Biology subjects

Gawlitt, S.

Publications and source records attributed to Gawlitt, S..

2 recordsLinked to original sources

bioRxiv2026

Dissecting multi-gene virulence phenotypes with base editing requires consideration of target-independent mutagenesis

Bacterial pathogenicity arises from complex genetic interactions that are difficult to characterise through single-gene deletions. CRISPR base editors can generate multiplexed gene knockouts, yet this technology remains unexplored for dissecting bacterial pathogenicity. Here, we developed a base-editing pipeline for multi-gene knockouts while revealing that target-independent editing can contribute to variability in clonal fitness. In the model pathogen Salmonella Typhimurium, we employed curable plasmids containing a cytidine deaminase base editor and a multi-spacer CRISPR array to introduce premature stop codons in up to nine genes encoding SPI-2 T3SS effector proteins. Target bases were efficiently edited, producing a multi-knockout strain that showed reduced virulence in vivo relative to single knockouts. However, whole-genome sequencing revealed off-target cytidine deaminase activity, which affected virulence in vivo in a clone-dependent manner. A statistical power analysis predicted how many edited mutants are needed to confidently measure fitness functions in the face of off-target mutations. Our work shows the potential and current limitations of multiplexed base editing in bacterial pathogens and highlights the need for properly addressing off-target mutations when deploying base editors to interrogate genotype-phenotype relationships.

microbiology↗

bioRxiv2022

Improved prediction of bacterial CRISPRi guide efficiency through data integration and automated machine learning

CRISPR interference (CRISPRi), the targeting of a catalytically dead Cas protein to block transcription, is the leading technique to silence gene expression in bacteria. However, design rules for CRISPRi remain poorly defined, limiting predictable design for gene interrogation, pathway manipulation, and high-throughput screens. Here we develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in multiple genome-wide essentiality screens, with the surprising discovery that gene-specific features such as transcriptional activity substantially impact prediction of guide activity. Accounting for these features as part of algorithm development allowed us to develop a mixed-effect random forest regression model that provides better estimates of guide efficiency than existing methods, as demonstrated in an independent saturating screen. We further applied methods from explainable AI to extract interpretable design rules from the model, such as sequence preferences in the vicinity of the PAM distinct from those previously described for genome engineering applications. Our approach provides a blueprint for the development of predictive models for CRISPR technologies where only indirect measurements of guide activity are available.

bioinformatics↗