High-resolution profiling and discovery of planarian small RNAs
Bibliographic record
Abstract
Freshwater planarian flatworms possess uncanny regenerative capacities mediated by abundant and collectively totipotent adult stem cells. Key functions of these cells during regeneration and tissue homeostasis have been shown to depend on PIWI, a molecule required for Piwi-interacting RNA (piRNA) expression in planarians. Nevertheless, the full complement of piRNAs and microRNAs (miRNAs) in this organism has yet to be defined. Here we report on the large-scale cloning and sequencing of small RNAs from the planarian Schmidtea mediterranea, yielding altogether millions of sequenced, unique small RNAs. We show that piRNAs are in part organized in genomic clusters and that they share characteristic features with mammalian and fly piRNAs. We further identify 61 novel miRNA genes and thus double the number of known planarian miRNAs. Sequencing, as well as quantitative PCR of small RNAs, uncovered 10 miRNAs enriched in planarian stem cells. These miRNAs are down-regulated in animals in which stem cells have been abrogated by irradiation, and thus constitute miRNAs likely associated with specific stem-cell functions. Altogether, we present the first comprehensive small RNA analysis in animals belonging to the third animal superphylum, the Lophotrochozoa, and single out a number of miRNAs that may function in regeneration. Several of these miRNAs are deeply conserved in animals.
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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 | 0.001 | 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) | 0.000 | 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".