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.
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ABSTRACT:
PROVIDER: PRJNA559461 | ENA |
REPOSITORIES: ENA
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Project description:Among the mineral elements necessary for plant growth, nitrogen (N) is the macronutrient required in larger amounts. Maize genotypic-specific responses to N shortage could provide more information useful to improve NUE.
2020-04-14 | GSE135613 | 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 | 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
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: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:Inferring phenotypic outcome from genomics features is the promise and challenge for basic and applied research. Functionally testing whether the feature(s) with predictive power can shed light on the underlying mechanism remains largely unexplored. We address these gaps by applying a machine learning approach to predict phenotypes based on transcriptome data. We validate the predictive models in silico using out-of-sample varieties and in planta by reverse genetics. Our study primarily focuses on a agronomic trait nitrogen use efficiency (NUE), but the described approach can be applied to any phenotype across biology, agriculture and medicine. We measure NUE and gene expression from natural and cultivated varieties of Arabidopsis and maize, respectively, under low and high N environments. We integrate transcriptomics across a model and a crop and demonstrate that identifying the evolutionarily conserved N-responsive differentially expressed genes is an efficient technique to reduce the feature dimensionality, which ultimately improves the predictive power of the gene-to-trait models learned. Furthermore, including a model organism with a comprehensive mutant collection has enabled the efficient validation of the role candidate genes with predictive power in NUE outcomes. Using the matched transcriptomic and phenotypic data from a model and crop species, we present an analysis pipeline to infer a complex trait from transcript abundance. This analysis pipeline can be applied to any other organisms to uncover novel genes underpinning physiological traits of interest.
2021-06-30 | GSE152249 | GEO
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: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