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Identifying your enemies - could envelope stress trigger microbial immunity?

2010· article· en· W2133887607 on OpenAlexaff
Tracy Raivio

Bibliographic record

VenueMolecular Microbiology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiologyCRISPRNucleic acidInnate immune systemImmunityGenomeAcquired immune systemArchaeaGeneticsPlasmidCell envelopeComputational biologyImmune systemGeneEscherichia coli

Abstract

fetched live from OpenAlex

Microbes utilize defence systems with fundamental similarities to our innate and adaptive immune responses to protect themselves from harmful invaders. One system, made up of CRISPR loci & Cas proteins, incorporates recognizable features from the genomes of viruses (bacteriophages) and plasmids into bacterial genomes, where they are later used to direct a ribonucleoprotein complex to destroy invading nucleic acids upon re-exposure. CRISPR-mediated defence against invasive nucleic acids is found in most archaea and many eubacteria. Many aspects of this newly described defence system have not been worked out, including the molecular mechanisms by which foreign nucleic acids are incorporated into microbial genomes during adaption and destroyed during interference. In this issue of Molecular Microbiology, DeLisa and colleagues provide insight into how this form of microbial immunity might be regulated in eubacteria. They demonstrate that Escherichia coli CRISPR-mediated immunity requires the presence of the BaeSR two-component system under certain conditions. Since BaeSR regulate an envelope stress response, their data imply that immunity against invading, foreign nucleic acids may be somehow linked to stresses to the bacterial membrane. These observations will help pave the way to understanding how and when CRISPR-based immunity may be important in driving evolution and adaptation in eubacteria.

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 categoriesMeta-epidemiology (narrow)
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 score0.086
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.285
Teacher spread0.277 · 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.

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

Citations16
Published2010
Admission routes1
Has abstractyes

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