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

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.