Donarski, E. D. | Search bioRxiv

bioRxiv2021

Independent Promoter Recognition by TcpP Precedes Cooperative Promoter Activation by TcpP and ToxR

Cholera is a diarrheal disease caused by the Gram-negative bacterium Vibrio cholerae. To reach the surface of intestinal epithelial cells, proliferate, and cause disease, V. cholerae tightly regulates the production of virulence factors such as cholera toxin (ctxAB) and the toxin co-regulated pilus (tcpA-F). ToxT is directly responsible for regulating these major virulence factors while TcpP and ToxR indirectly regulate virulence factor production by stimulating toxT expression. TcpP and ToxR are membrane-localized transcription activators (MLTAs) required to activate toxT expression. To gain a deeper understanding of how MLTAs identify promoter DNA while in the membrane, we tracked the dynamics of single TcpP-PAmCherry molecules in live cells using photoactivated localization microscopy and identified heterogeneous diffusion patterns. Our results provide evidence that: 1) TcpP exists in three biophysical states (fast diffusion, intermediate diffusion, and slow diffusion); 2) TcpP transitions between these different diffusion states; 3) TcpP molecules in the slow diffusion state are interacting with the toxT promoter; and 4) ToxR is not essential for TcpP to localize the toxT promoter. These data refine the current model of cooperativity between TcpP and ToxR in stimulating toxT expression and demonstrate that TcpP locates the toxT promoter independent of ToxR.

microbiology↗

bioRxiv2019

SMAUG: Analyzing single-molecule tracks with nonparametric Bayesian statistics

Single-molecule fluorescence microscopy probes nanoscale, subcellular biology in real time. Existing methods for analyzing single-particle tracking data provide dynamical information, but can suffer from supervisory biases and high uncertainties. Here, we introduce a new approach to analyzing single-molecule trajectories: the Single-Molecule Analysis by Unsupervised Gibbs sampling (SMAUG) algorithm, which uses nonparametric Bayesian statistics to uncover the whole range of information contained within a single-particle trajectory (SPT) dataset. Even in complex systems where multiple biological states lead to a number of observed mobility states, SMAUG provides the number of mobility states, the average diffusion coefficient of single molecules in that state, the fraction of single molecules in that state, the localization noise, and the probability of transitioning between two different states. In this paper, we provide the theoretical background for the SMAUG analysis and then we validate the method using realistic simulations of SPT datasets as well as experiments on a controlled in vitro system. Finally, we demonstrate SMAUG on real experimental systems in both prokaryotes and eukaryotes to measure the motions of the regulatory protein TcpP in Vibrio cholerae and the dynamics of the B-cell receptor antigen response pathway in lymphocytes. Overall, SMAUG provides a mathematically rigorous approach to measuring the real-time dynamics of molecular interactions in living cells.\n\nStatement of SignificanceSuper-resolution microscopy allows researchers access to the motions of individual molecules inside living cells. However, due to experimental constraints and unknown interactions between molecules, rigorous conclusions cannot always be made from the resulting datasets when model fitting is used. SMAUG (Single-Molecule Analysis by Unsupervised Gibbs sampling) is an algorithm that uses Bayesian statistical methods to uncover the underlying behavior masked by noisy datasets. This paper outlines the theory behind the SMAUG approach, discusses its implementation, and then uses simulated data and simple experimental systems to show the efficacy of the SMAUG algorithm. Finally, this paper applies the SMAUG method to two model living cellular systems--one bacterial and one mammalian--and reports the dynamics of important membrane proteins to demonstrate the usefulness of SMAUG to a variety of systems.

biophysics↗