Document Type

Dissertation - Open Access

Award Date

2020

Degree Name

Doctor of Philosophy (PhD)

Department

Biology and Microbiology

First Advisor

Nicholas C. Butzin

Keywords

Antibiotic persistence, Antibiotic tolerance, Proteolytic queueing, Single-cell tracking, Synthetic biology, Toxin-antitoxin systems

Abstract

This work combines traditional microbiology with bioinformatic and synthetic biology approaches to study antibiotic tolerance. Antibiotic tolerance is a widespread phenomenon that facilitates antibiotic resistance and decreases the effectiveness of antibiotic treatment. Tolerance is distinct from antibiotic resistance, because tolerance is short term survival and typically results from phenotypic variations rather than genetic variation. The molecular mechanisms underlying tolerance are varied and debated in the literature. I have explored two intracellular processes related to tolerance, toxin-antitoxin (TA) systems (Chapter 2) and proteases (Chapter 4). Specifically, I focus on the ratio of antitoxin-to-toxin in type II TA systems, because type II TA systems must be regulated in such a way that antitoxins are more prevalent than their toxins. Our analysis of RNA-sequencing and ribosome profiling data demonstrates that most type II TA systems in E. coli are regulated at the translational level, while others rely on various combinations of transcriptional and post-transcriptional regulation. Before publishing this article, researchers often cited transcriptional regulation as the primary method of regulating TA systems. Studying antibiotic tolerance and other subpopulations necessitates the ability to study single-cell dynamics in the context of the whole population. To facilitate single-cell analysis, we have developed single-cell tracking software that leverages machine learning to identify cells. The software then tracks the cell based on this classification and returns data on cell size, location, division and fluorescence. The software provides the means of quantifying cell behavior before and after antibiotic treatment. One such system we would like to apply this software to is our work on proteolytic queueing and antibiotic tolerance. Proteases are responsible for protein degradation and, as such, regulate many cellular functions. To better identify the role proteases play in persistence, we used proteolytic queueing to interfere with proteolytic activity. We found that interfering with degradation at the protease ClpXP increases antibiotic tolerance ~80 and ~60 fold in an E. coli population treated with ampicillin and ciprofloxacin, respectively. I used stochastic modeling to support our results, and we have experimentally determined that altering the expression of the synthetic system affects the level of tolerance in the population. I am currently using next-generation sequencing to identify the systems being affected by the queue.

Format

application/pdf

Number of Pages

100

Publisher

South Dakota State University

Rights

In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/

Included in

Microbiology Commons

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