Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning. Issue 7 (July 2021) - Allen, Jonathan P.; Snitkin, Evan; Pincus, Nathan B.; Hauser, Alan R.
Abstract : The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity within many bacterial species. Awareness of this genetic heterogeneity has corresponded with a greater appreciation of intraspecies variation in virulence. A number of comparative genomic strategies have been developed to link these genotypic and pathogenic differences with the aim of discovering novel virulence factors. Here, we review recent advances in comparative genomic approaches to identify bacterial virulence determinants, with a focus on genome-wide association studies and machine learning. Highlights: The plethora of bacterial whole-genome sequences generated in recent years has underscored the genetic diversity of strains within bacterial species, which has in turn suggested explanations for variable infectious manifestations caused by these strains. A number of sophisticated comparative genomic strategies, such as genome-wide association studies and machine learning algorithms, have been developed to take advantage of bacterial genetic diversity to uncover novel bacterial virulence determinants. Comparative genomic approaches have led to the identification of bacterial genes and polymorphisms linked to several disease endpoints, including cancer, invasive infection, mortality, cytotoxicity, and biofilm formation.