Abstract

This dissertation focuses on the development of diagnostic tools for mycobacteria using hybridization based technologies including binary deoxyribozyme (BiDz) sensors and microarrays. The genus Mycobacterium, is a diverse group of bacteria containing 150+ species including M. tuberculosis (M.tb) and non-tuberculous mycobacteria (NTM) which exhibit a range of pathogenicity, drug susceptibility and growth characteristics. M. tuberculosis (M.tb) is the causative agent of tuberculosis (TB) and the leading cause of infectious disease related deaths worldwide. The control of TB is limited by the lack of sensitive and specific diagnostic tools available at the point of care (POC). The studies presented here illustrate the advances in our technology for the detection and differentiation of M.tb and NTM. The use of BiDz sensors enables the selective recognition of DNA/RNA analytes containing single nucleotide polymorphisms associated with species-specific identification, drug susceptibility testing (DST) and strain typing. First, we developed a platform for the detection of M.tb and drug susceptibility using multiplex PCR and BiDz sensors. However, this method relies on the use of expensive instrumentation which is often not available in high TB burden countries. Therefore, additional studies focused on the development of tools for the detection of isothermal amplification products and the direct detection of rRNA. Based on these findings, we also developed an NTM species typing tool using BiDz sensors for species identification in ~1 hour. Despite the advantages of BiDz sensor technology, their use is limited to the detection of a few selected mutations. To address this limitation, we developed a 15-loci multiplex PCR followed by analysis with a custom microarray for high-throughput identification of SNPs. The work presented in this dissertation has the potential to enable the rapid, specific and sensitive identification of mycobacterial species necessary to reduce the diagnostic delay, ensure initiation of effective therapy, and prevent further transmission.

Notes

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Graduation Date

2017

Semester

Fall

Advisor

Kolpashchikov, Dmitry

Degree

Doctor of Philosophy (Ph.D.)

College

College of Medicine

Department

Burnett School of Biomedical Sciences

Degree Program

Biomedical Sciences

Format

application/pdf

Identifier

CFE0006856

URL

http://purl.fcla.edu/fcla/etd/CFE0006856

Language

English

Release Date

December 2022

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Open Access)

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