Mild reductions in cytosolic NADP-dependent isocitrate dehydrogenase activity result in lower amino acid contents and pigmentation without impacting growth

Transgenic tomato (Solanum lycopersicum) plants were generated targeting the cytosolic NADP-dependent isocitrate dehydrogenase gene (SlICDH1) via the RNA interference approach. The resultant transformants displayed a relatively mild reduction in the expression and activity of the target enzyme in the leaves. However, biochemical analyses revealed that the transgenic lines displayed a considerable shift in metabolism, being characterized by decreases in the levels of the TCA cycle intermediates, total amino acids, photosynthetic pigments, starch and NAD(P)H. The plants showed little change in photosynthesis with the exception of a minor decrease in maximum photosynthetic efficiency (Fv/Fm), and a small decrease in growth compared to the wild type. These results reveal that even small changes in cytosolic NADP-dependent isocitrate dehydrogenase activity lead to noticeable alterations in the activities of enzymes involved in primary nitrate assimilation and in the synthesis of 2-oxoglutarate derived amino acids. These data are discussed within the context of current models for the role of the various isoforms of isocitrate dehydrogenase within plant amino acid metabolism

Using genome-scale metabolic networks for analysis, visualization, and integration of targeted metabolomics data.

Interpretation of metabolomics data in the context of biological pathways is important to gain knowledge about underlying metabolic processes. In this chapter we present methods to analyze genome-scale models (GSMs) and metabolomics data together. This includes reading and mining of GSMs using the SBTab format to retrieve information on genes, reactions, and metabolites. Furthermore, the chapter showcases the generation of metabolic pathway maps using the Escher tool, which can be used for data visualization. Lastly, approaches to constrain flux balance analysis (FBA) by metabolomics data are presented

Reconstruction of the Saccharopolyspora erythraea genome-scale model and its use for enhancing erythromycin production

Genome-scale metabolic reconstructions are routinely used for the analysis and design of metabolic engineering strategies for production of primary metabolites. The use of such reconstructions for metabolic engineering of antibiotic production is not common due to the lack of simple design algorithms in the absence of a cellular growth objective function. Here, we present the metabolic network reconstruction for the erythromycin producer Saccharopolyspora erythraea NRRL23338. The model was manually curated for primary and secondary metabolism pathways and consists of 1,482 reactions (2,075 genes) and 1,646 metabolites. As part of the model validation, we explored the potential benefits of supplying amino acids and identified five amino acids "compatible" with erythromycin production, whereby if glucose is supplemented with this amino acid on a carbon mole basis, the in silico model predicts that high erythromycin yield is possible without lowering biomass yield. Increased erythromycin titre was confirmed for four of the five amino acids, namely valine, isoleucine, threonine and proline. In bioreactor experiments, supplementation with 2.5 % carbon mole of valine increased the growth rate by 20 % and simultaneously the erythromycin yield on biomass by 50 %. The model presented here can be used as a framework for the future integration of high-throughput biological data sets in S. erythraea and ultimately to realise strain designs capable of increasing erythromycin production closer to the theoretical yield