Filtern
Volltext vorhanden
- nein (43)
Erscheinungsjahr
Dokumenttyp
- Wissenschaftlicher Artikel (43) (entfernen)
Sprache
- Englisch (43) (entfernen)
Gehört zur Bibliographie
- ja (43)
Schlagworte
- Arabidopsis thaliana (2)
- Quantitative Trait Locus (2)
- Quantitative Trait Locus analysis (2)
- recombinant inbred line (2)
- Data standardisation and formatting (1)
- Experiment description (1)
- Experimental metadata (1)
- Hybrid prediction (1)
- LASSO (1)
- Maize (1)
- Minimum information recommendations (1)
- NF-X1 (1)
- Partial Little Square (1)
- Phenomics (1)
- Plant phenotyping (1)
- Regression (1)
- Solanaceae (1)
- Solanum lycopersicum (1)
- biomass (1)
- brassinosteroid (1)
- dominance effect (1)
- feature selection (1)
- fruit (1)
- heterosis (1)
- metabolite profiling (1)
- morphological analysis (1)
- primary metabolism (1)
- recombinant inbred line population (1)
- salt stress (1)
- seedlings (1)
- tomato (1)
- transcript profiling (1)
Microarray transcript profiling and RNA interference are two new technologies crucial for large-scale gene function studies in multicellular eukaryotes. Both rely on sequence-specific hybridization between complementary nucleic acid strands, inciting us to create a collection of gene-specific sequence tags (GSTs) representing at least 21,500 Arabidopsis genes and which are compatible with both approaches. The GSTs were carefully selected to ensure that each of them shared no significant similarity with any other region in the Arabidopsis genome. They were synthesized by PCR amplification from genomic DNA. Spotted microarrays fabricated from the GSTs show good dynamic range, specificity, and sensitivity in transcript profiling experiments. The GSTs have also been transferred to bacterial plasmid vectors via recombinational cloning protocols. These cloned GSTs constitute the ideal starting point for a variety of functional approaches, including reverse genetics. We have subcloned GSTs on a large scale into vectors designed for gene silencing in plant cells. We show that in planta expression of GST hairpin RNA results in the expected phenotypes in silenced Arabidopsis lines. These versatile GST resources provide novel and powerful tools for functional genomics
The AtNFXL1 gene encodes a NF-X1 type zinc finger protein required for growth under salt stress
(2006)
The human NF-X1 protein and homologous proteins in eukaryotes represent a class of transcription factors which are characterised. by NF-X1 type zinc finger motifs. The Arabidopsis genome encodes two NF-X1 homologs, which we termed AtNFXL1 and AtNFXL2. Growth and survival was impaired in atnfxl1 knock-out mutants and AtNFXL1-antisense plants under salt stress in comparison to wild-type plants. In contrast, 35S: :AtNFXL1 plants showed higher survival rates. The AtNFXL2 protein potentially plays an antagonistic role. The Arabidopsis NF-X1 type zinc finger proteins likely are part of regulatory mechanisms, which protect major processes such as photosynthesis.
Stomatal cell biology
(2003)
Rising demand for food and bioenergy makes it imperative to breed for increased crop yield. Vegetative plant growth could be driven by resource acquisition or developmental programs. Metabolite profiling in 94 Arabidopsis accessions revealed that biomass correlates negatively with many metabolites, especially starch. Starch accumulates in the light and is degraded at night to provide a sustained supply of carbon for growth. Multivariate analysis revealed that starch is an integrator of the overall metabolic response. We hypothesized that this reflects variation in a regulatory network that balances growth with the carbon supply. Transcript profiling in 21 accessions revealed coordinated changes of transcripts of more than 70 carbon-regulated genes and identified 2 genes (myo-inositol-1- phosphate synthase, a Kelch-domain protein) whose transcripts correlate with biomass. The impact of allelic variation at these 2 loci was shown by association mapping, identifying them as candidate lead genes with the potential to increase biomass production.
Background: Natural accessions of Arabidopsis thaliana are characterized by a high level of phenotypic variation that can be used to investigate the extent and mode of selection on the primary metabolic traits. A collection of 54 A. thaliana natural accession-derived lines were subjected to deep genotyping through Single Feature Polymorphism (SFP) detection via genomic DNA hybridization to Arabidopsis Tiling 1.0 Arrays for the detection of selective sweeps, and identification of associations between sweep regions and growth-related metabolic traits. Results: A total of 1,072,557 high-quality SFPs were detected and indications for 3,943 deletions and 1,007 duplications were obtained. A significantly lower than expected SFP frequency was observed in protein-, rRNA-, and tRNA-coding regions and in non- repetitive intergenic regions, while pseudogenes, transposons, and non-coding RNA genes are enriched with SFPs. Gene families involved in plant defence or in signalling were identified as highly polymorphic, while several other families including transcription factors are depleted of SFPs. 198 significant associations between metabolic genes and 9 metabolic and growth-related phenotypic traits were detected with annotation hinting at the nature of the relationship. Five significant selective sweep regions were also detected of which one associated significantly with a metabolic trait. Conclusions: We generated a high density polymorphism map for 54 A. thaliana accessions that highlights the variability of resistance genes across geographic ranges and used it to identify selective sweeps and associations between metabolic genes and metabolic phenotypes. Several associations show a clear biological relationship, while many remain requiring further investigation.
