WORKPACKAGE 1, LEAF GROWTH, is devoted to the phenotypic analysis of Arabidopsis plants. Analytical methods will be adapted to increase the throughput and resolution of growth kinematic studies, and these will be conducted in strictly controlled environmental conditions to allow comparison across independent experiments. Growth variables will be measured at all relevant spatial scales, from entire rosettes down to individual leaf cells. In addition to providing a refined description of leaf growth in time and space under optimal conditions, WP1 will also include the phenotypic characterization of plants growing under environmental stress commonly suffered by crops in agricultural settings. Finally, important genetic components will be identified via the comparative analysis of growth variables characterizing plant lines with varying genetic configurations.
WORKPACKAGE 2, GENETIC DIVERSITY, consists in the production and genotypic analysis of these plant variants. All sources of genetic variations currently available for Arabidopsis will be exploited: for reverse genetics approaches, insertional mutants, ectopic expression lines and RNAi silenced lines targeting genes known or suspected to be involved in leaf growth, based on prior knowledge and on results obtained within the consortium; for forward genetics approaches, chemically generated mutants, ecotypical variants and derived recombinant inbred lines (RILs). Accessions of interest will be either extracted from existing collections or generated within the project when necessary.
WORKPACKAGE 3, MOLECULAR PROFILING, focuses on the characterization of the main cellular components present in the leaves of genetic variants grown in optimal or stressful conditions. These studies will result in the identification of transcripts, proteins and metabolites that are up- or down-regulated at specific growth stages and in response to environmental cues. Assuming that many of these molecules play an important role under the specific conditions under scrutiny, the meta-analysis of the combined profiling data will indicate how the plant metabolism drives the growth and development of the leaf, and will highlight the molecular requirements in successive growth stages.
WORKPACKAGE 4, MOLECULAR INTERACTIONS, uses HTP methods to identify protein-protein and protein-DNA interactions involving genes and proteins acting in growth-related pathways. Furthermore, because protein localisation is key to devise and interpret interaction experiments, the compartment(s) in which proteins of interest are targeted will be investigated in vivo.
WORKPACKAGE 5, HTP GROWTH ASSAYS, utilizes two novel large-scale screening methods to study the consequences of genetic and chemical perturbations, respectively. The first approach, cell-based reverse genetics, will take advantage of the cloned DNA sequence repertoires (ORF and silencing constructs) that have been built in recent years to enable Arabidopsis functional genomics. A platform will be developed (i) to transform in parallel Arabidopsis cultured cells with large sets of constructs resulting in the overexpression or the knock-down of the targeted genes, and (ii) to record and automatically distinguish resulting mutant phenotypes in proliferating micro-calli. The second approach, chemical genetics, will be aimed at the discovery of small organic molecules that perturb specifically leaf growth and cell proliferation. Potential protein targets will be searched for compounds exhibiting particularly interesting growth regulation activity.
WORKPACKAGE 6, DATA ACCESS AND INTEGRATION, is dedicated to the dissemination of the biological materials, the experimental results and the software tools exploited and generated in the framework of the project. These efforts will be initiated with the construction of the knowledge base that delimits the molecular scaffold on which the project is built and that will accrue information throughout its lifetime. Coordination and integration will be supported by the definition and enforcement of standard operating procedures and of formal vocabulary (ontologies), adopted from existing standards or developed for the specific objectives of the project. Finally, tools will be implemented to provide on line access to the AGRON-OMICS data, from a central repository and via web services linking databases from particular partners dedicated to the specific technology platforms. At the very least, the mining of these combined datasets will help each participating group to generate novel hypotheses about their biological functional modules to investigate through further experimentation. In addition and importantly, taking advantage of the dissemination efforts within the consortium, all biomaterials, data and tools generated by the partners will be made available to the scientific community at large according to strict and rapid procedures.
WORKPACKAGE 7, MODELS, aims to developing computational and statistical data analysis tools to discriminate between plausible network models at different levels of complexity including definition of clusters in data matrices, inference of network topology, and temporal and spatial modelling of growth events. Modelling tools will be made available to wet-lab researchers with a focus on predictors of potential regulatory interactions to be tested experimentally.
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