Zinati Zahra, Nazari Leyla
Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran.
Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran.
PLoS One. 2025 Jul 23;20(7):e0317755. doi: 10.1371/journal.pone.0317755. eCollection 2025.
Understanding the complex networks underlying the biotic stress response in maize is crucial for developing effective approaches to improve tolerance. We identified 1449 differentially expressed genes (DEGs) by meta-analysis of the public microarray gene expression profile. Weighted Gene Co-expression Network Analysis on the DEGs resulted in positive module-trait correlation (0.71, 0.69, 0.58, and 0.46) in the brown, grey, blue, and green modules, respectively, and negative correlation in the turquoise module. The module membership (MM) and gene significance (GS) were strongly correlated (0.65 and 0.6) in the brown and grey modules, respectively. The enrichment in diterpene phytoalexin and diterpenoid biosynthetic process suggests the involvement of the brown module in the synthesis of compounds necessary for the defense against pathogens. For the grey module, the significant GO terms were related to lipid oxidation, oxylipin, and fatty acid biosynthetic process. Identification of DEGs encoding transcription factors revealed that the MYB, NAC, WRKY, and C2C2 families had the highest membership, each with six members. Noteworthy genes identified include zealexin A1 synthase, CPP synthase, linoleate 9S-lipoxygenase3 (lox3), linoleate 9S-lipoxygenase1 (lox1), and MYB8, were among the top 5% genes with the highest GS and MM values in the brown and grey modules. Codon usage analysis revealed specific preferences under biotic stress, characterized by high Codon Adaptation Index (CAI) and Relative Synonymous Codon Usage (RSCU) values, suggesting an adaptive mechanism for efficient translation and gene regulation during stress. This comprehensive study identified potential targets for genetic engineering aimed at optimizing gene expression for improved stress tolerance.
了解玉米生物胁迫响应背后的复杂网络对于开发提高耐受性的有效方法至关重要。我们通过对公共微阵列基因表达谱进行荟萃分析，鉴定出1449个差异表达基因（DEG）。对这些DEG进行加权基因共表达网络分析，结果显示棕色、灰色、蓝色和绿色模块分别存在正模块-性状相关性（0.71、0.69、0.58和0.46），而绿松石模块存在负相关性。棕色和灰色模块中的模块成员度（MM）和基因显著性（GS）分别具有很强的相关性（0.65和0.6）。二萜植保素和二萜生物合成过程的富集表明棕色模块参与了合成抵御病原体所需化合物的过程。对于灰色模块，显著的基因本体（GO）术语与脂质氧化、氧脂素和脂肪酸生物合成过程相关。对编码转录因子的DEG进行鉴定发现，MYB、NAC、WRKY和C2C2家族的成员最多，每个家族都有六个成员。鉴定出的值得注意的基因包括玉米抗毒素A1合酶、CPP合酶、亚油酸9S-脂氧合酶3（lox3）、亚油酸9S-脂氧合酶1（lox1）和MYB8，它们是棕色和灰色模块中GS和MM值最高的前5%基因。密码子使用分析揭示了生物胁迫下的特定偏好，其特征是密码子适应指数（CAI）和相对同义密码子使用（RSCU）值较高，这表明在胁迫期间存在一种用于高效翻译和基因调控的适应性机制。这项全面的研究确定了旨在优化基因表达以提高胁迫耐受性的基因工程潜在靶点。
