To kill or to piggyback: Switching of viral lysis-lysogeny strategies depending on host dynamics
Bibliographic record
Abstract
Viruses wield significant influence over microbial communities and ecosystem function in marine environments. However, the selection of viral life strategies and their impacts on microbial communities remains enigmatic. In this study, we utilized a large-scale macrocosm, established using water samples from a marine coastal region, to enable community-level investigation. Through a prolonged incubation experiment, we aimed to clarify the ramifications of lytic and lysogenic viral activities on microbial community dynamics. We observed a continuous succession in bacterial abundance, growth rate, and community diversity, tightly linked with time series switching between viral lysis and lysogeny. Elevated lytic viral production notably fostered greater bacterial diversity, whereas increased lysogenic viral production corresponded to bacterial communities characterized by heightened abundance and growth rate but reduced diversity. Moreover, discernible shifts in bacterial community compositions, associated with different abundant bacterial taxa, were synchronized with pronounced transitions between viral lysis and lysogeny. Notably, the switch from lysogeny to lysis facilitated the proliferation of initially rare bacterial populations. Our findings suggest that the Kill-the-Winner and Piggyback-the-Winner hypotheses, both elucidating dynamic patterns in virus-host interactions, can synergistically demonstrate the pivotal role of viruses in regulating microbial communities via the lysis-lysogeny switch in marine environments.
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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 | 0.000 | 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".