Deciphering Network Crosstalk: The Current Status and Potential of miRNA Regulatory Networks on the HSP40 Molecular Chaperone Network
Bibliographic record
Abstract
Molecular chaperone networks fulfill complex roles in protein homeostasis and are essential for maintaining cell health. Hsp40s (commonly referred to as J-proteins) have critical roles in development and are associated with a variety of human diseases, yet little is known regarding the J-proteins with respect to the post-transcriptional mechanisms that regulate their expression. With relatively small alterations in their abundance and stoichiometry altering their activity, post-transcriptional regulation potentially has significant impact on the functions of J-proteins. MicroRNAs (miRNAs) are a large group of non-coding RNAs that form a complex regulatory network impacting gene expression. Here we review and investigate the current knowledge and potential intersection of miRNA regulatory networks with the J-Protein chaperone network. Analysis of datasets from the current version of TargetScan revealed a great number of predicted microRNAs targeting J-proteins compared to the limited reports of interactions to date. There are likely unstudied regulatory interactions that influence chaperone biology contained within our analysis. We go on to present some criteria for prioritizing candidate interactions including potential cooperative targeting of J-Proteins by multiple miRNAs. In summary, we offer a view on the scope of regulation of J-Proteins through miRNAs with the aim of guiding future investigations by identifying key regulatory nodes within these two complex cellular networks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".