Paracrine rather than systemic glucocorticoids are biologically active in the thymus
Bibliographic record
Abstract
Abstract Thymocyte positive and negative selection are critical for generation of a competent and self-tolerant T cell repertoire. Glucocorticoids (GCs) protect thymocytes from T cell receptor (TCR)-induced death, and thymocyte-specific GC receptor (GR) deletion amplifies negative selection, weakening the TCR repertoire. Circulating GCs are secreted by the adrenals, but levels fluctuate widely with time of day and in response to stressors. To avoid such variation, thymus GCs might be regulated independently of the adrenals, as thymic epithelial cells (TECs) and possibly thymocytes express GC-synthetic enzymes. Whether local GC production is sufficient to affect thymocyte development in the presence of adrenal GCs, however, is unknown. Here, we have found that corticosterone, the major mouse GC, was locally elevated in the thymus compared to the blood, and that cultured thymus produced corticosterone from endogenous substrates via GC-synthetic enzyme activity. To test the source and importance of local GC synthesis in vivo, we generated mice with targeted deletion of the GC-synthetic enzyme Cyp11b1 in TECs (Cyp11b1foxn1-Cre) or thymocytes (Cyp11b1lck-Cre). As a measure of GC signaling we quantified thymocyte expression of the GC-responsive gene Gilz. Gilz mRNA was normal in Cyp11b1lck-Cre but reduced in Cyp11b1foxn1-Cre thymocytes, with a reduction equivalent to that in GR-deficient thymocytes. Basal GR signaling is thus driven overwhelmingly by TEC-rather than adrenal-derived corticosterone. These findings demonstrate the importance of paracrine GC function in vivo, and are consistent with a role for paracrine GCs in thymocyte selection.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".