Antibiotic resistance is a quickly emerging public health crisis, accounting for more than 700,000 annual global deaths. Global human antibiotic overuse and misuse has significantly expedited the rate at which bacteria become resistant to antibiotics. A renewed focus on discovering new antibiotics is one approach to addressing this crisis. However, it alone cannot solve the problem: historically, the introduction of a new antibiotic has consistently, and at times rapidly, been followed by the appearance and dissemination of resistant bacteria. It is thus crucial to develop strategies to improve how we select and deploy antibiotics so that we can control and prevent the emergence and transmission of antibiotic resistance.
Current gold-standard antibiotic susceptibility tests measure bacterial growth, which can take up to 72 hours. However, bacteria exhibit more immediate measurable phenotypes of antibiotic susceptibility, including changes in transcription, after brief antibiotic exposure. In this dissertation I develop a framework for building a paper-based cell-free toehold sensor antibiotic susceptibility test that can detect differential mRNA expression. I also explore how long-term lab evolution experiments can be used to prospectively uncover transcriptional signatures of antibiotic susceptibility.
Paper-based cell-free systems provide an opportunity for developing clinically tractable nucleic-acid based diagnostics that are low-cost, rapid, and sensitive. I develop a computational workflow to rapidly and easily design toehold switch sensors, amplification primers, and synthetic RNAs. I develop an experimental workflow, based on existing paper-based cell-free technology, for screening toehold sensors, amplifying bacterial mRNA, and deploying sensors for differential mRNA detection. I combine this work to introduce a paper-based cell-free toehold sensor antibiotic susceptibility test that can detect fluoroquinolone-susceptible E. coli. Next, I describe a methodology for long-term lab evolution and how it can be used to explore the relationship between a phenotype, such as gene expression, and antibiotic resistance acquisition. Using a set of E. coli strains evolved to acquire tetracycline resistance, I explore how each strain's transcriptome changes as resistance increases. Together, this work provides a set of computational and experimental methods that can be used to study the emergence of antibiotic resistance, and improve upon available methods for properly selecting and deploying antibiotics. / 2023-03-17T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44044 |
| Date | 17 March 2022 |
| Creators | Briars, Emma Ann |
| Contributors | Khalil, Ahmad |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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