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Reconstruction and computational analysis of the microRNA regulation gene network in wheat drought response mechanisms

https://doi.org/10.18699/vjgb-24-98

Abstract

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.

About the Authors

M. A. Kleshchev
Institute 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 University
Russian Federation

Novosibirsk



A. V. Maltseva
Institute 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 University
Russian Federation

Novosibirsk



E. A. Antropova
Institute 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 University
Russian Federation

Novosibirsk



P. S. Demenkov
Institute 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 University
Russian Federation

Novosibirsk



T. V. Ivanisenko
Institute 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 University
Russian Federation

Novosibirsk



Y. L. Orlov
Agrarian 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 Federation

Moscow



H. Chao
Department of Bioinformatics, College of Life Sciences, Zhejiang University
China

Hangzhou



M. Chen
Department of Bioinformatics, College of Life Sciences, Zhejiang University
China

Hangzhou



N. A. Kolchanov
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RAS; Novosibirsk State University
Russian Federation

Novosibirsk



V. A. Ivanisenko
Institute 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 University
Russian Federation

Novosibirsk



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