SummaryThe aim of this study was to evaluate the use of four nontargeted analytical methodologies in the detection of unintended effects that could be derived during genetic manipulation of crops. Three profiling technologies were used to compare the transcriptome, proteome and metabolome of two transgenic maize lines with the respective control line. By comparing the profiles of the two transgenic lines grown in the same location over three growing seasons, we could determine the extent of environmental variation, while the comparison with the control maize line allowed the investigation of effects caused by a difference in genotype. The effect of growing conditions as an additional environmental effect was also evaluated by comparing the Bt-maize line with the control line from plants grown in three different locations in one growing season. The environment was shown to play an important effect in the protein, gene expression and metabolite levels of the maize samples tested where 5 proteins, 65 genes and 15 metabolites were found to be differentially expressed. A distinct separation between the three growing seasons was also found for all the samples grown in one location. Together, these environmental factors caused more variation in the different transcript ⁄ protein ⁄ metabolite profiles than the different genotypes.

No abstract

A metabolite profiling approach based on gas chromatography-mass spectrometry (GC-MS) was applied to investigate the metabolite profiles of genetically modified (GM) Bt-maize (DKC78-15B, TXP 138F) and Roundup Ready-maize (DKC78-35R). For the comparative investigation of the impact of genetic modification versus environmental influence on the metabolite profiles, GM maize was grown together with the non-GM near-isogenic comparators under different environmental conditions, including several growing locations and seasons in Germany and South Africa. Analyses of variance (ANOVA) revealed significant differences between GM and non-GM maize grown in Germany and South Africa. For the factor genotype, 4 and 3%, respectively, of the total number of peaks detected by GC-MS showed statistically significant differences (p < 0.01) in peak heights as compared to the respective isogenic lines. However, ANOVA for the factor environment (growing location, season) revealed higher numbers of significant differences (p < 0.01) between the GM and the non-GM maize grown in Germany (42%) and South Africa (10%), respectively. This indicates that the majority of differences observed are related to natural variability rather than to the genetic modifications. In addition, multivariate data assessment by means of principal component analysis revealed that environmental factors, that is, growing locations and seasons, were dominant parameters driving the variability of the maize metabolite profiles.

The objective of this study was to investigate the metabolite variations during industrial pasta processing (from semolina to dried pasta) for five different commercial products. Up to 76 metabolites were detected. Significant differences were observed between wholemeal and refined pasta samples, with the wholemeal pasta richer in many classes of compounds such as phytosterols, policosanols, unsaturated fatty acids, amino acids, carotenoids, minerals, and so on. Significant differences were also observed between samples of refined pasta apparently similar for the actual parameters used for the assessment of pasta quality. The results indicated that a number of metabolites undergo a transformation during the pasta-making process depending on the processing conditions adopted. The approach used in this work shows the high potential of metabolite profiling for food investigations with regard to process-related transformation, safety, and nutrition.

The advances in the field of biotechnology (and bioengineering) over the past decades has allowed the precise development of new products across the agricultural, environmental, and pharmaceutical sectors. This has led to the need to evaluate the relevance and applicability of existing policies and frameworks that regulate the current transgenic technologies. On the African continent, there are delays in the development and implementation of biosafety policies and regulations. Most African countries formulate their policies, regulations, and frameworks by following The Convention on Biological Diversity’s (CBD) guidelines. Although the CBD documents are continually evolving, this happens at a slower pace. It is becoming increasingly important for countries to deal swiftly with the advances in biotechnology in a manner that balances the regulatory complexities, while safeguarding the net gains for human health, the environment, and the economy. For the African countries, some of these net gains are similar, while concerns and perceived risks associated with the adoption and use of the technology are also common. Furthermore, the challenges relating to capacity, knowledge, and skills to address some of the regulatory complexities. In this article we explore the advancement of some African countries in the development and implementation of various biosafety policies and detail the challenges and constraints faced by those countries that are lagging behind. We conclude by outlining identified opportunities for neighbouring and regional countries to assist one another and work in a more organised and coordinated approach towards developing, implementing, and strengthening their respective biosafety policies, regulations, and frameworks.