CRISPRI-based high-throughput functional genomic approaches for use in mycobacteria - De Wet, Timothy (Division of Medical Microbiology, 2022)
In the 20 years since the pioneering publication of the genome of Mycobacterium tuberculosis, significant efforts have been made to complete functional annotation of the genome. However, these efforts have generally been performed on a single-gene basis, ensuring slow progress and leaving large portions of the genome unannotated. High-throughput approaches to understanding the functional genome, such as transposon-insertion sequencing, have been developed and applied to mycobacteria in a variety of conditions; however, they have several limitations, particularly in their ability to study genes essential for viability. The recent optimisation of inducible CRISPR-interference for mycobacteria offers the potential to expand the high-throughput functional genomic toolkit. This thesis utilises CRISPR-interference for the development and validation of two high-throughput functional genomic approaches in the model mycobacterium M. smegmatis. The first approach combines large-scale pooled oligonucleotide synthesis and nextgeneration sequencing, and is termed CRISPRi-Seq. A pooled library of 11 367 mutants, targeting 2 385 M. smegmatis genes with M. tuberculosis homologues, was constructed and used to infer gene essentialities which were compared with corresponding predictions from transposon-insertion sequencing data. This process validated the CRISPRi-Seq technique and identified practical considerations for its future use. The second approach utilises data derived from CRISPRi-Seq to create an arrayed library of 263 individual M. smegmatis inducible CRISPRi mutants targeting essential genes. This library is applied to a quantitative imaging pipeline to produce detailed data-driven profiles of the morphological impact of essential gene suppression. These morphological profiles are used to statistically predict genetic function, as well as antimicrobial mechanism-of-action. The two novel approaches developed in this work represent valuable technical advances and produce large datasets of functional genomic data which are available interactively online. Taken individually, or in combination, these methodologies can be utilised to increase fundamental understanding of mycobacteria, including the pathogenic M. tuberculosis