Detecting spread of SARS-CoV-2 variants using PowerChek SARS-CoV-2 S-gene mutation detection kit.
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ABSTRACT:
SUBMITTER: Kim HJ
PROVIDER: S-EPMC9349760 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Publications
Detecting spread of SARS-CoV-2 variants using PowerChek SARS-CoV-2 S-gene mutation detection kit.
Kim Hyun Jin HJ Kim Seon Young SY Kwon Gye Cheol GC Choi Qute Q
Journal of clinical laboratory analysis 20220624 8
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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have received increasing attention globally because of their increased transmissibility and potential to escape immunity. Although whole-genome sequencing is the gold standard method for SARS-CoV-2 mutation detection and lineage determination, it is costly and time-consuming. However, SARS-CoV-2 variants can be identified based on select variant-specific single nucleotide polymorphisms (SNPs) in the spike protein-encoding gene (S). This study validated and compared the limit of detection (LOD) of L452R, N501Y, HV69/70 del and E484K as variant-specific SNPs of the S gene and RdRP as a SARS-CoV-2-specific gene, using the Novaplex SARS-CoV-2 variants assay kit series. For three SARS-CoV-2 lineages (B.1.617.2, B.1.1.7 and R.1), one strain per lineage was used. Variant-specific SNPs of the S gene were analysed using the Novaplex SARS-CoV-2 variants I assay and Novaplex SARS-CoV-2 variants II assay kits. Validation confirmed the LODs of the variant kits. The LOD for each target variant-specific SNP and RdRP was five RNA copies per reaction. The Novaplex SARS-CoV-2 variants assay kit series performs well and the LOD for SARS-CoV-2 detection and variant-specific SNP detection are consistent. The kits are suitable for use as routine laboratory tests for SARS-CoV-2 and variant-specific SNP detection in a single step, saving time and labour.
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Project description:Variants of severe acute respiratory syndrome coronavirus 2 raise concerns regarding the control of coronavirus disease epidemics. We analyzed 40,000 specific reverse transcription PCR tests performed on positive samples during January 26-February 16, 2021, in France. We found high transmission advantage of variants and more advanced spread than anticipated.
| S-EPMC8084510 | biostudies-literature
Project description:BackgroundCurrently, there are two rapid antigen detection (RAD) kits from the WHO Emergency Use List for detecting SARS-CoV-2.ObjectiveThe Panbio COVID-19 Ag Rapid Test Device was selected to evaluate the performance for detecting SARS-CoV-2.Study designAnalytical sensitivity for the detection of SARS-CoV-2 virus was determined by limit of detection (LOD) using RT-PCR as a reference method. Clinical sensitivity was evaluated by using respiratory specimens collected from confirmed COVID-19 patients.ResultsThe LOD results showed that the RAD kit was 100 fold less sensitive than RT-PCR. Clinical sensitivity of the RAD kit was 68.6 % for detecting specimens from COVID-19 patients.ConclusionsThe RAD kit evaluated in the present study shared similar performance with another kit from the WHO Emergency Use List, the Standard Q COVID-19 Ag. Understanding the clinical characteristics of RAD kits can guide us to decide different testing strategies in different settings.
| S-EPMC7716730 | biostudies-literature
Project description:During more than 3 years since its emergence, SARS-CoV-2 has shown great ability to mutate rapidly into diverse variants, some of which turned out to be very infectious and have spread throughout the world causing waves of infections. At this point, many countries have already experienced up to six waves of infections. Extensive academic work has focused on the development of models to predict the pandemic trajectory based on epidemiological data, but none has focused on predicting variant-specific spread. Moreover, important scientific literature analyzes the genetic evolution of SARS-CoV-2 variants and how it might functionally affect their infectivity. However, genetic attributes have not yet been incorporated into existing epidemiological modeling that aims to capture infection trajectory. Thus, this study leverages variant-specific genetic characteristics together with epidemiological information to systematically predict the future spread trajectory of newly detected variants. The study describes the analysis of 9.0 million SARS-CoV-2 genetic sequences in 30 countries and identifies temporal characteristic patterns of SARS-CoV-2 variants that caused significant infection waves. Using this descriptive analysis, a machine-learning-enabled risk assessment model has been developed to predict, as early as 1 week after their first detection, which variants are likely to constitute the new wave of infections in the following 3 months. The model's out-of-sample area under the curve (AUC) is 86.3% for predictions after 1 week and 90.8% for predictions after 2 weeks. The methodology described in this paper could contribute more broadly to the development of improved predictive models for variants of other infectious viruses.
