Screening the human miRNA interactome reveals coordinated upregulation in melanoma, adding bidirectional regulation to miRNA networks
Bibliographic record
Abstract
Abstract Cellular protein expression is coordinated post-transcriptionally by an intricate regulatory network. The current presumption is that miRNA work by repression of functionally related targets within a system. In recent work, upregulation of protein expression via direct interactions of mRNA with miRNA has been found in dividing cells, providing an additional mechanism of regulation. Herein, we demonstrate coordinated upregulation of functionally-coupled proteins by miRNA. We focused on CD98hc, the heavy chain of the amino acid transporter LAT1, and α-2,3-sialyltransferases ST3GAL1 and ST3GAL2, which are critical for CD98hc stability in melanoma. Profiling miRNA regulation using our high-throughput miRFluR assay, we identified miRNA that upregulated expression of both CD98hc and either ST3GAL1 or ST3GAL2. These co-upregulating miRNAs were enriched in melanoma datasets associated with transformation and progression. Our findings add co-upregulation by miRNA into miRNA regulatory networks and adds a new bidirectional twist to the impact miRNA have on protein regulation and glycosylation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".