Conditions for the spread of CRISPR-Cas immune systems into bacterial populations
Bibliographic record
Abstract
Bacteria contain a wide variety of innate and adaptive immune systems which provide protection to the host against invading genetic material, including bacteriophages (phages). It is becoming increasingly clear that bacterial immune systems are frequently lost and gained through horizontal gene transfer. However, how and when new immune systems can become established in a bacterial population have remained largely unstudied. We developed a joint epidemiological and evolutionary model that predicts the conditions necessary for the spread of a CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated) immune system into a bacterial population lacking this system. We found that whether bacteria carrying CRISPR-Cas will spread (increase in frequency) into a bacterial population depends on the abundance of phages and the difference in the frequency of phage resistance mechanisms between bacteria carrying a CRISPR-Cas immune system and those not (denoted as ${f}_{\Delta }$). Specifically, the abundance of cells carrying CRISPR-Cas will increase if there is a higher proportion of phage resistance (either via CRISPR-Cas immunity or surface modification) in the CRISPR-Cas-possessing population than in the cells lacking CRISPR-Cas. We experimentally validated these predictions in a model using Pseudomonas aeruginosa PA14 and phage DMS3vir. Specifically, by varying the initial ratios of different strains of bacteria that carry alternative forms of phage resistance, we confirmed that the spread of cells carrying CRISPR-Cas through a population can be predicted based on phage density and the relative frequency of resistance phenotypes. Understanding which conditions promote the spread of CRISPR-Cas systems helps to predict when and where these defences can become established in bacterial populations after a horizontal gene transfer event, both in ecological and clinical contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".