Zac1 functions through TGFβIIto negatively regulate cell number in the developing retina
Bibliographic record
Abstract
BACKGROUND: Organs are programmed to acquire a particular size during development, but the regulatory mechanisms that dictate when dividing progenitor cells should permanently exit the cell cycle and stop producing additional daughter cells are poorly understood. In differentiated tissues, tumor suppressor genes maintain a constant cell number and intact tissue architecture by controlling proliferation, apoptosis and cell dispersal. Here we report a similar role for two tumor suppressor genes, the Zac1 zinc finger transcription factor and that encoding the cytokine TGFbetaII, in the developing retina. RESULTS: Using loss and gain-of-function approaches, we show that Zac1 is an essential negative regulator of retinal size. Zac1 mutants develop hypercellular retinae due to increased progenitor cell proliferation and reduced apoptosis at late developmental stages. Consequently, supernumerary rod photoreceptors and amacrine cells are generated, the latter of which form an ectopic cellular layer, while other retinal cells are present in their normal number and location. Strikingly, Zac1 functions as a direct negative regulator of a rod fate, while acting cell non-autonomously to modulate amacrine cell number. We implicate TGFbetaII, another tumor suppressor and cytokine, as a Zac1-dependent amacrine cell negative feedback signal. TGFbetaII and phospho-Smad2/3, its downstream effector, are expressed at reduced levels in Zac1 mutant retinae, and exogenous TGFbetaII relieves the mutant amacrine cell phenotype. Moreover, treatment of wild-type retinae with a soluble TGFbeta inhibitor and TGFbeta receptor II (TGFbetaRII) conditional mutants generate excess amacrine cells, phenocopying the Zac1 mutant phenotype. CONCLUSION: We show here that Zac1 has an essential role in cell number control during retinal development, akin to its role in tumor surveillance in mature tissues. Furthermore, we demonstrate that Zac1 employs a novel cell non-autonomous strategy to regulate amacrine cell number, acting in cooperation with a second tumor suppressor gene, TGFbetaII, through a negative feedback pathway. This raises the intriguing possibility that tumorigenicity may also be associated with the loss of feedback inhibition in mature tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".