Micro-RNA Clusters Integrate Evolutionary Constraints on Expression and Target Affinities: The miR-6/5/4/286/3/309 Cluster in Drosophila
Bibliographic record
Abstract
A striking feature of micro-RNAs is that they are often clustered in the genomes of animals. The functional and evolutionary consequences of this clustering remain obscure. Here, we investigated a micro-RNA cluster miR-6/5/4/286/3/309 that is conserved across drosophilid lineages. Small RNA sequencing revealed expression of this micro-RNA cluster in Drosophila melanogaster leg discs, and conditional overexpression of the whole cluster resulted in leg appendage shortening. Transgenic overexpression lines expressing different combinations of micro-RNA cluster members were also constructed. Expression of individual micro-RNAs from the cluster resulted in a normal wild-type phenotype, but either the expression of several ancient micro-RNAs together (miR-5/4/286/3/309) or more recently evolved clustered micro-RNAs (miR-6-1/2/3) can recapitulate the phenotypes generated by the whole-cluster overexpression. Screening of transgenic fly lines revealed downregulation of leg-patterning gene cassettes in generation of the leg-shortening phenotype. Furthermore, cell transfection with different combinations of micro-RNA cluster members revealed a suite of downstream genes targeted by all cluster members, as well as complements of targets that are unique for distinct micro-RNAs. Considered together, the micro-RNA targets and the evolutionary ages of each micro-RNA in the cluster demonstrate the importance of micro-RNA clustering, where new members can reinforce and modify the selection forces on both the cluster regulation and the gene regulatory network of existing micro-RNAs. Key words: micro-RNA, cluster, evolution.
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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".