Identification of Differentially Regulated Secretome Components During Skeletal Myogenesis
Bibliographic record
Abstract
Myogenesis is a well-characterized program of cellular differentiation that is exquisitely sensitive to the extracellular milieu. Systematic characterization of the myogenic secretome (i.e. the ensemble of secreted proteins) is, therefore, warranted for the identification of novel secretome components that regulate both the pluripotency of these progenitor mesenchymal cells, and also their commitment and passage through the differentiation program. Previously, we have successfully identified 26 secreted proteins in the mouse skeletal muscle cell line C2C12 (1). In an effort to attain a more comprehensive picture of the regulation of myogenesis by its extracellular milieu, quantitative profiling employing stable isotope labeling by amino acids in cell culture was implemented in conjunction with two parallel high throughput online reverse phase liquid chromatography-tandem mass spectrometry systems. In summary, 34 secreted proteins were quantified, 30 of which were shown to be differentially expressed during muscle development. Intriguingly, our analysis has revealed several novel up- and down-regulated secretome components that may have critical biological relevance for both the maintenance of pluripotency and the passage of cells through the differentiation program. In particular, the altered regulation of secretome components, including follistatin-like protein-1, osteoglycin, spondin-2, and cytokine-induced apoptosis inhibitor-1, along with constitutively expressed factors, such as fibulin-2, illustrate dynamic changes in the secretome that take place when differentiation to a specific lineage occurs.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".