The evolutionary landscape of the chromatin modification machinery reveals lineage specific gains, expansions, and losses
Bibliographic record
Abstract
Model organisms such as yeast, fly, and worm have played a defining role in the study of many biological systems. A significant challenge remains in translating this information to humans. Of critical importance is the ability to differentiate those components where knowledge of function and interactions may be reliably inferred from those that represent lineage-specific innovations. To address this challenge, we use chromatin modification (CM) as a model system for exploring the evolutionary properties of their components in the context of their known functions and interactions. Collating previously identified components of CM from yeast, worm, fly, and human, we identified a "core" set of 50 CM genes displaying consistent orthologous relationships that likely retain their interactions and functions across taxa. In addition, we catalog many components that demonstrate lineage specific expansions and losses, highlighting much duplication within vertebrates that may reflect an expanded repertoire of regulatory mechanisms. Placed in the context of a high-quality protein-protein interaction network, we find, contrary to existing views of evolutionary modularity, that CM complex components display a mosaic of evolutionary histories: a core set of highly conserved genes, together with sets displaying lineage specific innovations. Although focused on CM, this study provides a template for differentiating those genes which are likely to retain their functions and interactions across species. As such, in addition to informing on the evolution of CM as a system, this study provides a set of comparative genomic approaches that can be generally applied to any biological systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".