Germ Granules Govern Small RNA Inheritance
Bibliographic record
Abstract
In C. elegans nematodes, components of liquid-like germ granules were shown to be required for transgenerational small RNA inheritance. Surprisingly, we show here that mutants with defective germ granules can nevertheless inherit potent small RNA-based silencing responses, but some of the mutants lose this ability after many generations of homozygosity. Animals mutated in pptr-1, which is required for stabilization of P granules in the early embryo, display extraordinarily strong heritable RNAi responses, lasting for tens of generations. Intriguingly, the RNAi capacity of descendants derived from mutants defective in the core germ granule proteins MEG-3 and MEG-4 is determined by the genotype of the ancestors and changes transgenerationally. Further, whether the meg-3/4 mutant alleles were present in the paternal or maternal lineages leads to different transgenerational consequences. Small RNA inheritance, rather than maternal contribution of the germ granules themselves, mediates the transgenerational defects in RNAi of meg-3/4 mutants and their progeny. Accordingly, germ granule defects lead to heritable genome-wide mis-expression of endogenous small RNAs. Upon disruption of germ granules, hrde-1 mutants can inherit RNAi, although HRDE-1 was previously thought to be absolutely required for RNAi inheritance. We propose that germ granules sort and shape the RNA pool, and that small RNA inheritance maintains this activity for multiple generations.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".