Back

A scalable framework for high-throughput identification of functional origins of replication in non-model bacteria

Gilbert, C.; Brumwell, S. L.; Crits-Christoph, A.; Kang, S. C.; Martin-Moldes, Z.; Alsharif, W.; Esmurria, A.; Nguyen, M.-A.; Lee, H. H.; Ostrov, N.

2023-05-20 synthetic biology Community evaluation
10.1101/2023.05.19.541510 bioRxiv
Show abstract

Microbial genetic manipulation requires access to engineerable plasmids that can be programmed to perturb genes, pathways and genomes. The extensive repertoire of plasmids available for model microbes, such as Escherichia coli, has facilitated fundamental biology studies and synthetic biology applications. However, the scarcity of plasmids for non-model microbes hinders efforts to broaden our biological knowledge and constrains the development of biotechnological solutions. In this study, we introduce a molecular toolkit and multiplexed screen to evaluate functional plasmids in non-model microbes. We constructed a collection of genetic parts consisting of 22 origins of replication (ORIs), 20 antibiotic selectable markers, and 30 molecular barcodes, which can be assembled combinatorially to create a library of plasmids trackable by next-generation DNA sequencing. We demonstrate our approach by delivering a pooled library of 22 ORIs to 12 bacterial species including extremophiles, electroactive bacteria and bioproduction strains. We report, for the first time, DNA delivery by conjugation and functional ORIs for Halomonas alkaliphila, Halomonas neptunia, and Shewanella electrodiphila. Furthermore, we expand the list of functional ORIs for Duganella zoogloeoides, Pseudomonas alcaliphila, Shewanella oneidensis and Shewanella putrefaciens. This screen provides a scalable high-throughput system to rapidly build and identify functional plasmids to establish genetic tractability in non-model microbes.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.