Leaf adaxial–abaxial (ad–abaxial) polarity is crucial for leaf morphology and function, but the genetic machinery governing this process remains unclear. To uncover critical genes involved in leaf ad–abaxial patterning, we applied a combination of prediction using machine learning (ML) and experimental analysis. A Random Forest model was trained using genes known to influence ad–abaxial polarity as ground truth. Gene expression data from various tissues and conditions as well as promoter regulation data derived from transcription factor chromatin immunoprecipitation sequencing (ChIP-seq) was used as input, enabling the prediction of novel ad–abaxial polarity-related genes and additional transcription factors. Parallel to this, available and newly-obtained transcriptome data enabled us to identify genes differentially expressed across leaf ad–abaxial sides. Based on these analyses, we obtained a set of 111 novel genes which are involved in leaf ad–abaxial specialization. To explore implications for vegetable crop breeding, we examined the conservation of expression patterns between and using single-cell transcriptomics. The results demonstrated the utility of our computational approach for predicting candidate genes in crop species. Our findings expand the understanding of the genetic networks governing leaf ad–abaxial differentiation in agriculturally important vegetables, enhancing comprehension of natural variation impacting leaf morphology and development, with demonstrable breeding applications.

中文翻译：

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基因表达、转录因子结合和组蛋白修饰预测叶近轴-远轴极性相关基因


叶片近轴-远轴极性对于叶片形态和功能至关重要，但控制这一过程的遗传机制仍不清楚。为了揭示参与叶片近轴图案形成的关键基因，我们结合使用机器学习（ML）和实验分析进行预测。使用已知影响远轴极性的基因作为基本事实来训练随机森林模型。来自不同组织和条件的基因表达数据以及来自转录因子染色质免疫沉淀测序 (ChIP-seq) 的启动子调控数据被用作输入，从而能够预测新的轴外极性相关基因和其他转录因子。与此同时，现有的和新获得的转录组数据使我们能够识别在叶背轴两侧差异表达的基因。基于这些分析，我们获得了一组 111 个与叶近轴特化相关的新基因。为了探索对蔬菜作物育种的影响，我们检查了单细胞转录组学之间和使用单细胞转录组学的表达模式的保守性。结果证明了我们的计算方法在预测作物物种候选基因方面的实用性。我们的研究结果扩大了对农业重要蔬菜中控制叶片近轴分化的遗传网络的理解，增强了对影响叶片形态和发育的自然变异的理解，并具有可证明的育种应用。