Different patterns of gene silencing in position-effect variegation
Bibliographic record
Abstract
Position-effect variegation (PEV) results when a fully functional gene is moved from its normal position to a position near to a broken heterochromatic-euchromatic boundary. In this new position, the gene, while remaining unaltered at the DNA level, is transcriptionally silenced in some cells but active in others, producing a diagnostic mosaic phenotype. Many variegating stocks show phenotypic instability, in that the level of variegation is dramatically different in different isolates or when out crossed. To test if this phenotypic instability was due to segregation of spontaneously accumulated mutations that suppress variegation, four different and well-characterized strains showing PEV for the white+ gene (wm4, wmMc, wm51b, and wmJ) and representing both large and small spot variegators were repeatedly out crossed to a strain free of modifiers, and the phenotypes of these variegators were monitored for 30 generations. Once free of modifiers, these variegating strains were then allowed to reaccumulate modifiers. The spontaneous suppressors of variegation were found to include both dominant and recessive, autosomal and X-linked alleles selected to reduce the detrimental effects of silencing white+ and adjacent genes. The time of peak sensitivity to temperature during development was also determined for these four variegators. Although large and small spot variegators have previously been attributed to early and late silencing events, respectively, the variegators we examined all shared a common early period of peak sensitivity to temperature. Once free of their variegation suppressors, the different variegating strains showed considerable differences in the frequency of inactivation at a cellular level (the number of cells showing silencing of a given gene) and the extent of variegation within the cell (the number of silenced genes). These results suggest that large and small spot variegation may be a superficial consequence of spontaneous variegation suppressors. The nature and number of these spontaneous variegation suppressors depends on the number of genes silenced in a given variegating rearrangement. These results are interpreted in the context of a model that proposes that the different underlying patterns of gene silencing seen in PEV can be attributed directly to the formation of heterochromatin domains possessing different properties of propagation during cell division.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".