Abstract

Neisseria gonorrhoeae, the bacterium which causes gonorrhea, is a public health threat as recognized by the CDC due to the risk of untreatable gonorrhea due to the high prevalence of resistance to antibiotics in the gonococcal population [1]. Current screening methods for resistance to antibiotics for clinical infections may take up to 2 days to produce results, which could result in delays in treatment, unnecessary follow-up appointments, inappropriate antibiotic prescriptions, and overall poor treatment outcomes [2]. In this study, we utilize RNA sequencing (RNA-seq) to characterize the expression patterns of resistant and susceptible strains of N. gonorrhoeae. Here, we look for transcripts that change their expression in susceptible strains in response to antibiotic treatment, as potentially diagnostic transcripts, with the overall goal of identifying transcripts that can be utilized to screen for resistance at the point of care. Furthermore, we conduct functional KEGG analysis to classify the functional classes of differentially expressed transcripts. Ultimately, a point of care (POC) diagnostic for gonococcal infections will allow for improved health outcomes and reduced misuse of antibiotics, which will help slow the epidemic of treatment resistant infections.

Publication Date

2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Bioinformatics (MS)

Department, Program, or Center

Thomas H. Gosnell School of Life Sciences

College

College of Science

Advisor

Crista Wadsworth

Advisor/Committee Member

Gary R. Skuse

Advisor/Committee Member

Michael V. Osier

Campus

RIT – Main Campus

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