High-Throughput miRFluR Platform Identifies miRNA Regulating B3GLCT That Predict Peters’ Plus Syndrome Phenotype, Supporting the miRNA Proxy Hypothesis
Bibliographic record
Abstract
MicroRNAs (miRNAs, miRs) finely tune protein expression and target networks of hundreds to thousands of genes that control specific biological processes. They are critical regulators of glycosylation, one of the most diverse and abundant post-translational modifications. In recent work, miRs have been shown to predict the biological functions of glycosylation enzymes, leading to the "miRNA proxy hypothesis" which states, "if a miR drives a specific biological phenotype..., the targets of that miR will drive the same biological phenotype." Testing of this powerful hypothesis is hampered by our lack of knowledge about miR targets. Target prediction suffers from low accuracy and a high false prediction rate. Herein, we develop a high-throughput experimental platform to analyze miR-target interactions, miRFluR. We utilize this system to analyze the interactions of the entire human miRome with beta-3-glucosyltransferase (B3GLCT), a glycosylation enzyme whose loss underpins the congenital disorder Peters' Plus Syndrome. Although this enzyme is predicted by multiple algorithms to be highly targeted by miRs, we identify only 27 miRs that downregulate B3GLCT, a >96% false positive rate for prediction. Functional enrichment analysis of these validated miRs predicts phenotypes associated with Peters' Plus Syndrome, although B3GLCT is not in their known target network. Thus, biological phenotypes driven by B3GLCT may be driven by the target networks of miRs that regulate this enzyme, providing additional evidence for the miRNA proxy hypothesis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".