Epigenetic memories and the evolution of infectious diseases
Bibliographic record
Abstract
Genes with identical DNA sequence may show differential expression because of epigenetic marks. Where epigenetic marks respond to past conditions, they represent a form of "memory". Despite their medical relevance, the impact of memories on the evolution of infectious diseases has rarely been considered. Here we explore the evolution of virulence in pathogens that carry memories of the sex of their previous host. We show that this form of memory provides information about the sex of present and future hosts when the sexes differ in their pathogen's transmission pattern. Memories of past hosts enable the evolution of greater virulence in infections originating from one sex and infections transmitted across sexes. Thus, our results account for patterns of virulence that have, to date, defied medical explanation. In particular, it has been observed that girls infected by boys (or boys infected by girls) are more likely to die from measles, chickenpox and polio than girls infected by girls (or boys infected by boys). We also evaluate epigenetic therapies that tamper with the memories of infecting pathogens. More broadly, our findings imply that pathogens can be selected to carry memories of past environments other than sex. This identifies new directions in personalised medicine.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".