Jerry Ampofo-Asiama

The metabolic response of cultured tomato cells to low oxygen stress

The storage of fruits and vegetables under a controlled atmosphere can induce low oxygen stress, which can lead to post-harvest losses through the induction of disorders such as core breakdown and browning. To gain better understanding of the metabolic response of plant organs to low oxygen, cultured tomato cells (Lycopersicum esculentum) were used as a model system to study the metabolic stress response to low oxygen (0 and 1 kPa O2). By adding 13C labelled glucose, changes in the levels of polar metabolites and their 13C label accumulation were quantified. Low oxygen stress altered the metabolite profile of tomato cells, with the accumulation of the intermediates of glycolysis in addition to increases in lactate and sugar alcohols. 13C label data showed reduced label accumulation in almost all metabolites except lactate and some sugar alcohols. The results showed that low oxygen stress in tomato cell culture activated fermentative metabolism and sugar alcohol synthesis while inhibiting the activity of the TCA cycle and the biosynthesis of metabolites whose precursors are derived from central metabolism, including fluxes to most organic acids, amino acids and sugars.

Multi-objective experimental design for 13C-based metabolic flux analysis

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of

Metabolic characterization of tomato fruit during preharvest development, ripening, and postharvest shelf-life

120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective design should stimulate its application within the field of (13)C-based metabolic flux analysis.