Molecular Pathogens 2024, Vol.15, No.4, 179-188 http://microbescipublisher.com/index.php/mp 179 Research Insight Open Access Transcriptomic Insights into Wheat Disease Resistance Xinguang Cai, Qiangsheng Qian Modern Agricultural Research Center of Cuixi Academy of Biotechology, Zhuji, 311800, Zhejiang, China Corresponding author: qiangsheng.qian@cuixi.org Molecular Pathogens, 2024, Vol.15, No.4 doi: 10.5376/mp.2024.15.0017 Received: 20 May, 2024 Accepted: 30 Jun., 2024 Published: 12 Jul., 2024 Copyright © 2024 Cai and Qian, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Cai X.G., and Qian Q.S., 2024, Transcriptomic insights into wheat disease resistance, Molecular Pathogens, 15(4): 179-188 (doi: 10.5376/mp.2024.15.0017) Abstract Wheat is one of the most important staple crops globally, but its production is threatened by various diseases, resulting in significant economic losses and yield reduction. Understanding the molecular mechanisms underlying wheat's disease resistance is crucial for enhancing its resilience. This study utilizes transcriptomics to systematically analyze gene expression patterns in wheat under different biotic stresses. Transcriptomics reveals key resistance genes, transcription factors regulating resistance responses, and specific molecular pathways involved in wheat-pathogen interactions, offering valuable tools and data for improving disease resistance in wheat. The aim of this research is to explore their potential applications in wheat breeding by summarizing current transcriptomic insights into wheat disease resistance. Keywords Wheat resistance; Transcriptomics; Pathogen interaction; Gene expression; Breeding improvement 1 Introduction Wheat, a staple food crop, is cultivated globally. However, wheat production is significantly threatened by various diseases, including rusts (leaf rust, stem rust, and stripe rust), powdery mildew, fusariumhead blight, and wheat blast. These diseases can cause substantial yield losses, with recent estimates indicating global wheat yield losses of up to 21% due to these pathogens (Singh et al., 2016; Hafeez et al., 2021). The continuous evolution of virulent pathogen strains exacerbates the problem, making it challenging to maintain effective disease control (Singh et al., 2016; Mapuranga et al., 2022). Additionally, climate change is expected to alter disease dynamics, potentially increasing the prevalence and severity of certain diseases in regions like Northwestern Europe (Miedaner and Juroszek, 2021). Resistance (R) genes play a critical role in recognizing and responding to pathogen attacks, and recent advances in genomic technologies have accelerated the identification and functional characterization of these genes (Deng et al., 2020). There are three primary resistance mechanisms in cereals: plasma membrane-localized receptor proteins, intracellular immune receptors, and quantitative disease resistance, each contributing uniquely to the plant's defense system (Krattinger and Keller, 2016). The integration of molecular breeding techniques, such as genome-wide association studies (GWAS) and CRISPR/Cas-9, has further enhanced our ability to develop wheat varieties with broad-spectrum and durable resistance (Jabran et al., 2023). This study aims to summarize the latest advances in understanding the molecular basis of disease resistance, highlight the importance of creating resources such as the Wheat Resistance Gene Atlas to rapidly deploy the R gene, and discuss the potential for integrating advanced molecular techniques into breeding programs to enhance disease resistance in wheat. 2 Transcriptomics in Wheat Research 2.1 Overview of transcriptomics and its role in plant research Transcriptomics, the study of the complete set of RNA transcripts produced by the genome under specific circumstances, has become a pivotal tool in plant research. This high-resolution, sensitive, and high-throughput next-generation sequencing (NGS) approach, commonly known as RNA-Sequencing (RNA-Seq), allows researchers to identify gene predictions and perform functional analyses to understand biological processes,
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