Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency.

ABSTRACT: Systems biology aims to uncover gene regulatory networks (GRNs) for agricultural traits, but validating them in crops is challenging. We addressed this challenge by learning and validating model-to-crop transcription factor (TF) regulons governing nitrogen use efficiency (NUE). First, a fine-scale time-course nitrogen (N) response transcriptome analysis revealed a conserved temporal N response cascade in maize (Zea mays) and Arabidopsis (Arabidopsis thaliana). These data were used to infer time-based causal TF target edges in N-regulated GRNs. By validating 23 maize TFs in a cell-based TF-perturbation assay (Transient Assay Reporting Genome-wide Effects of Transcription factors), precision/recall analysis enabled us to prune high-confidence edges between ∼200 TFs/700 maize target genes. We

SUBMITTER: Huang J 

PROVIDER: S-EPMC12124406 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature