miRNA analysis with Prost! reveals evolutionary conservation of organ-enriched expression and post-transcriptional modifications in three-spined stickleback and zebrafish
Bibliographic record
Abstract
MicroRNAs (miRNAs) can have organ-specific expression and functions; they can originate from dedicated miRNA genes, from non-canonical miRNA genes, or from mirror-miRNA genes and can also experience post-transcriptional variation. It remains unclear, however, which mechanisms of miRNA production or modification are organ-specific and the extent of their evolutionary conservation. To address these issues, we developed the software Prost! (PRocessing Of Short Transcripts), which, among other features, helps quantify mature miRNAs, accounts for post-transcriptional processing, such as nucleotide editing, and identifies mirror-miRNAs. Here, we applied Prost! to annotate and analyze miRNAs in three-spined stickleback (Gasterosteus aculeatus), a model fish for evolutionary biology reported to have a miRNome larger than most teleost fish. Zebrafish (Danio rerio), a distantly related teleost with a well-known miRNome, served as comparator. Our results provided evidence for the existence of 286 miRNA genes and 382 unique mature miRNAs (excluding mir430 gene duplicates and the vaultRNA-derived mir733), which doesn't represent a miRNAome larger than other teleost miRNomes. In addition, small RNA sequencing data from brain, heart, testis, and ovary in both stickleback and zebrafish identified suites of mature miRNAs that display organ-specific enrichment, many of which are evolutionarily-conserved in the brain and heart in both species. These data also supported the hypothesis that evolutionarily-conserved, organ-specific mechanisms may regulate post-transcriptional variations in miRNA sequence. In both stickleback and zebrafish, miR2188-5p was edited frequently with similar nucleotide changes in the seed sequence with organ specific editing rates, highest in the brain. In summary, Prost! is a new tool to identify and understand small RNAs, to help clarify a species' miRNA biology as shown here for an important model for the evolution of developmental mechanisms, and to provide insight into organ-enriched expression and the evolutionary conservation of miRNA post-transcriptional modifications.
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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".