Arrayed CRISPRi and quantitative imaging describe the morphotypic landscape of essential mycobacterial genes.

ABSTRACT

Mycobacterium tuberculosis possesses a large number of genes of unknown or predicted function, undermining fundamental understanding of pathogenicity and drug susceptibility. To address this challenge, we developed a high-throughput functional genomics approach combining inducible CRISPR-interference and image-based analyses of morphological features and sub-cellular chromosomal localizations in the related non-pathogen, M. smegmatis. Applying automated imaging and analysis to 263 essential gene knockdown mutants in an arrayed library, we derive robust, quantitative descriptions of bacillary morphologies consequent on gene silencing. Leveraging statistical-learning, we demonstrate that functionally related genes cluster by morphotypic similarity and that this information can be used to inform investigations of gene function. Exploiting this observation, we infer the existence of a mycobacterial restriction-modification system, and identify filamentation as a defining mycobacterial response to histidine starvation. Our results support the application of large-scale image-based analyses for mycobacterial functional genomics, simultaneously establishing the utility of this approach for drug mechanism-of-action studies.

Caused by the microorganism Mycobacterium tuberculosis, tuberculosis kills more people around the world than any other infectious disease. M. tuberculosis is also becoming increasingly resistant to treatments, which are particularly difficult for patients to complete. The M. tuberculosis genome carries about four thousand genes, with several hundred being vital for survival. Finding new ways to fight tuberculosis relies on understanding the exact role of these essential genes, but they are difficult to study in living bacteria. To investigate this question, de Wet et al. used the related, fast-dividing bacterial species called M. smegmatis as a model. Microscopic imaging was combined with CRISPR-interference ­ a method that temporarily disrupts expression of a specific gene ­ to examine how blocking an essential gene would affect the shape of the living microorganism. Experiments were conducted on a collection of 270 mutants, capturing single-cell data for hundreds of thousands of live bacteria. To analyze the data, a computational pipeline was built, which automatically clustered similar-shaped bacteria. These groups, or 'phenoprints', brought together genes of known and unknown roles; this indicated that these genes participate in similar biological networks ­ and, if unknown, hinted at their function. Finally, targeting essential genes with CRISPR-interference often yielded the same shape changes as blocking their encoded proteins with antibiotics. This suggests that phenoprints could be useful to understand the mode of action of potential new tuberculosis treatments. When applied to M. tuberculosis and other deadly bacteria, the approach developed by de Wet et al. might speed up drug development.