Pituitary-Specific Knockout of the Carney Complex Gene Prkar1a Leads to Pituitary Tumorigenesis
Bibliographic record
Abstract
Carney complex (CNC) is an inherited neoplasia syndrome characterized by spotty skin pigmentation, myxomas, endocrine tumors, and schwannomas. Among the endocrine tumors that comprise the syndrome, GH-producing pituitary tumors are seen in approximately 10% of patients, although biochemical abnormalities of the GH axis are much more common. To explore the role of loss of the CNC gene PRKAR1A on pituitary tumorigenesis, we produced a tissue-specific knockout (KO) of this gene in the mouse. For these studies, we generated a mouse line expressing the cre recombinase in pituitary cells using the rat GHRH receptor promoter. These mice were then crossed with Prkar1a conditional null animals to produce tissue-specific KOs. Although prolactinomas were observed in KO and control mice, the KO mice exhibited a significantly increased frequency of pituitary tumors compared with wild-type or conventional Prkar1a(+/-) mice. Characterization of the tumors demonstrated they were composed of cells of the Pit1 lineage that stained for GH, prolactin, and TSH. At the biochemical level, levels of GH in the serum of KO animals were markedly elevated compared with controls, regardless of the presence of a frank tumor. These data indicate that complete loss of Prkar1a is sufficient to allow the formation of pituitary tumors and abnormalities of the GH axis, in close analogy to human patients with CNC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".