Environmental DNA and the context of CRISPR adaptation
Bibliographic record
Abstract
The CRISPR-Cas system is a bacterial adaptative immune system which protects against infection by phages: viruses that infect bacteria. To develop immunity, bacteria integrate spacers — fragments of the invading nucleic acids — into their CRISPR array to serve as the basis for sequence-targeted DNA cleavage. However, upon infection, phages quickly take over the metabolism of the bacteria, leaving no time for the bacteria to acquire new spacers, transcribe them and use them to cut the invading DNA. To develop CRISPR immunity, bacteria must be safely exposed to phage DNA. Phage infection releases eDNA which could be involved in the development of CRISPR immunity. Using S. thermophilus and phages 2972 and 858 as a model for CRISPR immunity, I show that eDNA is crucial to the development of optimal CRISPR immunity, as generation of phage-immune bacterial colonies decrease with eDNA digestion. Furthermore, it is phage eDNA specifically that impacts CRISPR immunity since its addition increases the generation of phage-immune colonies. I also show that the effect of eDNA is phage-specific, sequence specific and can even be traced to a region of the genome covering the early-expressed genes which differ between phages 2972 and 858. While the acquisition of CRISPR spacers is not random and while the supplementation of eDNA influences that bias, eDNA is not used as a source of genetic information for spacer acquisition. This suggests that the effect of eDNA involves a new mechanism of phage resistance. Moreover, the effect of eDNA is highly dependent on environmental conditions as variation in media suppliers are sufficient to interfere with this effect. These results link environmental conditions, specifically eDNA, to the CRISPR-Cas system, providing a better understanding of the context of the emergence of CRISPR immunity and could inform our understanding of the mechanisms through which bacteria detect the presence of phages before infection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".