SARS-CoV-2 Alpha Variant Infection of a Patient Immunized by Inactive Sinovac (CoronaVac) Vaccine

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in December 2019, and shortly after pandemic has been declared by the World Health Organization (WHO) due to its unstoppable global spread. Considerable amount of effort has beenput around the World in order to develop a safe and effective vaccine against SARS-CoV-2. Inactivated and RNA vaccines have already passed phase three studies showing sufficient efficacy and safety, respectively. Nowadays, there is a noticeable dominance of SARS-CoV-2 variants with various mutations over the wild type SARS-CoV-2. However, there is no report showing the efficacy of these vaccines on these variants. This case study describes a thirty-eight-year-old male reported to be infected with SARS-CoV-2 alpha variant following two doses of inactive CoronaVac administration with a protective level of SARS-CoV-2 specific antibodies. The variant analysis of the virus reported to be positive for N501Y mutation.This is the first case in the literature demonstrating that inactive SARS-CoV-2 vaccine might have a lower efficacy on alpha variant.Copyright © 2022 Cenk Serhan Ozverel et al., published by Sciendo.

Long Short Term Memory based Covid-19 Mutation Detection for RNA Sequence dataset

Mutation detection for the various strains of Covid 19 evolves the constraints of time, accuracy and precision. RNA sequencing with deep learning enables the detection of the mutation variant from the sequence dataset and helps for the development of tests that are used for the diagnosis and future predictions. Analyzing and researching on Covid- 19 structure and the epidemiological study aid to the accurate methodology selection and process implementation. Efficient data preprocessing of the RNA sequence adds to the accuracy of the model which was built using LSTM. This paper proposes a Long Short Term Memory (LSTM) based deep learning modeling helps the RNA sequence dataset model to predict the RNA mutant variant. The model acquired an accuracy of 91.7 % and a loss function of 3.08%. © 2022 IEEE.

Rapid detection of multiple SARS-CoV-2 variants of concern by PAM-targeting mutations.

SARS-CoV-2 variants of concern (VOCs) that increase transmission or disease severity or reduce diagnostic or vaccine efficacy continue to emerge across the world. Current methods available to rapidly detect these can be resource intensive and thus sub-optimal for large-scale deployment needed during a pandemic response. Here, we describe a CRISPR-based assay that detects mutations in spike gene CRISPR PAM motif or seed regions to identify a pan-specific VOC single-nucleotide polymorphism (SNP)) ((D614G) and Alpha- and Delta-specific (S982A and D950N) SNPs. This assay exhibits good diagnostic sensitivity and strain specificity with nasal swabs and is designed for use in laboratory and point-of-care settings. This should enable rapid, high-throughput VOC identification required for surveillance and characterization efforts to inform clinical and public health decisions. Furthermore, the assay can be adapted to target similar SNPs associated with emerging SARS-CoV-2 VOCs, or other rapidly evolving viruses.

Highlight of potential impact of new viral genotypes of SARS-CoV-2 on vaccines and anti-viral therapeutics.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of the coronavirus disease (COVID-19) pandemic, has infected millions of people globally. Genetic variation and selective pressures lead to the accumulation of single nucleotide polymorphism (SNP) within the viral genome that may affect virulence, transmission rate, viral recognition and the efficacy of prophylactic and interventional measures. To address these concerns at the genomic level, we assessed the phylogeny and SNPs of the SARS-CoV-2 mutant population collected to date in Iran in relation to globally reported variants. Phylogenetic analysis of mutant strains revealed the occurrence of the variants known as B.1.1.7 (Alpha), B.1.525 (Eta), and B.1.617 (Delta) that appear to have delineated independently in Iran. SNP analysis of the Iranian sequences revealed that the mutations were predominantly positioned within the S protein-coding region, with most SNPs localizing to the S1 subunit. Seventeen S1-localizing SNPs occurred in the RNA binding domain that interacts with ACE2 of the host cell. Importantly, many of these SNPs are predicted to influence the binding of antibodies and anti-viral therapeutics, indicating that the adaptive host response appears to be imposing a selective pressure that is driving the evolution of the virus in this closed population through enhancing virulence. The SNPs detected within these mutant cohorts are addressed with respect to current prophylactic measures and therapeutic interventions.

AmpliCoV: Rapid Whole-Genome Sequencing Using Multiplex PCR Amplification and Real-Time Oxford Nanopore MinION Sequencing Enables Rapid Variant Identification of SARS-CoV-2.

Since the emergence of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in December 2019, the scientific community has been sharing data on epidemiology, diagnostic methods, and whole-genomic sequences almost in real time. The latter have already facilitated phylogenetic analyses, transmission chain tracking, protein modeling, the identification of possible therapeutic targets, timely risk assessment, and identification of novel variants. We have established and evaluated an amplification-based approach for whole-genome sequencing of SARS-CoV-2. It can be used on the miniature-sized and field-deployable sequencing device Oxford Nanopore MinION, with sequencing library preparation time of 10 min. We show that the generation of 50,000 total reads per sample is sufficient for a near complete coverage (>90%) of the SARS-CoV-2 genome directly from patient samples even if virus concentration is low (Ct 35, corresponding to approximately 5 genome copies per reaction). For patient samples with high viral load (Ct 18-24), generation of 50,000 reads in 1-2 h was shown to be sufficient for a genome coverage of >90%. Comparison to Illumina data reveals an accuracy that suffices to identify virus mutants. AmpliCoV can be applied whenever sequence information on SARS-CoV-2 is required rapidly, for instance for the identification of circulating virus mutants.