Lambdoid phages with abundant Chi recombination hotspots reflect diverse viral strategies for recombination-dependent growth
Bibliographic record
Abstract
Many phages encode recombination-mediating enzymes, but characterization of their roles in phage lifecycles is limited, and their impact on phage replication is controversial. To address these issues, we have searched for phages whose growth is impacted by the major recombination-promoting helicase-nuclease of Escherichia coli, the RecBCD enzyme. Although no phages inhibited by RecBCD are identified, growth of a newly isolated phage, named LLS, is enhanced by RecBCD. LLS's genome sequence reveals it is related to bacteriophage λ but encodes no recombination-promoting (Rec) proteins or associated RecBCD inhibitor. However, it contains an unexpectedly high number of Chi sites, activators of RecBCD-dependent recombination. Through analysis of 325 genomes of phages related to λ (lambdoid phages), we have found 71 other phage genomes that encode no Rec proteins but mostly possess large numbers of Chi sites. Conversely, phages encoding Rec proteins and a RecBCD inhibitor (collectively a Rec module) mostly lack Chi sites. Lambdoid phages of both diverse enteric bacteria and a pseudomonad have these properties. For this study, we thoroughly analyze the Rec modules of 246 lambdoid phage genomes. These analyses reveal a remarkable heterogeneity of Rec module protein types, both in sequence and in function, and allow us to identify phages that do not contain Rec modules. We conclude that phages lacking their own recombination systems have compensated by becoming enriched in Chi sites, enabling them to use the host's RecBCD to fulfill the requirement for recombination to efficiently replicate. This study highlights the importance of recombination for phage survival and the diversity of strategies to achieve it.
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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".