Ontology highlight
ABSTRACT:
PROVIDER: PRJNA626025 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11565168.fastq.gz | Fastqsanger.gz | |||
| SRR11565169.fastq.gz | Fastqsanger.gz | |||
| SRR11565170.fastq.gz | Fastqsanger.gz | |||
| SRR11565171.fastq.gz | Fastqsanger.gz | |||
| SRR11565172.fastq.gz | Fastqsanger.gz |
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Project description:Unraveling the KNOTTED1 regulatory network in maize meristems
2020-04-03 | E-GEOD-38487 | ExpressionAtlas
Project description:Background: Maize plants developed typical gray leaf spot disease (GLS) symptoms initiating at the lower leaves and progressing to upper leaves through the season. Leaf material was collected at 77 days after planting, at which stage there were a large number of GLS disease necrotic lesions on lower leaves (8% surface area on average determined by digital image analysis), but very few lesions and only at chlorotic stage on leaves above the ear (average of 0.2% lesion surface area). Method:To collect material that reflected a difference between C.zeina infected B73 leaves and control B73 leaf material, samples were collected from two lower GLS infected leaves (second and third leaf internode below ear) , and two upper leaves with minimal GLS symptoms (second and third internode above ear), respectively. The two lower leaves from each plant were pooled prior to RNA extraction, and the two upper leaves from each plant were pooled prior to RNA extraction. Upper and lower leaf samples from three maize B73 plants were subjected to RNA sequencing individually. The three maize plants were selected randomly as one plant per row from three rows of ten B73 plants each. Result: A systems genetics strategy revealed regions on the maize genome underlying co-expression of genes in susceptible and resistance responses, including a set of 100 genes common to the susceptible response of sub-tropical and temperate maize.
2016-10-15 | GSE81344 | 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:KNOTTED1(KN1)-like homeobox (KNOX) transcription factors function in plant meristems, self-renewing structures consisting of stem cells and their immediate daughters. Despite their importance for plant development, the genomic network targeted by KNOX proteins is poorly understood. Using ChIP-seq, we defined the KN1 cistrome in maize inflorescences and found that KN1 binds to several thousand loci. To understand how these binding occupancies correlate with changes in transcriptional regulation, we performed RNA-seq on immature ears and tassels, and compared expression profiles between normal and loss-of-function kn1 plants, in addition to immature leaves from normal and gain-of-function Kn1 plants. We found that 643 of the KN1 targets were modulated in one or multiple tissues, with a strong enrichment for transcription factors (including other homeobox genes) and genes participating in several hormonal pathways, most significantly auxin, implicating KN1 at the crossroads of plant hormone signaling. The loss-of-function kn1 phenotype is reminiscent of auxin mutants and kn1 mis-expression in leaves correlates with increased auxin signaling. Our results demonstrate that KN1 plays a key role in orchestrating the upper levels of a hierarchical gene regulatory network that impacts plant meristem identity and function. ChIP-seq was performed using ear primordia and tassel primordia. Input DNA from each sample was used as a normalization control
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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.
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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.
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