WNT2 Regulates DNA Synthesis in Mouse Granulosa Cells Through Beta-Catenin1
Bibliographic record
Abstract
WNTs are secreted extracellular signaling molecules that transduce their signals by binding to G protein-coupled receptors of the frizzled (FZD) family. They control diverse developmental processes, such as cell fate specification, cell proliferation, cell differentiation, and apoptosis. Although WNT signaling has been shown to be essential for development of the ovary, its mechanistic role in folliculogenesis within the adult ovary has not been studied extensively. Therefore, the objective of this study was to investigate the regulation and function of WNT2 signaling in mouse granulosa cells. Immunostaining identified WNT2 as being expressed in granulosa cells throughout folliculogenesis, but with varying signal strength: in sequential sections, WNT2 immunoreactivity was strongest in healthy antral follicles but weak in atretic follicles. Knockdown of WNT2 expression using transfected short interfering RNA decreased DNA synthesis in granulosa cells, whereas WNT2 overexpression using a recombinant viral vector enhanced it. WNT2 knockdown led to accumulation of glycogen synthase kinase-3beta (GSK3B) in the cytoplasm but reduced the expression of beta-catenin. Conversely, WNT2 overexpression reduced the expression of GSK3B in the cytoplasm and induced beta-catenin translocation from the membrane into the nucleus. Beta-catenin knockdown also inhibited DNA synthesis in granulosa cells and neutralized the effect of WNT2 overexpression. WNT2/beta-catenin signaling had a slight effect on the apoptosis of granulosa cells. Taken together, the data indicate that WNT2 regulates beta-catenin localization in granulosa cells, and WNT2/beta-catenin signaling contributes to regulating their proliferation.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".