Phosphorylation of Insulin-Like Growth Factor (IGF) Binding Protein-3 by Breast Cancer Cell Membranes Enhances IGF-I Binding
Bibliographic record
Abstract
Cross-linking of nonglycosylated biotinylated IGF binding protein (IGFBP)-3 to T-47D cell membranes identifies complexes with Mr of 32, 50, 70, and 100 kDa. Nonbiotinylated glycosylated IGFBP-3 competed for binding to each of these sites. The 32-kDa band approximated the size of intact nonglycosylated IGFBP-3, but its abundance was enhanced by cross-linking, and it had a more acidic isoelectric point on isoelectric focusing, suggesting that it had undergone phosphorylation. Immobilized IGFBP-3 was phosphorylated in the presence of (32)P-gamma ATP by both T-47D cell membranes and by intact cells treated with phenylarsine oxide to inhibit internalization. MCF-7 and COS-1 cells were also able to bind and phosphorylated IGFBP-3. IGF-I inhibited both IGFBP-3 binding to membranes and phosphorylation. However, incubation of T-47D cells with IGFBP-3 enhanced binding of (125)I-IGF-I to the cell monolayer indicating that membrane bound IGFBP-3 was able to bind IGF-I. Immobilized IGFBP-3 when phosphorylated by T-47D membranes bound significantly more (125)I-IGF-I than nonphosphorylated IGFBP-3. Treatment with alkaline phosphatase significantly reduced (125)I-IGF-I binding to phosphorylated immobilized IGFBP-3 and also reduced (125)I-IGF-I to T-47D cell monolayers preincubated with IGFBP-3. Phosphorylation of IGFBP-3 by T-47D membranes was partially blocked by inhibitors of both protein kinase A and C. These data demonstrate that binding of IGFBP-3 to breast cancer membranes is accompanied by phosphorylation at the plasma membrane and that both processes are inhibited by IGF-I. However, once phosphorylated the ability of IGFBP-3 to bind IGF-I is enhanced, resulting in increased association of the IGF-I with the cell membrane.
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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 | 0.001 |
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".