Costs of CRISPR-Cas-mediated resistance in<i>Streptococcus thermophilus</i>
Bibliographic record
Abstract
CRISPR-Cas is a form of adaptive sequence-specific immunity in microbes. This system offers unique opportunities for the study of coevolution between bacteria and their viral pathogens, bacteriophages. A full understanding of the coevolutionary dynamics of CRISPR-Cas requires knowing the magnitude of the cost of resisting infection. Here, using the gram-positive bacterium Streptococcus thermophilus and its associated virulent phage 2972, a well-established model system harbouring at least two type II functional CRISPR-Cas systems, we obtained different fitness measures based on growth assays in isolation or in pairwise competition. We measured the fitness cost associated with different components of this adaptive immune system: the cost of Cas protein expression, the constitutive cost of increasing immune memory through additional spacers, and the conditional costs of immunity during phage exposure. We found that Cas protein expression is particularly costly, as Cas-deficient mutants achieved higher competitive abilities than the wild-type strain with functional Cas proteins. Increasing immune memory by acquiring up to four phage-derived spacers was not associated with fitness costs. In addition, the activation of the CRISPR-Cas system during phage exposure induces significant but small fitness costs. Together these results suggest that the costs of the CRISPR-Cas system arise mainly due to the maintenance of the defence system. We discuss the implications of these results for the evolution of CRISPR-Cas-mediated immunity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".