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.