Peter J Gwynne, PhD
Research Assistant Professor
Tufts University Lyme Disease Initiative
B. Burgdorferi Metabolism
While my degree was in biochemistry and my PhD on bacterial gene regulation, my subsequent postdoctoral work has been heavily rooted in the translation of bench science to real-world applications. Through three successive postdoctoral positions I have worked to bridge academic research and industrial or commercial application. At the University of Edinburgh I was seconded to two separate companies, each with their own research project. The first was developing an antimicrobial medical device from concept through prototype development and towards a clinical trial. For this I worked with clinicians and engineers to design a bespoke series of assays to test the efficacy and mechanism of antimicrobial activity. In the second I leveraged my experience of bacterial genetics to optimize a production strain and gained valuable experience in bioprocess engineering and pharmaceutical manufacturing. I am pleased to use this experience to build relationships with commercial partners as the Director of Translational Research at the Tufts Lyme Disease Initiative.
At Tufts I have been motivated by the lack of diagnostic and treatment options for Lyme to develop a research program with potential to translate basic science into meaningful progress towards the clinic. Exploiting the unusual metabolism of the parasitic B. burgdorferi, I discovered the incorporation of a diverse array of host lipids into the bacterial membrane. We were then able to identify antibodies raised in response to these scavenged lipids, which we aim to study further here. Based on these studies of lipid metabolism, we have been able to develop a candidate diagnostic test which may improve the management of post-treatment Lyme symptoms.
As a complement to this work developing diagnostic methods, I am also developing a pipeline for the discovery of antimicrobial agents targeted specifically to B. burgdorferi. This work is based on comparative computational modeling of B. burgdorferi and other common pathogens and identified unique metabolic vulnerabilities which represent targets for the development of narrow-spectrum antimicrobials. Several of these computational predictions were validated in culture and we are currently exploring the repurposing of existing drugs to target them, as well as developing novel small molecule inhibitors.