Transcriptome profiling of mouse brains with qkI-deficient oligodendrocytes reveals major alternative splicing defects including self-splicing
Bibliographic record
Abstract
Abstract The qkI gene encodes a family of RNA binding proteins alternatively spliced at its 3′ end, giving rise to three major spliced isoforms: QKI-5, QKI-6 and QKI-7. Their expression is tightly regulated during brain development with nuclear QKI-5 being the most abundant during embryogenesis followed by QKI-6 and QKI-7 that peak during myelination. Previously, we generated a mouse conditional qkI allele where exon 2 is excised using Olig2-Cre resulting in QKI-deficient oligodendrocytes (OLs). These mice have dysmyelination and die at the third post-natal week. Herein, we performed a transcriptomic analysis of P14 mouse brains of QKI-proficient (QKI FL/FL;- ) and QKI-deficient (QKI FL/FL;Olig2-Cre ) OLs. QKI deficiency results in major global changes of gene expression and RNA processing with >1,800 differentially expressed genes with the top categories being axon ensheathment and myelination. Specific downregulated genes included major myelin proteins, suggesting that the QKI proteins are key regulators of RNA metabolism in OLs. We also identify 810 alternatively spliced genes including known QKI targets, MBP and Nfasc. Interestingly, we observe in QKI FL/FL;Olig2-Cre a switch in exon 2-deficient qkI mRNAs favoring the expression of the qkI-5 rather than the qkI-6 and qkI-7. These findings define QKI as regulators of alternative splicing in OLs including self-splicing.
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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".