Analysis of genetic recombination and the pan-genome of a highly recombinogenic bacteriophage species
Bibliographic record
Abstract
Bacteriophages are the most prevalent biological entities impacting on the ecosystem and are characterized by their extensive diversity. However, there are two aspects of phages that have remained largely unexplored: genetic flux by recombination between phage populations and characterization of specific phages in terms of the pan-genome. Here, we examined the recombination and pan-genome in Helicobacter pylori prophages at both the genome and gene level. In the genome-level analysis, we applied, for the first time, chromosome painting and fineSTRUCTURE algorithms to a phage species, and showed novel trends in inter-population genetic flux. Notably, hpEastAsia is a phage population that imported a higher proportion of DNA fragments from other phages, whereas the hpSWEurope phages showed weaker signatures of inter-population recombination, suggesting genetic isolation. The gene-level analysis showed that, after parameter tuning of the prokaryote pan-genome analysis program, H. pylori phages have a pan-genome consisting of 75 genes and a soft-core genome of 10 genes, which includes genes involved in the lytic and lysogenic life cycles. Quantitative analysis of recombination events of the soft-core genes showed no substantial variation in the intensity of recombination across the genes, but rather equally frequent recombination among housekeeping genes that were previously reported to be less prone to recombination. The signature of frequent recombination appears to reflect the host–phage evolutionary arms race, either by contributing to escape from bacterial immunity or by protecting the host by producing defective phages.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".