The decline of available fossil fuel reserves has triggered worldwide efforts to develop alternative energy sources based on plant biomass. Detailed knowledge of the relations of metabolism and biomass accumulation can be expected to yield powerful novel tools to accelerate and enhance energy plant breeding programs. We used metabolic profiling in the model Arabidopsis to study the relation between biomass and metabolic composition using a recombinant inbred line (RIL) population. A highly significant canonical correlation (0.73) was observed, revealing a close link between biomass and a specific combination of metabolites. Dividing the entire data set into training and test sets resulted in a median correlation between predicted and true biomass of 0.58. The demonstrated high predictive power of metabolic composition for biomass features this composite measure as an excellent biomarker and opens new opportunities to enhance plant breeding specifically in the context of renewable resources.biomass ͉ canonical correlation ͉ metabolic profiling ͉ recombinant inbred line population ͉ biomarker

The chemical composition of any wine sample contains numerous small molecules largely derived from three different sources: the grape berry, the yeast strain used for fermentation, and the containers used for wine making and storage. The combined sum of these small molecules present in the wine, therefore, might account for all wine specific features such as cultivar, vintage, origin, and quality. Still, most wine authentication procedures rely either on subjective human measures or if they are based on measurable features, they include a limited number of compounds. In this study, which is based on an untargeted UPLC-FT-ICR-MS-based approach, we provide data, demonstrating that unbiased and objective analytical chemistry in combination with multivariate statistical methods allows to reproducible classify/distinguish wine attributes like variety, origin, vintage, and quality.

Information about the total chemical composition of primary metabolites during grape berry development is scarce, as are comparative studies trying to understand to what extent metabolite modifications differ between cultivars during ripening. Thus, correlating the metabolic profiles with the changes occurring in berry development and ripening processes is essential to progress in their comprehension as well in the development of new approaches to improve fruit attributes. Here, the developmental metabolic profiling analysis across six stages from flowering to fully mature berries of two cultivars, Cabernet Sauvignon and Merlot, is reported at metabolite level. Based on a gas chromatography–mass spectrometry untargeted approach, 115 metabolites were identified and relative quantified in both cultivars. Sugars and amino acids levels show an opposite behaviour in both cultivars undergoing a highly coordinated shift of metabolite associated to primary metabolism during the stages involved in growth, development and ripening of berries. The changes are characteristic for each stage, the most pronounced ones occuring at fruit setting and pre-Veraison. They are associated to a reduction of the levels of metabolites present in the earlier corresponding stage, revealing a required catabolic activity of primary metabolites for grape berry developmental process. Network analysis revealed that the network connectivity of primary metabolites is stage- and cultivar-dependent, suggesting differences in metabolism regulation between both cultivars as the maturity process progresses. Furthermore, network analysis may represent an appropriate method to display the association between primary metabolites during berry developmental processes among different grapevine cultivars and for identifying potential biologically relevant metabolites.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-015-0927-z) contains supplementary material, which is available to authorized users.

Compared with linearly blended images, virtual monoenergetic reconstructions of DECT data at 60 keV significantly improve lesion enhancement and CNR, subjective overall image quality, and tumor delineation of head and neck squamous cell carcinoma.