Rapid detection and identification of foodborne pathogens using genomics

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Copyright: Zhang, Xiaomei
Abstract
Infectious diseases caused by Salmonella, Shigella and Shiga toxin-producing E. coli (STEC) place a heavy burden on human health and incur a massive economic cost. Timely detection and identification of these bacterial pathogens is vital for food safety and public health surveillance. Existing detection methods cannot easily distinguish different serotypes of these pathogens and are time-consuming. Early detection and identification of Salmonella, Shigella and STEC can be achieved by detection of highly specific and discriminatory pathogen genomic targets. Thus, comparative genomic analysis of many publicly available genomic sequences of Salmonella, Shigella and STEC has been applied to identify pathogen type-specific gene markers for rapid, highly sensitive and specific identification and differentiation of Salmonella, Shigella and STEC. In this thesis, pathogen type-specific gene markers for Salmonella, Shigella and STEC have been identified through comparative genomic analysis of pathogen genome sequences. For Salmonella, a set of 131 serovar-specific genes were identified for prediction of the 106 common serovars from genomic data with 95.3% accuracy. Seven laboratory diagnostic MCDA assays targeting seven Salmonella serovar-specific genes were then developed for the detection of five most prevalent Salmonella serovars in Australia with high specificity (>93.3%) and high sensitivity (>92.9%). These assays are rapid and can produce results in as short as 8 minutes. For Shigella, cluster-specific genes were identified for differentiation of Shigella and enteroinvasive E. coli (EIEC) from genomic data with 99.64% accuracy and were used to develop an in silico pipeline, ShigEiFinder for accurate differentiation, cluster typing and serotyping of Shigella and EIEC with 99.38% accuracy. For STEC, cluster/serotype-specific genes were identified for typing of STEC with 99.54% accuracy and were used to develop an in silico pipeline, STECFinder which can assign STEC isolates to STEC clusters and serotypes with 99.83% accuracy. These markers could be adapted for metagenomics or culture independent typing and could also be useful in the development of more cost-effective molecular assays. The outcome of this thesis can be applied to rapid typing of respective pathogens in food, clinical and environmental samples and facilitate surveillance of these pathogens for public health control and prevention
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Publication Year
2021
Resource Type
Thesis
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PhD Doctorate
UNSW Faculty
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