Severe drought stress is affecting selected primary metabolites, polyphenols, and volatile metabolites in grapevine leaves (Vitis vinifera cv. Pinot noir)

Bibliographische Detailangaben
Veröffentlicht in:	Plant physiology and biochemistry : PPB. - 1991. - 88(2015) vom: 02. März, Seite 17-26
1. Verfasser:	Griesser, Michaela (VerfasserIn)
Weitere Verfasser:	Weingart, Georg, Schoedl-Hummel, Katharina, Neumann, Nora, Becker, Manuel, Varmuza, Kurt, Liebner, Falk, Schuhmacher, Rainer, Forneck, Astrid
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2015
Zugriff auf das übergeordnete Werk:	Plant physiology and biochemistry : PPB
Schlagworte:	Journal Article Research Support, Non-U.S. Gov't Chlorophyll fluorescence Drought stress HS-SPME-GC-MS Metabolomics Vitis vinifera Aldehydes Oils, Volatile Polyphenols mehr... Water 059QF0KO0R Citric Acid 2968PHW8QP 2-methylbutanal 47H597M1YY Ribose 681HV46001 Acetaldehyde GO1N1ZPR3B phenylacetaldehyde U8J5PLW9MR

Beschreibung
Zusammenfassung:	Copyright © 2015 Elsevier Masson SAS. All rights reserved. Extreme weather conditions with prolonged dry periods and high temperatures as well as heavy rain events can severely influence grapevine physiology and grape quality. The present study evaluates the effects of severe drought stress on selected primary metabolites, polyphenols and volatile metabolites in grapevine leaves. Among the 11 primary metabolites, 13 polyphenols and 95 volatiles which were analyzed, a significant discrimination between control and stressed plants of 7 primary metabolites, 11 polyphenols and 46 volatile metabolites was observed. As single parameters are usually not specific enough for the discrimination of control and stressed plants, an unsupervised (PCA) and a supervised (PLS-DA) multivariate approach were applied to combine results from different metabolic groups. In a first step a selection of five metabolites, namely citric acid, glyceric acid, ribose, phenylacetaldehyde and 2-methylbutanal were used to establish a calibration model using PLS regression to predict the leaf water potential. The model was strong enough to assign a high number of plants correctly with a correlation of 0.83. The PLS-DA provides an interesting approach to combine data sets and to provide tools for the specific evaluation of physiological plant stresses
Beschreibung:	Date Completed 11.11.2015 Date Revised 30.09.2020 published: Print-Electronic Citation Status MEDLINE
ISSN:	1873-2690
DOI:	10.1016/j.plaphy.2015.01.004