<i>Pten</i>Regulates Retinal Amacrine Cell Number by Modulating Akt, Tgfβ, and Erk Signaling
Bibliographic record
Abstract
UNLABELLED: All tissues are genetically programmed to acquire an optimal size that is defined by total cell number and individual cellular dimensions. The retina contains stereotyped proportions of one glial and six neuronal cell types that are generated in overlapping waves. How multipotent retinal progenitors know when to switch from making one cell type to the next so that appropriate numbers of each cell type are generated is poorly understood. Pten is a phosphatase that controls progenitor cell proliferation and differentiation in several lineages. Here, using a conditional loss-of-function strategy, we found that Pten regulates retinal cell division and is required to produce the full complement of rod photoreceptors and amacrine cells in mouse. We focused on amacrine cell number control, identifying three downstream Pten effector pathways. First, phosphoinositide 3-kinase/Akt signaling is hyperactivated in Pten conditional knock-out (cKO) retinas, and misexpression of constitutively active Akt (Akt-CA) in retinal explants phenocopies the reduction in amacrine cell production observed in Pten cKOs. Second, Akt-CA activates Tgfβ signaling in retinal explants, which is a negative feedback pathway for amacrine cell production. Accordingly, Tgfβ signaling is elevated in Pten cKO retinas, and epistatic analyses placed Pten downstream of TgfβRII in amacrine cell number control. Finally, Pten regulates Raf/Mek/Erk signaling levels to promote the differentiation of all amacrine cell subtypes, which are each reduced in number in Pten cKOs. Pten is thus a positive regulator of amacrine cell production, acting via multiple downstream pathways, highlighting its diverse actions as a mediator of cell number control. SIGNIFICANCE STATEMENT: Despite the importance of size for optimal organ function, how individual cell types are generated in correct proportions is poorly understood. There are several ways to control cell number, including readouts of organ function (e.g., secreted hormones reach functional levels when enough cells are made) or counting of cell divisions or cell number. The latter applies to the retina, where cell number is regulated by negative feedback signals, which arrest differentiation of particular cell types at threshold levels. Herein, we show that Pten is a critical regulator of amacrine cell number in the retina, acting via multiple downstream pathways. Our studies provide molecular insights into how PTEN loss in humans may lead to uncontrolled cell division in several pathological conditions.
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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".