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Record W2910100593 · doi:10.4049/jimmunol.1701757

IL-33 Augments Virus-Specific Memory T Cell Inflation and Potentiates the Efficacy of an Attenuated Cytomegalovirus-Based Vaccine

2019· article· en· W2910100593 on OpenAlexaff
James E. McLaren, Mathew Clement, Morgan Marsden, Kelly L. Miners, Sian Llewellyn‐Lacey, Emma J. Grant, Anzelika Rubina, Silvia Gimeno Brias, Emma Gostick, Maria A. Stacey, Selinda J. Orr, Richard J. Stanton, Kristin Ladell, David A. Price, Ian R. Humphreys

Bibliographic record

VenueThe Journal of Immunology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilCardiff UniversityNational Health and Medical Research CouncilWellcome Trust
KeywordsAdjuvantImmunogenicityHeterologousVirologyCytomegalovirusImmunologyBiologyT cellMemory T cellViral vectorEffectorVirusImmune systemRecombinant DNAHerpesviridaeGeneViral disease

Abstract

fetched live from OpenAlex

Candidate vaccines designed to generate T cell-based immunity are typically vectored by nonpersistent viruses, which largely fail to elicit durable effector memory T cell responses. This limitation can be overcome using recombinant strains of CMV. Proof-of-principle studies have demonstrated the potential benefits of this approach, most notably in the SIV model, but safety concerns require the development of nonreplicating alternatives with comparable immunogenicity. In this study, we show that IL-33 promotes the accumulation and recall kinetics of circulating and tissue-resident memory T cells in mice infected with murine CMV. Using a replication-deficient murine CMV vector, we further show that exogenous IL-33 boosts vaccine-induced memory T cell responses, which protect against subsequent heterologous viral challenge. These data suggest that IL-33 could serve as a useful adjuvant to improve the efficacy of vaccines based on attenuated derivatives of CMV.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.142
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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 teacher head, 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

Citations32
Published2019
Admission routes1
Has abstractyes

Explore more

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