Strengthening Phage Resistance of Streptococcus thermophilus by Leveraging Complementary Defense Systems
Bibliographic record
Abstract
CRISPR-Cas and restriction-modification systems represent the core defense arsenal in Streptococcus thermophilus to block lytic phages, but their effectiveness is compromised by phages encoding anti-CRISPR proteins (ACRs) and other counter-defense strategies. Here, we explored the resistome of 263 S. thermophilus strains to uncover other anti-phage systems. The defense landscape of S. thermophilus was enriched by 21 accessory defense systems, 13 of which had not been previously investigated in this species. Experimental validation of 17 systems with 14 phages showed varying anti-phage levels, uncovering intra-genus specificities among the five viral genera infecting S. thermophilus. Interestingly, the resistance levels were even higher when some defense systems (Dodola and PD-Lambda-1) were expressed from a low-copy plasmid or when integrated into the chromosome. We also observed a synergistic effect when combining Gabija with CRISPR-Cas, underscoring the potential of these additional defense systems for developing more robust industrial S. thermophilus strains, particularly against ACR-encoding 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 | 0.000 | 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 | 0.000 |
| 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".