Extensive RNA editing and splicing increase immune self-representation diversity in medullary thymic epithelial cells
Bibliographic record
Abstract
BACKGROUND: In order to become functionally competent but harmless mediators of the immune system, T cells undergo a strict educational program in the thymus, where they learn to discriminate between self and non-self. This educational program is, to a large extent, mediated by medullary thymic epithelial cells that have a unique capacity to express, and subsequently present, a large fraction of body antigens. While the scope of promiscuously expressed genes by medullary thymic epithelial cells is well-established, relatively little is known about the expression of variants that are generated by co-transcriptional and post-transcriptional processes. RESULTS: Our study reveals that in comparison to other cell types, medullary thymic epithelial cells display significantly higher levels of alternative splicing, as well as A-to-I and C-to-U RNA editing, which thereby further expand the diversity of their self-antigen repertoire. Interestingly, Aire, the key mediator of promiscuous gene expression in these cells, plays a limited role in the regulation of these transcriptional processes. CONCLUSIONS: Our results highlight RNA processing as another layer by which the immune system assures a comprehensive self-representation in the thymus which is required for the establishment of self-tolerance and prevention of autoimmunity.
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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) | 0.002 | 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".