Computational clustering integration of metabolomics, transcriptomics and agronomical data for germplasm selection in highly diverse tomato landrace collection

CONTE M; ASIS R; BOGGIO SB; PERALTA IE; CARRARI F; CERNADAS RA; INSANI EM; ZANOR MI; ASPRELLI PD; STEGMAYER G; PIVIDORI M; D´ANGELO M; VALLE EM; MILONE D

Rosario

Simposio; 2nd Latin American Metabolic Profilling Syposium LAMPS; 2016

IBR (CONICET-UNR)

Tomato (S. lycopersicum) is a major vegetable crop consumed worldwide that provides a valuable source of vitamins and antioxidants for the human diet. Because of the variability constrains associated with breeding programs, the phenotypic and genetic diversity of heirloom cultivars (landraces) emerges as a landmark to rescue desired agronomic traits for crop improvement. Here, we surveyed a germplasm collection of 68 tomato Andean landraces maintained and cultivated by family farmers. Distinct sets of these accessions were cultivated in the Cuyo region (Mendoza) during several seasons (i.e. 2005-06, 2006-07, 2008-09, 2009-10, 2010-11 and 2011-12) and characterized by morpho-agronomic traits as well as by biochemical characters of the mature fruits. Our analyses undertook a combined approach using, i) GC-MS, NMR and HPLC to identify fruit soluble and volatile metabolites, ii) transcriptomics, and iii) computational biology to integrate the whole dataset. Preliminary results allow to define genotypic clusters according to agronomical traits, including metabolite profiles, antioxidant properties and vitamins accumulation. We also explored organoleptic properties of the different accessions to establish inter-cluster correlations between volatile content and fruit taste. Finally, a multi focus clustering analysis based on accessions diversity and environmental variation along the experimental seasons provides a method to infer the most probable traits to be stable inherited.