In this study we are going to use different omic-techniques to analyze fruits of three species of berries such as strawberry, raspberry and black currant. Berry fruit are well appreciated for their delicate flavor and nutraceutical properties, with consumer demand increasing over the last years. Furthermore, climate change and market globalization have made necessary to improve the production while maintaining fruit quality traits. Goodberry project is developping analytical platforms, covering from transcriptomic to metabolites and volatile compounds analysis, to find new factors controlling plant adaptation, fruit production and quality. In this study we implement the metabolomic analysis of strawberry, raspberry and black currant fruits from the 2017 harvest, as well as 2018 harvest during this year. To analyze and compare the data we use multiomic tools and bioinformatics to extract properly conclusion The analyses take different berry cultivars, adapted to diverse environments, were grown in 2017 and 2018 in different latitudes (Germany, France, Norway, Italy, Poland and Scotland). The data comes from a combination of gas-chromatography-mass spectrometry (GC-TOF-MS) and headspace solid phase micro extraction (HS-SPME) coupled with GC-MS was used to semi-quantify fruit primary metabolome and volatilome. Around 50 key primary metabolites, including sugars and acids, which are fundamental factors influencing fruit taste and 75 volatiles, responsible of the aroma, were identified across the different genotypes and climates. Multivariate statistical approaches allow us to point out the genetic and environmental factors underlying complex metabolic traits involved in fruit quality. Preliminary analysis showed that both climate and genetic factors influence primary metabolite and volatile content, even if the environment seems to have a stronger impact on the first one.
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