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Record W3194121618 · doi:10.1021/acscentsci.1c00525

Sialic Acid Ligands of CD28 Suppress Costimulation of T Cells

2021· article· en· W3194121618 on OpenAlexfundno aff
Landon J. Edgar, Andrew J. Thompson, Vincent F. Vartabedian, Chika Kikuchi, Jordan L. Woehl, John R. Teijaro, James C. Paulson

Bibliographic record

VenueACS Central Science · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaDivision of Intramural Research, National Institute of Allergy and Infectious Diseases
KeywordsCD28EffectorCD80Sialic acidCell biologyChemistryImmune systemT cellBiologyBiochemistryImmunologyCytotoxic T cellCD40In vitro

Abstract

fetched live from OpenAlex

Effector T cells comprise the cellular arm of the adaptive immune system and are essential for mounting immune responses against pathogens and cancer. To reach effector status, costimulation through CD28 is required. Here, we report that sialic acid-containing glycans on the surface of both T cells and APCs are alternative ligands of CD28 that compete with binding to its well-documented activatory ligand CD80 on the APC, resulting in attenuated costimulation. Removal of sialic acids enhances antigen-mediated activation of naïve T cells and also increases the revival of effector T cells made hypofunctional or exhausted via chronic viral infection. This occurs through a mechanism that is synergistic with antibody blockade of the inhibitory PD-1 axis. These results reveal a previously unrecognized role of sialic acid ligands in attenuation of CD28-mediated costimulation of T cells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.237
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations71
Published2021
Admission routes1
Has abstractyes

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Same venueACS Central ScienceSame topicImmune Cell Function and InteractionFrench-language works237,207