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postgraduate thesis: Development of multiplex droplet digital RT-PCR assays for the detection of influenza and other common respiratory virus infections

TitleDevelopment of multiplex droplet digital RT-PCR assays for the detection of influenza and other common respiratory virus infections
Authors
Advisors
Issue Date2022
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Leong, K. C. [梁景俊]. (2022). Development of multiplex droplet digital RT-PCR assays for the detection of influenza and other common respiratory virus infections. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.
AbstractThe novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first emerged in late December of 2019. Since then, coronavirus disease 2019 (COVID-19) pandemic has overburdened the economies and health care systems. The on-going spread of SARS-CoV-2 has raised concerns regarding the co- circulation of seasonal influenza viruses and other common respiratory viruses with SARS-CoV-2 in the future. There were influenza A and B viruses that caused up to 10% adults and 30% children globally infected in every year prior to the outbreak of SARS-CoV-2. The rapid, sensitive, and high throughput molecular tools that can identify and differentiate circulating respiratory viruses are essential for clinical and surveillance use. Currently, the most widely used diagnostics for the detection of respiratory viruses is quantitative reverse transcription PCR (qRT- PCR). However, this diagnostic approach is relatively sensitive to PCR inhibitors compared to end-point PCR, such as droplet digital reverse transcription PCR (ddRT-PCR). Absolute quantification of viral copy number is useful for clinical monitoring and prognosis evaluations, yet the quantification by qRT-PCR is challenging due to the high dependence on the quality of standard curve. The principle of ddRT-PCR is based on Poisson statistics where PCR reactions are partitioned into a minimum of 10,000 droplets for quantification without the need of standard curve. In this study, a novel six-plex ddRT-PCR assay was first developed for sensitive and accurate detection of contemporary influenza A and B viruses. The assay has a wide dynamic range and good reproducibility within and between runs. The limit of quantification (LoQ) of this assay was up to 45 copies per reaction. A reliable measurement in dual infection scenarios was demonstrated by testing a mixture of two influenza viruses in a highly disproportional ratio. A high concordance of typing, subtyping and lineage differentiation of influenza A and B viruses in testing 55 clinical samples was observed when compared to standard qRT-PCR. The six-plex ddRT-PCR assay was further expanded into a respiratory panel for the detection of fifteen common respiratory viruses including SARS-CoV-2 and influenza viruses. The respiratory panel consisted three multiplex ddRT-PCR assays with an optimized primer/probe sets. The performances of the respiratory panel were satisfied in linearity, LoQ, intra-/inter- assay variations and accuracy with 18 archived clinical samples. Furthermore, a co-infection screening of 98 imported COVID-19 case and 100 locally acquired COVID-19 case in Hong Kong was conducted using the respiratory panel. About 6.12% of the import cases and none of the locally acquired cases had viral co- infection with SARS-CoV-2, suggesting the mitigation strategies of COVID-19 pandemic may limit the spread of other respiratory viruses in Hong Kong. The multiplex ddRT-PCR assays developed in this study can serve as potential routine diagnostics for research and medical laboratories. (444 words)
DegreeDoctor of Philosophy
SubjectInfluenza - Diagnosis
Polymerase chain reaction - Diagnostic use
Respiratory infections - Diagnosis
Dept/ProgramPublic Health
Persistent Identifierhttp://hdl.handle.net/10722/335057

 

DC FieldValueLanguage
dc.contributor.advisorPoon, LML-
dc.contributor.advisorChan, MCW-
dc.contributor.authorLeong, Keng Chon-
dc.contributor.author梁景俊-
dc.date.accessioned2023-10-24T08:58:44Z-
dc.date.available2023-10-24T08:58:44Z-
dc.date.issued2022-
dc.identifier.citationLeong, K. C. [梁景俊]. (2022). Development of multiplex droplet digital RT-PCR assays for the detection of influenza and other common respiratory virus infections. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.-
dc.identifier.urihttp://hdl.handle.net/10722/335057-
dc.description.abstractThe novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first emerged in late December of 2019. Since then, coronavirus disease 2019 (COVID-19) pandemic has overburdened the economies and health care systems. The on-going spread of SARS-CoV-2 has raised concerns regarding the co- circulation of seasonal influenza viruses and other common respiratory viruses with SARS-CoV-2 in the future. There were influenza A and B viruses that caused up to 10% adults and 30% children globally infected in every year prior to the outbreak of SARS-CoV-2. The rapid, sensitive, and high throughput molecular tools that can identify and differentiate circulating respiratory viruses are essential for clinical and surveillance use. Currently, the most widely used diagnostics for the detection of respiratory viruses is quantitative reverse transcription PCR (qRT- PCR). However, this diagnostic approach is relatively sensitive to PCR inhibitors compared to end-point PCR, such as droplet digital reverse transcription PCR (ddRT-PCR). Absolute quantification of viral copy number is useful for clinical monitoring and prognosis evaluations, yet the quantification by qRT-PCR is challenging due to the high dependence on the quality of standard curve. The principle of ddRT-PCR is based on Poisson statistics where PCR reactions are partitioned into a minimum of 10,000 droplets for quantification without the need of standard curve. In this study, a novel six-plex ddRT-PCR assay was first developed for sensitive and accurate detection of contemporary influenza A and B viruses. The assay has a wide dynamic range and good reproducibility within and between runs. The limit of quantification (LoQ) of this assay was up to 45 copies per reaction. A reliable measurement in dual infection scenarios was demonstrated by testing a mixture of two influenza viruses in a highly disproportional ratio. A high concordance of typing, subtyping and lineage differentiation of influenza A and B viruses in testing 55 clinical samples was observed when compared to standard qRT-PCR. The six-plex ddRT-PCR assay was further expanded into a respiratory panel for the detection of fifteen common respiratory viruses including SARS-CoV-2 and influenza viruses. The respiratory panel consisted three multiplex ddRT-PCR assays with an optimized primer/probe sets. The performances of the respiratory panel were satisfied in linearity, LoQ, intra-/inter- assay variations and accuracy with 18 archived clinical samples. Furthermore, a co-infection screening of 98 imported COVID-19 case and 100 locally acquired COVID-19 case in Hong Kong was conducted using the respiratory panel. About 6.12% of the import cases and none of the locally acquired cases had viral co- infection with SARS-CoV-2, suggesting the mitigation strategies of COVID-19 pandemic may limit the spread of other respiratory viruses in Hong Kong. The multiplex ddRT-PCR assays developed in this study can serve as potential routine diagnostics for research and medical laboratories. (444 words)-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.lcshInfluenza - Diagnosis-
dc.subject.lcshPolymerase chain reaction - Diagnostic use-
dc.subject.lcshRespiratory infections - Diagnosis-
dc.titleDevelopment of multiplex droplet digital RT-PCR assays for the detection of influenza and other common respiratory virus infections-
dc.typePG_Thesis-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplinePublic Health-
dc.description.naturepublished_or_final_version-
dc.date.hkucongregation2022-
dc.identifier.mmsid991044729933303414-

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