Bilateral BBSRC NSF/ Bio: The Roles of Contact-dependent Inhibition in Bacterial Communities

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

A wide range of bacterial species communicate with one another by a mechanism called contact-dependent growth inhibition (CDI). In this mechanism proteins deliver toxins to a neighboring bacterium, thereby causing inhibition of cell growth and even death. Little is known about how CDI affects bacterial interactions within communities containing the same species as opposed to mixed communities containing multiple different bacterial species. In this project, a collaborative team of US (University of California, Santa Barbara) and UK (University of York) researchers will determine how CDI systems influence the development of bacterial communities, using genetically engineered bacterial cells that express different CDI toxin systems and mathematical modeling of the bacterial populations. This project will enable a better understanding of the mechanisms by which bacteria interact with one another and build microbial communities, and has direct relevance to plant, animal, and human health. Students (including women and members of underrepresented minorities) will receive interdisciplinary training in biology, engineering and biophysics. Contact Dependent Inhibition systems (CDI) are ubiquitous bacterial toxin delivery systems that require direct interaction between the toxin delivery CdiAB apparatus of the CDI+ cell and a defined receptor protein on the target susceptible cell. Many fundamental questions about CDI remain unanswered including the role of CDI in bacterial ecology and evolution. This project will examine both the mechanism of CDI, and how CDI systems shape the development and dynamics of microbial communities using a combination of molecular biology and biochemical approaches. The investigators will generate strains with well-characterized CDI systems in the two model organisms Escherichia coli and Enterobacter cloacae. Subsequently, the effect of these CDI systems on population development will be determined based on a set of qualitative and quantitative imaging analyses at the single cell, microcolony, and biofilm levels. These data will be used to parameterize a suite of computational models with stochastic implementation to capture important transient interactions that ultimately define the population structure. An integrated model that allows in silico predictions of the CDI effect on mixed strain population structure will be validated in several rounds of combined model refinement and experimentation. This collaborative US/UK project is supported by the US National Science Foundation and the UK Biotechnology and Biological Sciences Research Council.