Dataset Information

Respiratory mucosal delivery of next-generation COVID-19 vaccine provides robust protection against both ancestral and variant strains of SARS-CoV-2.

ABSTRACT: The emerging SARS-CoV-2 variants of concern (VOCs) threaten the effectiveness of current COVID-19 vaccines administered intramuscularly and designed to only target the spike protein. There is a pressing need to develop next-generation vaccine strategies for broader and long-lasting protection. Using adenoviral vectors (Ad) of human and chimpanzee origin, we evaluated Ad-vectored trivalent COVID-19 vaccines expressing spike-1, nucleocapsid, and RdRp antigens in murine models. We show that single-dose intranasal immunization, particularly with chimpanzee Ad-vectored vaccine, is superior to intramuscular immunization in induction of the tripartite protective immunity consisting of local and systemic antibody responses, mucosal tissue-resident memory T cells and mucosal trained innate immunity. We further show that intranasal immunization provides protection against both the ancestral SARS-CoV-2 and two VOC, B.1.1.7 and B.1.351. Our findings indicate that respiratory mucosal delivery of Ad-vectored multivalent vaccine represents an effective next-generation COVID-19 vaccine strategy to induce all-around mucosal immunity against current and future VOC.

SUBMITTER: Afkhami S

PROVIDER: S-EPMC8825346 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

Respiratory mucosal delivery of next-generation COVID-19 vaccine provides robust protection against both ancestral and variant strains of SARS-CoV-2.

Afkhami Sam S D'Agostino Michael R MR Zhang Ali A Stacey Hannah D HD Marzok Art A Kang Alisha A Singh Ramandeep R Bavananthasivam Jegarubee J Ye Gluke G Luo Xiangqian X Wang Fuan F Ang Jann C JC Zganiacz Anna A Sankar Uma U Kazhdan Natallia N Koenig Joshua F E JFE Phelps Allyssa A Gameiro Steven F SF Tang Shangguo S Jordana Manel M Wan Yonghong Y Mossman Karen L KL Jeyanathan Mangalakumari M Gillgrass Amy A Medina Maria Fe C MFC Smaill Fiona F Lichty Brian D BD Miller Matthew S MS Xing Zhou Z

Cell 20220209 5

The emerging SARS-CoV-2 variants of concern (VOCs) threaten the effectiveness of current COVID-19 vaccines administered intramuscularly and designed to only target the spike protein. There is a pressing need to develop next-generation vaccine strategies for broader and long-lasting protection. Using adenoviral vectors (Ad) of human and chimpanzee origin, we evaluated Ad-vectored trivalent COVID-19 vaccines expressing spike-1, nucleocapsid, and RdRp antigens in murine models. We show that single- ...[more]

PMID: 35180381

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Project description:As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci.

Project description:BackgroundNext-generation sequencing (NGS) offers unprecedented opportunities to expand clinical genomics. It also presents challenges with respect to integration with data from other sequencing methods and historical data. Provision of consistent, clinically applicable variant annotation of NGS data has proved difficult, particularly of indels, an important variant class in clinical genomics. Annotation in relation to a reference genome sequence, the DNA strand of coding transcripts and potential alternative variant representations has not been well addressed. Here we present tools that address these challenges to provide rapid, standardized, clinically appropriate annotation of NGS data in line with existing clinical standards.MethodsWe developed a clinical sequencing nomenclature (CSN), a fixed variant annotation consistent with the principles of the Human Genome Variation Society (HGVS) guidelines, optimized for automated variant annotation of NGS data. To deliver high-throughput CSN annotation we created CAVA (Clinical Annotation of VAriants), a fast, lightweight tool designed for easy incorporation into NGS pipelines. CAVA allows transcript specification, appropriately accommodates the strand of a gene transcript and flags variants with alternative annotations to facilitate clinical interpretation and comparison with other datasets. We evaluated CAVA in exome data and a clinical BRCA1/BRCA2 gene testing pipeline.ResultsCAVA generated CSN calls for 10,313,034 variants in the ExAC database in 13.44 hours, and annotated the ICR1000 exome series in 6.5 hours. Evaluation of 731 different indels from a single individual revealed 92 % had alternative representations in left aligned and right aligned data. Annotation of left aligned data, as performed by many annotation tools, would thus give clinically discrepant annotation for the 339 (46 %) indels in genes transcribed from the forward DNA strand. By contrast, CAVA provides the correct clinical annotation for all indels. CAVA also flagged the 370 indels with alternative representations of a different functional class, which may profoundly influence clinical interpretation. CAVA annotation of 50 BRCA1/BRCA2 gene mutations from a clinical pipeline gave 100 % concordance with Sanger data; only 8/25 BRCA2 mutations were correctly clinically annotated by other tools.ConclusionsCAVA is a freely available tool that provides rapid, robust, high-throughput clinical annotation of NGS data, using a standardized clinical sequencing nomenclature.

Dataset Information

Respiratory mucosal delivery of next-generation COVID-19 vaccine provides robust protection against both ancestral and variant strains of SARS-CoV-2.

Publications

Respiratory mucosal delivery of next-generation COVID-19 vaccine provides robust protection against both ancestral and variant strains of SARS-CoV-2.

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