Phage Response to CRISPR-Encoded Resistance in <i>Streptococcus thermophilus</i>
Bibliographic record
Abstract
Clustered regularly interspaced short palindromic repeats (CRISPR) and their associated genes are linked to a mechanism of acquired resistance against bacteriophages. Bacteria can integrate short stretches of phage-derived sequences (spacers) within CRISPR loci to become phage resistant. In this study, we further characterized the efficiency of CRISPR1 as a phage resistance mechanism in Streptococcus thermophilus. First, we show that CRISPR1 is distinct from previously known phage defense systems and is effective against the two main groups of S. thermophilus phages. Analyses of 30 bacteriophage-insensitive mutants of S. thermophilus indicate that the addition of one new spacer in CRISPR1 is the most frequent outcome of a phage challenge and that the iterative addition of spacers increases the overall phage resistance of the host. The added new spacers have a size of between 29 to 31 nucleotides, with 30 being by far the most frequent. Comparative analysis of 39 newly acquired spacers with the complete genomic sequences of the wild-type phages 2972, 858, and DT1 demonstrated that the newly added spacer must be identical to a region (named proto-spacer) in the phage genome to confer a phage resistance phenotype. Moreover, we found a CRISPR1-specific sequence (NNAGAAW) located downstream of the proto-spacer region that is important for the phage resistance phenotype. Finally, we show through the analyses of 20 mutant phages that virulent phages are rapidly evolving through single nucleotide mutations as well as deletions, in response to CRISPR1.
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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".