High-throughput functional genomics: A (myco)bacterial perspective.

Advances in sequencing technologies have enabled unprecedented insights into bacterial genome composition and dynamics. However, the disconnect between the rapid acquisition of genomic data and the (much slower) confirmation of inferred genetic function threatens to widen unless techniques for fast, high-throughput functional validation can be applied at scale. This applies equally to Mycobacterium tuberculosis, the leading infectious cause of death globally and a pathogen whose genome, despite being among the first to be sequenced two decades ago, still contains many genes of unknown function. Here, we summarize the evolution of bacterial high-throughput functional genomics, focusing primarily on transposon (Tn)-based mutagenesis and the construction of arrayed mutant libraries in diverse bacterial systems. We also consider the contributions of CRISPR interference as a transformative technique for probing bacterial gene function at scale. Throughout, we situate our analysis within the context of functional genomics of mycobacteria, focusing specifically on the potential to yield insights into M. tuberculosis pathogenicity and vulnerabilities for new drug and regimen development. Finally, we offer suggestions for future approaches that might be usefully applied in elucidating the complex cellular biology of this major human pathogen.

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

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.