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Cristiano Bianchetti

Publications and source records attributed to Cristiano Bianchetti.

2 recordsLinked to original sources

A metabonomic study of transgenic maize (Zea mays) seeds revealed variations in osmolytes and branched amino acids.

The aim of the research was to investigate metabolic variations associated with genetic modifications in the grains of Zea mays using metabonomic techniques. With this in mind, the non-targeted characteristic of the technique is useful to identify metabolites peculiar to the genetic modification and initially undefined. The results obtained showed that the genetic modification, introducing Cry1Ab gene expression, induces metabolic variations involving the primary nitrogen pathway. Concerning the methodological aspects, the experimental protocol used has been applied in this field for the first time. It consists of a combination of partial least square-discriminant analysis and principal component analysis. The most important metabolites for discrimination were selected and the metabolic correlations linking them are identified. Principal component analysis on selected signals confirms metabolic variations, highlighting important details about the changes induced on the metabolic network by the presence of a Bt transgene in the maize genome.

Amino Acids, Branched-Chain↗

NMR-based metabonomic study of transgenic maize.

The aim of this research was to verify the possibility of identifying and classifying maize seeds obtained from transgenic plants, in different classes according to the modification, on the basis of the concerted variation in metabolite levels detected by NMR spectra. It was possible to recognise the discriminant metabolites of transgenic samples as well as to classify non-a priori defined samples of maize. It is important to underline that the obtained results are useful to point out the metabolic consequences of a specific genic modification on a plant, without using a targeted analysis of the different metabolites, in fact it was possible to classify the seeds also without the complete assignment of the spectra. The analysis was performed by applying multivariate techniques (principal component analysis and partial least squares-discriminant analysis) to NMR data.

Gene Expression↗