The Lemieux laboratory investigates the pathogenesis and epidemiology tick-borne and respiratory pathogens using computational and experimental methods. Our primary focus is defining and characterizing the microbial genetic factors that influence Lyme disease, a bacterial infection caused by Borrelia burgdorferi spirochetes. There is strong evidence that microbial genetic factors influence the clinical course of Lyme disease because clinical manifestations vary markedly depending on the infecting B. burgdorferi genotype; however, the specific microbial genes responsible for these differences are not known. Our laboratory is interested in identifying the genes that contribute to the distinct clinical manifestations of Lyme disease and defining the mechanisms by which they contribute to Lyme disease pathogenesis.
To find candidate B. burgdorferi loci linked to clinical heterogeneity in Lyme disease, we have conducted microbial genome wide association (GWAS) studies of B. burgdorferi phenotypes including dissemination, neuroborreliosis, and chronic skin infection (acrodermatitis). To characterize the function of candidate loci, we use the many experimental tools available for B. burgdorferi, including robust genetic tools available for genome modification, CRISPR systems for high-throughput screening, a mouse model that recapitulates important features of the disease, and access to human patients seen at MGH’s multidisciplinary Lyme disease clinic. We are particularly interested in lipoproteins expressed on the surface of the spirochete that are in contact with host tissues and immune defenses. Sequence homology and the results of prior investigations suggest that surface lipoproteins act in part to inactive the host immune system and/or promote adherence to host tissues, making them attractive targets for novel therapeutics and vaccines.
A second focus of the laboratory is leveraging the growing quantity of genome sequence data to understand how microbes spread and cause disease. Our major interest in this area is in linking genomic epidemiology to mechanistic studies of microbial function by incorporating individual genetic variants in computational models. We have developed new methods to conduct approximate phylogenetic inference using millions of SARS-CoV-2 genomes, enabling us to understand the effect of individual amino acid changes on viral fitness. The fundamental idea behind our approaches is to model genomes and lineages as a collection of individual mutations whose effects are individually inferred using model-based analyses of large-scale data. As microbial genomic datasets grow, we are interested in applying such models to other respiratory viruses and bacterial pathogens.
A final area of focus is the use of microbial genomics to study the evolution and epidemiology of emerging pathogens, particularly those associated with ticks. Previous and ongoing work in the laboratory has defined the genetic basis of resistance to first-line therapy to human babesiosis caused by Babesia microti (a tick-borne Apicomplexan parasite closely related to malaria). We are also interested in Powassan virus (POWV), a highly pathogenic tick-borne Flavivirus emerging in New England for which there are no available treatments. We have recently adapted methods from other Flaviviruses that allow us to safely characterize specific POWV proteins with the goals of defining the molecular factors important for infection and identifying treatments for POWV infection.
We welcome inquiries from graduate students and postdocs interested in experimental and computational studies in any of those areas.
