Vibrio vulnificus, a natural inhabitant of brackish and estuarine environments, is a fatal opportunistic human pathogen with a mortality rate of over 50%. Unlike for other pathogenic Vibrio species, and despite its high mortality rate, there is no conclusive approach to indicate the pathogenic potential of V. vulnificus isolates from the environment. To this day, a single gene encoding a hemolysin, vvh, has been used to detect V. vulnificus, and one phenotype - indole production, has been used to assess the pathogenic potential and classification of a given strain. In this study, we use genomic-based approaches to identify distinct phenotypes that can characterize V. vulnificus strains with highest pathogenic potential (cluster 1 – C1), and to determine novel genes that can be used to accurately detect and identify V. vulnificus strains from natural reservoirs. Our phenotypic analyses indicate that strains from C1 utilize a more diverse range of carbon sources compared to strains less likely to emerge as pathogenic (cluster 2 – C2). We found that C1 and C2 prefer to inhabit different niches leading to behavioral separation. Physiological adaptations like motility in the presence of mucin and growth at different salinities indicate markedly different lifestyles for these clusters. These phenotypes can thus be used as markers to predict the pathogenic potential of unknown V. vulnificus isolates from the environment based on their clusters. Furthermore, we identified six candidate genes that can distinctly discriminate between the two clusters and are more sensitive in detecting V. vulnificus compared to existing typing techniques. The high degree of resolution offered by this simple, reproducible approach can thus be used to identify V. vulnificus strains from natural reservoirs, as validated in our study using environmental isolates from oysters and water.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-7858 |
| Date | 01 January 2019 |
| Creators | Jayakumar, Jane Maureen |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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