Never in Mitosis Kinase 2 regulation of metabolism is required for neural differentiation
Bibliographic record
Abstract
1. Abstract Wnt and Hh are known signalling pathways involved in neural differentiation and recent work has shown the cell cycle regulator, Never in Mitosis Kinase 2 (Nek2) is able to regulate both pathways. Despite its known function in pathway regulation, few studies have explored Nek2 within embryonic development. The P19 embryonal carcinoma cell model was used to investigate Nek2 and neural differentiation through CRISPR knockout and overexpression studies. Loss of Nek2 reduced cell proliferation in the undifferentiated state and during directed differentiation, while overexpression increased cell proliferation. Despite these changes in proliferation rates, Nek2 deficient cells maintained pluripotency markers after neural induction while Nek2 overexpressing cells lost these markers in the undifferentiated state. Nek2 deficient cells lost the ability to differentiate into both neurons and astrocytes, although Nek2 overexpressing cells enhanced neuron differentiation at the expense of astrocytes. Hh and Wnt signaling were explored, however there was no clear connection between Nek2 and these pathways causing the observed changes to differentiation phenotypes. Mass spectrometry was also used during wildtype and Nek2 knockout cell differentiation and we identified reduced electron transport chain components in the knockout population. Immunoblotting confirmed the loss of these components and additional studies showed cells lacking Nek2 were exclusively glycolytic. Interestingly, hypoxia inducible factor 1α was stabilized in these Nek2 knockout cells despite culturing them under normoxic conditions. Since neural differentiation requires a metabolic switch from glycolysis to oxidative phosphorylation, we propose a mechanism where Nek2 prevents HIF1α stabilization, thereby allowing cells to use oxidative phosphorylation to facilitate neuron and astrocyte 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".