Genomic fossils reveal adaptation of non-autonomous pararetroviruses driven by concerted evolution of noncoding regulatory sequences
Bibliographic record
Abstract
The interplay of different virus species in a host cell after infection can affect the adaptation of each virus. Endogenous viral elements, such as endogenous pararetroviruses (PRVs), have arisen from vertical inheritance of viral sequences integrated into host germline genomes. As viral genomic fossils, these sequences can thus serve as valuable paleogenomic data to study the long-term evolutionary dynamics of virus-virus interactions, but they have rarely been applied for this purpose. All extant PRVs have been considered autonomous species in their parasitic life cycle in host cells. Here, we provide evidence for multiple non-autonomous PRV species with structural defects in viral activity that have frequently infected ancient grass hosts and adapted through interplay between viruses. Our paleogenomic analyses using endogenous PRVs in grass genomes revealed that these non-autonomous PRV species have participated in interplay with autonomous PRVs in a possible commensal partnership, or, alternatively, with one another in a possible mutualistic partnership. These partnerships, which have been established by the sharing of noncoding regulatory sequences (NRSs) in intergenic regions between two partner viruses, have been further maintained and altered by the sequence homogenization of NRSs between partners. Strikingly, we found that frequent region-specific recombination, rather than mutation selection, is the main causative mechanism of NRS homogenization. Our results, obtained from ancient DNA records of viruses, suggest that adaptation of PRVs has occurred by concerted evolution of NRSs between different virus species in the same host. Our findings further imply that evaluation of within-host NRS interactions within and between populations of viral pathogens may be important.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".