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Frontiers in microbiology2022; 13; 1009610; doi: 10.3389/fmicb.2022.1009610

Establishment of a reverse transcription real-time quantitative PCR method for Getah virus detection and its application for epidemiological investigation in Shandong, China.

Abstract: Getah virus (GETV) is a mosquito-borne, single-stranded, positive-sense RNA virus belonging to the genus of the family . Natural infections of GETV have been identified in a variety of vertebrate species, with pathogenicity mainly in swine, horses, bovines, and foxes. The increasing spectrum of infection and the characteristic causing abortions in pregnant animals pose a serious threat to public health and the livestock economy. Therefore, there is an urgent need to establish a method that can be used for epidemiological investigation in multiple animals. In this study, a real-time reverse transcription fluorescent quantitative PCR (RT-qPCR) method combined with plaque assay was established for GETV with specific primers designed for the highly conserved region of GETV gene. The results showed that after optimizing the condition of RT-qPCR reaction, the minimum detection limit of the assay established in this study was 7.73 PFU/mL, and there was a good linear relationship between viral load and value with a correlation coefficient ( ) of 0.998. Moreover, the method has good specificity, sensitivity, and repeatability. The established RT-qPCR is 100-fold more sensitive than the conventional RT-PCR. The best cutoff value for the method was determined to be 37.59 by receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) was 0.956. Meanwhile, we collected 2,847 serum specimens from swine, horses, bovines, sheep, and 17,080 mosquito specimens in Shandong Province in 2022. The positive detection rates by RT-qPCR were 1%, 1%, 0.2%, 0%, and 3%, respectively. In conclusion, the method was used for epidemiological investigation, which has extensive application prospects.
Copyright © 2022 Cao, Qiu, Shi, Ha, Zhang, Xie, Wang, Zhu, Zhao, Zhao, Jin and Lu.
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Publication Date: 2022-09-23 PubMed ID: 36212868PubMed Central: PMC9538719DOI: 10.3389/fmicb.2022.1009610Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The study details the establishment of a real-time reverse transcription quantitative PCR (shortened to RT-qPCR) method for detecting the Getah virus, a mosquito-borne RNA virus known to cause illnesses in several species of animals. The researchers tested the method on numerous serum and mosquito samples collected in the Shandong Province, China, and concluded that the method can be very useful for epidemiological investigations.

Development of the RT-qPCR method for Getah virus detection

  • The researchers set out to design a RT-qPCR method for Getah virus (GETV) detection with the use of specific primers for the highly conserved region of the GETV NSP1 gene. This method was created in response to the virus’s increasing spectrum of infection and harmful effects, particularly in pregnant animals.
  • GETV is known to infect a range of vertebrate species and causes severe health complications, including the potential to cause abortion in pregnant animals, thus posing a significant risk not only to public health but also to the livestock economy.

Features and accuracy of the RT-qPCR method

  • The RT-qPCR method showed high accuracy, having a minimum detection limit established at 7.73 PFU/mL. In simpler terms, this refers to how sensitive the test is in detecting the smallest amount of virus present in test samples.
  • The correlation coefficient of the method (shown as R) was 0.998, meaning there was a very high degree of accuracy between viral load and the ‘Ct’ value obtained during testing.
  • The method showed strong specificity, sensitivity, and repeatability, making it a reliable way to detect GETV.
  • The researchers also noted that their RT-qPCR method was 100-fold more sensitive than the conventional RT-PCR method.
  • The best cutoff value for the method was determined to be 37.59 by receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC), another measure of test accuracy, was 0.956–another indication of the test’s high accuracy.

Application of the RT-qPCR method for epidemiological investigation

  • The developed method was used to test on 2,847 serum specimens from swine, horses, bovines, sheep, and 17,080 mosquito specimens collected in 2022 in the Shandong province of China.
  • Results showed varying positive detection rates for different species: 1% for swine and horses, 0.2% for bovines, 0% for sheep, and 3% for the mosquito specimens.
  • The researchers concluded that the RT-qPCR method they developed holds potential for broader application in epidemiological investigations of GETV.

Cite This Article

APA
Cao X, Qiu X, Shi N, Ha Z, Zhang H, Xie Y, Wang P, Zhu X, Zhao W, Zhao G, Jin N, Lu H. (2022). Establishment of a reverse transcription real-time quantitative PCR method for Getah virus detection and its application for epidemiological investigation in Shandong, China. Front Microbiol, 13, 1009610. https://doi.org/10.3389/fmicb.2022.1009610

Publication

Frontiers in microbiology
ISSN: 1664-302X
NlmUniqueID: 101548977
Country: Switzerland
Language: English
Volume: 13
Pages: 1009610
PII: 1009610

Researcher Affiliations

Cao, Xinyu
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, China.
Qiu, Xiangshu
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • College of Animal Sciences, Institute of Preventive Veterinary Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Shi, Ning
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • Key Laboratory of Zoonoses Research, Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Ha, Zhuo
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
Zhang, He
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
Xie, Yubiao
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
Wang, Peng
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
Zhu, Xiangyu
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
Zhao, Wenxin
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
Zhao, Guanyu
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • Key Laboratory of Zoonoses Research, Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Jin, Ningyi
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, China.
  • College of Animal Sciences, Institute of Preventive Veterinary Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
  • Key Laboratory of Zoonoses Research, Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, China.
Lu, Huijun
  • Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
  • College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, China.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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