Interferon γ Induces Neurite Outgrowth by Up-regulation of p35 Neuron-specific Cyclin-dependent Kinase 5 Activator via Activation of ERK1/2 Pathway
Bibliographic record
Abstract
Interferon gamma (IFN-gamma) is a cytokine predominantly involved in antiproliferative and antiviral responses, immune surveillance, and tumor suppression. However, it has been shown that IFN-gamma is also involved in central nervous system development. Here we studied the underlying mechanism for IFN-gamma-induced neuronal differentiation using the human neuroblastoma Paju cell line. Our results indicate that IFN-gamma treatment led to neurite outgrowth followed by growth arrest in the G1 phase of the cell cycle. IFN-gamma induced ERK1/2 phosphorylation and subsequently the transcription factor early gene response 1, which in turn up-regulated p35 expression and increased cyclin-dependent kinase 5 (Cdk5) activity. IFN-gamma-induced neurite outgrowth was abolished by the treatment of MEK1/2 kinase inhibitors, such as U0126 and PD98059, which inhibit the ERK1/2 activation and subsequently prevent the up-regulation of p35 expression and Cdk5 activity. In agreement with the role of p35-Cdk5 in neuronal differentiation, small interfering RNA targeting Cdk5 abrogate the IFN-gamma-induced neurite outgrowth. Together, these results demonstrate for the first time that IFN-gamma-triggered neuronal differentiation mediated through the up-regulation of p35-associated Cdk5 depends on the activation of the ERK1/2 pathway. Therefore, the present study suggests that IFN-gamma is not only involved in tumorigenicity but also involved in neurogenesis by regulating cell proliferation and differentiation.
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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".