Development of High-Throughput Tools for the Investigation of Quorum Sensing in Vibrio cholerae

Abstract

The global pathogen Vibrio cholerae uses a process called quorum sensing (QS) to detect the presence of other bacteria in its environment through small molecules called autoinducers (AIs) and it alters behavior accordingly. While the last few decades have uncovered remarkable information about the genes and mechanisms that control QS, most studies to date have been performed in well mixed cultures in which only one or two AIs are present, conditions that are not necessarily reflective of the complex habitats in which bacteria naturally reside. In the first part of this thesis, I establish a new assay for probing a three-dimensional matrix of 512 intermediate conditions containing three AIs: CAI-1, AI-2, and DPO. I discovered that CAI-1 and AI-2 each have only a modest effect on the readout, light production, and that this effect is greatly amplified when the other AI is present. DPO functions to amplify the output signal further without inducing light when in isolation or when only CAI-1 or AI-2 is also present. This result provides evidence of the QS circuit as a coincidence detector. Importantly, I describe novel methods of data visualization that enable analysis of the complex data sets that result from these assays. While measurements of bulk population-wide reporters are helpful to make general conclusions about the state of QS, a major hurdle in our ability to understand the QS circuit at the single-cell level stems from the lack of functional fusions of QS proteins to fluorescent proteins. In this thesis, I establish a method to identify functional fusions using randomized linkers between the protein and the fluorescent tag. With this high-throughput approach, the hope is to identify principles dictating the functionality of different linker fusions and to extrapolate this understanding to other proteins of interest. Finally, I show that establishing a large linker library through a novel application of a Gibson Assembly is viable for applications in high-throughput single cell sorting, isolating fusions by functionality, and identifying them by subsequent sequencing.