energy metabolism, amino acid/protein metabolism, and lipid metabolism, etc. DNA methylation plays a crucial role in responding to heavy ions stressors and regulates the aforementioned processes. Discussion To the best of our knowledge, this is the first study to report the multi-omics changes in plants after exposure to heavy ions with different LET values under spaceflight conditions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpls.2024.1457587
- OA Status
- gold
- Cited By
- 2
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404197000
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404197000Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fpls.2024.1457587Digital Object Identifier
- Title
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Using single-sample networks and genetic algorithms to identify radiation-responsive genes in rice affected by heavy ions of the galactic cosmic radiation with different LET valuesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-11-08Full publication date if available
- Authors
-
Yan Zhang, Wei Wang, Meng Zhang, Binquan Zhang, Shuai Gao, Hao Meng, D. Zhou, Lei Zhao, G. Reitz, Yeqing SunList of authors in order
- Landing page
-
https://doi.org/10.3389/fpls.2024.1457587Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3389/fpls.2024.1457587Direct OA link when available
- Concepts
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Linear energy transfer, Gene, COSMIC cancer database, Ion, Cosmic ray, Biology, Linear regression, Physics, Computational biology, Genetics, Astrophysics, Computer science, Machine learning, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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