Desialylation of insulin receptors and IGF-1 receptors by neuraminidase-1 controls the net proliferative response of L6 myoblasts to insulin
Bibliographic record
Abstract
We recently established that the subunit of cell surface-residing elastin receptor, neuraminidase-1 (Neu1), can desialylate adjacent insulin-like growth factor 1 receptors (IGF-1R) of arterial smooth muscle cells, thereby quenching their proliferative response to insulin-like growth factor II. In this study, we explored whether Neu1 would also desialylate the insulin receptors (IR), as well as the IGF-1R on rat skeletal L6 myoblasts, and whether desialylation of IR and IGF-1R would affect a net proliferative effect of insulin. First, we found that physiological (0.5-1 nM) and high therapeutic (10 nM) insulin concentrations induced a modest increase in proliferation rate of cultured L6 myoblasts. While IR kinase inhibitor could abolish the mitogenic effect of these insulin concentrations, the observed more pronounced proliferative response to supraphysiological concentration (100 nM) of insulin could be eliminated only by specific inhibition of IGF-1R. Then, we found that treatment of L6 cells with mouse-derived Neu1 or with Clostridium perfringens neuraminidase caused desialylation of IR, which coincided with a significant increase of their proliferative response to lower (0.5-10 nM) concentrations of insulin. In contrast, experimental desialylation of IGF-1R coincided with elimination of the heightened proliferative response of L6 myoblasts to 100 nM insulin. Importantly, we also found that inhibition of endogenous Neu1 abolished the increase in proliferation of L6 cells induced by 1 and 10 nM of insulin, but amplified the proliferative effect of 100 nM insulin. We therefore conclude that desialylation of both IR and IGF-1R by Neu1 controls the net proliferative response of skeletal myoblasts to insulin.
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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".