Evidence map›Paper›PMID 38793544›Full record

ArticleViruses2024

Design and Development of an Antigen Test for SARS-CoV-2 Nucleocapsid Protein to Validate the Viral Quality Assurance Panels.

Partha Ray, Melissa Ledgerwood-Lee, Howard Brickner, Alex E Clark, Aaron Garretson, Rishi Graham, Westley Van Zant, Aaron F Carlin, Eliah S Aronoff-Spencer

Abstract read
In one paragraph

Article in Viruses, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Partha RayDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA 92093, USA.ORCID 0000-0002-7075-5463
Melissa Ledgerwood-LeeDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA 92093, USA.
Howard BricknerDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA 92093, USA.ORCID 0000-0003-0075-7399
Alex E ClarkDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA 92093, USA.
Aaron GarretsonDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA 92093, USA.ORCID 0000-0003-3855-4482
Rishi GrahamDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA 92093, USA.
Westley Van ZantDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA 92093, USA.ORCID 0000-0002-4123-3551
Aaron F CarlinDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA 92093, USA.ORCID 0000-0002-1669-8066
Eliah S Aronoff-SpencerDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA 92093, USA.ORCID 0000-0002-6279-5027

Funding

RADx-Rad Discoveries & Data: Consortium Coordination Center Program OrganizationU24LM013755 · NLM · YALE UNIVERSITY · PI ARONOFF-SPENCER, ELIAH S, OHNO-MACHADO, LUCILA · 2021 to 2023
$23.3M
NLM NIH HHS U24 LM013755
6 · The paper itself

Abstract

The continuing mutability of the SARS-CoV-2 virus can result in failures of diagnostic assays. To address this, we describe a generalizable bioinformatics-to-biology pipeline developed for the calibration and quality assurance of inactivated SARS-CoV-2 variant panels provided to Radical Acceleration of Diagnostics programs (RADx)-radical program awardees. A heuristic genetic analysis based on variant-defining mutations demonstrated the lowest genetic variance in the Nucleocapsid protein (Np)-C-terminal domain (CTD) across all SARS-CoV-2 variants. We then employed the Shannon entropy method on (Np) sequences collected from the major variants, verifying the CTD with lower entropy (less prone to mutations) than other Np regions. Polyclonal and monoclonal antibodies were raised against this target CTD antigen and used to develop an Enzyme-linked immunoassay (ELISA) test for SARS-CoV-2. Blinded Viral Quality Assurance (VQA) panels comprised of UV-inactivated SARS-CoV-2 variants (XBB.1.5, BF.7, BA.1, B.1.617.2, and WA1) and distractor respiratory viruses (CoV 229E, CoV OC43, RSV A2, RSV B, IAV H1N1, and IBV) were assembled by the RADx-rad Diagnostics core and tested using the ELISA described here. The assay tested positive for all variants with high sensitivity (limit of detection: 1.72-8.78 ng/mL) and negative for the distractor virus panel. Epitope mapping for the monoclonal antibodies identified a 20 amino acid antigenic peptide on the Np-CTD that an in-silico program also predicted for the highest antigenicity. This work provides a template for a bioinformatics pipeline to select genetic regions with a low propensity for mutation (low Shannon entropy) to develop robust 'pan-variant' antigen-based assays for viruses prone to high mutational rates.

Indexed as

Antigens, ViralCoronavirus Nucleocapsid ProteinsCOVID-19PhosphoproteinsSARS-CoV-2AnimalsAntibodies, MonoclonalAntibodies, ViralComputational BiologyCOVID-19 Serological TestingEnzyme-Linked Immunosorbent AssayHumansMutationAntibodies, MonoclonalAntibodies, ViralAntigens, ViralCoronavirus Nucleocapsid Proteinsnucleocapsid phosphoprotein, SARS-CoV-2PhosphoproteinsCOVID-19 diagnosticsEnzyme-linked immunoassaymonoclonal and polyclonal antibodiesnucleocapsid proteinpeptide epitope mappingRADxSARS-CoV-2viral quality assurance

Identifiers

PMID38793544
PMCPMC11125937

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.