Date of Award

2017

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Biology

Abstract

The presence of pathogenic bacteria on the surface of meat and produce causes many issues for the consumer including food-borne disease and infection. There are many techniques that have been proposed for food sterilization. Most of these techniques pose many problems including meat discoloration, toxic residues, inefficient application, and high cost of use. One compound, Cetylpyridinium Chloride (CPC), has become the gold standard for food antimicrobials. However, CPC is in need of being replaced due to the development of bacterial resistance, toxic residue developed during the treatment process, and environmental hazards. Using Quantitative Structural-Activity Relationships (QSAR) we have developed predictive models for compounds against three pathogenic foodborne bacteria: Escherichia coli, Salmonella typhimurium , and Listeria monocytogenes . Using structural similarity analysis we have found 13 potential compounds with similarity scores of 75% or greater. These compounds show promise as effective and safe replacements for CPC. Small noncoding regulatory RNAs (sRNA) are regulators of mRNA, protein, and DNA that have recently been identified in many bacterial species. sRNA are sometimes transcribed from the intergenic regions of a bacterial genome and are thought to be regulated by RNase III. In Streptococcus pyogenes , a common and potentially deadly pathogen, there are 3 sRNA (FasX, RivX, and PelRNA) that regulate virulence. Our goal was to use RNA sequencing (RNA-Seq) data of wild-type and RNase III deleted mutant S. pyogenes to detect and identify new trans -acting sRNA. We developed a custom program that can detect RNA reads supporting intergenic regions of the S. pyogenes genome. Together with two differential expression programs (Cufflinks and RSEM/EBseq), a differential expression analysis was performed comparing expressed transcripts of the intergenic regions, which could be affected by RNase III. This yielded 376 potential sRNA regions. The top 12 candidates that had greater than 2 fold change and a probability value less than or equal to 0.05 were used for further analysis. Using Artemis Genome Viewer software, these potential regions were verified as sRNA. The detection of new sRNA will not only expand our knowledge on the regulatory elements involved in S. pyogenes but may generate new insights into the elements that are key in the pathogenicity and virulence of this common bacterium.

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