Generation of an animal model of <i>Pten</i> hamartoma tumour syndrome in the retina
Bibliographic record
Abstract
PTEN Hamartoma Tumor Syndrome (PHTS) is a heterogeneous group of rare, autosomal dominant disorders associated with PTEN germline mutations. PHTS patients routinely develop hamartomas, which are benign tissue overgrowths comprised of disorganized ‘normal’ cells. Efforts to generate PHTS animal models have been largely unsuccessful due to the early lethality of homozygous germline mutations in Pten, together with the lack of hamartoma formation in most conditional mutants generated to date. We report herein a novel PHTS mouse model that reproducibly forms hamartoma-like lesions in the central retina by postnatal day 21. Specifically, we generated a Pten conditional knock-out (cKO) using a retinal-specific Pax6-Cre-driver that leads to a nearly complete deletion of Pten in the peripheral retina but produces a mosaic of ‘wild-type’ and Pten cKO cells centrally. Structural defects were only observed in the mosaic central retina, including in Müller glia and in the outer and inner limiting membranes, suggesting that defective mechanical integrity partly underlies the hamartoma-like pathology. Finally, we used this newly-developed model to test whether rapamycin, an mTOR inhibitor that is currently the only PHTS therapy, can block hamartoma growth. When administered in the early postnatal period, prior to hamartoma formation, rapamycin reduces hamartoma size, but also induces new morphological abnormalities in the Pten cKO retinal periphery. In contrast, administration of rapamycin after hamartoma initiation fails to reduce lesion size. We have thus generated and used an animal model of retinal PHTS to show that while current therapies reduce hamartoma formation, they also induce new retinal dysmorphologies.
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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 | 0.000 | 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 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".