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Detection of hepatitis a virus from oyster by nested PCR using efficient extraction and concentration method
Duwoon Kim1, Seok-Ryel Kim, Ki-Sung Kwon
1Division of Food Science and Aqualife Medicine, Chonnam National University, Yeosu 550-749, Republic of Korea.
Journal of Microbiology (Seoul, Korea)
|September 2, 2008
Summary
A new TPTT protocol enhances the detection of hepatitis A virus (HAV) in oysters. This method improves sensitivity for identifying viral pathogens in shellfish, crucial for food safety.
Area of Science:
- Food safety and microbiology
- Molecular virology
- Public health
Background:
- Polymerase chain reaction (PCR) is vital for detecting viral pathogens in food and water.
- Current PCR methods struggle with low sensitivity due to inhibitors and inefficient virus concentration/purification.
- Hepatitis A virus (HAV) detection in shellfish requires sensitive and reliable methods.
Purpose of the Study:
- To develop an improved protocol for detecting hepatitis A virus (HAV) in oyster digestive glands.
- To enhance the sensitivity of PCR-based viral detection in complex food matrices.
- To establish a more efficient method for concentrating and purifying viral nucleic acids.
Main Methods:
- Development of the TPTT (tris elution buffer-PEG-TRIzol-poly(dT) magnetic bead) protocol.
- Comparison of HAV precipitation methods using zirconium hydroxide versus PEG/NaCl.
- Nested PCR for sensitive detection of HAV in oyster homogenates.
Main Results:
- The TPTT protocol demonstrated significantly higher sensitivity compared to zirconium hydroxide precipitation.
- Detection limit achieved was 0.0148 PFU/g of oyster homogenate using PEG/NaCl precipitation.
- The TPTT method proved 10(5) fold more sensitive than traditional methods.
Conclusions:
- The TPTT protocol offers an efficient and highly sensitive method for detecting HAV in oysters.
- This method is suitable for routine detection of HAV in shellfish, improving food safety surveillance.
- The developed protocol surpasses the sensitivity of most existing published tests for HAV detection.

