The contribution of abortive infection to preventing populations of <i>Lactococcus lactis</i> from succumbing to infections with bacteriophage
Bibliographic record
Abstract
Abstract In the dairy industry bacteriophage (phage) contamination significantly impairs the production and quality of products like yogurt and cheese. To combat this issue, the strains of bacteria used as starter cultures possess mechanisms that make them resistant to phage infection, such as envelope resistance, or processes that render them immune to phage infection, such as restriction-modification and CRISPR-Cas. Lactococcus lactis , used to manufacture cheese and other dairy products, can also block the reproduction of infecting phages by abortive infection (Abi), a process in which phage-infected cells die before the phage replicate. We employ mathematical-computer simulation models and experiments with two Lactococcus lactis strains and two lytic phages to investigate the conditions under which Abi can limit the proliferation of phages in L. lactis populations and prevent the extinction of their populations by these viruses. According to our model, if Abi is almost perfect and there are no other populations of bacteria capable of supporting the replication of the L. lactis phages, Abi can protect bacterial populations from succumbing to infections with these viruses. This prediction is supported by the results of our experiment, which indicate that Abi can help protect L. lactis populations from extinction by lytic phage infections. However, our results also predict abortive infection is only one element of L. lactis defenses against phage infection. Mutant phages that can circumvent the Abi systems of these bacteria emerge. The survival of L. lactis populations then depends on the evolution of envelope mutants that are resistant to the evolved host-range phage.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".