Our lab is currently interested in: (1) understanding how toxin-antitoxin systems and other immunity mechanisms help bacteria defend themselves against phage predation and (2) elucidating the molecular basis of protein evolution and the coevolution of interacting proteins. We use a combination of genetics, biochemistry, microscopy, computational analyses, and genome-scale approaches like RNA-seq. Our work is rooted in a desire to develop a deep, fundamental understanding of how bacteria function and evolve, but it also has implications for and applications in areas such as protein engineering and phage therapy.
Toxin-antitoxin (TA) systems are prevalent genetic modules in bacteria, with many species encoding dozens of distinct systems. Despite decades of study, the functions of TA have remained unclear and controversial. We have recently found in E. coli that many TA systems play crucial roles in helping cells to defend themselves against a range of different bacteriophage (phage). Current work seeks to identify phage-defensive TA systems, characterize how the toxins are activated or released following phage infection, and understand how the toxins ultimately disrupt phage development. This work has also led to a keen interest in elucidating the clever mechanisms that phages use to overcome certain TA systems. Finally, we have parallel efforts aimed at identifying and characterizing new phage defense systems other than TA systems.
We continue to pursue a long-standing interest in the specificity and evolution of protein-protein interactions, using TA systems as well as two-component signaling proteins as our models. We seek to understand how cells orchestrate the action of paralogous TA systems and two-component pathways to avoid unwanted cross-talk, and to elucidate the mechanisms by which interacting pairs of proteins coevolve to either retain or change their interaction specificity. These studies include experimental investigations of protein evolution both in vitro and in vivo, ancestral protein reconstructions, directed evolution studies, and often the use of comprehensive and combinatorially complete mutant libraries coupled to deep sequencing readouts.
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