Bibliographic record
Abstract
Glycosylation is one of the most abundant and diverse post‐translational modifications. Glycans participate in various key biological functions and processes including immunity, cell adhesion, endocytosis, exocytosis, molecular trafficking, and signal transduction. Previous work from our lab identified microRNAs (miRNAs, miRs) as key regulators of glycosylation genes (glycogenes). miRs are small non‐coding RNAs that fine‐tune protein expression through binding to messenger RNA (mRNA). miRs regulate networks of genes that work to control a specific biological process, tightening the expression range for critical genes. By mapping the targets of miRNA involved in specific biological processes or disease states, we can determine genes that are important drivers in that process or disease, defined as the miRNA Proxy Approach . However, this approach is dependent on an accurate understanding of miRNA:mRNA interactions. Current identification of miR:glycogene interactomes is hindered by the low accuracy of prediction (17‐66%), the low expression of glycogenes (complicating transcriptomic analysis and RISC complex pulldowns), and suboptimal throughput of more direct miR:mRNA validations (e.g. luciferase assays). To date, around 0.01% of all predicted human miR:target interactions have been validated experimentally. To overcome these obstacles, our laboratory has developed a method for high‐throughput experimental mapping of miR: glycogene interactions. Based on previous work, we chose the B3GLCT gene as our first target. Herein, we present a comprehensive analysis of 2303 interactions between B3GLCT and human miRs. We identified miRs that both repress and, surprisingly, up‐regulate B3GLCT. Our findings indicated high rates of both false positive and negative in the miR target predictions by common algorithms (e.g. Targetscan). Our data also shows that miRs that hit B3GLCT predict disease characteristics represented in Peters plus syndrome, caused by B3GLCT mutations. This supports our miRNA Proxy hypothesis and points to the potential utility of miRs to determine drivers of disease. In summary, this work provides a platform to quickly expand the network of experimentally validated miR‐target interactions and enables us to understand more about miR:mRNA interactions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".