|ZHOU, XIUJUAN - Shanghai Jiaotong University|
|ZHANG, LIDA - Shanghai Jiaotong University|
|SHI, CHINLEI - Shanghai Jiaotong University|
|LIU, BIN - Northwest Agriculture And Forestry University|
|DAN, XIANLONG - Shanghai Jiaotong University|
|ZHUANG, XIAOFEI - Shanghai Jiaotong University|
|CUI, YAN - Shanghai Jiaotong University|
|WANG, DAPENG - Shanghai Jiaotong University|
|SHI, XIANMING - Shanghai Jiaotong University|
Submitted to: International Journal of Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/16/2015
Publication Date: 3/1/2016
Citation: Zhou, X., Zhang, L., Shi, C., Fratamico, P.M., Liu, B., Paoli, G., Dan, X., Zhuang, X., Cui, Y., Wang, D., Shi, X. 2016. Genome-scale screening and validation of targets for identification of Salmonella enterica and serovar prediction. International Journal of Food Microbiology. 79:376-383.
Interpretive Summary: Salmonella is one of the most common causes of bacterial foodborne illness in the US and worldwide. In order to reduce the incidence of food poisoning by Salmonella and other foodborne pathogens, food producers and regulatory agencies require rapid and effective methods to detect these harmful bacteria in foods. Here we report the discovery of 15 Salmonella-specific DNA sequences using a computer-based method, and show experimentally that these DNA sequences can be used for the specific detection of Salmonella. In addition, one of the Salmonella-specific DNA sequences was used to determine the specific type of Salmonella that is present. The identified Salmonella-specific genes will allow for effective methods for the detection of Salmonella from foods and the method used to identify the specific type of Salmonlla can be applied to tracking outbreaks of foodborne illness. The application of these methods is expected to positively impact food safety and public health.
Technical Abstract: Salmonella enterica is the most common foodborne pathogen worldwide, with a great diversity of 2500 recognized serovars. Detection of S. enterica and its classification into serovars are essential for food safety surveillance and clinical diagnosis. Recently, the polymerase chain reaction (PCR) method has shown promise in these applications because of its rapidity and high accuracy. In the current study, we obtained 412 candidate detection targets for S. enterica using a comparative genomics mining approach. It was shown by GO function enrichment analysis of these candidate targets that the GO term with the largest number of unigenes with known function (38/177, 21.5%) was significantly involved in pathogenesis (p < 10*24). All the candidate targets were then evaluated by PCR assays. PCR test results indicated that 15 targets showed high specificity for the detection of S. enterica by verification among 151 S. enterica strains and 34 non-Salmonella strains, and the detection limits were 27.6 fg/PCR for S. enterica Enteritidis ATCC 13076 genomic DNA and 17.8 fg/PCR for S. enterica Typhimurium ATCC 14028 genomic DNA. In addition, the phylogenetic trees of verified targets were highly comparable with those of housekeeping genes, especially for differentiating S. enterica strains into serovars. Furthermore, the serovar-prediction ability was validated by sequencing one target (S9) for 39 S. enterica strains belonging to 6 serovars. Identical mutation sites existed in the same serovar and different mutation sites occurred in diverse serovars, including their numbers, locations, and variations in nucleotide bases. In all, our findings revealed that 15 verified targets can be potentially used for molecular detection and part of them for the serotyping of S. enterica.