Abstract
Deep mutational scanning can provide significant insights into the function of essential genes in bacteria. Here, we developed a high‐throughput method for mutating essential genes of Escherichia coli in their native genetic context. We used Cas9‐mediated recombineering to introduce a library of mutations, created by error‐prone PCR, within a gene fragment on the genome using a single gRNA pre‐validated for high efficiency. Tracking mutation frequency through deep sequencing revealed biases in the position and the number of the introduced mutations. We overcame these biases by increasing the homology arm length and blocking mismatch repair to achieve a mutation efficiency of 85% for non‐essential genes and 55% for essential genes. These experiments also improved our understanding of poorly characterized recombineering process using dsDNA donors with single nucleotide changes. Finally, we applied our technology to target rpoB, the beta subunit of RNA polymerase, to study resistance against rifampicin. In a single experiment, we validate multiple biochemical and clinical observations made in the previous decades and provide insights into resistance compensation with the study of double mutants.
Highlights
The function of proteins, which drive all cellular processes, can be uncovered by studying mutations in their sequence
In the CRISPR/Cas9mediated genomic error-prone editing (CREPE) workflow (Fig 1), we initially screened for a guide RNA (gRNA) centered around the genomic target of interest that enables over 95% editing efficiency for replacing the NGG PAM with the synonymous PAM mutation (SPM) to be used in the repair template
We ensured that the SPM does not affect the fitness of the cells and that their growth is comparable to wild-type E. coli
Summary
The function of proteins, which drive all cellular processes, can be uncovered by studying mutations in their sequence. The advent of next-generation DNA sequencing platforms has paved way for the emergence of high-throughput sequence-to-function mapping such as deep mutational scanning (DMS), which expands our access to previously unexplored areas of fitness landscapes for proteins (Araya & Fowler, 2011; Fowler & Fields, 2014). In DMS, activities of thousands of mutants in large libraries, covering most or potentially all possible substitutions in the protein sequence, are measured simultaneously using next-generation sequencing (Araya & Fowler, 2011; Fowler & Fields, 2014). Comprehensive DMS of essential genes using these approaches has remained elusive, especially in bacteria
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