The scientists then sampled a number of metabolites such as carbohydrates, pigments, and hormones, among others, throughout flower and fruit development. They also used microarrays to determine which genes were expressed at those same times. Pairwise comparisons and network analyses were then made to determine which of those genes and metabolites are associated in possible functional networks. These associations do not establish causality or regulatory direction, because they are only correlational. Expression of certain genes may regulate metabolite activity, but metabolites may also have a regulatory effect on gene expression. To begin to define causal direction, Carrari and his colleagues perturbed these systems by treatment with external metabolites and followed the transmission of information from metabolite to gene. In continuing research, Carrari and co-workers are using these methods, as well as RNA interference and transgenesis to map QMLs and to identify and utilize candidate genes that function at network nodes.
These systems approaches make it possible to model the whole organism throughout its development. Moreover, an understanding of metabolic networks will make it possible to alter metabolic pathways to produce fruits with different secondary compounds that influence texture, taste, aroma, and nutrition, as well as to improve yield. Metabolite analysis also has possible applications in drug discovery, nutrient enhancement and biofuel production. One important goal is the use of ancestral genetic resources in place of simplistic genetic modification to avoid possible deleterious environmental effects as well as resistance by consumers to genetically modified food.
|Contact: Dr. Jim Giovannoni|
American Society of Plant Biologists