Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.514401
Title: Molecular characterization and diagnosis of salmonella enterica strains in the United Kingdom
Author: Ben-Darif, Elloulu Taher
Awarding Body: The University of Manchester
Current Institution: University of Manchester
Date of Award: 2010
Availability of Full Text:
Access through EThOS:
Abstract:
Food-borne salmonellosis is a major cause of gastrointestinal disease in humans and domestic animals worldwide. In this study, the population structure and genetic relationship of various Salmonella serovars were investigated using Multilocus Sequence Typing (MLST). The MLST scheme, using seven housekeeping genes, was used to characterize and differentiate 153 Salmonella isolates of 34 serovars into 51 sequence types (STs) with fourteen novel STs identified among 13 serovars in the collection. The scheme provided good sub-type discrimination among various Salmonella serovars. Comparison of 51 allelic profiles against the database using START and eBURST software indicated a strong association of STs with serovars. The phylogenetic structure of each serovar was represented in one lineage of a cluster, with the exceptions that isolates of serovars Newport and Java were clustered into three and two distinct lineages, respectively. In addition, 17 STs of seven serovars were differentiated into nine clonal complexes. These results allowed the development of a molecular serotyping scheme based on detection of serovar specific single nucleotide polymorphism~SNPs) seen in the MLST data. The SNaPshot system is a multiplex primer extension assay (MPEA) that enables multiplex SNP analysis. Here, the method has been developed for the identification of five Salmonella serotypes commonly detected in the UK, based on the above serotype specific SNPs. The SNPs, in genes hemD, thrA, purE and sucA, acted as surrogate markers for serovars Typhimurium, Enteritidis, Virchow, Infantis and Bracnderup. The MPEA was performed using two separate panels of MPEA reactions and evaluated using 152 S. enterica isolates that had been characterized by MLST. The MPEA was shown to be 100% specific and sensitive for this collection of isolates. Furthermore, the MPEA was applied to identify the serovar or ST of DNA recovered from clinical specimens (faecal samples from human Salmonella infection) (n=15) and food samples (n= 10). The isolation of Salmonella from these specimens/samples and their subsequent serotyping indicated that use of the MPEA had allowed accurate identification of the serovar of 96% of isolates present in the samples. Interestingly, the assay also allowed identification of the serovar of two Salmonella isolates is from the above samples that were not able to be fully typed using serological methods. The method could be applied in less than six hours and has potential for improved patient care, public health investigation of Salmonella outbreaks and source tracing. Recently, the DiversiLab rep-Pf'R system has been developed using microfluidic chips to provide standardized semi-automated fingerprinting for pathogens including S. enterica. In the current study, 71 isolates of S. enterica, representing 21 different serovars, were analysed using MLST and the DiversiLab rep-Pf'R system. MLST was able to identify 31 STs, while the DiversiLab system revealed 38 Diversil.ab types (OTs). The DiversiLab rep-P(R approach distinguished isolates of different serovars and showed a greater discriminatory power (0.95) than MLST (0.89). The OiversiLab system exhibited 92% concordance with MLST and 90% with serotyping, while the concordance level of MLST with serotyping was 96%, representing a strong association. MLST and the DiversiLab reppeR system may provide useful additional informative techniques for the molecular identification of S. enterica isolates. In addition, the OiversiLab rep-PCR system may provide a rapid (less than 4 hours) and standardized method for the identification of S. enterica isolates.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.514401  DOI: Not available
Share: