Exploring Heterogeneous Phenotypes in Response to Stress

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

Supplementary Software

Python and FiJi scripts V1.0 (the linked GitHub repository contains the latest available version of these scripts); Supplementary Softwar

Supplementary Software

Python and FiJi scripts V1.0 (the linked GitHub repository contains the latest available version of these scripts); Supplementary Softwar

Cell Tracking Tools based on Machine Learning

Dataset and tools for the segmentation and tracking of growing bacterial cell population

Practice Dataset

A small dataset for use with the softwar

Videos

Supplementary videos showing the result of a segmentation/tracking process This channel also provides demo videos to further explain the usage of these tools (under construction

Practice Dataset

A small dataset for use with the softwar

Videos

Supplementary videos showing the result of a segmentation/tracking process This channel also provides demo videos to further explain the usage of these tools (under construction

Antibiotic tolerance is associated with a broad and complex transcriptional response in E. coli

AbstractAntibiotic treatment kills a large portion of a population, while a small, tolerant subpopulation survives. Tolerant bacteria disrupt antibiotic efficacy and increase the likelihood that a population gains antibiotic resistance, a growing health concern. We examined how E. coli transcriptional networks changed in response to lethal ampicillin concentrations. We are the first to apply transcriptional regulatory network (TRN) analysis to antibiotic tolerance by leveraging existing knowledge and our transcriptional data. TRN analysis shows that gene expression changes specific to ampicillin treatment are likely caused by specific sigma and transcription factors typically regulated by proteolysis. These results demonstrate that to survive lethal concentration of ampicillin specific regulatory proteins change activity and cause a coordinated transcriptional response that leverages multiple gene systems.</jats:p

Antibiotic tolerance is associated with a broad and complex transcriptional response in <i>E. coli</i>

SummaryAntibiotic treatment kills a large portion of a population, while a small, tolerant subpopulation survives. Tolerant bacteria disrupt antibiotic efficacy and increase the likelihood that a population gains antibiotic resistance, a growing health concern. We examined how E. coli transcriptional networks changed in response to lethal ampicillin concentrations. We are the first to apply transcriptional regulatory network (TRN) analysis to antibiotic tolerance by leveraging existing knowledge and our transcriptional data. TRN analysis shows that gene expression changes specific to ampicillin treatment are likely caused by specific sigma and transcription factors typically regulated by proteolysis. These results demonstrate that to survive lethal concentration of ampicillin specific regulatory proteins change activity and cause a coordinated transcriptional response that leverages multiple gene systems.</jats:p