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Record W4392642708 · doi:10.1101/2024.03.08.24304006

Dynamics of T cell responses to COVID-19 vaccines and breakthrough infection in people living with HIV receiving antiretroviral therapy

2024· preprint· en· W4392642708 on OpenAlexafffundabout
Sneha Datwani, Rebecca Kalikawe, Rachel Waterworth, Francis Mwimanzi, Richard Liang, Yurou Sang, Hope R. Lapointe, Peter K. Cheung, F. Harrison Omondi, Maggie C. Duncan, Evan Barad, Sarah Speckmaier, Nadia Moran-Garcia, Mari L. DeMarco, Malcolm Hedgcock, Cecilia T. Costiniuk, Mark Hull, Marianne Harris, Marc G. Romney, Julio Montaner, Zabrina L. Brumme, Mark A. Brockman

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of British ColumbiaProvidence Health CareAIDS VancouverMcGill University Health CentreSimon Fraser University
FundersCanadian Institutes of Health ResearchAfrican Academy of SciencesMichael Smith Health Research BCMenzies School of Health ResearchSimon Fraser UniversityProvidence Health CarePublic Health AgencyPublic Health Agency of Canada
KeywordsMedicineSeroconversionVaccinationImmunologyCD8Immune systemAntiretroviral therapyHIV vaccineT cellVirologyHuman immunodeficiency virus (HIV)AntibodyAntigenVaccine trialInternal medicineViral load

Abstract

fetched live from OpenAlex

ABSTRACT Introduction People living with HIV (PLWH) can exhibit impaired immune responses to vaccines. Accumulating evidence indicates that PLWH, particularly those receiving antiretroviral therapy, mount strong antibody responses to COVID-19 vaccination, but fewer studies have examined cellular immune responses to vaccination. We measured SARS-CoV-2 spike-specific CD4+ and CD8+ T cell responses generated by two and three doses of COVID-19 vaccine in PLWH receiving antiretroviral therapy, compared to control participants without HIV. We also quantified T cell responses after post-vaccine breakthrough infection, and receipt of fourth vaccine doses, in a subset of PLWH. Methods We quantified CD4+ and CD8+ T cells reactive to overlapping peptides spanning the ancestral SARS-CoV-2 spike protein in 50 PLWH and 87 controls without HIV, using an activation induced marker (AIM) assay. All participants remained SARS-CoV-2 naïve until at least one month after their third vaccine dose. SARS-CoV-2 infection was determined by seroconversion to nucleocapsid (N) antigen, which occurred in 21 PLWH and 38 controls post-third dose. Multivariable regression analyses were used to investigate relationships between sociodemographic, health and vaccine-related variables and vaccine-induced T cell responses, as well as breakthrough infection risk. Results A third vaccine dose boosted spike-specific CD4+ and CD8+ T cell frequencies significantly above those measured after the second dose (all p<0.0001). Median T cell frequencies did not differ between PLWH and controls after the second dose (p>0.1), but CD8+ T cell responses were modestly lower in PLWH after the third dose (p=0.02), an observation that remained significant after adjustment for sociodemographic, health and vaccine-related variables (p=0.045). In PLWH who experienced breakthrough infection, median T cell frequencies increased even higher than those observed after three vaccine doses (p<0.03), and CD8+ T cell responses in this group remained higher even after a fourth vaccine dose (p=0.03). In multivariable analysis, the only factor associated with increased breakthrough infection risk was younger age, consistent with the rapid increases in SARS-CoV-2 seropositivity among younger adults in Canada after the initial appearance of the Omicron variant. Conclusion PLWH receiving antiretroviral therapy mount strong T cell responses to COVID-19 vaccines that can be enhanced by booster doses or breakthrough infection.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.029
GPT teacher head0.342
Teacher spread0.312 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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