Abstract
Toxin–antitoxin systems (TAs) are abundant in bacterial chromosomes and can arrest growth under stress, but usually remain inactive. TAs have been increasingly implicated in halting the growth of infected bacteria from bacteriophages or foreign genetic elements1,2 to protect the population (abortive infection, Abi). The vast diversity and abundance of TAs and other Abi systems3 suggest they play an important immunity role, yet what allows them to sense attack remains largely enigmatic. Here, we describe a method called toxin activation–inhibition conjugation (TAC–TIC), which we used to identify gene products that trigger or block the toxicity of phage-defending tripartite retron-TAs4. TAC–TIC employs high-density arrayed mobilizable gene-overexpression libraries, which are transferred into cells carrying the full TA system or only its toxic component, on inducible vectors. The double-plasmid transconjugants are then pinned on inducer-containing agar plates and their colony fitness is quantified to identify gene products that trigger a TA to inhibit growth (TAC), or that block it from acting (TIC). TAC–TIC is optimized for the Singer ROTOR pinning robot, but can also be used with other robots or manual pinners, and allows screening tens of thousands of genes against any TA or Abi (with toxicity) within a week. Finally, we present a dual conjugation donor/cloning strain (Escherichia coli DATC), which accelerates the construction of TAC–TIC gene-donor libraries from phages, enabling the use of TAC–TIC for identifying TA triggers and antidefense mechanisms in phage genomes.
Key points
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TAC–TIC employs gene-overexpression plasmid libraries conjugated into E. coli strains carrying the full toxin–antitoxin or phage defense system or only its toxin/effector. Colony fitness of double-plasmid transconjugants is quantified to reveal genes that trigger the system to inhibit growth or block its toxin from acting.
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TAC–TIC can identify all potential phage genes that trigger/block a defense system, while phage escapee studies identify the strongest genetic elements.
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Code availability
Code used to analyze TAC–TIC data in ref. 4 can be found at https://git.embl.de/kritikos/tic-tac.
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Acknowledgements
We thank the Typas laboratory for discussions and for advice when first establishing the automated steps of this protocol, especially M. Wartel and A. Koumoutsi. This work was funded by EMBL.
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J.B. and A.T. conceived the TAC–TIC protocol, J.B., A.L.J.Y. and C.G.P.V. optimized the protocol for constructing gene–donor phage libraries, A.L.J.Y. performed experiments for Supplementary Fig. 1a,b, J.B. designed figures with comments from all authors. J.B. and A.L.J.Y. wrote the manuscript, with extensive comments and edits from C.G.P.V. and A.T.
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Bobonis, J. et al. Nature 609, 144–150 (2022): https://doi.org/10.1038/s41586-022-05091-4
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Supplementary Methods, Table 1 and Figs. 1 and 2.
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Bobonis, J., Yang, A.L.J., Voogdt, C.G.P. et al. TAC–TIC, a high-throughput genetics method to identify triggers or blockers of bacterial toxin–antitoxin systems. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-00988-y
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DOI: https://doi.org/10.1038/s41596-024-00988-y
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