Comparative genomic analysis of upstream miRNA regulatory motifs in <i>Caenorhabditis</i>
Bibliographic record
Abstract
MicroRNAs (miRNAs) comprise a class of short noncoding RNA molecules that play diverse developmental and physiological roles by controlling mRNA abundance and protein output of the vast majority of transcripts. Despite the importance of miRNAs in regulating gene function, we still lack a complete understanding of how miRNAs themselves are transcriptionally regulated. To fill this gap, we predicted regulatory sequences by searching for abundant short motifs located upstream of miRNAs in eight species of Caenorhabditis nematodes. We identified three conserved motifs across the Caenorhabditis phylogeny that show clear signatures of purifying selection from comparative genomics, patterns of nucleotide changes in motifs of orthologous miRNAs, and correlation between motif incidence and miRNA expression. We then validated our predictions with transgenic green fluorescent protein reporters and site-directed mutagenesis for a subset of motifs located in an enhancer region upstream of let-7 We demonstrate that a CT-dinucleotide motif is sufficient for proper expression of GFP in the seam cells of adult C. elegans, and that two other motifs play incremental roles in combination with the CT-rich motif. Thus, functional tests of sequence motifs identified through analysis of molecular evolutionary signatures provide a powerful path for efficiently characterizing the transcriptional regulation of miRNA genes.
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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) | 0.000 | 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".