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Project description:Tracking genetic variations from positive SARS-CoV-2 samples yields crucial information about the number of variants circulating in an outbreak and the possible lines of transmission but sequencing every positive SARS-CoV-2 sample would be prohibitively costly for population-scale test and trace operations. Genotyping is a rapid, high-throughput and low-cost alternative for screening positive SARS-CoV-2 samples in many settings. We have designed a SNP identification pipeline to identify genetic variation using sequenced SARS-CoV-2 samples. Our pipeline identifies a minimal marker panel that can define distinct genotypes. To evaluate the system, we developed a genotyping panel to detect variants-identified from SARS-CoV-2 sequences surveyed between March and May 2020 and tested this on 50 stored qRT-PCR positive SARS-CoV-2 clinical samples that had been collected across the South West of the UK in April 2020. The 50 samples split into 15 distinct genotypes and there was a 61.9% probability that any two randomly chosen samples from our set of 50 would have a distinct genotype. In a high throughput laboratory, qRT-PCR positive samples pooled into 384-well plates could be screened with a marker panel at a cost of < £1.50 per sample. Our results demonstrate the usefulness of a SNP genotyping panel to provide a rapid, cost-effective, and reliable way to monitor SARS-CoV-2 variants circulating in an outbreak. Our analysis pipeline is publicly available and will allow for marker panels to be updated periodically as viral genotypes arise or disappear from circulation.
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Project description: Not available
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Project description:BackgroundPrecise public health and clinical interventions for the COVID-19 pandemic has spurred a global rush on SARS-CoV-2 variant tracking, but current approaches to variant tracking are challenged by the flood of viral genome sequences leading to a loss of timeliness, accuracy, and reliability. Here, we devised a new co-mutation network framework, aiming to tackle these difficulties in variant surveillance.MethodsTo avoid simultaneous input and modeling of the whole large-scale data, we dynamically investigate the nucleotide covarying pattern of weekly sequences. The community detection algorithm is applied to a co-occurring genomic alteration network constructed from mutation corpora of weekly collected data. Co-mutation communities are identified, extracted, and characterized as variant markers. They contribute to the creation and weekly updates of a community-based variant dictionary tree representing SARS-CoV-2 evolution, where highly similar ones between weeks have been merged to represent the same variants. Emerging communities imply the presence of novel viral variants or new branches of existing variants. This process was benchmarked with worldwide GISAID data and validated using national level data from six COVID-19 hotspot countries.ResultsA total of 235 co-mutation communities were identified after a 120 weeks' investigation of worldwide sequence data, from March 2020 to mid-June 2022. The dictionary tree progressively developed from these communities perfectly recorded the time course of SARS-CoV-2 branching, coinciding with GISAID clades. The time-varying prevalence of these communities in the viral population showed a good match with the emergence and circulation of the variants they represented. All these benchmark results not only exhibited the methodology features but also demonstrated high efficiency in detection of the pandemic variants. When it was applied to regional variant surveillance, our method displayed significantly earlier identification of feature communities of major WHO-named SARS-CoV-2 variants in contrast with Pangolin's monitoring.ConclusionAn efficient genomic surveillance framework built from weekly co-mutation networks and a dynamic community-based variant dictionary tree enables early detection and continuous investigation of SARS-CoV-2 variants overcoming genomic data flood, aiding in the response to the COVID-19 pandemic.
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Project description:Since severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began to spread in late 2019, laboratories around the world have widely used whole genome sequencing (WGS) to continuously monitor the changes in the viral genes and discovered multiple subtypes or branches evolved from SARS-CoV-2. Recently, several novel SARS-CoV-2 variants have been found to be more transmissible. They may affect the immune response caused by vaccines and natural infections and reduce the sensitivity to neutralizing antibodies. We analyze the distribution characteristics of prevalent SARS-CoV-2 variants and the frequency of mutant sites based on the data available from GISAID and PANGO by R 4.0.2 and ArcGIS 10.2. Our analysis suggests that B.1.1.7, B.1.351, and P.1 are more easily spreading than other variants, and the key mutations of S protein, including N501Y, E484K, and K417N/T, have high mutant frequencies, which may have become the main genotypes for the spread of SARS-CoV-2.
| S-EPMC8364952 | biostudies-literature
Project description:There is an urgent need to understand the functional effects of mutations in emerging variants of SARS-CoV-2. Variants of concern (alpha, beta, gamma and delta) acquired four patterns of spike glycoprotein mutations that enhance transmissibility and immune evasion: 1) mutations in the N-terminal domain (NTD), 2) mutations in the Receptor Binding Domain (RBD), 3) mutations at interchain contacts of the spike trimer, and 4) furin cleavage site mutations. Most distinguishing mutations among variants of concern are exhibited in the NTD, localized to sites of high structural flexibility. Emerging variants of interest such as mu, lambda and C.1.2 exhibit the same patterns of mutations as variants of concern. There is a strong likelihood that SARS-CoV-2 variants will continue to emerge with mutations in these defined patterns, thus providing a basis for the development of next line antiviral drugs and vaccine candidates.
| S-EPMC8606318 | biostudies-literature
Project description:In January 2021, after detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, genomic surveillance was established on Réunion Island to track the introduction and spread of SARS-CoV-2 lineages and variants of concern. This system identified 22 SARS-CoV-2 lineages, 71% of which were attributed to the Beta variant.
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