W783
The Quest for Tolerant Varieties: The use of Proteomics to Understand Stress and Identify Variety Specific Alleles.

Date: Tuesday, January 12, 2016
Time: 11:30 AM
Room: Pacific Salon 3
Sebastien C Carpentier , SYBIOMA, Leuven, Belgium
Ewaut Kissel , KU Leuven, Leuven, Belgium
Jelle Van Wesemael , KU Leuven, Leuven, Belgium
Nadia Campos , KU Leuven, Leuven, Belgium
The primary objective of crop breeding is to improve yield and/or harvest quality while minimizing inputs.  There is a need for improvement of crops and new tools to mine biodiversity for new promising alleles. While significant progress has been made in molecular and genetic analysis of model plants, the exploration of crop biodiversity and the correlation of cellular responses that can be analysed by omics to stress tolerance at the plant level is currently a challenge. New developments offered by proteomics techniques will contribute to this improvement. 

We at KU Leuven host the Bioversity International collection of banana (more than 1500 accessions). We believe that a profound characterization of the available diversity enables to discover tolerance related alleles. Drought is a priority in banana breeding and drought tolerance is a complex quantitative trait, controlled by many genes. Due to the sterile nature of edible banana varieties traditional QTL mapping is not straightforward. 

In 2011, we already proved that a gel based proteomics approach was able to select allele specific proteins involved in drought (Carpentier et al. 2011). Now we present a gel free based workflow to visualize the peptides that bear amino acid polymorfism(s) and are able to select those allele specific peptides that are correlated to the trait of interest via integrated multivariate analysis. Allele specific peptides are identified via clustering of MSMS spectra, de novo identification or searching against species specific mRNA databeses. Once performing high-throughput shotgun proteomics, annotation becomes quite challenging in a non-model crop. Therefore we additionally propose a cytoscape based workflow to cluster related proteins based on their GO annotations.