SARS-CoV-2 variants Omicron BA.4/5 and XBB.1.5 significantly escape T cell recognition in solid organ transplant recipients vaccinated against the ancestral strain
Bibliographic record
Abstract
Abstract Background Immune-suppressed solid organ transplant recipients (SOTRs) display impaired humoral responses to COVID-19 vaccination, but T cell responses are incompletely understood. The highly infectious SARS-CoV-2 variants Omicron BA.4/5 and XBB.1.5 escape neutralization by antibodies induced by vaccination or infection with earlier strains, but T cell recognition of these lineages in SOTRs is unclear. Methods We characterized Spike-specific T cell responses to ancestral SARS-CoV-2, Omicron BA.4/5 and XBB.1.5 peptides in a prospective study of kidney, lung and liver transplant recipients (n = 42) throughout a three- or four-dose ancestral Spike mRNA vaccination schedule. Using an optimized activation-induced marker assay, we quantified circulating Spike-specific CD4+ and CD8+ T cells based on antigen-stimulated expression of CD134, CD69, CD25, CD137 and/or CD107a. Results Vaccination strongly induced SARS-CoV-2-specific T cells, including BA.4/5- and XBB.1.5-reactive T cells, which remained detectable over time and further increased following a fourth dose. However, responses to Omicron BA.4/5 and XBB.1.5 were significantly lower in magnitude compared to ancestral strain responses. Antigen-specific CD4+ T cell frequencies correlated with anti-receptor-binding domain (RBD) antibody titres, with post-second dose T cell responses predicting subsequent antibody responses. Patients receiving prednisone, lung transplant recipients and older adults displayed weaker responses. Conclusions Ancestral strain vaccination stimulates BA.4/5 and XBB.1.5-cross-reactive T cells in SOTRs, but responses to these variants are diminished. Antigen-specific T cells can predict future antibody responses and identify vaccine responses in seronegative individuals. Our data support monitoring both humoral and cellular immunity in SOTRs to track effectiveness of COVID-19 vaccines against emerging variants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".