TY - JOUR

T1 - Computational identification of promising genetic markers associated with molecular mechanisms of reduced rice resistance to Rhizoctonia solani under excess nitrogen fertilization using gene network reconstruction and analysis methods

AU - Antropova, E. A.

AU - Volyanskaya, A. R.

AU - Adamovskaya, A. V.

AU - Demenkov, P. S.

AU - Yatsyk, I. V.

AU - Ivanisenko, T. V.

AU - Orlov, Y. L.

AU - Haoyu, Ch

AU - Chen, M.

AU - Ivanisenko, V. A.

N1 - The work of EAA, ARV, AVA, PSD, IVY, TVI, YLO, and VAI was supported by the Russian-Chinese grant from the Russian Science Foundation No. 23-44-00030. The work of ChH and MCh was supported by the National Natural Science Foundation of China (No. 32261133526).

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - ANDSystem software and information system

KW - Oryza sativa

KW - Rhizoctonia solani

KW - Smart crop knowledge base

KW - associative gene networks

KW - differentially expressed genes

KW - fungal response

KW - genetic regulation

KW - nitrogen fertilizer

KW - plant bioinformatics

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85217195453&origin=inward&txGid=e3d406b854f974dd54be993d7a22bc92

UR - https://www.mendeley.com/catalogue/d4c95b66-fc78-3e35-9075-1f9c9025032a/

U2 - 10.18699/vjgb-24-103

DO - 10.18699/vjgb-24-103

M3 - Article

C2 - 39944814

VL - 28

SP - 960

EP - 973

JO - Вавиловский журнал генетики и селекции

JF - Вавиловский журнал генетики и селекции

SN - 2500-0462

IS - 8

ER -