Evidence map›Paper›PMID 34909774›Full record

ArticlebioRxiv : the preprint server for biology2021

Macaque-human differences in SARS-CoV-2 Spike antibody response elicited by vaccination or infection.

Alexandra C Willcox, Kevin Sung, Meghan E Garrett, Jared G Galloway, Megan A O'Connor, Jesse H Erasmus, Jennifer K Logue, David W Hawman, Helen Y Chu, Kim J Hasenkrug and 3 more

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

13 authors at 3 institutions in 1 country.

Alexandra C WillcoxHuman Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Kevin SungPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Meghan E GarrettHuman Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Jared G GallowayPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Megan A O'ConnorDepartment of Microbiology, University of Washington, Seattle, WA, USA.
Jesse H ErasmusDepartment of Microbiology, University of Washington, Seattle, WA, USA.
Jennifer K LogueDepartment of Medicine, University of Washington, Seattle, WA, USA.
David W HawmanLaboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA.
Helen Y ChuDepartment of Medicine, University of Washington, Seattle, WA, USA.
Kim J HasenkrugLaboratory of Persistent Viral Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA.
Deborah H FullerDepartment of Microbiology, University of Washington, Seattle, WA, USA.
Frederick A MatsenPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Julie OverbaughHuman Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
University of Washington · USFred Hutch Cancer Center · USNational Institutes of Health · US

Funding

Washington National Primate Research CenterP51OD010425 · OD · UNIVERSITY OF WASHINGTON · PI Mari Ostendorf · 2012 to 2026
$201.8M
Blending deep learning with probabilistic mechanistic models to predict and understand the evolution and function of adaptive immune receptorsR01AI146028 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2019 to 2024
$3.4M
NIAID NIH HHS R01 AI146028NIH HHS P51 OD010425
6 · The paper itself

Abstract

Macaques are a commonly used model for studying immunity to human viruses, including for studies of SARS-CoV-2 infection and vaccination. However, it is unknown whether macaque antibody responses recapitulate, and thus appropriately model, the response in humans. To answer this question, we employed a phage-based deep mutational scanning approach (Phage-DMS) to compare which linear epitopes are targeted on the SARS-CoV-2 Spike protein in humans and macaques following either vaccination or infection. We also used Phage-DMS to determine antibody escape pathways within each epitope, enabling a granular comparison of antibody binding specificities at the locus level. Overall, we identified some common epitope targets in both macaques and humans, including in the fusion peptide (FP) and stem helix-heptad repeat 2 (SH-H) regions. Differences between groups included a response to epitopes in the N-terminal domain (NTD) and C-terminal domain (CTD) in vaccinated humans but not vaccinated macaques, as well as recognition of a CTD epitope and epitopes flanking the FP in convalescent macaques but not convalescent humans. There was also considerable variability in the escape pathways among individuals within each group. Sera from convalescent macaques showed the least variability in escape overall and converged on a common response with vaccinated humans in the SH-H epitope region, suggesting highly similar antibodies were elicited. Collectively, these findings suggest that the antibody response to SARS-CoV-2 in macaques shares many features with humans, but with substantial differences in the recognition of certain epitopes and considerable individual variability in antibody escape profiles, suggesting a diverse repertoire of antibodies that can respond to major epitopes in both humans and macaques.

Identifiers

PMID34909774
PMCPMC8669841
OpenAlexW4200307241

What OpenQuestion holds

Textmetadata
LicenceCC0
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.