Functional screening identifies miR-315 as a potent activator of Wingless signaling
Bibliographic record
Abstract
The existence of vast regulatory networks mediated by microRNAs (miRNAs) suggests broad potential for miRNA dysfunction to contribute to disease. However, relatively few miRNA-target interactions are likely to make detectable contributions to phenotype, and effective strategies to identify these few interactions are currently wanting. We hypothesized that signaling cascades represent critical points of susceptibility to miRNA dysfunction, and we developed a strategy to test this theory by using quantitative cell-based screens. Here we report a screen for miRNAs that affect the Wingless (Wg) pathway, a conserved pathway that regulates growth and tissue specification. This process identified ectopic miR-315 as a potent and specific activator of Wg signaling, an activity that we corroborated in transgenic animals. This miR-315 activity was mediated by direct inhibition of Axin and Notum, which encode essential, negatively acting components of the Wg pathway. Genetic interaction tests substantiated both of these genes as key functional targets of miR-315. The ability of ectopic miR-315 to activate Wg signaling was not a trivial consequence of predicted miRNA-target relationships because other miRNAs with conserved sites in the Axin 3' UTR neither activated Wg outputs nor inhibited an Axin sensor. In summary, activity-based screening can selectively identify miRNAs whose deregulation can lead to interpretable phenotypic consequences.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".