Evidence map›Paper›PMID 35215130›Full record

ArticlePathogens (Basel, Switzerland)2022

Formation and Expansion of Memory B Cells against Coronavirus in Acutely Infected COVID-19 Individuals.

A Karim Embong, Phuong Nguyen-Contant, Jiong Wang, Preshetha Kanagaiah, Francisco A Chaves, Theresa F Fitzgerald, Qian Zhou, Gabrielle Kosoy, Angela R Branche, Benjamin L Miller and 3 more

Open access · goldAbstract read
In one paragraph

Article in Pathogens (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 38% of its field
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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. 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

13 authors at 2 institutions in 1 country.

A Karim EmbongDavid H. Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY 14620, USA.ORCID 0000-0002-7224-4640
Phuong Nguyen-ContantACM Global Laboratories, Rochester, NY 14624, USA.
Jiong WangDivision of Nephrology, Department of Medicine, University of Rochester Medical Center, Rochester, NY 14620, USA.ORCID 0000-0002-4938-5074
Preshetha KanagaiahDavid H. Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY 14620, USA.
Francisco A ChavesDavid H. Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY 14620, USA.
Theresa F FitzgeraldDavid H. Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY 14620, USA.
Qian ZhouDivision of Nephrology, Department of Medicine, University of Rochester Medical Center, Rochester, NY 14620, USA.
Gabrielle KosoyDepartment of Biochemistry and Biophysics, University of Rochester, Rochester, NY 14620, USA.
Angela R BrancheDepartment of Medicine, University of Rochester Medical Center, Rochester, NY 14620, USA.
Benjamin L MillerDepartment of Biochemistry and Biophysics, University of Rochester, Rochester, NY 14620, USA.ORCID 0000-0001-9168-8047
Martin S ZandDivision of Nephrology, Department of Medicine, University of Rochester Medical Center, Rochester, NY 14620, USA.
Mark Y SangsterDavid H. Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY 14620, USA.ORCID 0000-0002-1599-5894
David J TophamDavid H. Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY 14620, USA.ORCID 0000-0002-9435-8673
University of Rochester Medical Center · USUniversity of Rochester · US

Funding

Training in Wellness and Resiliency at the University of Rochester Medical Center and College of Arts, Sciences & EngineeringT32GM135134 · NIGMS · UNIVERSITY OF ROCHESTER · PI Jeffrey J Hayes, Lynne E Maquat · 2020 to 2026
$2.9M
NIAID NIH HHS HHSN272201400005CNIGMS NIH HHS T32 GM135134
6 · The paper itself

Abstract

Infection with the β-coronavirus SARS-CoV-2 typically generates strong virus-specific antibody production. Antibody responses against novel features of SARS-CoV-2 proteins require naïve B cell activation, but there is a growing appreciation that conserved regions are recognized by pre-existing memory B cells (MBCs) generated by endemic coronaviruses. The current study investigated the role of pre-existing cross-reactive coronavirus memory in the antibody response to the viral spike (S) and nucleocapsid (N) proteins following SARS-CoV-2 infection. The breadth of reactivity of circulating antibodies, plasmablasts, and MBCs was analyzed. Acutely infected subjects generated strong IgG responses to the S protein, including the novel receptor binding domain, the conserved S2 region, and to the N protein. The response included reactivity to the S of endemic β-coronaviruses and, interestingly, to the N of an endemic α-coronavirus. Both mild and severe infection expanded IgG MBC populations reactive to the S of SARS-CoV-2 and endemic β-coronaviruses. Avidity of S-reactive IgG antibodies and MBCs increased after infection. Overall, findings indicate that the response to the S and N of SARS-CoV-2 involves pre-existing MBC activation and adaptation to novel features of the proteins, along with the potential of imprinting to shape the response to SARS-CoV-2 infection.

Indexed as

antibodiesB cellsmemorySARS-CoV-2

Identifiers

PMID35215130
PMCPMC8876169
OpenAlexW4214505071

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.