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Background: Molecular characterization is an essential step of risk/safety assessment of genetically modified (GM) crops. Holistic approaches for molecular characterization using omics platforms can be used to confirm the intended impact of the genetic engineering, but can also reveal the unintended changes at the omics level as a first assessment of potential risks. The potential of omics platforms for risk assessment of GM crops has rarely been used for this purpose because of the lack of a consensus reference and statistical methods to judge the significance or importance of the pleiotropic changes in GM plants. Here we propose a meta data analysis approach to the analysis of GM plants, by measuring the transcriptome distance to untransformed wild-types. Results: In the statistical analysis of the transcriptome distance between GM and wild-type plants, values are compared with naturally occurring transcriptome distances in non-GM counterparts obtained from a database. Using this approach we show that the pleiotropic effect of genes involved in indirect insect defence traits is substantially equivalent to the variation in gene expression occurring naturally in Arabidopsis. Conclusion: Transcriptome distance is a useful screening method to obtain insight in the pleiotropic effects of genetic modification.

As a result of rapid biotechnological developments in the past century, genetically modified (GM) crops were developed and introduced for field application. Despite the advantages of these crops and the professional marketing policies, people also started questioning the safety of GM products for humans and the environment. In response to that, scientific advisory bodies (such as COGEM, The Netherlands Commission on Genetic Modification) suggested that, among other measurements, an environmental risk assessment (ERA) of a GM crop should be done before introduction into the field. Ecological knowledge about the possible effects was considered a vital component of that assessment. In 2007, the Dutch Government initiated the ERGO (Ecology Regarding Gene-modified Organisms) research programme to generate a scientific basis for a sound ecological risk analysis. The main objective of the ERGO-programme was to develop ecology-based guidelines for how to best assess the possible ecological side-effects of new GM crops. Also the European Food Safety Authority (EFSA) recognised the interaction of a GM crop with non-target organisms as a potential environmental risk and therefore they provided guidelines for selection of a range of non-target organisms and phenotypes to be studied under laboratory conditions as part of a GM crop risk assessment study. These guidelines formed the basis for the ERGO research themes. Parallel to the new biotechnological developments leading to the introduction of GM plants into the environment, new analytical techniques were also introduced that revolutionized the field of analytical biology. High throughput analytical platforms, collectively called omics technologies, created opportunities for untargeted analysis of cellular components with biological and ecological functions including mRNAs (transcriptomics), proteins (proteomics) and metabolites (metabolomics). These analytical platforms were recommended by several researchers in the field of

Metabolite fingerprinting is widely used to unravel the chemical characteristics of biological samples. Multivariate data analysis and other statistical tools are subsequently used to analyze and visualize the plasticity of the metabolome and/or the relationship between those samples. However, there are limitations to these approaches for example because of the multi-dimensionality of the data that makes interpretation of the data obtained from untargeted analysis almost impossible for an average human being. These limitations make the biological information that is of prime importance in untargeted studies be partially exploited. Even in the case of full exploitation, current methods for relationship elucidation focus mainly on between groups variation and differences. Therefore, a measure that is capable of exploiting both between- and within-group biological variation would be of great value. Here, we examined the natural variation in the metabolome of nine Arabidopsis thaliana accessions grown under various environmental conditions and established a measure for the metabolic distance between accessions and across environments. This data analysis approach shows that there is just a minor correlation between genetic and metabolic diversity of the nine accessions. On the other hand, it delivers so far in Arabidopsis unexplored chemical information and is shown to be biologically relevant for resistance studies.

Tomato (Solanum lycopersicum) is the most cultivated crop in the Solanaceae family and is a host for Oidium neolycopersici, the cause agent of powdery mildew disease. In wild species of tomato, genes (Ol-1–Ol-6) for monogenic resistance have been identified. Moreover, three quantitative resistance loci (QRLs), namely Ol-qtl1, Ol-qtl2 and Ol-qtl3, have been mapped in Solanum neorickii G1.1601. In this work, we developed several advanced backcross populations in order to fine-map these Ol-qtls. Resistant lines harboring individual Ol-qtl were produced and used in recombinant screening. Ten recombinants were identified in chromosomal regions carrying Ol-qtl1s. The recombinant individuals were used to produce recombinant families (RFs). By screening these RFs with molecular markers and testing them with O. neolycopersici, we could localize Ol-qtl1 in a region of about 2.3 Mbp on the long arm of chromosome 6 and Ol-qtl2 in a region of 2.5 Mbp on the short arm of chromosome 12. On the other hand, the presence of Ol-qtl3 locus was not confirmed in this study. The fine-mapping results further demonstrated the co-localization between Ol-qtls and genes for monogenic resistance; the Ol-qtl1 interval contains the Ol-1 gene and the Ol-qtl2 interval harbors the Lv gene that confers monogenic resistance to Leveillula taurica, another species of tomato powdery mildew.

Tillering in cereals is a complex process in the regulation of which also signals from the roots in the form of strigolactones play an important role. The strigolactones are signalling molecules that are secreted into the rhizosphere where they act as germination stimulants for root parasitic plants and hyphal branching factors for arbuscular mycorrhizal fungi. On the other hand, they are also transported from the roots to the shoot where they inhibit tillering or branching. In the present study, the genetic variation in strigolactone production and tillering phenotype was studied in twenty rice varieties collected from all over the world and correlated with S. hermonthica infection. Rice cultivars like IAC 165, IAC 1246, Gangweondo and Kinko produced high amounts of the strigolactones orobanchol, 2'-epi-5-deoxystrigol and three methoxy-5-deoxystrigol isomers and displayed low amounts of tillers. These varieties induced high S. hermonthica germination, attachment, emergence as well as dry biomass. In contrast, rice cultivars such as Super Basmati, TN 1, Anakila and Agee displayed high tillering in combination with low production of the aforementioned strigolactones. These varieties induced only low S. hermonthica germination, attachment, emergence and dry biomass. Statistical analysis across all the varieties confirmed a positive correlation between strigolactone production and S. hermonthica infection and a negative relationship with tillering. These results show that genetic variation in tillering capacity is the result of genetic variation in strigolactone production and hence could be a helpful tool in selecting rice cultivars that are less susceptible to S. hermonthica infection.