Expression of murine muscle-enriched A-type lamin-interacting protein (MLIP) is regulated by tissue-specific alternative transcription start sites
Bibliographic record
Abstract
Muscle-enriched lamin-interacting protein ( Mlip ) is an alternatively spliced gene whose splicing specificity is dictated by tissue type. MLIP is most abundantly expressed in brain, cardiac, and skeletal muscle. In the present study, we systematically mapped the transcriptional start and stop sites of murine Mlip . Rapid amplification of cDNA ends (RACE) of Mlip transcripts from the brain, heart, and skeletal muscle revealed two transcriptional start sites (TSSs), exon 1a and exon 1b, and only one transcriptional termination site. RT-PCR analysis of the usage of the two identified TSSs revealed that the heart utilizes only exon 1a for MLIP expression, whereas the brain exclusively uses exon 1b TSS. Loss of Mlip exon 1a in mice resulted in a 7-fold increase in the prevalence of centralized nuclei in muscle fibers with the Mlip exon1a–deficient satellite cells on single fibers exhibiting a significant delay in commitment to a MYOD-positive phenotype. Furthermore, we demonstrate that the A-type lamin-binding domain in MLIP is encoded in exon 1a, indicating that MLIP isoforms generated with exon 1b TSS lack the A-type lamin-binding domain. Collectively these findings suggest that Mlip tissue-specific expression and alternative splicing play a critical role in determining MLIP's functions in mice.
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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".