The QKI-6 and QKI-7 RNA Binding Proteins Block Proliferation and Promote Schwann Cell Myelination
Bibliographic record
Abstract
BACKGROUND: The quaking viable (qk(v)) mice have uncompacted myelin in their central and peripheral nervous system (CNS, PNS). The qk gene encodes 3 major alternatively spliced isoforms that contain unique sequence at their C-terminus dictating their cellular localization. QKI-5 is a nuclear isoform, whereas QKI-6 and QKI-7 are cytoplasmic isoforms. The qk(v) mice harbor an enhancer/promoter deletion that prevents the expression of isoforms QKI-6 and QKI-7 in myelinating cells resulting in a dysmyelination phenotype. It was shown that QKI regulates the differentiation of oligodendrocytes, the myelinating cells of the CNS, however, little is known about the role of the QKI proteins, or RNA binding proteins in PNS myelination. METHODOLOGY/PRINCIPAL FINDINGS: To define the role of the QKI proteins in PNS myelination, we ectopically expressed QKI-6 and QKI-7 in primary rat Schwann cell/neuron from dorsal root ganglia cocultures. We show that the QKI isoforms blocked proliferation and promoted Schwann cell differentiation and myelination. In addition, these events were coordinated with elevated proteins levels of p27(KIP1) and myelin basic protein (MBP), markers of Schwann cell differentiation. QKI-6 and QKI-7 expressing co-cultures contained myelinated fibers that had directionality and contained significantly thicker myelin, as assessed by electron microscopy. Moreover, QKI-deficient Schwann cells had reduced levels of MBP, p27(KIP1) and Krox-20 mRNAs, as assessed by quantitative RT-PCR. CONCLUSIONS/SIGNIFICANCE: Our findings suggest that the QKI-6 and QKI-7 RNA binding proteins are positive regulators of PNS myelination and show that the QKI RNA binding proteins play a key role in Schwann cell differentiation and myelination.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".