Hsp10 and Hsp60 Suppress Ubiquitination of Insulin-like Growth Factor-1 Receptor and Augment Insulin-like Growth Factor-1 Receptor Signaling in Cardiac Muscle
Bibliographic record
Abstract
We have investigated the effects of two heat shock proteins, Hsp10 and Hsp60, on insulin-like growth factor-1 receptor (IGF-1R) signaling in cardiac muscle cells. Neonatal cardiomyocytes were transduced with Hsp10 or Hsp60 via adenoviral vector. Compared with the cells transduced with a control vector, overexpression of Hsp10 or Hsp60 increased the abundance of IGF-1R and IGF-1-stimulated receptor autophosphorylation. Thus, Hsp10 and Hsp60 overexpression increased the number of functioning receptors and amplified activation of IGF-1R signaling. IGF-1 stimulation of MEK, Erk, p90Rsk, and Akt were accordingly augmented. Transducing cardiomyocytes with antisense Hsp60 oligonucleotides reduced Hsp60 expression, decreased the abundance of IGF-1R, attenuated IGF-1R autophosphorylation, and suppressed the pro-survival action of IGF-1 in cardiomyocytes. Using cycloheximide to inhibit protein synthesis did not alter the effect of Hsp60 on IGF-1R signaling, and IGF-1R mRNA levels were not up-regulated by Hsp10 or Hsp60. Additional experiments showed that Hsp10 and Hsp60 suppressed polyubiquitination of IGF-1 receptor. These data indicate that Hsp10 and Hsp60 can modulate IGF-1R signaling through post-translational modification. In animal models of diabetes, diabetic myocardium is associated with decreased abundance of Hsp60, increased ubiquitination of IGF-1R, and lower level of IGF-1R protein. Declined myocardial protection is a major feature of diabetic cardiomyopathy. These data suggest that decreased Hsp60 expression and subsequent decline of IGF-1R signaling may be a fundamental mechanism underlying the development of diabetic cardiomyopathy.
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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".