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Record W4414410029 · doi:10.1128/msphere.00453-25

CRISPR adaptation in <i>Streptococcus thermophilus</i> benefits from phage environmental DNA

2025· article· en· W4414410029 on OpenAlexafffund
F. R. Croteau, Jenny Tran, Alexander P. Hynes

Bibliographic record

VenuemSphere · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcMaster UniversityHamilton Health Sciences
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCRISPRContext (archaeology)BacteriaDNAGenomeAcquired immune systemImmune systemAdaptation (eye)Cas9

Abstract

fetched live from OpenAlex

ABSTRACT The CRISPR-Cas system is a bacterial adaptive immune system that 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, a phage quickly takes over the metabolism of the bacterium, leaving little time for the bacterium 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 environmental DNA (eDNA) which could be involved in the development of CRISPR immunity. Using Streptococcus thermophilus and phages 2972 and 858 as a model for CRISPR immunity, we show that eDNA is involved in CRISPR immunity, as generation of phage-immune bacterial colonies decreases with eDNA digestion. Furthermore, it is phage eDNA specifically that impacts CRISPR immunity since only its addition increases the generation of phage-immune colonies. We 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. However, we also show that eDNA is not used as a source of genetic information for spacer acquisition. These results link 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 phage infection. IMPORTANCE How can a bacterial adaptive immune system (the CRISPR-Cas system) exist at all, when exposure to a virulent phage is so consistently lethal? We proposed that bacteria might actively sample their genetic environment for phage DNA through natural competence. In testing this hypothesis, we revealed that free phage DNA is important to CRISPR immunity—but not as the source of the immunological memory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.246
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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