Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance

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.

     

Effects of intraspecific variation in white cabbage (Brassica oleracea var. capitata) on soil organisms

Intraspecific variation in plants can affect soil organisms. However, little is known about whether the magnitude of the effect depends on the degree of interaction with the roots. We analyzed effects of plant intraspecific variation on root herbivores and other soil organisms that interact directly with living plant roots, as well as on decomposer organisms that interact more indirectly with roots. We used four different white cabbage (Brassica oleracea var. capitata) cultivars exhibiting a high degree of intraspecific variation in root glucosinolate profiles. Intraspecific variation affected root-feeding nematodes, whereas decomposer organisms such as earthworms and Collembola were not affected. Root-feeding nematodes were most abundant in one of the cultivars, Badger Shipper, which lacked the glucosinolate gluconasturtiin. The effect of the intraspecific variation in glucosinolate composition may have been restricted to root-feeding nematodes due to the rapid degradation of glucosinolates and their breakdown products in the soil. Additionally, the low biomass of root-feeding nematodes, relative to other soil organisms, limits the possibility to affect higher trophic level organisms. Our results show that variation in root chemistry predominantly affects belowground herbivores and that these effects do not extend into the soil food web.     

Characterization of the natural variation in <Emphasis Type="Italic"> Arabidopsis thaliana</Emphasis> metabolome by the analysis of metabolic distance

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.     

How genetic modification of roots affects rhizosphere processes and plant performance

Genetic modification of plants has become common practice. However, root-specific genetic modifications have only recently been advocated. Here, a review is presented regarding how root-specific modifications can have both plant internal and rhizosphere-mediated effects on aboveground plant properties and plant performance. Plant internal effects refer to pleiotropic processes such as transportation of the modified gene product. Rhizosphere-mediated effects refer to altered plant-rhizosphere interactions, which subsequently feed back to the plant. Such plant-soil feedback mechanisms have been demonstrated both in natural systems and in crops. Here how plant internal and rhizosphere-mediated effects could enhance or counteract improvements in plant properties for which the genetic modification was intended is discussed. A literature survey revealed that rice is the most commonly studied crop species in the context of root-specific transgenesis, predominantly in relation to stress tolerance. Phytoremediation, a process in which plants are used to clean up pollutants, is also often an objective when transforming roots. These two examples are used to review potential effects of root genetic modifications on shoots. There are several examples in which root-specific genetic modifications only lead to better plant performance if the genes are specifically expressed in roots. Constitutive expression can even result in modified plants that perform worse than non-modified plants. Rhizosphere effects have rarely been examined, but clearly genetic modification of roots can influence rhizosphere interactions, which in turn can affect shoot properties. Indeed, field studies with root-transformed plants frequently show negative effects on shoots that are not seen in laboratory studies. This might be due to the simplified environments that are used in laboratories which lack the full range of plant-rhizosphere interactions that are present in the field.