Synthetic Combinatorial Minimisation of Cell Cycle Control in Yeast
Malyshava, A.; Ciurkot, K.; Can_izares Alonso, L.; Grollemund, A.; Shaw, W. M.; Barberis, M.; Ellis, T.
Show abstract
The eukaryotic cell cycle, with its inherent regulatory redundancy, provides an ideal target for exploring genome modularisation and minimisation through synthetic genomics. Building upon principles established by the Synthetic Yeast Genome (Sc2.0) project, we used CRISPR-mediated genome engineering to relocate nine key cell cycle genes into a synthetic gene cluster in the S. cerevisiae genome, to allow combinatorial study of these genes in yeast. We employed Cre/loxP recombination to rapidly generate hundreds of strains with different gene deletion combinations in the module, with the objective of identifying minimal gene sets that permit robust cell cycle function. Using FACS-sorting and POLAR (Pool of Long Amplified Reads) sequencing, we conducted high-throughput analysis of gene deletion combinations in large cell pools, detecting approximately 80% of theoretically possible gene combinations, including those predicted from prior mathematical modelling studies. Our findings demonstrate that the cell cycle gene set can be minimised while maintaining viability, though only select combinations of gene deletions ensure robust fitness across different conditions. This work establishes a framework for genome minimisation, opening the door to the design of simplified, modular synthetic genomes for diverse applications.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- The conserved elongation factor Spn1 is required for normal transcription, histone modifications, and splicing in Saccharomyces cerevisiae 96%
- Bacterial cell cycle and growth phase switch by the essential transcriptional regulator CtrA 96%
- Inducible Directed Evolution of Complex Phenotypes in Bacteria 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.