miRNA analysis with <i>Prost!</i> reveals evolutionary conservation of organ-enriched expression and post-transcriptional modifications in three-spined stickleback and zebrafish
Bibliographic record
Abstract
Abstract MicroRNAs (miRNAs) can have tissue-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 variations. It remains unclear, however, which mechanisms of miRNA production or modification are tissue-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, allows accurate quantification of mature miRNAs, takes into account post-transcriptional processing, such as nucleotide editing, and helps identify 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. Despite reports suggesting that stickleback had a large miRNome, results showed that stickleback has 277 evolutionary-conserved mir genes and 366 unique mature miRNAs (excluding mir430 gene replicates and the vaultRNA-derived mir733 ), similar to zebrafish. 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, which is, for many miRNAs, evolutionarily-conserved. These data also supported the hypothesis that evolutionarily-conserved, organ-specific mechanisms regulate miRNA post-transcriptional variations. In both stickleback and zebrafish, miR2188-5p was edited frequently with similar nucleotide editing patterns in the seed sequence in various tissues, and the editing rate was organ-specific with higher editing in the brain. In summary, Prost! is a critical new tool to identify and understand small RNAs and can help clarify a species’ miRNA biology, as shown here for an important fish model for the evolution of developmental mechanisms, and can provide insight into organ-specific expression and evolutionary-conserved miRNA post-transcriptional mechanisms.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".