Systemic Sclerosis Immunoglobulin Induces Growth and a Pro-Fibrotic State in Vascular Smooth Muscle Cells through the Epidermal Growth Factor Receptor
Bibliographic record
Abstract
OBJECTIVE: It has been suggested that autoantibodies in systemic sclerosis (SSc) may induce the differentiation of cultured fibroblasts into myofibroblasts through platelet-derived growth factor receptor (PDGFR) activation. The present study aims to characterize the effects of SSc IgG on vascular smooth muscle cells (VSMCs) and to determine if stimulatory autoantibodies directed to the PDGFR can be detected, and whether they induce a profibrotic response in primary cultured VSMCs. METHODS: Cultured VSMCs were exposed to IgG fractions purified from SSc-patient or control sera. VSMC responses were then analyzed for ERK1/2 and Akt phosphorylation, PDGFR immunoprecipitation, cellular proliferation, protein synthesis, and pro-fibrotic changes in mRNA expression. RESULTS: Stimulatory activity in IgG fractions was more prevalent and intense in the SSc samples. SSc IgG immunoprecipitated the PDGFR with greater avidity than control IgG. Interestingly, activation of downstream signaling events (e.g. Akt, ERK1/2) was independent of PDGFR activity, but required functional EGFR. We also detected increased protein synthesis in response to SSc IgG (p<0.001) and pro-fibrotic changes in gene expression (Tgfb1 +200%; Tgfb2 -23%; p<0.001)) in VSMCs treated with SSc IgG. CONCLUSION: When compared to control IgG, SSc IgG have a higher stimulation index in VSMCs. Although SSc IgG interact with the PDGFR, the observed remodeling signaling events occur through the EGFR in VSMC. Our data thus favour a model of transactivation of the EGFR by SSc-derived PDGFR autoantibodies and suggest the use of EGFR inhibitors in future target identification studies in the field of SSc.
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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) | 0.002 | 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".