Computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to <i>Rhizoctonia solani</i> under excess nitrogen fertilization using gene network reconstruction and analysis methods
Although nitrogen fertilizers increase rice yield, their excess can impair plant resistance to diseases, particularly sheath blight caused by Rhizoctonia solani. This pathogen can destroy up to 50 % of the crop, but the mechanisms underlying reduced resistance under excess nitrogen remain poorly und...
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| Format: | Article |
| Language: | English |
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Siberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and Breeders
2025-01-01
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| Series: | Вавиловский журнал генетики и селекции |
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| Online Access: | https://vavilov.elpub.ru/jour/article/view/4417 |
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| author | E. A. Antropova A. R. Volyanskaya A. V. Adamovskaya P. S. Demenkov I. V. Yatsyk T. V. Ivanisenko Y. L. Orlov Ch. Haoyu M. Chen V. A. Ivanisenko |
| author_facet | E. A. Antropova A. R. Volyanskaya A. V. Adamovskaya P. S. Demenkov I. V. Yatsyk T. V. Ivanisenko Y. L. Orlov Ch. Haoyu M. Chen V. A. Ivanisenko |
| author_sort | E. A. Antropova |
| collection | DOAJ |
| description | Although nitrogen fertilizers increase rice yield, their excess can impair plant resistance to diseases, particularly sheath blight caused by Rhizoctonia solani. This pathogen can destroy up to 50 % of the crop, but the mechanisms underlying reduced resistance under excess nitrogen remain poorly understood. This study aims to identify potential marker genes to enhance rice resistance to R. solani under excess nitrogen conditions. A comprehensive bioinformatics approach was applied, including differential gene expression analysis, gene network reconstruction, biological process overrepresentation analysis, phylostratigraphic analysis, and non-coding RNA co-expression analysis. The Smart crop cognitive system, ANDSystem, the ncPlantDB database, and other bioinformatics resources were used. Analysis of the molecular genetic interaction network revealed three potential mechanisms explaining reduced resistance of rice to R. solani under excess nitrogen: the OsGSK2-mediated pathway, the OsMYB44-OsWRKY6-OsPR1 pathway, and the SOG1-Rad51-PR1/PR2 pathway. Potential markers for breeding were identified: 7 genes controlling rice responses to various stresses and 11 genes modulating the immune system. Special attention was given to key participants in regulatory pathways under excess nitrogen conditions. Non-coding RNA analysis revealed 30 miRNAs targeting genes of the reconstructed gene network. For two miRNAs (Osa-miR396 and Osa-miR7695), about 7,400 unique long non-coding RNAs (lncRNAs) with various co-expression indices were found. The top 50 lncRNAs with the highest co-expression index for each miRNA were highlighted, opening new perspectives for studying regulatory mechanisms of rice resistance to pathogens. The results provide a theoretical basis for experimental work on creating new rice varieties with increased pathogen resistance under excessive nitrogen nutrition. This study opens prospects for developing innovative strategies in rice breeding aimed at optimizing the balance between yield and disease resistance in modern agrotechnical conditions. |
| format | Article |
| id | doaj-art-aa68aab6ff3945bea610579c15a04cde |
| institution | Kabale University |
| issn | 2500-3259 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Siberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and Breeders |
| record_format | Article |
| series | Вавиловский журнал генетики и селекции |
| spelling | doaj-art-aa68aab6ff3945bea610579c15a04cde2025-02-01T09:58:14ZengSiberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and BreedersВавиловский журнал генетики и селекции2500-32592025-01-0128896097310.18699/vjgb-24-1031530Computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to <i>Rhizoctonia solani</i> under excess nitrogen fertilization using gene network reconstruction and analysis methodsE. A. Antropova0A. R. Volyanskaya1A. V. Adamovskaya2P. S. Demenkov3I. V. Yatsyk4T. V. Ivanisenko5Y. L. Orlov6Ch. Haoyu7M. Chen8V. A. Ivanisenko9Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Artificial Intelligence Research Center, Novosibirsk State UniversityInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Artificial Intelligence Research Center, Novosibirsk State UniversityInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Artificial Intelligence Research Center, Novosibirsk State UniversityInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Artificial Intelligence Research Center, Novosibirsk State University; Novosibirsk State University; Kurchatov Genomic Center of ICG SB RASInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Artificial Intelligence Research Center, Novosibirsk State University; Kurchatov Genomic Center of ICG SB RASInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Artificial Intelligence Research Center, Novosibirsk State