Maintenance of Immune Tolerance to a Neo-Self Acetylcholine Receptor Antigen with Aging: Implications for Late-Onset Autoimmunity
Bibliographic record
Abstract
Age-related changes in immune regulation are likely to account for the age-associated increase in serum autoantibody levels and in certain autoimmune disorders, such as myasthenia gravis (MG). To demonstrate directly a loss of immune tolerance in older individuals, responses to the acetylcholine receptor, the autoantigen in MG, were assessed in transgenic mice expressing the Torpedo californica acetylcholine receptor (TAChR) alpha-chain as a neo-self Ag. T cells from young transgenic mice had been shown to be tolerant to p146-162, the TAChR alpha-chain peptide that dominated young nontransgenic T cell responses in vitro. The immunodominance of p146-162 was not lost with age; fine specificity was preserved. Moreover, T cell tolerance to p146-162, as well as to other epitopes of the TAChR alpha-chain extracellular domain, was maintained in old transgenic mice. Even multiple TAChR immunizations coupled with the MG-enhancing cytokine, IL-12, did not break tolerance. In addition, T cells exhibiting CD4 upregulation, an early activation marker, were reduced in frequency equivalently in old and young transgenic animals, suggesting that immune regulation in this model was not impacted by aging. Moreover, B cell tolerance was also maintained with age. The persistence of immune tolerance was accompanied by an increase in the proportion of T regulatory cells; it is speculated that this may compensate for deficiencies in central tolerance that occur owing to thymic involution. In summary, our study reveals, for the first time, that some immune tolerance mechanisms do survive aging; this suggests that certain late-onset autoimmune disorders may be induced by a specific insult that disrupts immune homeostasis.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".