Multiplex Molecular Test Developed for Three Arboviruses
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By LabMedica International staff writers Posted on 30 Jun 2016 |
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Image: The 7500 Fast Dx Real-Time polymerase chain reaction (rt-PCR) instrument (Photo courtesy of Applied Biosystems).
Clinical manifestations of Zika virus, chikungunya virus and dengue virus infections can be similar. To improve virus detection, streamline molecular workflow, and decrease test costs, a multiplex real-time reverse transcription polymerase chain reaction (rt-PCR) for these viruses has been developed and evaluated.
The diagnosis of human Zika virus infections is confounded by a nonspecific clinical presentation, which overlaps substantially with that of dengue virus (DENV) and chikungunya virus (CHIKV) and by cross-reaction with DENV immunoglobulin M (IgM) and DENV nonstructural protein 1 in assays for Zika virus.
Scientists at the Stanford University School of Medicine, Stanford, CA, USA) evaluated the single-reaction multiplex rRT-PCR for Zika virus, CHIKV, and DENV (referred to as the ZCD assay) by testing clinical samples from persons with suspected cases in Nicaragua. They tested 216 samples by using the ZCD assay and the pan–DENV-CHIKV rRT-PCR, which is a validated duplex assay containing the DENV and CHIKV primers and probes used in the ZCD assay.
All rRT-PCR reactions were performed on an ABI 7500 instrument (Applied Biosystems, Foster City, CA, USA). A total of 173 samples were positive, 25 for DENV alone, 110 for CHIKV alone, or 38 for both. The ZCD assay and pan–DENV-CHIKV rRT-PCR showed very good agreement for DENV detection. The two assays demonstrated good agreement for CHIKV detection and the cycle threshold (Ct) for the 35 CHIKV discrepant samples were reached significantly later than the 113 concordant samples. Of the 56 Zika virus–positive samples in the ZCD assay, 39 were positive only for Zika virus, and 17 showed evidence of mixed infection.
The authors concluded that the single-reaction multiplex ZCD assay detected and differentiated Zika virus, CHIKV, and DENV. This assay should streamline molecular workflow and decrease test costs while improving detection of these three human arboviruses. The study was published in the July 2016 issue of the journal Emerging Infectious Diseases.
Related Links:
Stanford University School of Medicine
Applied Biosystems
The diagnosis of human Zika virus infections is confounded by a nonspecific clinical presentation, which overlaps substantially with that of dengue virus (DENV) and chikungunya virus (CHIKV) and by cross-reaction with DENV immunoglobulin M (IgM) and DENV nonstructural protein 1 in assays for Zika virus.
Scientists at the Stanford University School of Medicine, Stanford, CA, USA) evaluated the single-reaction multiplex rRT-PCR for Zika virus, CHIKV, and DENV (referred to as the ZCD assay) by testing clinical samples from persons with suspected cases in Nicaragua. They tested 216 samples by using the ZCD assay and the pan–DENV-CHIKV rRT-PCR, which is a validated duplex assay containing the DENV and CHIKV primers and probes used in the ZCD assay.
All rRT-PCR reactions were performed on an ABI 7500 instrument (Applied Biosystems, Foster City, CA, USA). A total of 173 samples were positive, 25 for DENV alone, 110 for CHIKV alone, or 38 for both. The ZCD assay and pan–DENV-CHIKV rRT-PCR showed very good agreement for DENV detection. The two assays demonstrated good agreement for CHIKV detection and the cycle threshold (Ct) for the 35 CHIKV discrepant samples were reached significantly later than the 113 concordant samples. Of the 56 Zika virus–positive samples in the ZCD assay, 39 were positive only for Zika virus, and 17 showed evidence of mixed infection.
The authors concluded that the single-reaction multiplex ZCD assay detected and differentiated Zika virus, CHIKV, and DENV. This assay should streamline molecular workflow and decrease test costs while improving detection of these three human arboviruses. The study was published in the July 2016 issue of the journal Emerging Infectious Diseases.
Related Links:
Stanford University School of Medicine
Applied Biosystems
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