vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/vjgb-24-103vavilov-4417Research ArticleСИСТЕМНАЯ КОМПЬЮТЕРНАЯ БИОЛОГИЯSYSTEMS COMPUTATIONAL BIOLOGYПоиск перспективных генетических маркеров, ассоциированных с молекулярными механизмами снижения устойчивости риса к Rhizoctonia solani при избытке азотных удобрений, методом реконструкции и анализа генных сетейComputational 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 methodshttps://orcid.org/0000-0003-2158-3252АнтроповаЕ. А.AntropovaE. A.

Новосибирск

Novosibirsk

nzhenia@bionet.nsc.ruhttps://orcid.org/0009-0003-8472-4945ВолянскаяА. Р.VolyanskayaA. R.

Новосибирск

Novosibirsk

https://orcid.org/0009-0002-5923-3709АдамовскаяА. В.AdamovskayaA. V.

Новосибирск

Novosibirsk

https://orcid.org/0000-0001-9433-8341ДеменковП. С.DemenkovP. S.

Новосибирск

Novosibirsk

https://orcid.org/0009-0001-9245-8988ЯцыкИ. В.YatsykI. V.

Новосибирск

Novosibirsk

https://orcid.org/0000-0002-0005-9155ИванисенкоТ. В.IvanisenkoT. V.

Новосибирск

Novosibirsk

https://orcid.org/0000-0003-0587-1609ОрловЮ. Л.OrlovY. L.

Новосибирск;

Москва

Novosibirsk;

Moscow

https://orcid.org/0000-0002-5475-0443ЧаоХ.HaoyuCh.

Ханчжоу

Hangzhou

https://orcid.org/0000-0002-9677-1699ЧэньМ.ChenM.

Ханчжоу

Hangzhou

https://orcid.org/0000-0002-1859-4631ИванисенкоВ. А.IvanisenkoV. A.

Новосибирск

Novosibirsk

Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Исследовательский центр в сфере искусственного интеллекта Новосибирского национального исследовательского государственного университетаРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Artificial Intelligence Research Center, Novosibirsk State UniversityRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Исследовательский центр в сфере искусственного интеллекта Новосибирского национального исследовательского государственного университета; Новосибирский национальный исследовательский государственный университет; Курчатовский геномный центр ИЦиГ СО РАНРоссияInstitute 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 RASRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Исследовательский центр в сфере искусственного интеллекта Новосибирского национального исследовательского государственного университета; Курчатовский геномный центр ИЦиГ СО РАНРоссияInstitute 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 RASRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Новосибирский национальный исследовательский государственный университет; Аграрно-технологический институт Российского университета дружбы народов им. Патриса Лумумбы; Центр цифровой медицины, Первый Московский государственный медицинский университет им. И.М. Сеченова Минздрава России (Сеченовский Университет), МоскваРоссияInstitute 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)Russian FederationОтдел биоинформатики, Колледж естественных наук, Чжэцзянский университетКитайDepartment of Bioinformatics, College of Life Sciences, Zhejiang UniversityChina202426012025288960973Copyright © Антропова Е.А., Волянская А.Р., Адамовская А.В., Деменков П.С., Яцык И.В., Иванисенко Т.В., Орлов Ю.Л., Чао Х., Чэнь М., Иванисенко В.А., 20252025Антропова Е.А., Волянская А.Р., Адамовская А.В., Деменков П.С., Яцык И.В., Иванисенко Т.В., Орлов Ю.Л., Чао Х., Чэнь М., Иванисенко В.А.Antropova E.A., Volyanskaya A.R., Adamovskaya A.V., Demenkov P.S., Yatsyk I.V., Ivanisenko T.V., Orlov Y.L., Haoyu C., Chen M., Ivanisenko V.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/4417

Азотные удобрения, повышающие урожайность риса, при избытке могут снижать устойчивость растений к заболеваниям, в частности к ризоктониозу, вызываемому Rhizoctonia solani. Этот патоген способен уничтожить до 50 % урожая, однако механизмы, лежащие в основе снижения устойчивости при избытке азота, остаются малоизученными. Данное исследование направлено на выявление потенциальных генов-маркеров для повышения устойчивости риса к R. solani в условиях избытка азота. Применен комплексный биоинформатический подход, включающий анализ дифференциальной экспрессии генов, реконструкцию генных сетей, анализ перепредставленности биологических процессов, филостратиграфический анализ и анализ коэкспрессии некодирующих РНК. Использованы когнитивная система Smart crop, ANDSystem, база данных ncPlantDB и другие биоинформатические ресурсы. Анализ молекулярно-генетической сети взаимодействий выявил три потенциальных механизма, объясняющих снижение устойчивости риса к R. solani при избытке азота: OsGSK2-опосредованный путь, путь OsMYB44-OsWRKY6-OsPR1 и путь SOG1-Rad51-PR1/PR2. Идентифицированы потенциальные маркеры для селекции: 7 генов, контролирующих ответы риса на широкий круг стрессов, и 11 генов-модуляторов иммунной системы. Особое внимание уделено ключевым участникам регуляторных путей в условиях избытка азота. Анализ некодирующих РНК выявил 30 микроРНК, мишенями которых являются гены из реконструированной генной сети. Для двух микроРНК (Osa-miR396 и Osa-miR7695) обнаружено около 7400 тыс. уникальных длинных некодирующих РНК (днРНК) с различными индексами коэкспрессии. Выделены топ-50 днРНК с наибольшим индексом коэкспрессии для каждой микроРНК, что открывает новые перспективы в изучении регуляторных механизмов устойчивости риса к патогенам. Полученные результаты создают теоретическую основу для экспериментальных работ по созданию новых сортов риса с повышенной устойчивостью к патогенам в условиях избыточного азотного питания.

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.

Oryza sativaRhizoctonia solaniбиоинформатика растенийдифференциально экспрессируемые геныгенетическая регуляцияассоциативные генные сетибаза знаний Smart cropпрограммно-информационная система ANDSystemазотные удобренияответ на грибную инфекциюOryza sativaRhizoctonia solaniplant bioinformaticsdifferentially expressed genesgenetic regulationassociative gene networksSmart crop knowledge baseANDSystem software and information systemnitrogen fertilizerfungal responseThe 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).ReferencesBragina E.Y., Tiys E.S., Freidin M.B., Koneva L.A., Demenkov P.S., Ivanisenko V.A., Kolchanov N.A., Puzyrev V.P. Insights into pathophysiology of dystropy through the analysis of gene networks: an example of bronchial asthma and tuberculosis. 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The authors declare that there are no conflicts of interest present.