Genomics approaches to elucidating pathways to antibiotic resistance in Neisseria gonorrhoeae

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Project Summary/Abstract The rise of antibiotic resistant bacteria poses a grave threat to public health. To outcompete susceptible bacteria and increase in prevalence, resistant strains must be able to compensate for fitness costs incurred by resistance-conferring mutations and genes. However, not every strain of a bacterial species can compensate equally well, yielding a complex evolutionary landscape between susceptibility and resistance. Elucidating the nature and diversity of the mechanisms that support acquisition and maintenance of resistance will allow us to understand how resistant strains emerge and spread and thereby accelerate development of desperately needed new strategies to prevent and treat resistant infections. We use the clinically important pathogen Neisseria gonorrhoeae (the gonococcus) as a model system, given its high burden of disease (820,000 cases in the US and nearly 80 million cases globally each year), the imminent threat of untreatable infection, and the ease of experimental manipulation. Our goal is to define the genetic networks that support acquisition and maintenance of resistance to three of the clinically most important antibiotics for treatment of gonococcus: the extended spectrum cephalosporins, azithromycin, and the quinolones. To do so, we will leverage our unique dataset of >1100 epidemiologically and genetically diverse clinical gonococcal isolates for which we have full genome sequences and antibiotic susceptibility profiles. We will use population-based computational and experimental methods that incorporate the diversity of susceptible and resistant populations and thus represent a fundamental shift from single reference strain studies. These methods include unbiased statistical tools to identify genetic differences in sub-populations; high-throughput transposon mutagenesis screens to define the loci that impact resistance as a function of genetic background; and a system for genome manipulation to validate links between genotype and resistance phenotype. We expect that the results from these studies will define the interacting loci that contribute to resistance in natural populations. These results can be applied to improving public health surveillance efforts and development of therapeutics. Moreover, the system we establish here can be used to further probe the biology of gonococcus and provides a framework for the development of similar systems to dissect of the genetic networks of resistance in other bacterial pathogens.