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<p>Gray leaf spot (GLS) is a devastating foliar disease that causes substantial yield losses in maize. The identification and utilization of GLS resistance genes in hybrid cultivation is a critical strategy for GLS-resistance breeding. The mapped quantitative trait loci (QTLs), the genome-wide associated quantitative trait nucleotides (QTNs), and multi-omics datasets provide foundational resources for gene discovery. Here, we integrated GLS resistance QTLs and QTNs to identify 164 QTL hotspots, 13 meta-QTL (MQTL) regions, and 86 QTN hotspots. We further extracted a total of 1,343 recurrent differentially expressed genes (DEGs) from multiple transcriptomic studies, including 200 high-confidence DEGs. These DEGs exhibited significant enrichment in key biological pathways, including ‘metabolic pathways’ and ‘plant-pathogen interaction’, implicating them in defense resistance. Through integrative analysis, we identified 340 high-confidence candidate genes in multiple dimensions, with five locating in both QTL hotspots and MQTL regions. Key candidate genes function within known resistant network, such as chitinase (<i>chn2</i>, <i>chn7</i>, <i>chn17</i>), wall-associated kinase-like (WAKLs) (<i>wakl23</i>, <i>wakl56</i>, and <i>wakl27</i>), and calcium-dependent protein kinase (CPKs) (<i>cpk20</i>, <i>cpk36</i>, and <i>cpk25</i>). Expression profiling revealed that most candidate genes were consistently down- or up-regulated at the reproductive stage. These findings provide novel genetic resources and mechanistic insights for GLS-resistance breeding in maize.</p>

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Mining of candidate genes regulating Gray leaf spot resistance in maize through integrative biological analysis

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Yonghui Zhu,
  • Xiao Zhang,
  • Xuantao Wen,
  • Lin Yang,
  • Yuanyuan He,
  • Wenzhu He

摘要

Gray leaf spot (GLS) is a devastating foliar disease that causes substantial yield losses in maize. The identification and utilization of GLS resistance genes in hybrid cultivation is a critical strategy for GLS-resistance breeding. The mapped quantitative trait loci (QTLs), the genome-wide associated quantitative trait nucleotides (QTNs), and multi-omics datasets provide foundational resources for gene discovery. Here, we integrated GLS resistance QTLs and QTNs to identify 164 QTL hotspots, 13 meta-QTL (MQTL) regions, and 86 QTN hotspots. We further extracted a total of 1,343 recurrent differentially expressed genes (DEGs) from multiple transcriptomic studies, including 200 high-confidence DEGs. These DEGs exhibited significant enrichment in key biological pathways, including ‘metabolic pathways’ and ‘plant-pathogen interaction’, implicating them in defense resistance. Through integrative analysis, we identified 340 high-confidence candidate genes in multiple dimensions, with five locating in both QTL hotspots and MQTL regions. Key candidate genes function within known resistant network, such as chitinase (chn2, chn7, chn17), wall-associated kinase-like (WAKLs) (wakl23, wakl56, and wakl27), and calcium-dependent protein kinase (CPKs) (cpk20, cpk36, and cpk25). Expression profiling revealed that most candidate genes were consistently down- or up-regulated at the reproductive stage. These findings provide novel genetic resources and mechanistic insights for GLS-resistance breeding in maize.