Investigating Lactococcus lactis MG1363 Response to Phage p2 Infection at the Proteome Level
Bibliographic record
Abstract
Phages are viruses that specifically infect and eventually kill their bacterial hosts. Bacterial fermentation and biotechnology industries see them as enemies, however, they are also investigated as antibacterial agents for the treatment or prevention of bacterial infections in various sectors. They also play key ecological roles in all ecosystems. Despite decades of research some aspects of phage biology are still poorly understood. In this study, we used label-free quantitative proteomics to reveal the proteotypes of Lactococcus lactis MG1363 during infection by the virulent phage p2, a model for studying the biology of phages infecting Gram-positive bacteria. Our approach resulted in the high-confidence detection and quantification of 59% of the theoretical bacterial proteome, including 226 bacterial proteins detected only during phage infection and 6 proteins unique to uninfected bacteria. We also identified many bacterial proteins of differing abundance during the infection. Using this high-throughput proteomic datasets, we selected specific bacterial genes for inactivation using CRISPR-Cas9 to investigate their involvement in phage replication. One knockout mutant lacking gene llmg_0219 showed resistance to phage p2 because of a deficiency in phage adsorption. Furthermore, we detected and quantified 78% of the theoretical phage proteome and identified many proteins of phage p2 that had not been previously detected. Among others, we uncovered a conserved small phage protein (pORFN1) coded by an unannotated gene. We also applied a targeted approach to achieve greater sensitivity and identify undetected phage proteins that were expected to be present. This allowed us to follow the fate of pORF46, a small phage protein of low abundance. In summary, this work offers a unique view of the virulent phages' takeover of bacterial cells and provides novel information on phage-host interactions.
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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) | 0.001 | 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".