Objective
Protein secretion is a universal trait among organisms, involving multiple steps of quality control, sorting, trafficking, and export; as well as various organelles. Numerous pressing challenges faced by society can be addressed by thoroughly understanding protein secretion. Disease mechanisms can be elucidated, new antimicrobials can be discovered, and high-value proteins can be produced. However, exactly because of its complexity, a full mechanistic understanding of protein secretion is still lacking, impeding full exploitation.
The yeast Saccharomyces cerevisiae is a promising model organism for studying protein secretion and is also a major expression host for industrial heterologous protein production. So far research has only focused on a few lab strains, that are inefficient protein secretors and that do not fully represent this yeast’s natural biodiversity. My preliminary data show that properties considered to be optimal in lab strains often are inferior to those observed in some other, more ‘wild’ yeast. Herein, I propose that natural S. cerevisiae strains can have more efficient protein production and secretion pathways and that the responsible alleles can be identified and exploited creating a robust protein-producing strain. In ProteoYeast, I will combine my skills in omics analyses and systems biology with the host lab’s QTL mapping expertise, unique yeast collection (>1200 strains) and advanced robotic systems to address the existing knowledge gap. This work will be divided into three parts: 1) High-throughput investigation of the natural biodiversity for protein secretion in a high-throughput manner; 2) ‘Round Robin’ QTL approach to identify novel alleles affecting protein secretion; and 3) development of robust strains for high-titer secretion of added-value proteins. Today’s urgent demand for a sustainable bio-based economy, combined with progress in laboratory automation and systems biology make this project timely and fitting to societal needs
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- social sciencessociologyindustrial relationsautomation
- natural sciencesbiological sciencesecologyecosystems
- social scienceseconomics and businesseconomicsbioeconomy
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
9052 ZWIJNAARDE - GENT
Belgium