Label-free proteomic analysis reveals large dynamic changes to the cellular proteome upon expression of the miRNA-23a-27a-24-2 microRNA cluster
Bibliographic record
Abstract
In deciphering the regulatory networks of gene expression controlled by the small non-coding RNAs known as microRNAs (miRNAs), a major challenge has been with the identification of the true mRNA targets by these RNAs within the context of the enormous numbers of predicted targets for each of these small RNAs. To facilitate the system-wide identification of miRNA targets, a variety of system wide methods, such as proteomics, have been implemented. Here we describe the utilization of quantitative label-free proteomics and bioinformatics to identify the most significant changes to the proteome upon expression of the miR-23a-27a-24-2 miRNA cluster. In light of recent work leading to the hypothesis that only the most pronounced regulatory events by miRNAs may be physiologically relevant, our data reveal that label-free analysis circumvents the limitations of proteomic labeling techniques that limit the maximum differences that can be quantified. The result of our analysis identifies a series of novel candidate targets that are reduced in abundance by more than an order of magnitude upon the expression of the miR-23a-27a-24-2 cluster.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".