University; Novosibirsk State University; Kurchatov Genomic Center of ICG SB RASInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Novosibirsk State University; Agrarian and Technological Institute, Peoples’ Friendship University of Russia; Digital Health Center, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenovskiy University)Department of Bioinformatics, College of Life Sciences, Zhejiang UniversityDepartment of Bioinformatics, College of Life Sciences, Zhejiang UniversityInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Artificial Intelligence Research Center, Novosibirsk State University; Novosibirsk State University; Kurchatov Genomic Center of ICG SB RASAlthough nitrogen fertilizers increase rice yield, their excess can impair plant resistance to diseases, particularly sheath blight caused by Rhizoctonia solani. This pathogen can destroy up to 50 % of the crop, but the mechanisms underlying reduced resistance under excess nitrogen remain poorly understood. This study aims to identify potential marker genes to enhance rice resistance to R. solani under excess nitrogen conditions. A comprehensive bioinformatics approach was applied, including differential gene expression analysis, gene network reconstruction, biological process overrepresentation analysis, phylostratigraphic analysis, and non-coding RNA co-expression analysis. The Smart crop cognitive system, ANDSystem, the ncPlantDB database, and other bioinformatics resources were used. Analysis of the molecular genetic interaction network revealed three potential mechanisms explaining reduced resistance of rice to R. solani under excess nitrogen: the OsGSK2-mediated pathway, the OsMYB44-OsWRKY6-OsPR1 pathway, and the SOG1-Rad51-PR1/PR2 pathway. Potential markers for breeding were identified: 7 genes controlling rice responses to various stresses and 11 genes modulating the immune system. Special attention was given to key participants in regulatory pathways under excess nitrogen conditions. Non-coding RNA analysis revealed 30 miRNAs targeting genes of the reconstructed gene network. For two miRNAs (Osa-miR396 and Osa-miR7695), about 7,400 unique long non-coding RNAs (lncRNAs) with various co-expression indices were found. The top 50 lncRNAs with the highest co-expression index for each miRNA were highlighted, opening new perspectives for studying regulatory mechanisms of rice resistance to pathogens. The results provide a theoretical basis for experimental work on creating new rice varieties with increased pathogen resistance under excessive nitrogen nutrition. This study opens prospects for developing innovative strategies in rice breeding aimed at optimizing the balance between yield and disease resistance in modern agrotechnical conditions.https://vavilov.elpub.ru/jour/article/view/4417<i>oryza sativarhizoctonia solani</i>plant bioinformaticsdifferentially expressed genesgenetic regulationassociative gene networkssmart crop knowledge baseandsystem software and information systemnitrogen fertilizerfungal response |
| spellingShingle | E. A. Antropova A. R. Volyanskaya A. V. Adamovskaya P. S. Demenkov I. V. Yatsyk T. V. Ivanisenko Y. L. Orlov Ch. Haoyu M. Chen V. A. Ivanisenko Computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to <i>Rhizoctonia solani</i> under excess nitrogen fertilization using gene network reconstruction and analysis methods Вавиловский журнал генетики и селекции <i>oryza sativa rhizoctonia solani</i> plant bioinformatics differentially expressed genes genetic regulation associative gene networks smart crop knowledge base andsystem software and information system nitrogen fertilizer fungal response |
| title | Computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to <i>Rhizoctonia solani</i> under excess nitrogen fertilization using gene network reconstruction and analysis methods |
| title_full | Computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to <i>Rhizoctonia solani</i> under excess nitrogen fertilization using gene network reconstruction and analysis methods |
| title_fullStr | Computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to <i>Rhizoctonia solani</i> under excess nitrogen fertilization using gene network reconstruction and analysis methods |
| title_full_unstemmed | Computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to <i>Rhizoctonia solani</i> under excess nitrogen fertilization using gene network reconstruction and analysis methods |
| title_short | Computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to <i>Rhizoctonia solani</i> under excess nitrogen fertilization using gene network reconstruction and analysis methods |
| title_sort | computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to i rhizoctonia solani i under excess nitrogen fertilization using gene network reconstruction and analysis methods |
| topic | <i>oryza sativa rhizoctonia solani</i> plant bioinformatics differentially expressed genes genetic regulation associative gene networks smart crop knowledge base andsystem software and information system nitrogen fertilizer fungal response |
| url | https://vavilov.elpub.ru/jour/article/view/4417 |
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