A new large set of reciprocal recombinant inbred lines (RILs) was created between the Arabidopsis accessions Col-0 and C24 for quantitative trait mapping approaches, consisting of 209 Col-0 x C24 and 214 C24 x Col-0 F-7 RI lines. Genotyping was performed using 110 evenly distributed framework single nucleotide polymorphism markers, yielding a genetic map of 425.70 cM, with an average interval of 3.87 cM. Segregation distortion (SD) was observed in several genomic regions during the construction of the genetic map. Linkage disequilibrium analysis revealed an association between a distorted region at the bottom of chromosome V and a non-distorted region on chromosome IV. A detailed analysis of the RILs for these two regions showed that an SD occurred when homozygous Col-0 alleles on chromosome IV coincided with homozygous C24 alleles at the bottom of chromosome V. Using nearly isogenic lines segregating for the distorted region we confirmed that this genotypic composition leads to reduced fertility and fitness.
The main objective of this study was to identify genomic regions involved in biomass heterosis using QTL, generation means, and mode-of-inheritance classification analyses. In a modified North Carolina Design III we backcrossed 429 recombinant inbred line and 140 introgression line populations to the two parental accessions, C24 and Col-0, whose F 1 hybrid exhibited 44% heterosis for biomass. Mid-parent heterosis in the RILs ranged from −31 to 99% for dry weight and from −58 to 143% for leaf area. We detected ten genomic positions involved in biomass heterosis at an early developmental stage, individually explaining between 2.4 and 15.7% of the phenotypic variation. While overdominant gene action was prevalent in heterotic QTL, our results suggest that a combination of dominance, overdominance and epistasis is involved in biomass heterosis in this Arabidopsis cross.
Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers
(2009)
A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.
Phenomic experiments are carried out in large-scale plant phenotyping facilities that acquire a large number of pictures of hundreds of plants simultaneously. With the aid of automated image processing, the data are converted into genotype-feature matrices that cover many consecutive days of development. Here, we explore the possibility of predicting the biomass of the fully grown plant from early developmental stage image-derived features. We performed phenomic experiments on 195 inbred and 382 hybrid maizes varieties and followed their progress from 16 days after sowing (DAS) to 48 DAS with 129 image-derived features. By applying sparse regression methods, we show that 73% of the variance in hybrid fresh weight of fully-grown plants is explained by about 20 features at the three-leaf-stage or earlier. Dry weight prediction explained over 90% of the variance. When phenomic features of parental inbred lines were used as predictors of hybrid biomass, the proportion of variance explained was 42 and 45%, for fresh weight and dry weight models consisting of 35 and 36 features, respectively. These models were very robust, showing only a small amount of variation in performance over the time scale of the experiment. We also examined mid-parent heterosis in phenomic features. Feature heterosis displayed a large degree of variance which resulted in prediction performance that was less robust than models of either parental or hybrid predictors. Our results show that phenomic prediction is a viable alternative to genomic and metabolic prediction of hybrid performance. In particular, the utility of early-stage parental lines is very encouraging. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
Novel aspects of symbiotic nitrogen fixation uncovered by transcript profiling with cDNA arrays
(2002)
Brassinosteroids (BRs) are steroidal plant hormones with important regulatory roles in various physiological processes, including growth, xylem differentiation, disease resistance, and stress tolerance. Several components of the BR signal transduction pathway have been identified. The extracellular domains of receptor kinases such as BRI1 perceive BRs and transduce the signal via intracellular kinase domains. Within the cell further kinases and phosphatases determine the phosphorylation status of transcription factors such as BES1 and BZR1. These factors mediate major BR effects. Studies of BR-regulated genes shed light on the molecular mode of BR action. Genes encoding cell-wall-modifying enzymes, enzymes of the BR biosynthetic pathway, transcription factors, and proteins involved in primary and secondary metabolism are subject to BR-regulation. Gene expression data also point at interactions with other phytohormones and a role of BR in stress responses. This article gives a survey of the BR-signaling pathway. Two BR-responsive genes, OPR3 and EXO, are described in detail
Measures for interoperability of phenotypic data: minimum information requirements and formatting
(2016)
Background: Plant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combining existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible provided that the collected observations are equipped with an adequate set of metadata. So far there have been no common standards governing phenotypic data description, which hampered data exchange and reuse. Results: In this paper we propose the guidelines for proper handling of the information about plant phenotyping experiments, in terms of both the recommended content of the description and its formatting. We provide a document called "Minimum Information About a Plant Phenotyping Experiment", which specifies what information about each experiment should be given, and a Phenotyping Configuration for the ISA-Tab format, which allows to practically organise this information within a dataset. We provide examples of ISA-Tab-formatted phenotypic data, and a general description of a few systems where the recommendations have been implemented. Conclusions: Acceptance of the rules described in this paper by the plant phenotyping community will help to achieve findable, accessible, interoperable and reusable data.