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

ABSTRACT: 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.

ORGANISM(S): Zea mays

PROVIDER: GSE280346 | GEO | 2025/05/09

REPOSITORIES: GEO

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

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/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.

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

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

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Elucidation of the Gene Regulatory Networks Governing Terpenoid-Mediated Plant-Environment Interactions to Enable Advanced Bioenergy Crops

Project description:The proposed project aims to elucidate the gene-regulatory networks (GRNs) underlying terpenoid metabolism in three major bioenergy crops, switchgrass (Panicum virgatum), maize (Zea mays), and sorghum (Sorghum bicolor), to aid crop development for enhanced stress resilience and biofuel production from biomass feedstock. Prior studies identified more than 50 terpene synthases (TPS) and cytochrome P450 monooxygenases (P450), as key enzymes in generating bioactive terpenoids, in several bioenergy plants including switchgrass and maize. This knowledge now enables a precise investigation of the GRNs that govern common and species-specific terpenoid pathways. We will integrate transcription factor (TF) functional studies, TF binding site mapping, transcript and metabolite profiling, and terpenoid pathway engineering to identify the terpenoid-metabolic TF networks mediating stress responses in switchgrass, maize and sorghum. The work (proposal:https://doi.org/10.46936/10.25585/60001378) conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231.

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Project description:Development of crop varieties with high nitrogen use efficiency (NUE) is crucial for minimizing N loss, reducing environmental pollution and decreasing input cost. Maize is one of the most important crops cultivated worldwide and its productivity is closely linked to the amount of fertilizer used. A survey of the transcriptomes of shoot and root tissues of a maize hybrid line and its two parental inbred lines grown under sufficient and limiting N conditions by mRNA-Seq has been conducted to have a better understanding of how different maize genotypes respond to N limitation.

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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

Transcriptome-based prediction of nitrogen use efficiency in Arabidopsis and maize - I

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.

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