Understanding gene regulatory networks(GRNs)is essential for improving maize yield and quality through molecular breeding approaches.The lack of comprehensive transcription factor(TF)-DNA interaction data has hindered...Understanding gene regulatory networks(GRNs)is essential for improving maize yield and quality through molecular breeding approaches.The lack of comprehensive transcription factor(TF)-DNA interaction data has hindered accurate GRN predictions,limiting our insight into the regulatory mechanisms.In this study,we performed large-scale profiling of maize TF binding sites.We obtained and collected reliable binding profiles for 513 TFs,identified 394,136 binding sites,and constructed an accuracy-enhanced maize GRN(mGRN+)by integrating chromatin accessibility and gene expression data.The mGRN+comprises 397,699 regulatory relationships.We further divided the mGRN+into multiple modules across six major tis-sues.Using machine-learning algorithms,we optimized the mGRN+to improve the prediction accuracy of gene functions and key regulators.Through independent genetic validation experiments,we further confirmed the reliability of these predictions.This work provides the largest collection of experimental TF binding sites in maize and highly optimized regulatory networks,which serve as valuable resources forstudyingmaize genefunctionand crop improvement.展开更多
基金supported by the Biological Breeding-Major Projects(2023ZD0403005)the National Natural Science Foundation of China(32372123,32301846)+1 种基金the National Key Research and Development Program of China(2023YFF1000400)supported by the University of Arizona College of Agriculture,Life and Environmental Sciences,and the USDA.
文摘Understanding gene regulatory networks(GRNs)is essential for improving maize yield and quality through molecular breeding approaches.The lack of comprehensive transcription factor(TF)-DNA interaction data has hindered accurate GRN predictions,limiting our insight into the regulatory mechanisms.In this study,we performed large-scale profiling of maize TF binding sites.We obtained and collected reliable binding profiles for 513 TFs,identified 394,136 binding sites,and constructed an accuracy-enhanced maize GRN(mGRN+)by integrating chromatin accessibility and gene expression data.The mGRN+comprises 397,699 regulatory relationships.We further divided the mGRN+into multiple modules across six major tis-sues.Using machine-learning algorithms,we optimized the mGRN+to improve the prediction accuracy of gene functions and key regulators.Through independent genetic validation experiments,we further confirmed the reliability of these predictions.This work provides the largest collection of experimental TF binding sites in maize and highly optimized regulatory networks,which serve as valuable resources forstudyingmaize genefunctionand crop improvement.
