vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/vjgb-24-98vavilov-4412Research ArticleСИСТЕМНАЯ КОМПЬЮТЕРНАЯ БИОЛОГИЯSYSTEMS COMPUTATIONAL BIOLOGYРеконструкция и компьютерный анализ генной сети, отражающей роль микроРНК в регуляции ответа пшеницы на засухуReconstruction and computational analysis of the microRNA regulation gene network in wheat drought response mechanismshttps://orcid.org/0000-0002-7537-2525КлещевМ. А.KleshchevM. A.

Новосибирск

Novosibirsk

https://orcid.org/0009-0000-8510-7496МальцеваА. В.MaltsevaA. V.

Новосибирск

Novosibirsk

https://orcid.org/0000-0003-2158-3252АнтроповаЕ. А.AntropovaE. A.

Новосибирск

Novosibirsk

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

Новосибирск

Novosibirsk

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

Новосибирск

Novosibirsk

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

Москва

Moscow

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

Ханчжоу

Hangzhou

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

Ханчжоу

Hangzhou

https://orcid.org/0000-0001-6800-8787КолчановН. А.KolchanovN. A.

Новосибирск

Novosibirsk

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

Новосибирск

Novosibirsk

salix@bionet.nsc.ruФедеральный 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; Research Center in the Field of Artificial Intelligence of Novosibirsk State UniversityRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Новосибирский национальный исследовательский государственный университет; 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; Novosibirsk State University; Research Center in the Field of Artificial Intelligence of Novosibirsk State UniversityRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Курчатовский геномный центр ИЦиГ СО РАН; Новосибирский национальный исследовательский государственный университет; 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; Kurchatov Genomic Center of ICG SB RAS; Novosibirsk State University; Research Center in the Field of Artificial Intelligence of Novosibirsk State UniversityRussian FederationАграрно-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)РоссияAgrarian and Technological Institute, Peoples’ Friendship University of Russia named after Patrice Lumumba; Digital Health Center, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)Russian FederationОтдел биоинформатики, Колледж естественных наук, Чжэцзянский университетКитайDepartment of Bioinformatics, College of Life Sciences, Zhejiang UniversityChinaФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Курчатовский геномный центр ИЦиГ СО РАН; Новосибирский национальный исследовательский государственный университетРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RAS; Novosibirsk State UniversityRussian Federation202425012025288904917Copyright © Клещев М.А., Мальцева А.В., Антропова Е.А., Деменков П.С., Иванисенко Т.В., Орлов Ю.Л., Чао Х., Чэнь М., Колчанов Н.А., Иванисенко В.А., 20252025Клещев М.А., Мальцева А.В., Антропова Е.А., Деменков П.С., Иванисенко Т.В., Орлов Ю.Л., Чао Х., Чэнь М., Колчанов Н.А., Иванисенко В.А.Kleshchev M.A., Maltseva A.V., Antropova E.A., Demenkov P.S., Ivanisenko T.V., Orlov Y.L., Chao H., Chen M., Kolchanov N.A., Ivanisenko V.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/4412

Недостаток влаги – критический фактор, ограничивающий продуктивность мягкой пшеницы (Triticum aestivum L.), одной из ключевых сельскохозяйственных культур. Адаптация пшеницы к водному дефициту обеспечивается комплексными молекулярно-генетическими механизмами, включающими согласованную работу множества генов, регулируемых транскрипционными факторами и сигнальными некодирующими РНК, в частности микроРНК. микроРНК – опосредованная регуляция экспрессии генов – рассматривается как один из основных механизмов устойчивости растений к абиотическим стрессам. Изучение этих сложных молекулярно-генетических механизмов требует применения методов компьютерной системной биологии. Цель данной работы – реконструкция и компьютерный анализ генной сети, связанной с микроРНК-регуляцией адаптации мягкой пшеницы к условиям недостаточного увлажнения. Для достижения этой цели использованы программно-информационная система ANDSystem и специализированная база знаний Smart crop, адаптированная для области генетики и селекции пшеницы. Нами была реконструирована генная сеть ответа пшеницы на водный дефицит, включающая 144 гена, 1017 белков и 21 микроРНК пшеницы. Анализ сети выявил, что микроРНК преимущественно регулируют гены, контролирующие процессы морфогенеза побегов и корней растений, что играет важную роль в морфологических адаптациях к засухе. Ключевыми компонентами генной сети, регулируемыми микроРНК, оказались транскрипционные факторы семейств MYB и WRKY, а также белок теплового шока HSP90 и белок RPM1. Эти белки связаны с сигнальными путями фитогормонов и кальций-зависимыми протеинкиназами, играющими существенную роль в адаптации растений к водному дефициту. Было идентифицировано несколько микроРНК (MIR7757, MIR9653a, MIR9671, MIR9672b), ранее не обсуждавшихся в контексте адаптации пшеницы к засухе, которые являются кандидатами для дальнейших экспериментальных исследований, направленных на усиление устойчивости пшеницы к недостатку влаги. Полученные результаты могут быть полезными для создания новых сортов пшеницы с повышенной устойчивостью к водному дефициту, что имеет существенное значение для сельского хозяйства в условиях изменения климата.

Drought is a critical factor limiting the productivity of bread wheat (Triticum aestivum L.), one of the key agricultural crops. Wheat adaptation to water deficit is ensured by complex molecular genetic mechanisms, including the coordinated work of multiple genes regulated by transcription factors and signaling non-coding RNAs, particularly microRNAs (miRNAs). miRNA-mediated regulation of gene expression is considered one of the main mechanisms of plant resistance to abiotic stresses. Studying these mechanisms necessitates computational systems biology methods. This work aims to reconstruct and analyze the gene network associated with miRNA regulation of wheat adaptation to drought. Using the ANDSystem software and the specialized Smart crop knowledge base adapted for wheat genetics and breeding, we reconstructed a wheat gene network responding to water deficit, comprising 144 genes, 1,017 proteins, and 21 wheat miRNAs. Analysis revealed that miRNAs primarily regulate genes controlling the morphogenesis of shoots and roots, crucial for morphological adaptation to drought. The key network components regulated by miRNAs are the MYBa and WRKY41 family transcription factors, heat-shock protein HSP90, and the RPM1 protein. These proteins are associated with phytohormone signaling pathways and calcium-dependent protein kinases significant in plant water deficit adaptation. Several miRNAs (MIR7757, MIR9653a, MIR9671 and MIR9672b) were identified that had not been previously discussed in wheat drought adaptation. These miRNAs regulate many network nodes and are promising candidates for experimental studies to enhance wheat resistance to water deficiency. The results obtained can find application in breeding for the development of new wheat varieties with increased resistance to water deficit, which is of substantial importance for agriculture in the context of climate change.

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The authors declare that there are no conflicts of interest present.