DOI
https://doi.org/10.25772/V3QD-DP65
Defense Date
2015
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Microbiology & Immunology
First Advisor
Dr. Ping Xu
Second Advisor
Dr. W. Mike Holmes
Third Advisor
Dr. Glen E. Kellogg
Fourth Advisor
Dr. Todd Kitten
Fifth Advisor
Dr. Yan Zhang
Abstract
The human body is colonized by more than 100 trillion microbes which make up an essential part of the body and plays a significant role in health. We now know the over use and misuse of broad-spectrum antibiotics can disrupt this microbiome contributing to the onset of disease and runs the risk of promoting antibiotic resistance. With antibiotic research still on the decline, new strategies are greatly needed to combat emerging pathogens while maintaining a healthy microbiome. We therefore set out to present a novel species-selective antimicrobial drug discovery strategy.
Disruption of the homeostasis within the oral cavity can trigger the onset of one of the most common bacterial infections, periodontal disease. Even though the oral cavity is one of the most diverse sites on the human body, the Gram-negative colonizer, Porphyromonas gingivalis has long been considered a key player in the initiation of periodontitis, suggesting the potential for novel narrow-spectrum therapeutics. By targeting key pathogens, it may be possible to treat periodontitis while allowing for the recolonization of the beneficial, healthy flora. Therefore, we set out to use P. gingivalis and periodontal disease as a model for pathogen-specific antimicrobial drug discovery.
In this study we present a unique approach to predict essential gene targets selective for the periodontal pathogen within the oral environment. Using our knowledge of metabolic networks and essential genes we identified a “druggable” essential target, meso-diaminopimelate dehydrogenase, which is found in a limited number of species. This enzyme, meso-diaminopimelate dehydrogenase from P. gingivalis, was first expressed and purified, then characterized for enzymatic inhibitor screening studies. We then applied a computer-based drug discovery method, combining pharmacophore models, high-throughput virtual screening and molecular docking. Utilizing the ZINC database we virtually screened over 9 million small-molecules to identify several potential target-specific inhibitors. Finally, we used target-based and whole-cell based biochemical screening to assess in vitro activity. We conclude that the establishment of this target and screening strategy provides a framework for the future development of new antimicrobials and drug discovery.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
10-29-2015