Diversification of the muscle proteome through alternative splicing
Bibliographic record
Abstract
BACKGROUND: Skeletal muscles express a highly specialized proteome that allows the metabolism of energy sources to mediate myofiber contraction. This muscle-specific proteome is partially derived through the muscle-specific transcription of a subset of genes. Surprisingly, RNA sequencing technologies have also revealed a significant role for muscle-specific alternative splicing in generating protein isoforms that give specialized function to the muscle proteome. MAIN BODY: In this review, we discuss the current knowledge with respect to the mechanisms that allow pre-mRNA transcripts to undergo muscle-specific alternative splicing while identifying some of the key trans-acting splicing factors essential to the process. The importance of specific splicing events to specialized muscle function is presented along with examples in which dysregulated splicing contributes to myopathies. Though there is now an appreciation that alternative splicing is a major contributor to proteome diversification, the emergence of improved "targeted" proteomic methodologies for detection of specific protein isoforms will soon allow us to better appreciate the extent to which alternative splicing modifies the activity of proteins (and their ability to interact with other proteins) in the skeletal muscle. In addition, we highlight a continued need to better explore the signaling pathways that contribute to the temporal control of trans-acting splicing factor activity to ensure specific protein isoforms are expressed in the proper cellular context. CONCLUSIONS: An understanding of the signal-dependent and signal-independent events driving muscle-specific alternative splicing has the potential to provide us with novel therapeutic strategies to treat different myopathies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".