Identification of novel signalling roles and targets for Gα and Gβγ downstream of the insulin-like growth factor 1 receptor in vascular smooth muscle cells
Bibliographic record
Abstract
Vascular dysfunction is the underlying cause of nearly 80% of heart disease cases, and its initiation and progression can be exacerbated by circulating factors, such as IGF-1 (insulin-like growth factor 1). IGF-1, which is highly homologous with insulin, elicits a response via a classical tyrosine kinase receptor, the IGF-1R (IGF-1 receptor). However, it has been suggested that the IGF-1R may also be coupled to a heterotrimeric G-protein and can thus modulate cellular processes via this alternate pathway. The objective of the present study was to investigate the structural aspects of IGF-1R coupling to a heterotrimeric G-protein in VSMCs [vascular SMCs (smooth muscle cells)], as well as examine the contribution of this pathway to cellular responses that are related to vascular disease. We found that the intracellular subunit of the IGF-1R precipitates with two G-protein subunits. The G(βγ)-mediated pathway contributes to both proliferation and migration. We also show that IGF-1 specifically activates G(αi) and can directly interact with both G(αi1) and G(αi2). A phospho-screen using a novel specific G(αi)-peptide inhibitor reveals a number of potential downstream effectors of this pathway, although our results show that it is not essential for SMC proliferation or migration.
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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".