Biology subjects

Karslake, J.

Publications and source records attributed to Karslake, J..

4 recordsLinked to original sources

bioRxiv2021

Mapping biochemical states associated with HP1 target recognition at sites of heterochromatin formation in living cells

HP1 proteins bind with low affinity but high specificity to histone H3 lysine 9 methylation (H3K9me), forming transcriptionally inactive genomic compartments referred to as heterochromatin. How HP1 proteins traverse a complex and crowded chromatin landscape on the millisecond timescale and yet recognize H3K9me with high specificity remains paradoxical. Here, we visualize the single-molecule dynamics of an HP1 homolog, the fission yeast Swi6, in its native chromatin environment. By analyzing the motions of individual Swi6 molecules, we identify mobility states that map to discrete biochemical intermediates. Using mutants that perturb Swi6 H3K9me recognition, oligomerization, or nucleic acid binding, we parse the mechanism by which each biochemical property affects protein dynamics. We find that rather than enhancing chromatin binding, nucleic acid interactions, compete with and titrates Swi6 away from heterochromatin. However, as few as four tandem Swi6 chromodomains are necessary and sufficient to restore H3K9me-dependent localization. Our studies propose propose that HP1 oligomerization stabilizes higher-order protein configurations of a defined stoichiometry that facilitates high-specificity H3K9me recognition and outcompetes the inhibitory effects of nucleic acid-binding.

biophysics↗

bioRxiv2019

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

Bacteria exploit a diverse set of defenses to survive exposure to antibiotics. While the molecular and genetic underpinnings of antibiotic resistance are increasingly understood, less is known about how these molecular events influence microbial dynamics on the population scale. In this work, we show that the dynamics of E. faecalis communities exposed to antibiotics can be surprisingly rich, revealing scenarios where-for example-increasing population size or delaying drug exposure can promote population collapse. Specifically, we combine experiments in computer-controlled bioreactors with simple mathematical models to reveal density-dependent feedback loops that couple population growth and antibiotic efficacy when communities include drug-resistant ({beta}-lactamase producing) subpopulations. The resulting communities exhibit a wide range of behavior, including population survival, population collapse, or one of two qualitatively distinct bistable behaviors where survival is favored in either small or large populations. These dynamics reflect competing density-dependent effects of different subpopulations, with growth of drug-sensitive cells increasing but growth of drug-resistant cells decreasing effective drug inhibition. Guided by these results, we experimentally demonstrate how populations receiving immediate drug influx may sometimes thrive, while identical populations exposed to delayed drug influx (and lower average drug concentrations) collapse. These results illustrate that the spread of drug resistant determinants--even in a simplified single-species communities--may be governed by potentially counterintuitive dynamics driven by population-level interactions.

microbiology↗

bioRxiv2019

Sensitive cells extend antibiotic-driven containment of drug-resistant bacterial populations

Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, it also removes drug-sensitive cells as quickly as possible, removing competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, the containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the \"escape time\" required for drug-resistant E. coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape containment, but we found that matched populations with the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density-the acceptable bacterial burden-is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.

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↗