A cell-free transcription-translation pipeline for recreating methylation patterns boosts DNA transformation in bacteria

Published: 2 April 2024| Version 1 | DOI: 10.17632/z9g4b9s299.1
Justin Vento, Deniz Durmusoglu1, Tianyu Li, Constantinos Patinios, Sean Sullivan, Fani Ttofali, John van Schaik, Yanying Yu, Yanyan Wang, Lars Barquist, Nathan Crook, Chase Beisel


The bacterial world offers diverse strains for understanding medical and environmental processes and for engineering synthetic-biology chasses. However, genetically manipulating these strains has faced a long-standing bottleneck: how to efficiently transform DNA. Here we report IMPRINT, a generalized, rapid and scalable approach based on cell-free transcription-translation (TXTL) to overcome DNA restriction, a prominent barrier to transformation. IMPRINT utilizes TXTL to express DNA methyltransferases from a bacterium’s restriction-modification systems. The expressed methyltransferases then methylate DNA in vitro to match the bacterium’s DNA methylation pattern, circumventing restriction and enhancing transformation. With IMPRINT, we efficiently multiplex methylation by diverse DNA methyltransferases and enhance plasmid transformation in gram-negative and gram-positive bacteria. We also developed a high-throughput pipeline that identifies the most consequential methyltransferases, and we apply IMPRINT to screen a ribosome-binding site library in a hard-to-transform Bifidobacterium. Overall, IMPRINT can enhance DNA transformation, enabling use of sophisticated genetic manipulation tools across the bacterial world.


Steps to reproduce

1. The sequence data of RBS library screening is available with accession number GSE240651. After merging the pair-end reads, run "Sequencing_match_count.py" to obtain the counts of RBSs. A summary table of counts from all samples is "summary_table.tsv" 2. To obtain the differential abundance of RBSs, run "differential_abundance_RBS.R". The result files "*_QLFTest.csv" are included here. 3. To generate the FASTA files for Weblogo and plot the base frequency difference, see the code in "plots.py".


Data Analysis