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Record W4389029119 · doi:10.1093/ofid/ofad500.1971

2349. Natural Killer Cells and BNT162b2 mRNA Vaccine Reactogenicity and Durability

2023· article· en· W4389029119 on OpenAlexaff
Elizabeth Graydon, Tonia L. Conner, Kim Dunham, Cara H. Olsen, Emilie Goguet, Allison Segard, Si’Ana A. Coggins, Marana S Tso, E Samuels, Belinda Jackson‐Thompson, Matthew Moser, Alyssa R. Lindrose, Monique Hollis-Perry, Gregory Wang, Santina Maiolatesi, Yolanda Alcorta, Anatalio Reyes, Mimi Wong, Kathy Ramsey, Julian Davies, Edward Parmelee, Orlando Ortega, Margaret Edwards, Sydney Moller, Jon R. Inglefield, David R. Tribble, Timothy Burgess, Robert J. O’Connell, Allison M. W. Malloy, Simon Pollett, Christopher C. Broder, Eric D. Laing, Stephen K. Anderson, Edward Mitre

Bibliographic record

VenueOpen Forum Infectious Diseases · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsGeneral Dynamics (Canada)
Fundersnot available
KeywordsVaccinationMedicineImmunologyImmune systemPeripheral blood mononuclear cellCD16ReactogenicityAntibodyFlow cytometryImmunogenicityCD8In vitroBiology

Abstract

fetched live from OpenAlex

Abstract Background Natural killer (NK) cells can both amplify and diminish immune responses to vaccination. Studies in humans and animals have observed NK cell activation within days after mRNA vaccination. In this study, we sought to determine if baseline NK cell frequencies, phenotype, or function correlate with antibody responses or inflammatory side effects induced by the Pfizer-BioNTech COVID-19 vaccine (BNT162b2). Methods We analyzed serum and peripheral blood mononuclear cells (PBMCs) from 188 participants in the Prospective Assessment of SARS-CoV-2 Seroconversion study, an observational study evaluating immune responses in healthcare workers. Baseline serum samples and PBMCs were collected from all participants prior to any SARS-CoV-2 infection or vaccination. Spike-specific IgG antibodies were quantified at one and six months post-vaccination by microsphere-based multiplex immunoassay. NK cell frequencies and phenotypes were assessed on pre-vaccination PBMCs from all participants by multi-color flow cytometry, and on a subset of participants at time points after the 1st and 2nd doses of BNT162b2. Inflammatory side effects were assessed by structured symptom questionnaires, and baseline NK cell functionality was quantified by an in vitro killing assay on participants that reported high or low post-vaccination symptom scores. Results Key observations include: 1) circulating NK cells exhibit an increase in CD56dim CD16- NK cells compared to baseline levels, providing evidence of NK cell activation in the week following vaccination, 2) individuals with high symptom scores after 1st vaccination had higher pre-vaccination NK cytotoxicity indices compared to individuals with low symptoms scores (195.7 [SD 170.1] vs 118.5 [SD 77.5], p=0.04), and 3) pre-vaccination NK cell numbers were negatively correlated with spike-specific IgG levels six months after two BNT162b2 doses (Rho= -0.14, p=0.043). Conclusion These results suggest that NK cell activation by BNT162b2 vaccination may contribute to vaccine-induced inflammatory symptoms and reduce durability of vaccine-induced antibody responses. Disclosures David Tribble, MD, DrPH, AstraZeneca: The IDCRP and HJF were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial as part of US Govt COVID response Timothy Burgess, MD, MPH, AstraZeneca: The IDCRP and the Henry M. Jackson Foundation (HJF) were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial Simon Pollett, MBBS, AstraZeneca: The IDCRP and the Henry M. Jackson Foundation (HJF) were funded to conduct an unrelated phase III COVID-19 monoclonal antibody immunoprophylaxis trial

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.246
Teacher spread0.238 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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