HERI-1 is a Chromodomain Protein that Negatively Regulates Transgenerational Epigenetic Inheritance
Bibliographic record
Abstract
Abstract Transgenerational epigenetic inheritance (TEI) is the inheritance of epigenetic information for two or more generations. In most cases, TEI is limited to 2-3 generations. This short-term nature of TEI could be set by innate biochemical limitations to TEI or by genetically encoded systems that actively limit TEI. dsRNA-mediated gene silencing (RNAi) can be inherited in C. elegans (termed RNAi inheritance or RNA-directed TEI). To identify systems that might actively limit RNA-directed TEI, we conducted a forward genetic screen for factors whose mutation enhanced RNAi inheritance. This screen identified the gene h eritable e nhancer of R NA i ( heri-1 ), whose mutation causes RNAi inheritance to last longer (>20 generations) than normal. heri-1 encodes a protein with a chromodomain and a kinase-homology domain that is expressed in germ cells and localizes to nuclei. In C. elegans , a nuclear branch of the RNAi pathway (nuclear RNAi or NRDE pathway) is required for RNAi inheritance. We find that this NRDE pathway is hyper-responsive to RNAi in heri-1 mutant animals, suggesting that a normal function of HERI-1 is to limit nuclear RNAi and that limiting nuclear RNAi may be the mechanism by which HERI-1 limits RNAi inheritance. Interestingly, we find that HERI-1 binds to genes targeted by RNAi, suggesting that HERI-1 may have a direct role in limiting nuclear RNAi and, therefore, RNAi inheritance. Surprisingly, recruitment of the negative regulator HERI-1 to genes depends upon that same NRDE factors that drive co-transcriptional gene silencing during RNAi inheritance. We therefore speculate that the generational perdurance of RNAi inheritance is set by competing pro- and anti-silencing outputs of the NRDE nuclear RNAi machinery.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".