Epigenetic inheritance and the evolution of infectious diseases
Bibliographic record
Abstract
Abstract Genes with identical DNA sequences may show differential expression because of epigenetic marks. These marks in pathogens are key to their virulence and are being evaluated as targets for medical treatment. Where epigenetic marks were created in response to past conditions (epigenetically inherited), they represent a form of memory, the impact of which has not been considered in the evolution of infectious diseases. We fill this gap by exploring the evolution of virulence in pathogens that inherit epigenetic information on the sex of their previous host. We show that memories of past hosts can also provide clues about the sex of present and future hosts when women and men differ in their immunity to infection and/or their interactions with the sexes. These biological and social differences between the sexes are pervasive in humans. We show that natural selection can favour the evolution of greater virulence in infections originating from one sex. Furthermore, natural selection can favour the evolution of greater virulence in infections across sexes (or within sexes). Our results explain certain patterns of virulence in diseases like measles, chickenpox and polio that have puzzled medical researchers for decades. In particular, they address why girls infected by boys (or boys infected by girls) are more likely to die from the infection than girls infected by girls (or boys infected by boys). We propose epigenetic therapies to treat infections by tampering with the memories of infecting pathogens. Counterintuitively, we predict that successful therapies should target pathogen’s genes that inhibit virulence, rather than those enhancing virulence. Our findings imply that pathogens can carry memories of past environments other than sex (e.g. those related to socioeconomic status) that may condition their virulence and could signify an important new direction 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".