Root gene expression profiles of two maize inbred lines (Lo5 and T250) showing different nitrogen use efficiency (NUE) in response to the growth without N.
Ontology highlight
ABSTRACT:
ORGANISM(S): Zea mays
PROVIDER: GSE135613 | GEO | 2020/04/14
REPOSITORIES: GEO
Similar Datasets
Project description:Among the mineral elements necessary for plant growth, nitrogen (N) is the macronutrient required in larger amounts. The optimization of N use efficiency (NUE) in maize could be obtained through both genetic improvement and agronomic practices in order to enhance production and reduce the negative impact of the N fertilizer use on the environment. Physiological characterizations of inbred lines with different NUE are available in maize.
2014-05-19 | GSE49681 | GEO
Project description: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/R424 targets) and the orthologous Arabidopsis TF (AtDIV123 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.
2025-05-09 | GSE280342 | GEO
Project description: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/R424 targets) and the orthologous Arabidopsis TF (AtDIV123 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.
2025-05-09 | GSE280346 | GEO
Project description: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/R424 targets) and the orthologous Arabidopsis TF (AtDIV123 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.
2025-05-09 | GSE280345 | GEO
Project description: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/R424 targets) and the orthologous Arabidopsis TF (AtDIV123 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.
2025-05-09 | GSE280343 | GEO
Project description: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/R424 targets) and the orthologous Arabidopsis TF (AtDIV123 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.
2025-05-09 | GSE280344 | GEO
Project description:Nitrogen (N) is an essential macronutrient for agricultural production, and its excessive use causes not only economic but also environmental damage. However, improving nitrogen use efficiency (NUE) remains a complex task for achieving more productive crops under conditions of limited nitrogen availability. To the best of our knowledge, this is the first study aiming to integrate the morphophysiological responses and proteomic profiles of leaves and roots of popcorn (Zea mays everta). For this, two inbred lines contrasting for NUE, i.e., P2 (high NUE) and L80 (low NUE), were cultivated under high (100% of N supply) and low (10% of N supply) nitrogen conditions. Morphological and physiological traiits such as photochemical quenching (qP), non-photochemical quenching (NPQ), quantum yield of photosystem II (ΦPSII), and potential photosynthesis (Apot) were evaluated. P2 showed more pronounced vegetative growth under low N, as well as higher values of qP, NPQ, and ΦPSII. Comparative proteomic analysis of the leaves identified 215 differentially accumulated proteins (DAPs) in P2 and 168 DAPs in L80, while in roots, 127 DAPs were observed in P2 and 172 in L80. In leaves, DAPs involved in the response to oxidative stress, energy metabolism, and photosynthesis represented the main differences between P2 and L80. In roots, DAPs involved in nitrate transport, ammonium assimilation, and amino acid metabolism seem to have contributed to the improved NUE in P2. This study provides valuable insights into the molecular mechanisms underlying NUE and opens avenues for molecular breeding aimed at selecting superior genotypes for the development of a more sustainable agriculture.
2025-03-11 | PXD048990 | Pride
Project description:Among the mineral elements necessary for plant growth, nitrogen (N) is the macronutrient required in larger amounts. The optimization of N use efficiency (NUE) in maize could be obtained through both genetic improvement and agronomic practices in order to enhance production and reduce the negative impact of the N fertilizer use on the environment. Physiological characterizations of inbred lines with different NUE are available in maize. Maize (Zea mays L.) seeds of two inbred lines (T250, Lo5) previously soaked in running water for 24 h, were allowed to germinate in the dark at 28M-BM-0C for 72 h over an aerated solution of 0.5 mM CaSO4. Half part of two-d-old seedlings was grown in a nutrient solution with 200 M-NM-<M of KNO3 (treated samples, +NO3-) and the other half with appropriate amounts of the corresponding chloride salt (KCl) (control samples, -NO3-). Three biological replicates were obtained by three independent experiments and collecting roots of treated (200 M-NM-<M NO3-) and not treated plants after 0, 1, 2 e 4 h for Lo5 inbred line and after 0, 5, 11 and 12 h for T250.
2014-05-19 | E-GEOD-49681 | biostudies-arrayexpress
Project description:Nitrogen (N) is an essential nutrient element for crop productivity. Unfortunately, the nitrogen use efficiency (NUE) of crop plants gradually decreases with the increase of the nitrogen application rate. Nevertheless, little has been known about the molecular mechanisms of differences in NUE among genotypes of wheat. The use of near-isogenic lines (NILs) in transcriptome analysis can reduce genetic background noise. In this study, we used RNA-Sequencing (RNA-Seq) to compare the transcriptome profiling in NILs (1Y, high-NUE, and 1W, low-NUE) under normal nitrogen conditions. The results showed that 7,023 DEGs (4,738 up-regulated and 2,285 down-regulated) were identified in the 1Y vs 1W comparison. The responses of 1Y and 1W to normal nitrogen differed significantly in the transcriptional regulation mechanisms. Several genes belonging to the GS and GOGAT gene families were up-regulated in 1Y compared with 1W, and the enhanced carbon metabolism might lead 1Y to produce more C skeletons, metabolic energy, and reductants for nitrogen metabolism. A subset of transcription factors (TFs) family members such as ERF, WRKY, NAC, and MYB were also identified. Collectively, these identified candidate genes provided new information for a further understanding of the genotypic difference in NUE
2021-08-31 | GSE179179 | GEO
Project description:Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency
| PRJNA1177888 | ENA