Genome analysis. The availability of the high-quality draft genome sequence of Golden Delicious at FEM-IASMA (Velasco et al. submitted) and technological advances now enables us to sequence and analyse the genome from any other apple cultivar or accession at a fraction of the cost of the initial sequencing project. In this WP we will use Next-Generation sequencing technologies (Roche-454 and/or Illumina) in order to obtain the genome sequence of 10 cultivars of apple. Protocols for genome complexity reduction (Van Tassel et al., 2008; Nat Methods, 5:247-252) will be considered. The cultivars to be sequenced will be selected among a panel of founders (Noiton & Alspach 1996; J. Am. Soc. Hort. Sci 121:773-782) and donors of resistance genes. The re-sequencing effort will provide a large number of molecular markers (SNPs) expected to be highly informative (i.e. heterozygous across a large portion of the germplasm and with balanced allelic frequencies). SNPs identified in this project together with the information about the level of linkage disequilibrium within apple germplasm (under acquisition at FEM-IASMA. Micheletti et al., 2010) will enable the design of a high density SNP genotyping array. Collaboration with international research groups will be considered under this topic. The SNPs/genotyping tools will contribute to the fine mapping of the genes/QTLs identified in other WPs..
Transcriptomics. In this project we will also carry out a comprehensive survey of the apple transcriptome in order to identify the expressed gene set from a comprehensive list of tissues/organs, developmental stages and response to specific triggers (i.e. pathogens). On the whole, we plan to sequence 40 cDNA libraries, including 10 developmental stages from 2 apple cultivars, and further 20 libraries derived from resistant and susceptible genotypes where tissues will be inoculated with Venturia and Erwinia. The analysis of transcriptome will be fundamental in order to improve the annotation of the Golden genome sequence and enable the production of gene-expression microarrays, which will be directly used within this project (WP3a2 - transcriptome analysis of self-thinning; WP3b - transcriptome analysis of the effect of ABA treatments; WP4a -transcriptomics analysis of the effects of ethylene controlling agents; WP5a - transcription profiles of apple genotypes with diverse allergenic potentials).
WP1b Plant Metabolomics.
The project will use the FEM platform for metabolic profiling including an array of 6 mass spectrometers interfaced with UPLC and HRGC chromatographs. The project will exploit ongoing systematic studies carried on at FEM-IASMA, aimed to build state-of-the-art libraries for the characterization of the organic compounds constituting the apple metabolome. Both metabolite profiling and biomarkers discovery will be carried on with an array of 3 different separation techniques, which will be validated in order to cover the polar metabolites (mostly primary metabolites, by UPLC-MS/MS, normal phase), the less polar metabolites (mostly secondary metabolites, by UPLC-MS/MS, reversed phase) and the volatile compounds (by HRGC-MS/MS). Extensive libraries of apple metabolites will be built and regularly updated during the project, to give access to the molecular characterization of ideally all the apple metabolites of interest for fruit quality. With a set of three complementary analyses running in parallel under optimized conditions, it will then be possible to accurately analyse hundreds of low-molecular-weight organic compounds, thus covering all the major metabolic pathways of interest. In addition, in order to highlight possible complex metabolite patterns and include unknown and uncharacterised compounds, advanced metabolomic methods will be adopted along with a “discovery-based research” strategy (by UPLC-Q-TOF HDMS and/or FTMS coupled with the appropriate interfaces, such as MALDI, DESI, Triversa Nanomate) and these will be integrated with and complete the conventional “hypothesis-driven research” method. A portion of the metabolites will be quantified from fruit samples of two experimental mapping populations available at FEM-IASMA and on a panel of apple germplasm collection in order to identify genes/QTLs responsible for the genetic control of their accumulation. Samples will be collected over at least two years, and results will be cross-compared with standard fruit quality and sensorial traits. Appropriate bioinformatic and biostatistic techniques will supervise the automation of the process, from the design of the experiments up to data processing and mining. WPs 2, 3 and 5 will identify the main compounds of interest for fruit quality within the wide set of metabolites which also includes unknown and uncharacterised compounds and, on the basis of these, a high-throughput, target monitoring method for the exhaustive characterization of the chemical phenotype will be validated and made available.
WP1c Non-invasive fruit quality analyses. The project will use the innovative DA-Meter and NIRs non-invasive analytical tools, currently being developed at the University of Bologna. These tools will be validated using data either already available or produced within the project to allow the development of an apple-specific system. In addition, a GPS will be integrated into both the classic DA-Meter and the apple-specific version to allow georeferencing of data obtained with portable tools. A simplified version of the equipment designed to follow ongoing ripening of fruit during cold storage will also be developed. At the same time, fixed equipment will also be validated. All these techniques will provide support for WPs 3 and 4, in particular. Finally, new storage technologies with low environmental impact are to be developed in collaboration with WP4, using 1-MCP together with an increase in the thermal regime: it is presumed that an increase in the storage temperature may have a considerable effect on costs and environmental sustainability.