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 

机构地区：[1]State Key Laboratory of Vegetable Biobreeding,Institute of Vegetables and Flowers,Chinese Academy of Agricultural Sciences,Beijing 100081,China [2]Sino-Dutch Joint Lab of Vegetable Genomics,Beijing 100081,China [3]Bioinformatics Group,Wageningen University and Research,6708 PB Wageningen,the Netherlands [4]Plant Breeding,Wageningen University and Research,6708 PB Wageningen,the Netherlands [5]Biosystems Data Analysis,Swammerdam Institute for Life Sciences,University of Amsterdam,1090 GE Amsterdam,the Netherlands

出　　处：《Horticultural Plant Journal》2024年第4期971-982,共12页园艺学报（英文版）

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)。

摘　　要：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 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 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 Arabidopsis and Brassica rapa 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.

关 键 词：Machine learning Leaf polarity Arabidopsis thaliana Brassica rapa Transcription factor 

分 类 号：S336[农业科学—作物遗传育种]