BCG Vaccination at Birth Shapes the TCR Usage and Functional Profile of MR1T Cells at 9 Weeks of Age
Bibliographic record
Abstract
Abstract Tuberculosis (TB) is the leading infectious disease killer worldwide and children suffer disproportionately. The ongoing burden of disease, despite widespread vaccination with BCG, highlights the need for novel vaccines. MR1-restricted T (MR1T) cells recognize small molecules, including microbial-derived molecules, presented by the monomorphic MHC class 1- related molecule (MR1). They have both “innate” effector capacity, allowing them to quickly respond to pathogens including Mycobacterium tuberculosis (Mtb), while also having adaptive features (effector memory cell surface phenotype and selective TCR usage). Feasibility of an MR1T cell-based vaccination remains unexplored and critical to this is whether or not MR1T cells possess the capacity for immunological memory. To begin to address this question, peripheral blood mononuclear cells (PBMC) were collected at 9-weeks of age from healthy term- infants in South Africa, who had either received BCG vaccination at birth (n=10) or who had BCG vaccination delayed (n=10). MR1/5-OP-RU tetramer positive cells were sorted using flow cytometry and single-cell RNA and TCR-seq was performed. Ex-vivo MR1T cells from vaccinated infants demonstrated increased expression of type I interferon response genes consistent with a cytokine mediated response to BCG vaccination. Using the TCR clustering algorithm TCRdist3, similar TCRs were grouped together, revealing a cluster significantly enriched in BCG-vaccinated infants. This cluster exhibited elevated expression of pro- inflammatory and cytotoxic genes, consistent with a recall response to prior vaccination and evidence of possible recognition of Mtb. This work provides the first step in addressing if MR1T cells demonstrate immunological memory, however, further work is needed to understand if these clonal expansions persist and possess the capacity for antigenic recall.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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.
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".