Cryptic genetic variation shapes the fate of gene duplicates in a protein interaction network
Bibliographic record
Abstract
Paralogous genes are often functionally redundant for long periods of time. While their functions are preserved, paralogs accumulate cryptic changes in sequence and expression, which could modulate the impact of future mutations through epistasis. We examine the impact of mutations on redundant myosin proteins that have maintained the same binding preference despite having accumulated differences in expression levels and amino acid substitutions in the last 100 million years. By quantifying the impact of all single-amino acid substitutions in their SH3 domains on the physical interaction with their interaction partners, we show that the same mutations in the paralogous SH3s change binding in a paralog-specific and interaction partner-specific manner. This contingency is explained by the difference in promoter strength of the two paralogous myosin genes and epistatic interactions between the mutations introduced and cryptic divergent sites within the SH3s. One significant consequence of this contingency is that while some mutations would be sufficient to nonfunctionalize one paralog, they would have minimal impact on the other. Our results reveal how cryptic divergence, which accumulates while maintaining functional redundancy in cellular networks, could bias gene duplicates to specific fates. Over time, paralogous genes accumulate changes in their sequences that do not affect their function, which is called cryptic variation. Using paralogous myosins, this study shows how cryptic variation modulates the functional effect of mutations and biases duplicates to distinct evolutionary fates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".