Diagnostic Testing for SARS-CoV-2 Infection

Thomas, Emmanuel; Delabat, Stephanie; Andrews, David

doi:10.1007/s11901-021-00567-9

Cited by 10 publications

(6 citation statements)

References 72 publications

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“…However, most of the early testing protocols had limitations including sensitivity, specificity, and background crossreactivity. Moreover, diagnostic tests were required to be processed at off-site centralized laboratories, requiring a long turnaround time with almost a week needed for results to become available [27][28][29]. Some of these limitations were later improved by newly developed commercial assays that have their limit of detection-analytical sensitivity better defined than previous tests.…”

Section: The Methodologies Of Covid-19 Diagnostic Testsmentioning

confidence: 99%

The significance of advanced COVID-19 diagnostic testing in pandemic control measures

Kwok¹

2022

Int. J. Biol. Sci.

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Section: The Methodologies Of Covid-19 Diagnostic Testsmentioning

confidence: 99%

The significance of advanced COVID-19 diagnostic testing in pandemic control measures

Kwok¹

2022

Int. J. Biol. Sci.

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“…Another limitation associated with this method is primer-variant overlap with heavily mutated lineages (e.g., Omicron), which can result in PCR amplification failure and a subsequent decrease in coverage [ 28 , 29 , 30 , 31 ]. In contrast, PCR-based approaches represent a simpler approach to track specific VOCs [ 32 ]. Molecular techniques involving PCR have played an important role in COVID-19 diagnostic testing and are widely used for viral nucleic acid detection due to their high sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Combined Use of RT-qPCR and NGS for Identification and Surveillance of SARS-CoV-2 Variants of Concern in Residual Clinical Laboratory Samples in Miami-Dade County, Florida

Carattini

Griswold

Williams

et al. 2023

Viruses

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Over the course of the COVID-19 pandemic, SARS-CoV-2 variants of concern (VOCs) with increased transmissibility and immune escape capabilities, such as Delta and Omicron, have triggered waves of new COVID-19 infections worldwide, and Omicron subvariants continue to represent a global health concern. Tracking the prevalence and dynamics of VOCs has clinical and epidemiological significance and is essential for modeling the progression and evolution of the COVID-19 pandemic. Next generation sequencing (NGS) is recognized as the gold standard for genomic characterization of SARS-CoV-2 variants, but it is labor and cost intensive and not amenable to rapid lineage identification. Here we describe a two-pronged approach for rapid, cost-effective surveillance of SARS-CoV-2 VOCs by combining reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) and periodic NGS with the ARTIC sequencing method. Variant surveillance by RT-qPCR included the commercially available TaqPath COVID-19 Combo Kit to track S-gene target failure (SGTF) associated with the spike protein deletion H69-V70, as well as two internally designed and validated RT-qPCR assays targeting two N-terminal-domain (NTD) spike gene deletions, NTD156-7 and NTD25-7. The NTD156-7 RT-qPCR assay facilitated tracking of the Delta variant, while the NTD25-7 RT-qPCR assay was used for tracking Omicron variants, including the BA.2, BA.4, and BA.5 lineages. In silico validation of the NTD156-7 and NTD25-7 primers and probes compared with publicly available SARS-CoV-2 genome databases showed low variability in regions corresponding to oligonucleotide binding sites. Similarly, in vitro validation with NGS-confirmed samples showed excellent correlation. RT-qPCR assays allow for near-real-time monitoring of circulating and emerging variants allowing for ongoing surveillance of variant dynamics in a local population. By performing periodic sequencing of variant surveillance by RT-qPCR methods, we were able to provide ongoing validation of the results obtained by RT-qPCR screening. Rapid SARS-CoV-2 variant identification and surveillance by this combined approach served to inform clinical decisions in a timely manner and permitted better utilization of sequencing resources.

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“…The viral attachment machinery (the set of proteins responsible for host cell attachment and cell entry) has served as a historically important focus for the development of molecular tests (for example for influenza [ 2 ] and SARS-CoV-2 [ 3 , 4 ]) as well as medical countermeasures such as vaccines [ 5 – 7 ]. Thus, the accurate and efficient identification of the viral attachment machinery is a critical first step in the design and deployment of biomedical countermeasures.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the identification of respiratory viral attachment machinery from sequences data

Walker

Shwarts²,

Demidikin³

et al. 2023

PLoS ONE

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At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral “spike” proteins from sequence can significantly accelerate the development of medical countermeasures. For six families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor. In this report it is shown that sequence data for an unknown virus belonging to one of the six families above provides sufficient information to identify the protein(s) responsible for viral attachment. Random forest models that take as input a set of respiratory viral sequences can classify the protein as “spike” vs. non-spike based on predicted secondary structure elements alone (with 97.3% correctly classified) or in combination with N-glycosylation related features (with 97.0% correctly classified). Models were validated through 10-fold cross-validation, bootstrapping on a class-balanced set, and an out-of-sample extra-familial validation set. Surprisingly, we showed that secondary structural elements and N-glycosylation features were sufficient for model generation. The ability to rapidly identify viral attachment machinery directly from sequence data holds the potential to accelerate the design of medical countermeasures for future pandemics. Furthermore, this approach may be extendable for the identification of other potential viral targets and for viral sequence annotation in general in the future.

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Diagnostic Testing for SARS-CoV-2 Infection

Cited by 10 publications

References 72 publications

The significance of advanced COVID-19 diagnostic testing in pandemic control measures

The significance of advanced COVID-19 diagnostic testing in pandemic control measures

Combined Use of RT-qPCR and NGS for Identification and Surveillance of SARS-CoV-2 Variants of Concern in Residual Clinical Laboratory Samples in Miami-Dade County, Florida

Machine learning for the identification of respiratory viral attachment machinery from sequences data

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