Genome-wide metabolic modelling and data integration of organic acid production A. niger and potato

Organic acids like itaconic and fumaric acid have a strong potential as building blocks to replaced naphtha-based chemicals. In the field of the proposal, Dutch industries hold a strong position as producers and as end users of the chemical building blocks. Also the Dutch biotech fermentation industry and the plant breeding industry have a world-wide strong position.

Producing chemical building blocks

This proposal involves the modelling of the production of itaconic acid, a metabolic derivative of citric acid, and fumaric acid by Aspergillus niger and by a plant, potato (Solanum tuberosum). The proposal relies on the strength of both production platforms; A. niger is the production organism for the fermentative production of citric acid. Existing industrial processes can easily adapted for the production of itaconic acid and fumaric acid. Starch potato is a low cost crop that is already used for the production of starch for food and non-food applications. Preliminary experiments have shown significant production of itaconic acid after introduction of a microbial cis-aconitate decarboxylase.

Metabolic modelling

The proposal involves the metabolic modelling of citric acid and itaconic acid production in A. niger based on transcript profiling, sub-cellular proteomics and sub-cellular metabolomics. Cellular imaging data would provide input into the model for cellular localization of enzyme activities and the sub-cellular pH at the side of action of the enzyme. This will provide unique input data to base the metabolic model on. By comparative modelling transcriptomics, proteomics and metabolomics data from Aspergillus terreus, Rhizopus oryzae and potato will be used to determine the metabolic differences in these organisms and to redesign A. niger and potato for the efficient production of itaconic acid and fumaric acid.

Connecting with other projects

Essentially the data needed for this modelling comes from matching projects. These projects already have been granted, guaranteeing the input of data to make the project successful. Also these projects involve essential collaborations to implement the necessary methodology efficiently.

Supervising team

L.H. de Graaff, P.J. Schaap, V.A.P. Martins dos Santos, I.M. van der Meer and A.J. Koops

PhD Student

Dorett Odoni (project page for up-to-date information)