Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1122864 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The western corn rootworm (WCR, Diabrotica virgifera virgifera LeConte) is an important pest of corn (Zea mays) in the US. Annual crop rotation between corn and soybean (Glycine max) disrupts the corn-dependent WCR lifecycle and was widely adopted to manage WCR. However, this strategy selected for a rotation-resistant (RR) variant with reduced ovipositional fidelity to cornfields. Previous studies indicated that RR-WCR adults exhibit greater tolerance of soybean tissue diet, different gut physiology, and host-microbe interactions compared to wild-types (WT). To identify genetic mechanisms underlying these phenotypic changes, a de novo assembly of the WCR adult gut transcriptome was constructed and used for RNA-sequencing analyses on RNA libraries from different WCR phenotypes (RR and WT) fed with corn or soybean diets. Differential gene expression analyses and network-based methods were used to identify gene modules transcriptionally correlated with the RR phenotype. Gene ontology enrichment analyses on these modules were then conducted to understand their potential functions and biological importance. Differential gene expression analyses on RNA libraries from adult guts of different WCR phenotypes (rotation-resistant and wild-type) fed with corn or soybean diets
2015-11-23 | E-GEOD-62480 | biostudies-arrayexpress
Project description:The western corn rootworm (WCR, Diabrotica virgifera virgifera LeConte) is an important pest of corn (Zea mays) in the US. Annual crop rotation between corn and soybean (Glycine max) disrupts the corn-dependent WCR lifecycle and was widely adopted to manage WCR. However, this strategy selected for a rotation-resistant (RR) variant with reduced ovipositional fidelity to cornfields. Previous studies indicated that RR-WCR adults exhibit greater tolerance of soybean tissue diet, different gut physiology, and host-microbe interactions compared to wild-types (WT). To identify genetic mechanisms underlying these phenotypic changes, a de novo assembly of the WCR adult gut transcriptome was constructed and used for RNA-sequencing analyses on RNA libraries from different WCR phenotypes (RR and WT) fed with corn or soybean diets. Differential gene expression analyses and network-based methods were used to identify gene modules transcriptionally correlated with the RR phenotype. Gene ontology enrichment analyses on these modules were then conducted to understand their potential functions and biological importance.
2015-11-23 | GSE62480 | GEO
Project description:Nitrate is the major source of nitrogen available for many crop plants and is often the limiting factor for plant growth and agricultural productivity especially for maize. Many studies have been done identifying the transcriptome changes under low nitrate conditions. However, the microRNAs (miRNAs) varied under nitrate limiting conditions in maize has not been reported. MiRNAs play important roles in abiotic stress responses and nutrient deprivation. We used the microarray systems to detect miRNAs responding to the chronic nitrate limiting conditions in maize leaves and roots.
2011-09-01 | GSE31373 | GEO
Project description:Crop Rotation Sequences affects Subsequent Soybean Yield explained by Soil Microbiome
| PRJNA521547 | ENA
Project description:Nitrate is the major source of nitrogen available for many crop plants and is often the limiting factor for plant growth and agricultural productivity especially for maize. Many studies have been done identifying the transcriptome changes under low nitrate conditions. However, the microRNAs (miRNAs) varied under nitrate limiting conditions in maize has not been reported. MiRNAs play important roles in abiotic stress responses and nutrient deprivation. Root is the organ that plants transport nitrate. we used the microarray systems to perform a genome-wide search to detect miRNAs responding to the chronic and transient nitrate limiting conditions in maize.
2011-09-01 | GSE31231 | 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