Generation of β cell-specific human cytotoxic T cells by lentiviral transduction and their survival in immunodeficient human leucocyte antigen-transgenic mice.
Bibliographic record
Abstract
Several β cell antigens recognized by T cells in the non-obese diabetic (NOD) mouse model of type 1 diabetes (T1D) are also T cell targets in the human disease. While numerous antigen-specific therapies prevent diabetes in NOD mice, successful translation of rodent findings to patients has been difficult. A human leucocyte antigen (HLA)-transgenic mouse model incorporating human β cell-specific T cells might provide a better platform for evaluating antigen-specific therapies. The ability to study such T cells is limited by their low frequency in peripheral blood and the difficulty in obtaining islet-infiltrating T cells from patients. We have worked to overcome this limitation by using lentiviral transduction to 'reprogram' primary human CD8 T cells to express three T cell receptors (TCRs) specific for a peptide derived from the β cell antigen islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP265-273 ) and recognized in the context of the human class I major histocompatibility complex (MHC) molecule HLA-A2. The TCRs bound peptide/MHC multimers with a range of avidities, but all bound with at least 10-fold lower avidity than the anti-viral TCR used for comparison. One exhibited antigenic recognition promiscuity. The β cell-specific human CD8 T cells generated by lentiviral transduction with one of the TCRs released interferon (IFN)-γ in response to antigen and exhibited cytotoxic activity against peptide-pulsed target cells. The cells engrafted in HLA-A2-transgenic NOD-scid IL2rγ(null) mice and could be detected in the blood, spleen and pancreas up to 5 weeks post-transfer, suggesting the utility of this approach for the evaluation of T cell-modulatory therapies for T1D and other T cell-mediated autoimmune diseases. Clin Exp Immunol 2015 Mar; 179(3):398-413.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".