Combinatorial RNA Interference In Caenorhabditis elegans Reveals That Redundancy Between Gene Duplicates Can Be Maintained For More Than 80 Million Years of Evolution
Bibliographic record
Abstract
Background: Systematic analyses of loss-of-function phenotypes have been carried out for mostgenes in Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster. Although suchstudies vastly expand our knowledge of single gene function, they do not address redundancy ingenetic networks. Developing tools for the systematic mapping of genetic interactions is thus a keystep in exploring the relationship between genotype and phenotype.Results: We established conditions for RNA interference (RNAi) in C. elegans to target multiplegenes simultaneously in a high-throughput setting. Using this approach, we can detect the greatmajority of previously known synthetic genetic interactions. We used this assay to examine theredundancy of duplicated genes in the genome of C. elegans that correspond to single orthologs inS. cerevisiae or D. melanogaster and identified 16 pairs of duplicated genes that have redundantfunctions. Remarkably, 14 of these redundant gene pairs were duplicated before the divergence ofC. elegans and C. briggsae 80-110 million years ago, suggesting that there has been selective pressureto maintain the overlap in function between some gene duplicates.Conclusion: We established a high throughput method for examining genetic interactions usingcombinatorial RNAi in C. elegans. Using this technique, we demonstrated that many duplicatedgenes can retain redundant functions for more than 80 million years of evolution. This providesstrong support for evolutionary models that predict that genetic redundancy between duplicatedgenes can be actively maintained by natural selection and is not just a transient side effect of recentgene duplication events.
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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) | 0.001 | 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".