Nitrogen starvation and re-supply-Improving the nitrogen use efficiency : from model plant to crop species.
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ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE8031
PROVIDER: PRJNA100841 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:gnp3-b4_nitrogen_starvation - nitrogen starvation and re-supply - What are the transcriptomic short- and long-term plant responses to nitrogen starvation and nitrogen re-supply? - WS Arabidopsis ecotype were grown on 6mM nitrate as sole nitrogen source during 35 days under short days . At T0, plants were then starved for nitrate for 10 days and root and shoot samples were harvested separately 2 and 10 days after treatment (T2, T10). Then, nitrate (6 mM) was re-supplied for 1 and 24 hours (T+1, T+24). Keywords: time course
2007-06-20 | GSE8031 | GEO
Project description:adt06-04_mirna_nitrogen - mirna_nitrogen - Nitrogen starvation and re-supply. - To discover miRNA implied following a deficiency out of nitrogen and after reinduction.
2010-11-03 | GSE25010 | GEO
Project description:In comparison with provision of either ammonium or nitrate alone, simultaneously supplying both forms of N results in superior growth and yield for the majority of plants including rice. Using a rice 22K oligo-array, we performed transcriptome analysis to identify genes of rice (Oryza sativa L. ssp. japonica) responsive to change of N-supply forms and N-starvation. Using the supply of ammonium nitrate (one to one molar ratio) as control, the total number of root genes that were equal or more than two fold up- or down- regulated was 445, 324, and 781 by upon supply of either ammonium or nitrate or continuous N starvation, respectively for 96 h. In the shoot the equivalent numbers were much smaller only 32, 58, and 165, respectively. Clustering of the rice genes associated with different environmental stresses revealed substantial organ specificity of the root and shoot to N starvation, and also to the N supply form. Genes encoding transporters for ammonium and nitrate, nitrate reductase, glutamate dehydrogenase, and aspartate amino transferase, showed great response to change of the N supply form, especially to N starvation. Some of the genes involved in chlorophyll metabolism, carbon fixation and assimilation, were enhanced by ammonium supply only, but significantly suppressed by N-starvation. In the shoot there was increased expression of more general stress genes under nitrate when compared to ammonium nutrition. In the root the reverse situation was true with more apparent stress under ammonium nutrition. The microarray approach has revealed new levels of complexity in the response of rice to the form of N supply. Keywords: Rice; root; shoot; nitrogen starvation; nitrogen form; ammonium; nitrate; gene expression
2006-12-07 | GSE4409 | GEO
Project description:adt06-04_mirna_nitrogen - mirna_nitrogen - Nitrogen starvation and re-supply. - To discover miRNA implied following a deficiency out of nitrogen and after reinduction. 6 dye-swap - time course
2010-11-03 | E-GEOD-25010 | biostudies-arrayexpress
Project description:Nitrate (NO3-) is crucial for optimal plant growth and development and often limits crop productivity at the low availability. In comparison with model plant Arabidopsis, the molecular mechanisms underlying NO3- acquisition and utilization remain largely unclear in maize. In particular, only a few genes have been exploited to improve nitrogen use efficiency (NUE). Here, we demonstrated that NO3--inducible ZmNRT1.1B (ZmNPF6.6) positively regulated NO3--dependent growth and NUE in maize. We showed that the tandem duplicated proteoform ZmNRT1.1C is irrelevant to maize seedling growth under nitrate supply, however, loss-of-function of ZmNRT1.1B significantly weakened plant growth under adequate NO3- supply in both hydroponic and field conditions. 15N-labeled NO3- absorption assay indicated that ZmNRT1.1B mediated high-affinity NO3--transport and root-to-shoot NO3- translocation. Furthermore, upon NO3- supply, ZmNRT1.1B promotes cytoplasmic-to-nuclear shuttling of ZmNLP3.1 (ZmNLP8), which co-regulates the expression of genes involved in NO3- response, cytokinin biosynthesis and carbon metabolism. Remarkably, overexpression of ZmNRT1.1B in modern maize hybrids improved grain yield under nitrogen (N) limiting fields. Taken together, our study revealed a crucial role of ZmNRT1.1B in high-affinity NO3- transport and signaling and offers valuable genetic resource for breeding nitrogen use efficient high-yield cultivars.
2023-04-25 | GSE227472 | 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: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 | 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 | GSE280344 | 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