Evidence map›Paper›PMID 38014024›Full record

ArticlebioRxiv : the preprint server for biology2023

Full-spike deep mutational scanning helps predict the evolutionary success of SARS-CoV-2 clades.

Bernadeta Dadonaite, Jack Brown, Teagan E McMahon, Ariana G Farrell, Daniel Asarnow, Cameron Stewart, Jenni Logue, Ben Murrell, Helen Y Chu, David Veesler and 1 more

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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, 23 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Bernadeta DadonaiteBasic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, 98109, USA.ORCID 0000-0003-0908-6982
Jack BrownDepartment of Biochemistry, University of Washington, Seattle, Washington, USA.
Teagan E McMahonBasic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, 98109, USA.
Ariana G FarrellBasic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, 98109, USA.
Daniel AsarnowDepartment of Biochemistry, University of Washington, Seattle, Washington, USA.
Cameron StewartDepartment of Biochemistry, University of Washington, Seattle, Washington, USA.
Jenni LogueUniversity of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA.
Ben MurrellDepartment of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0002-0393-4445
Helen Y ChuUniversity of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA.ORCID 0000-0003-1499-4096
David VeeslerDepartment of Biochemistry, University of Washington, Seattle, Washington, USA.ORCID 0000-0002-6019-8675
Jesse D BloomBasic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, 98109, USA.ORCID 0000-0003-1267-3408
University of Washington · USFred Hutch Cancer Center · USHoward Hughes Medical Institute · USKarolinska Institutet · SE

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Eric Collisson · 1985 to 2026
$296.4M
NIAID Centers of Excellence for Influenza Research and Response: Universal Influenza Vaccine Research Activities75N93021C00015 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI HENSLEY, SCOTT · 2021 to 2025
$50.7M
Centers for Research on Structural Biology of Infectious Diseases: Universal Influenza Vaccine Research75N93022C00036 · NIAID · SEATTLE CHILDREN'S HOSPITAL · PI STAKER, BART · 2022 to 2025
$21.7M
Structure-based design of broadly protective coronavirus vaccinesP01AI167966 · NIAID · UNIVERSITY OF WASHINGTON · PI BALI PULENDRAN · 2022 to 2026
$15.3M
Unraveling the bat humoral immune response against zoonotic viruses to guide the design of next-generation therapeuticsDP1AI158186 · NIAID · UNIVERSITY OF WASHINGTON · PI VEESLER, DAVID · 2020 to 2024
$5.4M
Prospectively characterizing the functional and antigenic effects of mutations to viral entry proteinsR01AI141707 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BLOOM, JESSE D · 2018 to 2022
$2.1M
High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases ResearchS10OD028685 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRADLEY, PHILIP · 2020 to 2020
$2.0M
FHCRC High-Performance Computing ClusterS10OD020069 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI KOOPERBERG, CHARLES L · 2015 to 2015
$600k
NCI NIH HHS P30 CA015704NIAID NIH HHS 75N93021C00015NIAID NIH HHS 75N93022C00036NIAID NIH HHS DP1 AI158186NIAID NIH HHS P01 AI167966NIAID NIH HHS R01 AI141707NIH HHS S10 OD020069NIH HHS S10 OD028685
6 · The paper itself

Abstract

SARS-CoV-2 variants acquire mutations in spike that promote immune evasion and impact other properties that contribute to viral fitness such as ACE2 receptor binding and cell entry. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning to measure how >9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry, or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully impacted ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456, and 473-however, the antigenic impacts of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.

Identifiers

PMID38014024
PMCPMC10680755
OpenAlexW4388680968

What Socratic 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 Socratic graph.