Objective
Gas fermentation provides a unique opportunity for the circular bioeconomy by enabling carbon recycling from gaseous waste feedstocks into value-added bioproducts using microbes. Acetogens are ideal biocatalysts for gas fermentation as they use gas (CO and/or CO2+H2) as their sole carbon and energy source, evidenced by the commercialisation of the waste gas-to-ethanol conversion process. Despite recent efforts, our understanding of genotype-phenotype relationships in acetogens is still minimal. This, however, is required for rational metabolic engineering and expanding the product spectrum of acetogens to unlock their full potential. GENESYS aims to engineer superior gas-fermenting cell factories through revolutionising systems-level understanding of acetogens and pioneering a new workflow for making large-scale arrayed CRISPR-engineered microbial strain libraries. First, essential genes for autotrophy of the model acetogen Clostridium autoethanogenum will be determined using a genome-wide CRISPRi screen. We then create ~750 single-gene knock-down strains of essential genes, transcription factors, and proteome “dark matter” using a novel “pooled-to-arrayed” workflow combining synthetic biology, automation, and DNA sequencing. Next, we will generate systems-level phenotype maps for up to ~750 strains through high-throughput gas fermentation and –omics analyses. Big data will be translated into a systems-level acetogen knowledgebase–A-BASE–through integrated bioinformatics and machine learning, metabolic modelling, and data engineering. Finally, we apply A-BASE to engineer acetogen cell factories from the “unexplored territories” of essential genes, transcription factors, and proteome “dark matter” for improving production of ethanol and acetone. This project will create superior gas-fermenting cell factories, advance fundamental understanding of acetogen metabolism, and pioneer a new workflow for creating large-scale arrayed engineered microbial strain libraries.
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.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social sciencessociologyindustrial relationsautomation
- natural scienceschemical sciencesorganic chemistryalcohols
- engineering and technologyindustrial biotechnologybiomaterials
- engineering and technologyindustrial biotechnologybioprocessing technologiesfermentation
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
51005 Tartu
Estonia