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DOI 10.1016/j.jviromet.2016.05.020

Медицинская библиотека · Journal of virological methods · 2016

Quantitative real-time PCR assays for detection and type-specific identification of the endemic species C human adenoviruses.

Lu Xiaoyan, Erdman Dean D

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Аннотация

Human adenoviruses (HAdVs) are medically important respiratory pathogens. Among the 7 recognized species (A-G), species C HAdVs (serotypes 1, 2, 5 and 6) are globally endemic and infect most people early in life. Species C HAdV infections are most often subclinical or mild and can lead to persistent shedding from the gastrointestinal and upper respiratory tracts. They can also cause severe disseminated disease in newborn and immunocompromised persons, where rapid and quantitative detection and identification of the virus would help guide therapeutic intervention. To this end, we developed quantitative type-specific real-time PCR (qPCR) assays for HAdV-1, -2, -5 and -6 targeting the HAdV hexon gene. All type-specific qPCR assays reproducibly detected as few as 5 copies/reaction of quantified hexon recombinant plasmids with a linear dynamic range of 8 log units (5-5×107 copies). No non-specific amplifications were observed with concentrated nucleic acid from other HAdV types or other common respiratory pathogens. Of 199 previously typed HAdV field isolates and positive clinical specimens, all were detected and correctly identified to type by the qPCR assays; 10 samples had 2 HAdV types and 1 sample had 3 types identified which were confirmed by amplicon sequencing. The species C HAdV qPCR assays permit rapid, sensitive, specific and quantitative detection and identification of four recognized endemic HAdVs. Together with our previously developed qPCR assays for the epidemic respiratory HAdVs, these assays provide a convenient alternative to classical typing methods.

Для цитирования: Lu Xiaoyan, Erdman Dean D. Quantitative real-time PCR assays for detection and type-specific identification of the endemic species C human adenoviruses.. Journal of virological methods. 2016. DOI: 10.1016/j.jviromet.2016.05.020.

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