Gene expression,transcription factor binding and histone modification predict leaf adaxial-abaxial polarity related genes

作     者：Wei Sun Zhicheng Zhang Guusje Bonnema Xiaowu Wang Aalt Dirk Jan van Dijk Wei Sun;Zhicheng Zhang;Guusje Bonnema;Xiaowu Wang;Aalt Dirk Jan van Dijk

作者机构：State Key Laboratory of Vegetable BiobreedingInstitute of Vegetables and FlowersChinese Academy of Agricultural SciencesBeijing 100081China Sino-Dutch Joint Lab of Vegetable GenomicsBeijing 100081China Bioinformatics GroupWageningen University and Research6708 PB Wageningenthe Netherlands Plant BreedingWageningen University and Research6708 PB Wageningenthe Netherlands Biosystems Data AnalysisSwammerdam Institute for Life SciencesUniversity of Amsterdam1090 GE Amsterdamthe Netherlands 

出 版 物：《Horticultural Plant Journal》 (园艺学报（英文版）)

年 卷 期：2024年第10卷第4期

页      面：971-982页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 08[工学] 09[农学] 071007[理学-遗传学] 0901[农学-作物学] 0836[工学-生物工程] 090102[农学-作物遗传育种] 

基　　金：supported by the National Key Research and Development Program of China (Grant No.2022YFF1003003) the Central Public-interest Scientific Institution Basal Research Fund (Grant No.Y2023PT16) the Agricultural Science and Technology Innovation Program (ASTIP) supported by China Scholarship Council (Grant No.202103250097) 

主　　题：Machine learning Leaf polarity Arabidopsis thaliana Brassica rapa Transcription factor 

摘      要：Leaf adaxial-abaxial(ad-abaxial)polarity is crucial for leaf morphology and function,but the genetic machinery governing this process remains *** uncover critical genes involved in leaf ad-abaxial patterning,we applied a combination of in silico prediction using machine learning(ML)and experimental analysis.A Random Forest model was trained using genes known to influence ad-abaxial polarity as ground *** 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 *** to this,available and newly-obtained transcriptome data enabled us to identify genes differentially expressed across leaf ad-abaxial *** on these analyses,we obtained a set of 111 novel genes which are involved in leaf ad-abaxial *** explore implications for vegetable crop breeding,we examined the conservation of expression patterns between Arabidopsis and Brassica rapa using single-cell *** results demonstrated the utility of our computational approach for predicting candidate genes in crop *** 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.