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  • Title: Five omic technologies are concordant in differentiating the biochemical characteristics of the berries of five grapevine (Vitis vinifera L.) cultivars.
    Author: Ghan R, Van Sluyter SC, Hochberg U, Degu A, Hopper DW, Tillet RL, Schlauch KA, Haynes PA, Fait A, Cramer GR.
    Journal: BMC Genomics; 2015 Nov 16; 16():946. PubMed ID: 26573226.
    Abstract:
    BACKGROUND: Grape cultivars and wines are distinguishable by their color, flavor and aroma profiles. Omic analyses (transcripts, proteins and metabolites) are powerful tools for assessing biochemical differences in biological systems. RESULTS: Berry skins of red- (Cabernet Sauvignon, Merlot, Pinot Noir) and white-skinned (Chardonnay, Semillon) wine grapes were harvested near optimum maturity (°Brix-to-titratable acidity ratio) from the same experimental vineyard. The cultivars were exposed to a mild, seasonal water-deficit treatment from fruit set until harvest in 2011. Identical sample aliquots were analyzed for transcripts by grapevine whole-genome oligonucleotide microarray and RNAseq technologies, proteins by nano-liquid chromatography-mass spectroscopy, and metabolites by gas chromatography-mass spectroscopy and liquid chromatography-mass spectroscopy. Principal components analysis of each of five Omic technologies showed similar results across cultivars in all Omic datasets. Comparison of the processed data of genes mapped in RNAseq and microarray data revealed a strong Pearson's correlation (0.80). The exclusion of probesets associated with genes with potential for cross-hybridization on the microarray improved the correlation to 0.93. The overall concordance of protein with transcript data was low with a Pearson's correlation of 0.27 and 0.24 for the RNAseq and microarray data, respectively. Integration of metabolite with protein and transcript data produced an expected model of phenylpropanoid biosynthesis, which distinguished red from white grapes, yet provided detail of individual cultivar differences. The mild water deficit treatment did not significantly alter the abundance of proteins or metabolites measured in the five cultivars, but did have a small effect on gene expression. CONCLUSIONS: The five Omic technologies were consistent in distinguishing cultivar variation. There was high concordance between transcriptomic technologies, but generally protein abundance did not correlate well with transcript abundance. The integration of multiple high-throughput Omic datasets revealed complex biochemical variation amongst five cultivars of an ancient and economically important crop species.
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