Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency
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https://www.ncbi.nlm.nih.gov/sra/SRP540911
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In this data-rich era, the promise of systems biology is to learn gene regulatory networks controlling key agricultural traits. However, validating these networks in crops remains challenging. By integrating gene regulatory network and machine learning, we functionally validated network regulons predicting nitrogen use efficiency (NUE) in Arabidopsis and maize. Our time-course nitrogen response transcriptome analysis uncovered a conserved N-response cascade between maize and Arabidopsis. Using Dynamic Factor Graph, we inferred N-regulated gene regulatory networks (N-GRNs) in maize and validated TF-target interactions for 23 maize TFs with the TARGET, a cell-based TF-perturbation assay. We pruned the N-GRNs by Precision-Recall analysis. Combining these data, we uncovered a previously unknown role for KNOTTED1 in the dynamic N-signaling network. We learned gene-to-NUE trait models across 16 maize varieties using XGBoost trained on N-response genes conserved model-to-crop. Integrating NUE importance scores within our GRN, we ranked maize TFs by their NUENet scores. In a model-to-crop approach, we validated orthologous N-regulated TF-targets for the top-ranked maize NUENet TFs (MYB34/R4?24 targets) and the orthologous Arabidopsis TF (AtDIV1?23 targets) using the cell-based TARGET assay. The genes in this orthologous model-to-crop NUENet regulons were superior at predicting NUE traits in XGBoost models learned in both maize and Arabidopsis. Thus, our model-to-crop approach combining GRNs, machine learning, and orthologous network modules offers a strategic framework for crop trait improvement. Overall design: We conducted the fine time-course nitrogen treatement experiment in maize B73 seedlings.On the day of the sampling (day ten), two hours after the start of the light period (10 am), seedlings were transferred to the fresh hydroponic medium containing either the Nitrogen in MS medium (20 mM KNO3 + 20 mM NH4NO3) or 20 mM KCl and harvested at time intervals 0, 5, 10, 15, 20, 30, 45, 60, 90, and 120 minutes.
创建时间:
2025-05-15



