TGF-β1 Increases GDNF Production by Upregulating the Expression of GDNF and Furin in Human Granulosa-Lutein Cells
Bibliographic record
Abstract
Glial cell line-derived neurotrophic factor (GDNF) is expressed at a high level in the human ovary and GDNF signaling is involved in the direct control of follicular activation and oocyte maturation. Transforming growth factor-β1 (TGF-β1) plays an important role in the regulation of various ovarian functions. Furin is an intracellular serine endopeptidase of the subtilisin family that is closely associated with the activation of multiple protein precursors. Despite the important roles of GDNF and TGF-β1 in the regulation of follicular development, whether TGF-β is able to regulate the expression and production of GDNF in human granulosa cells remains to be determined. The aim of this study was to investigate the effect of TGF-β1 on the production of GDNF and its underlying mechanisms in human granulosa-lutein (hGL) cells. We used two types of hGL cells (primary hGL cells and an established immortalized hGL cell line, SVOG cells) as study models. Our results show that TGF-β1 significantly induced the expression of GDNF and furin, which, in turn, increased the production of mature GDNF. Using a dual inhibition approach combining RNA interference and kinase inhibitors against cell signaling components, we showed that the TβRII type II receptor and ALK5 type I receptor are the principal receptors that mediated TGF-β1-induced cellular activity in hGL cells. Additionally, Sma- and Mad-related protein (SMAD)3 and SMAD4 are the downstream signaling transducers that mediate the biological response induced by TGF-β1. Furthermore, furin is the main proprotein convertase that induces the production of GDNF. These findings provide additional regulatory mechanisms by which an intrafollicular factor influences the production of another growth factor through a paracrine or autocrine interaction in hGL cells.
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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 | 0.000 |
| 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".