Abstract
In spite of their involvement in foodborne illness, the epidemiological relevance of staphylococcal enterotoxin C (SEC) subtypes is poorly documented may be due to high sequence similarity. Among subtypes, SEC1, SEC2, and SEC3 exhibit more than 97 % homology because of which specific detection tools are seldom available to identify and differentiate them. In this study, a SYBR Green-based RT-PCR followed by melt curve analysis was developed for differentiation of entC1 from entC2/entC3 using a single primer pair. Nucleotide sequences of all three subtypes were analyzed using Clustal Omega program and the region with significant sequence variation/heterogeneity (where utmost SNPs were closely located and accessible for RT-PCR) was selected for amplification by designing a single primer pair that could amplify all three subtypes. In spite of same amplicon size, entC1 showed distinct melt peak at 76 °C. However, due to high similarity between entC2 and entC3, the developed format was deficient to discriminate between them and both showed melt peak at 82 °C. Reliability of developed RT-PCR was evaluated using various naturally contaminated samples and 91 food and clinical Staphylococcus aureus isolates where satisfactory results were obtained in comparison with commercial immunoassay kit and conventional PCRs using validated primers. To the best of our knowledge, this is the first method being reported to differentiate entC1 from entC2/entC3 using single primer pair which is unachievable by conventional PCR due to same amplicon size. As benefits, the method is sensitive, rapid, and inexpensive with no requirement of fluorescent probes, multiple primers, and post-PCR procedures. Thus, the assay might find its utility as a detection tool in epidemiological survey of foodborne outbreaks for simultaneous identification and differentiation of entC1 from entC2/entC3.
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Nagaraj, S., Ramlal, S., Venkataswamachari, B.P. et al. Differentiation of entC1 from entC2/entC3 with a single primer pair using simple and rapid SYBR Green-based RT-PCR melt curve analysis. Appl Microbiol Biotechnol 100, 8495–8506 (2016). https://doi.org/10.1007/s00253-016-7637-y
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DOI: https://doi.org/10.1007/s00253-016-7637-y