A stimulatory TSH receptor antibody enhances adipogenesis via phosphoinositide 3-kinase activation in orbital preadipocytes from patients with Graves' ophthalmopathy
Bibliographic record
Abstract
Graves' ophthalmopathy (GO) is characterized by expanded volume of the orbital tissues associated with elevated serum levels of TSH receptor (TSHR) autoantibodies. Because previous studies have demonstrated evidence of adipogenesis within the GO orbit, we sought to determine whether M22, a human monoclonal antibody directed against TSHR, enhances adipogenesis in orbital fibroblasts from patients with GO and, if so, to identify signaling mechanisms involved. GO orbital fibroblast cultures (n=10) were treated for 10 days with bovine TSH (1 or 10.0 U/l) or M22 (1 or 10 ng/ml) in serum-free adipocyte differentiation medium. Some cultures also received a phosphoinositide 3-kinase (PI3K) inhibitor or an inhibitor of cAMP production. In other experiments, confluent cultures (n=8) were treated for between 1 and 30 min with TSH (0.1-10.0 U/l) or M22 (0.1-100 ng/ml) with measurement of cAMP production or levels of phosphorylated AKT (pAKT). We found levels of adiponectin, leptin, and TSHR mRNA to be increased in GO cultures treated for 10 days with either M22 (2.6 mean fold ± 0.7; P=0.03) or TSH (13.2 ± 5.8-fold, P=0.048). In other studies, M22 and TSH stimulated cAMP production and pAKT levels in GO cells. Inhibition of PI3K activity during 10 days in culture decreased the levels of M22-stimulated mRNA encoding adiponectin (67 ± 12%; P=0.021), as well as adiponectin and CCAAT/enhancer-binding protein α protein levels. In conclusion, M22 is a pro-adipogenic factor in GO orbital preadipocytes. This antibody appears to act via the PI3K signaling cascade, suggesting that inhibition of PI3K signaling may represent a potential novel therapeutic approach in GO.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".