Expression data from Oryza sativa and Arabidopsis thaliana
Ontology highlight
ABSTRACT:
ORGANISM(S): Arabidopsis thaliana Oryza sativa
PROVIDER: GSE38102 | GEO | 2015/06/10
SECONDARY ACCESSION(S): PRJNA167206
REPOSITORIES: GEO
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Project description:Cultivated rice (Oryza sativa L.) is frequently exposed to multiple stresses, including Schizotetranychus oryzae mite infestation. Rice domestication has narrowed the genetic diversity of the species, leading to a wide susceptibility. This work aimed to observe the response of two wild rice species (Oryza barthii and O. glaberrima) and two O. sativa genotypes (cv. Nipponbare and f. spontanea) to S. oryzae infestation. Surprisingly, leaf damage, histochemistry, chlorophyll concentration and fluorescence showed that the wild species present higher level of leaf damage, increased accumulation of H2O2 and lower photosynthetic capacity when compared to O. sativa genotypes under infested conditions. Infestation decreased tiller number, except in Nipponbare. Infestation also caused the death of wild plants during the reproductive stage. While infestation did not affect the weight of 1,000 grains in both O. sativa genotypes, the number of panicles per plant was affected only in f. spontanea, and the percentage of full seeds per panicle and seed length were increased only in Nipponbare. Using proteomic analysis, we identified 195 differentially abundant proteins when comparing susceptible (O. barthii) and tolerant (Nipponbare) genotypes under control and infested conditions. O. barthii has a less abundant antioxidant arsenal and is unable to modulate proteins involved with general metabolism and energy production under infested condition. Nipponbare presents high abundance of detoxification-related proteins, general metabolic processes and energy production, suggesting that, under infested condition, the primary metabolism is maintained more active compared to O. barthii. Also, under infested conditions, Nipponbare presents higher levels of proline and a greater abundance of defense-related proteins, such as osmotin, ricin B-like lectin, and protease inhibitors. These differentially abundant proteins can be used as biotechnological tools in breeding programs aiming increased tolerance to mite infestation.
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Project description:The goal of this work is to identify the gene regulatory hubs that control nitrogen-use in Oryza sativa, one of the most important crop plants, by using a combination of genomics, bioinformatics and systems biology approaches. Here, we evaluate the role of bZIP1, a transcription factor involved in light and nitrogen sensing, by exposing wild-type (WT) and bZIP1 T-DNA null mutant plants to a combinatorial space of N and L treatment conditions. We use ANOVA analysis combined with clustering and Boolean modeling, to evaluate the role of bZIP1 in mediating L and N signaling genome-wide. We also study the interspecies conservation, comparing rice with Arabidopsis thaliana nitrogen transcriptomes, to help identify conserved nitrogen regulation.
2015-06-10 | E-GEOD-38102 | biostudies-arrayexpress
Project description:Illumina RNA Sequencing (RNA-Seq) of Oryza sativa, Nipponbare for the Conserved Poaceae Specific Genes project. Authors: Robin Buell, Ning Jiang, Haining Lin, Rebecca Davidson, Malali Gowda, John Hamilton, Brieanne Vaillancourt
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Project description:Rice is one of the most important global food crops, and is also a model organism for cereal research 31 . Complete genome sequencing of rice, together with advances in transcriptomics and proteomics, has had a dramatic impact on plant growth and 5 breeding programs 32 . Genomic analysis of DNA methylation in rice has revealed methylation patterns associated with gene bodies and promoters, and the occurrence of high levels of DNA methylation in the centromeric domain 33 . A genome-wide investigation of acetylation in rice revealed that H3K9ac and H3K27ac are mainly enriched at transcription start sites associated with active transcription 34 . Furthermore, global proteome analysis has shown that phosphorylation and succinylation are involved in diverse cellular and metabolic processes 35, 36 . However, despite these considerable advances in our knowledge, additional large-scale analysis of the lysine acetylome in rice is expected to identify many more Kac sites and acetylated proteins in this improtant crop plant. In this study, affinity enrichment and high-resolution LC-MS/MS were used for large-scale analysis of the lysine acetylome in rice variety Nipponbare. In total, 1353 lysine acetylation sites were detected in 866 protein groups in rice seedlings. Proteomic analysis showed that Kac occurs in proteins involved in diverse biological processes with varied cellular functions and subcellular localization.
2017-11-13 | PXD004870 | 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.
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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.
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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.
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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.
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Project description:Here, we first reported the construction of a phosphoproteomic landscape of 6 tissues, including callus, leaves, roots, shoot meristem (SM), young panicles (YP) and mature panicles (MP), from Nipponbare (Oryza sativa ssp. japonica). By employing a non-gel, quantitative phosphoproteomic approach, a total of 4792 phosphopeptides from 2657 phosphoproteins were identified, which were found to be differentially phosphorylated among tissues.
2018-04-11 | PXD005241 | Pride