Germ Granules Functions are Memorized by Transgenerationally Inherited Small RNAs
Bibliographic record
Abstract
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 ( pptr-1 , meg-3/4 , pgl-1 ) 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 extremely strong heritable RNAi responses, which last for tens of generations, long after the responses in wild type animals peter out. The phenotype of mutants defective in the core germ granules proteins MEG-3 and MEG-4, depends on the genotype of the ancestors: Mutants that derive from maternal lineages that had functional MEG-3 and MEG-4 proteins exhibit enhanced RNAi inheritance for multiple generations. While functional ancestral meg-3/4 alleles correct, and even potentiates the ability of mutant descendants to inherit RNAi, defects in germ granules functions can be memorized as well; Wild type descendants that derive from lineages of mutants show impaired RNAi inheritance for many (>16) generations, although their germ granules are intact. Importantly, while P granules are maternally deposited, wild type progeny derived from meg-3/4 male mutants also show reduced RNAi inheritance. Unlike germ granules, small RNAs are inherited also from the sperm. Moreover, we find that the transgenerational effects that depend on the ancestral germ granules require the argonaute protein HRDE-1, which carries heritable small RNAs in the germline. Indeed, small RNA sequencing reveals imbalanced levels of many endogenous small RNAs in germ granules mutants. Strikingly, we find that hrde-1;meg-3/4 triple mutants inherit RNAi, although hrde-1 was previously thought to be essential for heritable silencing. We propose that germ granules sort and shape the RNA pool, and that small RNA inheritance memorizes 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".