16S ribosomal RNA (rRNA) gene sequences are commonly analyzed for taxonomic and phylogenetic purposes because they contain variable regions that help distinguish genera. However, intra-genus classification is difficult due to high sequence similarity among closely related species. The biological impact of nucleotide variants in 16S variable regions are often unknown and hence their sequence differences are weighted evenly during classification, which provides poor species identity confidence. In this dissertation, I determined that analysis of intra-genus 16S allelic variants can provide species information and that nucleotide changes in 16S rRNA variable regions can impact ribosome quality. In one study, I analyzed ribosomal gene sequences, including 16S variable regions, to identify microbes that can spoil different retail draft beers. Based on relative sequence abundance changes of variable region sequences, I determined that certain bacteria preferred growth on draft lines rather than beers. Sequences of certain species were consistently detected at ratios indicative of their 16S gene copies, suggesting they came from specific strains. In a second study, I computationally interrogated 16S variable sequences in closely related genera Escherichia and Shigella and discovered that certain species could be differentiated. I demonstrated that Escherichia coli ribosomes were compromised when they carried 16S rRNA with these species-informative nucleotides, suggesting that variable region nucleotides may be constrained to respective species. In a third study, metagenomic sequencing was used to identify organisms that resided on cables submerged off the coast of Florida. Relative abundances of DNA for putative polymer-degrading organisms reduced over time and DNA for putative polymer-degrading enzymes were present at low relative abundance. Altogether, this dissertation shows the capabilities of DNA-based microbial identification and suggests that acknowledgment 16S alleles can improve intra-genus bacterial classification.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2877 |
| Date | 01 January 2023 |
| Creators | Bose, Nikhil |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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