A Systems Biology Approach to the Elucidation of Metabolic Networks underlying Health Based Quality Traits in Tomato Fruit.

'We are what we eat' is a phrase that has been used for many years to describe the importance of good dietary components. Although this may not be strictly true, it has been shown that diets rich in fruits and vegetables are beneficial to human well-being and health. The beneficial effects of fruits and vegetables have been attributed to the synergistic effects of various phytochemicals present. Many of these compounds like carotenoids, vitamins E and C, as well as flavonoids and phenylpropanoids are potent antioxidants, with the ability to dissipate damaging reactive molecules produced by the body's metabolism. Tomato fruit are a major dietary source of important antioxidants such as carotenoids (especially the red-coloured lycopene of ripe fruit). The consumers demands for improved aesthetic and nutritional quality has lead to increased efforts to increase the levels of these compounds in crop plants. Recently tomato varieties have been produced that contain high levels of multiple antioxidants. These varieties were produced by manipulating the plants ability to perceive light, in this instance GM technology was used. Although this technology is not acceptable presently to the consumer the plants do provide a valuable research tool to study the underlying events leading to nutritional enhancement in crop plants such as tomato. In the present proposal we aim to use modern techniques to determine the chemical composition and genes expressed in these tomato varieties as their fruit develops and ripens. A mathematical approach will then be used to collate, integrate and decipher the information. The collective data will reveal the interaction between the genes and metabolites in these tomato varieties from which a dynamic model can be constructed. This approach is termed a systems approach because it does not look at specific molecule entities but how they interact in a cell. The dynamic models showing the genes and molecules interacting will enable us to determine cascades of events occurring in the cell from which putative master regulators can be identified. These regulators will be tested by transient expression in the tomato fruit cells to see if they can elevate health related phytochemical in tomato fruit. The knowledge acquired will enable show to apply modern non-GM plant breeding techniques to produce improved tomato varieties that are nutrient rich and more beneficial to human health.

Diets rich in fruits and vegetables are beneficial to human well-being and health. These health promoting properties have been attributed to the synergistic effects of various phytochemicals, such as carotenoids, vitamins E and C, flavonoids and phenylpropanoids. Todays consumer demands improved nutritional quality and several strategies have been employed to create nutrient-rich foods. Ripe tomato fruit contain significant basal levels of several health promoting phytochemicals. Recently the manipulation in tomato of a signal transduction component involved in light perception, namely De /Etiolated-1 (DET-1) has lead to tomato varieties with unparalleled simultaneous elevation of multiple classes of health-related compounds. This experimental system will form the basis of the proposed project. Using a developmental and ripening series of tomato fruits derived from RNAi-suppressed DET-1, transcriptomic and metabolomic analyses have been performed. A systems biology approach will be used to integrate and de-convolute these datasets, elucidating gene to gene, metabolite to metabolite, gene to metabolite, as well as gene/metabolite to cellular properties and phenotype correlations. Time Series Network Inference (TSNI) and Bayesian approaches will be employed to decipher the cascade of molecular and metabolic events that leads to these important chemotypes. Collectively these data will enable the identification of putative regulatory and biosynthetic genes that contribute to DET-1 chemotypes/phenoypes. Functional testing of these putative regulators and their role in the networks predicted will be carried out using Virus VIGS and TILLING approaches to down regulate the target genes, or Agrobacterium infiltration to over-express gene products. The datasets derived from functional testing will be analysed to refine and extend our predictive models of the networks underlying health based traits conferred by DET-1 down regulation