Plants produce some of the most potent human therapeutics and have been used for millennia to treat illnesses. The monoterpenoid indole alkaloids (MIAs) are plant secondary metabolites that show a remarkable structural diversity and pharmaceutically valuable biological activities with more than 2,000 MIAs derived from the common precursor strictosidine. However, because most MIA chemicals do not have their biosynthetic pathways elucidated and MIA-producing plants are not genetically trackable, MIAs are under-represented in recently introduced medicines. In the consortium for Refactoring of Monoterpenoid Indole Alkaloids in Microbial Cell Factories (MIAMi) our main objective is to develop sustainable microbial production of new human therapies for the benefit of the European biotech industry, human health, and the environment. To do so, MIAMi will i) develop a new approach for MIA biosynthetic pathway discovery in plants founded on supervised learning algorithms based on omics data sampled from > 20 MIA producing plants, ii) contribute to standardisation of bioengineering by development of SOPs for characterisation of > 100 DNA elements for control of gene expression, protein interactions, and sub-cellular localisation, iii) build a public parts repository and Bio-CAD for forward engineering of compartmentalised biosynthetic pathway designs in yeast, and iv) apply automated genome engineering to prototype > 1,000 new-to-nature MIA biosynthetic pathway designs in order to identify robust designs for scale-up microbial MIA production processes, and evaluate the environmental benefits and risks compared to existing value chains. The excellent, interdisciplinary and inter-sectorial consortium will showcase the use of the new approaches and standardised data inventory to produce both commercially available and new-to-market MIAs rauwolscine, tabersonine and alstonine in yeast, and finally test their bioactivity as new cancer and psychosis treatment drug leads.
Field of science
- /social sciences/economics and business/economics/production economics
- /natural sciences/computer and information sciences/artificial intelligence/machine learning/supervised learning
Call for proposal
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Funding SchemeRIA - Research and Innovation action
2333 BE Leiden