The difference of detection rate of avian influenza virus in the wild bird surveillance using various methods
- Author:
Gang San KIM
1
;
Tae Sik KIM
;
Joo Sung SON
;
Van Dam LAI
;
Jung Eun PARK
;
Seung Jun WANG
;
Weon Hwa JHEONG
;
In Pil MO
Author Information
- Publication Type:Original Article
- Keywords: Avian influenza; conventional method; next generation sequencing; Korea; wild bird
- MeSH: Animals; Birds; Feces; Influenza in Birds; Korea; Methods; Ovum; Seasons
- From:Journal of Veterinary Science 2019;20(5):e56-
- CountryRepublic of Korea
- Language:English
- Abstract: Korea is located within the East Asian-Australian flyway of wild migratory birds during the fall and winter seasons. Consequently, the likelihood of introduction of numerous subtypes and pathotypes of the Avian influenza (AI) virus to Korea has been thought to be very high. In the current study, we surveyed wild bird feces for the presence of AI virus that had been introduced to Korea between September 2017 and February 2018. To identify and characterize the AI virus, we employed commonly used methods, namely, virus isolation (VI) via egg inoculation, real-time reverse transcription-polymerase chain reaction (rRT-PCR), conventional RT-PCR (cRT-PCR) and a newly developed next generation sequencing (NGS) approach. In this study, 124 out of 11,145 fresh samples of wild migratory birds tested were rRT-PCR positive; only 52.0% of VI positive samples were determined as positive by rRT-PCR from fecal supernatant. Fifty AI virus specimens were isolated from fresh fecal samples and typed. The cRT-PCR subtyping results mostly coincided with the NGS results, although NGS detected the presence of 11 HA genes and four NA genes that were not detected by cRT-PCR. NGS analysis confirmed that 12% of the identified viruses were mixed-subtypes which were not detected by cRT-PCR. Prevention of the occurrence of AI virus requires a workflow for rapid and accurate virus detection and verification. However, conventional methods of detection have some limitations. Therefore, different methods should be combined for optimal surveillance, and further studies are needed in aspect of the introduction and application of new methods such as NGS.
