Transcriptome-based prediction of nitrogen use efficiency in Arabidopsis and maize - I
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA638728 | ENA |
REPOSITORIES: ENA
Similar Datasets
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:NPI enriched more down-regulation genes involved in starch and sucrose metabolism, phenylpropanoid biosynthesis, amino sugar and nucleotide sugar metabolism, glycolysis/gluconeogenesis pathway, leading to restricted root growth. Three GO terms including channel activity, water transport, and water channel activity caused a high degree of gene enrichment. Thirteen genes related to water absorption and transport such as ZM00001D003006, ZM00001D014285 et al. were highly up-regulated, which might be the key genes for the significant increase in water use efficiency of maize under negative pressure irrigation.
2021-07-22 | GSE180352 | GEO
Project description:Molecular mechanism of negative pressure irrigation inhibiting root growth and improving water use efficiency in maize
| PRJNA747940 | ENA
Project description:Root-associated microbiome of maize genotypes with contrasting P use efficiency
| PRJEB24131 | ENA
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:nitrogen starvation and re-supply-Improving the nitrogen use efficiency : from model plant to crop species.
| PRJNA100841 | ENA
Project description:In sub-Saharan Africa, one of the major challenges to smallholder farmers is soil with low fertility and inability to apply nitrogen fertilizer externally due to the cost. Development of maize hybrids, which perform better in nitrogen depleted soils, is one of the promising solutions. However, breeding maize for nitrogen use efficiency (NUE) is hindered by expensive phenotypic evaluations and trait complexity under low N stress. Genome-wide association study (GWAS) and genomic prediction (GP) are promising tools to circumvent this interference. Here, we evaluated a mapping panel in diverse environments both under optimum and low N management. The objective of this study was to identify SNPs significantly associated with grain yield (GY) and other traits through GWAS and assess the potential of GP under low N and optimum conditions. Testcross progenies of 411 inbred lines were planted under optimum and low N conditions in several locations in Africa and Latin America. In all locations, low N fields were previously depleted over several seasons, and no N fertilizer was applied throughout the growing season. All inbred lines were genotyped with genotyping by sequencing. Genotypic and GxE interaction variances were significant, and heritability estimates were moderate to high for all traits under both optimum and low N conditions. Genome-wide LD decay at r 2 = 0.2 and r 2 = 0.34 were 0.24 and 0.19 Mbp, respectively. Chromosome-specific LD decays ranged from 0.13 to 0.34 Mbps with an average of 0.22 Mbp at r 2 = 0.2. GWAS analyses revealed 38 and 45 significant SNPs under optimum and low N conditions, respectively. Out of these 83 significant SNPs, 3 SNPs on chromosomes 1, 2, and 6 were associated either with different traits or the same trait under different management conditions, suggesting pleiotropic effects of genes. A total of 136 putative candidate genes were associated with the significant SNPs, of which seven SNPs were linked with four known genes. Prediction accuracies were moderate to high for all traits under both optimum and low N conditions. These results can be used as useful resources for further applications to develop hybrids or lines with better performance under low N conditions.
| S-EPMC7198882 | biostudies-literature
Project description:mRNA sequencing of leaf in the flowering stage was performed to reveals gene regulation in relation to water use efficiency in soybean
2020-09-30 | GSE158762 | GEO
Project description:Improving fertilizer recommendations for farmers is essential to increase food security in smallholder landscapes. Currently, blanket recommendations are provided across agro-ecological zones, although fertilizer response and nutrient use efficiency by maize crop are spatially variable. We aimed to identify factors that could help to refine fertilizer recommendation by analyzing the variability in fertilizer response (FR) and the agronomic nitrogen use efficiency (N-AE). A literature search for on-farm studies across Kenya and Sub-Sahara Africa (SSA), excluding Kenya, yielded 71 publications. The variability in FR was studied using a meta-analysis whereas key factors that influence FR and N-AE were studied with linear regression models. On average, the FR was 2, but it varied considerably from 1 to 28.5 (excluding outliers). In SSA, 18% of the plots were non-responsive plots with an FR < 1. The main factors affecting N-AE for Kenya were P-Olsen, silt content, soil pH, clay and rainfall, whereas only soil pH, exchangeable K and texture were important for SSA. However, our study indicates that available data on soil, climate and management factors could explain only a small part (< 33%) of the variation in FR and N-AE. Soil pH, P-Olsen, silt content, and rainfall had significant but low levels of power in explaining variation in FR and N-AE. Our findings indicate that strategies to refine fertilizer recommendation should include information on soil types and soil properties.
| S-EPMC7357725 | biostudies-literature