Binding between Insulin-like Growth Factor 1 and Insulin-like Growth Factor-binding Protein 3 Is Not Influenced by Glucose or 2-Deoxy-d-glucose
Bibliographic record
Abstract
A recent report (Zhong, D., Xiong, L., Liu, T., Liu, X., Liu, X., Chen, J., Sun, S. Y., Khuri, F. R., Zong, Y., Zhou, Q., and Zhou, W. (2009) J. Biol. Chem. 284, 23225-23233) details that 2-deoxy-D-glucose (2-DG), a well known inhibitor of glycolysis and a candidate antineoplastic agent, also induces insulin-like growth factor 1 receptor (IGF-1R) signaling through the inhibition of insulin-like growth factor 1-insulin-like growth factor-binding protein 3 (IGF-1-IGFBP-3) complex formation. Zhong et al. hypothesized that disrupted IGF-1/IGFBP-3 binding by 2-DG led to increased free IGF-1 concentrations and, consequently, activation of IGF-1R downstream pathways. Because their report suggests unprecedented off-target effects of 2-DG, this has profound implications for the fields of metabolism and oncology. Using ELISA, surface plasmon resonance, and novel "intensity-fading" mass spectrometry, we now provide a detailed characterization of complex formation between IGF-1 and IGFBP-3. All three of these independent methods demonstrated that there was no effect of glucose or 2-DG on the interaction between IGF-1 and IGFBP-3. Furthermore, we show examples of 2-DG exposure associated with reduced rather than increased IGF-1R and AKT activation, providing further evidence against a 2-DG increase in IGF-1R activation by IGF-1-IGFBP-3 complex disruption.
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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 | 0.001 | 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".