Evan Snitkin, PhD

We are interested in the application of innovative computational and experimental approaches to study the epidemiology, evolution and biology of multi-drug resistant (MDR) bacteria that cause healthcare-associated infections (HAI). HAIs affect 5% of hospitalized patients, result in 99,000 deaths and cost $30 billion each year in the United States alone. The impact of HAI is poised to become even greater, as MDR organisms continue to spread, and the number of effective antibiotics dwindles. Bacteria resistant to all antibiotics are now being reported, bringing treatment of afflicted patients back to the pre-antibiotic era. The diminishing efficacy of available drugs has put even more onus on infection prevention. More effective prevention will require detailed insights into how infections spread in our healthcare system, thereby allowing for the optimization and implementation of containment strategies. To help meet this challenge, part of our laboratory works in the emerging field of genomic hospital epidemiology. The goal of this research is to take advantage of the molecular resolution provided by whole genome sequencing (WGS), to link together bacterial isolates from different patients, and in turn understand how infections spread. Through the integration of genomic and epidemiological analyses, we hope to elucidate both patient factors and treatments associated with the spread of infection, and thereby facilitate the implementation of evidence-based infection control measures. In addition to allowing insight into the epidemiology of hospital pathogens, WGS can provide fundamental insights into bacterial evolution. In particular, by sequencing bacteria isolated from patients, we can begin to decipher the molecular mechanisms underlying bacterial adaptations to the pressures imposed by antibiotic treatment, the host immune response and the other stresses encountered during the pathogen lifecycle. We believe that a better understanding of bacterial evolution in the context of the healthcare system will be essential for designing more effective treatments and diagnostics. Finally, we are also interested in understanding the molecular functions critical to the success of hospital pathogens. Central to both the pathogenicity and transmissibility of many hospital pathogens is their capacity to reach high population densities in the guts of antibiotic treated patients. Recent work has provided evidence that eliminating the desired nutritional niche of pathogens can reduce their absolute levels in the gut. Thus, an understanding of how pathogens depend upon the gut environment could potentially be exploited to reduce pathogen load, and thereby decrease both transmission and infection rates. To this end, we will apply tools of bacterial genetics, metabolic modeling and computational biology to develop frameworks by which the nutritional niche of a bacterial pathogen may be characterized.