Evidence map›Paper›PMID 34717659›Full record

ArticleRetrovirology2021

Diverse antiviral IgG effector activities are predicted by unique biophysical antibody features.

Hao D Cheng, Karen G Dowell, Chris Bailey-Kellogg, Brittany A Goods, J Christopher Love, Guido Ferrari, Galit Alter, Johannes Gach, Donald N Forthal, George K Lewis and 4 more

Abstract read
In one paragraph

Article in Retrovirology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. 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

14 authors.

Hao D ChengThayer School of Engineering, Dartmouth College, Hanover, NH, USA.
Karen G DowellDepartment of Computer Science, Dartmouth College, Hanover, 03755, USA.
Chris Bailey-KelloggDepartment of Computer Science, Dartmouth College, Hanover, 03755, USA.
Brittany A GoodsDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
J Christopher LoveDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Guido FerrariDepartment of Surgery, Duke University Medical Center, Durham, NC, 27710, USA.
Galit AlterRagon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA.
Johannes GachDivision of Infectious Diseases, Irvine School of Medicine, University California, Irvine, CA, 92697, USA.
Donald N ForthalDivision of Infectious Diseases, Irvine School of Medicine, University California, Irvine, CA, 92697, USA.
George K LewisDivision of Vaccine Research, Institute of Human Virology, University Maryland School of Medicine, Baltimore, MD, 21201, USA.
Kelli GreeneDepartment of Surgery, Duke University Medical Center, Durham, NC, 27710, USA.
Hongmei GaoDepartment of Surgery, Duke University Medical Center, Durham, NC, 27710, USA.
David C MontefioriDepartment of Surgery, Duke University Medical Center, Durham, NC, 27710, USA.
Margaret E AckermanThayer School of Engineering, Dartmouth College, Hanover, NH, USA. Margaret.e.ackerman@dartmouth.edu.ORCID 0000-0002-4253-3476

Funding

University of California Health Participation in the National COVID Cohort Collaborative (N3C)UL1TR001414 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI COOPER, DAN M, VILAIN, ERIC J. · 2015 to 2023
$35.1M
Physical Resources CoreP01AI120756 · NIAID · DUKE UNIVERSITY · PI TOMARAS, GEORGIA DORIS · 2016 to 2020
$17.1M
Project 3- Mucosal Determinants of Virus TransmissionP01AI124912 · NIAID · UNIVERSITY OF MARYLAND BALTIMORE · PI SILVESTRI, GUIDO · 2016 to 2019
$14.4M
Directing Antibody Effector Activity via Controlled IgG Subclass SwitchingR01AI131975 · NIAID · DARTMOUTH COLLEGE · PI ACKERMAN, MARGARET E · 2017 to 2020
$2.6M
NCATS NIH HHS UL1 TR001414NIAID NIH HHS P01 AI120756NIAID NIH HHS P01 AI124912NIAID NIH HHS R01 AI131975
6 · The paper itself

Abstract

backgroundThe critical role of antibody Fc-mediated effector functions in immune defense has been widely reported in various viral infections. These effector functions confer cellular responses through engagement with innate immune cells. The precise mechanism(s) by which immunoglobulin G (IgG) Fc domain and cognate receptors may afford protection are poorly understood, however, in the context of HIV/SHIV infections. Many different in vitro assays have been developed and utilized to measure effector functions, but the extent to which these assays capture distinct antibody activities has not been fully elucidated.

resultsIn this study, six Fc-mediated effector function assays and two biophysical antibody profiling assays were performed on a common set of samples from HIV-1 infected and vaccinated subjects. Biophysical antibody profiles supported robust prediction of diverse IgG effector functions across distinct Fc-mediated effector function assays. While a number of assays showed correlated activities, supervised machine learning models indicated unique antibody features as primary contributing factors to the associated effector functions. Additional experiments established the mechanistic relevance of relationships discovered using this unbiased approach.

conclusionsIn sum, this study provides better resolution on the diversity and complexity of effector function assays, offering a clearer perspective into this family of antibody mechanisms of action to inform future HIV-1 treatment and vaccination strategies.

Indexed as

HIV-1HIV AntibodiesHIV InfectionsHumansImmunoglobulin Fc FragmentsImmunoglobulin GHIV AntibodiesImmunoglobulin Fc FragmentsImmunoglobulin GAntibodyEffector functionHIVIgGVaccine

Identifiers

PMID34717659
PMCPMC8557579

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.