Bibliographic record
Abstract
Micro-RNA (miRNA) genes encode abundant small regulatory RNAs that play key roles during development and in homeostasis by fine tuning and buffering gene expression. This layer of regulatory control over transcriptional networks is preserved by selection across deep evolutionary time, yet selection pressures on individual miRNA genes in contemporary populations remain poorly characterized in any organism. Here, we quantify nucleotide variability for 129 miRNAs in the genome of the nematode Caenorhabditis remanei to understand the microevolution of this important class of regulatory genes. Our analysis of three population samples and C. remanei's sister species revealed ongoing natural selection that constrains evolution of all sequence domains within miRNA hairpins. We also show that new miRNAs evolve faster than older miRNAs but that selection nevertheless favors their persistence. Despite the ongoing importance of purging of new mutations, we discover a trove of >400 natural miRNA sequence variants that include single nucleotide polymorphisms in seed motifs, indels that ablate miRNA functional domains, and origination of new miRNAs by duplication. Moreover, we demonstrate substantial nucleotide divergence of pre-miRNA hairpin alleles between populations and sister species. These findings from the first global survey of miRNA microevolution in Caenorhabditis support the idea that changes in gene expression, mediated through divergence in miRNA regulation, can contribute to phenotypic novelty and adaptation to specific environments in the present day as well as the distant past.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".