What can PIWI-interacting RNA research learn from chickens, and vice versa?
Bibliographic record
Abstract
P-element induced wimpy testis (PIWI) interacting RNA (piRNA) are essential for fertility, by protecting the integrity of the germ-line genome via silencing of transposable elements (TE). Because new TE are constantly invading the host genome, piRNA-producing loci are under continuous pressure to undergo rapid evolution. This arms race between TE and piRNA is a prime example of the genome being more plastic than previously thought. Historically, the study of piRNA and TE has benefited from the use of diverse model organisms, including worms, fruit fly, zebrafish, frogs, and mice. In domestic chickens, we recently identified a new mode of piRNA acquisition in which the host hijacks and converts a pre-existing provirus into a piRNA-producing locus to defend against Avian leukosis virus, an adaptive immune strategy similar to the prokaryotic CRISPR–Cas [clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas)] system. This finding reveals a previously unrecognized mechanism of the host piRNA repertoire to rapidly evolve and target TE specifically. In this review, we will focus on both the unique and common features of chicken piRNA, as well as the advantages of using chickens as a model system, to address fundamental questions regarding piRNA acquisition in hosts. We will also comment on the potential application of piRNA for improving poultry health and reproductive efficiency.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".