FGF-induced Pea3 transcription factors program the genetic landscape for cell fate determination
Bibliographic record
Abstract
FGF signaling is a potent inducer of lacrimal gland development in the eye, capable of transforming the corneal epithelium into glandular tissues.Here, we show that genetic ablation of the Pea3 family of transcription factors not only disrupted the ductal elongation and branching of the lacrimal gland, but also biased the lacrimal gland epithelium toward an epidermal cell fate.Analysis of high-throughput gene expression and chromatin immunoprecipitation data revealed that the Pea3 genes directly control both the positive and negative feedback loops of FGF signaling.Importantly, Pea3 genes are also required to suppress aberrant Notch signaling which, if gone unchecked, can compromise lacrimal gland development by preventing the expression of both Sox and Six family genes.These results demonstrate that Pea3 genes are key FGF early response transcriptional factors, programing the genetic landscape for cell fate determination. Author summaryFGF signaling regulates cell fate decision by inducing genome-wide changes in gene expression.We identified Pea3 family transcription factors as the key effectors of FGF signaling in reprograming the epithelia transcriptome.Pea3 factors control both the feedback and feedforward circuities of FGF signaling in lacrimal gland development.They also activate specific expression of Six and Sox family genes and suppress aberrant activation of Notch signaling.In the absence of Pea3 genes, the lacrimal gland progenitors become epidermal-like in their gene expression patterns.The study of Pea3 function resolves the long standing conundrum of how FGF induces the lacrimal gland fate, providing direction for regenerating the lacrimal gland to treat dry eye diseases.
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".