Nuclear Factor κB-inducing Kinase and IκB Kinase-α Signal Skeletal Muscle Cell Differentiation
Bibliographic record
Abstract
Nuclear factor kappaB (NF-kappaB)-inducing kinase (NIK), IkappaB kinase (IKK)-alpha and -beta, and IkappaBalpha are common elements that signal NF-kappaB activation in response to diverse stimuli. In this study, we analyzed the role of this pathway during insulin-like growth factor II (IGF-II)-induced myoblast differentiation. L6E9 myoblasts differentiated with IGF-II showed an induction of NF-kappaB DNA-binding activity that correlated in time with the activation of IKKalpha, IKKbeta, and NIK. Moreover, the activation of IKKalpha, IKKbeta, and NIK by IGF-II was dependent on phosphatidylinositol 3-kinase, a key regulator of myogenesis. Adenoviral transduction with the IkappaBalpha(S32A/S36A) mutant severely impaired both IGF-II-dependent NF-kappaB activation and myoblast differentiation, indicating that phosphorylation of IkappaBalpha at Ser-32 and Ser-36 is an essential myogenic step. Adenoviral transfer of wild-type or kinase-deficient forms of IKKalpha or IKKbeta revealed that IKKalpha is required for IGF-II-dependent myoblast differentiation, whereas IKKbeta is not essential for this process. Finally, overexpression of kinase-proficient wild-type NIK showed that the activation of NIK is sufficient to generate signals that trigger myogenin expression and multinucleated myotube formation in the absence of IGF-II.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.005 | 0.002 |
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".