Comparison of methods for detection of norovirus in oysters

Research output: Contribution to journalJournal articleResearchpeer-review

In the absence of culture methods for noroviruses, detection in foods relies on molecular techniques such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) on extracted viral RNA followed by PCR product confirmation by hybridisation and/or sequencing. However, in order to obtain a successful detection it is of great importance to remove the tissue inhibitors during the viral RNA extraction. To select the most efficient extraction procedure of oysters we have compared four protocols. A pool of digestive gland material from oyster samples was divided into 1.5 g portions and spiked with 10-fold dilutions of human faecal samples containing norovirus genogroup II. The samples were tested on three different occasions using four different sample treatment protocols. The protocols were assessed with regard to their ability to recover viral RNA and detect norovirus in spiked oysters and for their in-house reproducibility. One method using viral elution by a Mixer Mill Cell Disrupter resulted in a 10-fold better recovery than the other three protocols when an RT-seminested PCR (G2SKR/COG2F and G2SKR/G2SKF) detection approach was applied. Although less distinctive this was also the case when NoV was detected by a single round RT-PCR approach using the primers JV13i and JV12y. The second most efficient method was a method using chloroform extraction and polyethylene precipitation.
Original languageEnglish
JournalInternational Journal of Food Microbiology
Issue number3
Pages (from-to)352-6
Number of pages5
Publication statusPublished - 20 Mar 2007

    Research areas

  • Animals, Chemical Precipitation, Chloroform, Consumer Product Safety, Feces, Food Contamination, Food Microbiology, Humans, Norovirus, Ostreidae, Polyethylene, RNA, Viral, Reproducibility of Results, Reverse Transcriptase Polymerase Chain Reaction, Sensitivity and Specificity, Shellfish
  • Faculty of Health and Medical Sciences

ID: 46987017