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Record W2133920592 · doi:10.1128/jb.01093-12

Dynamics of the SetCD-Regulated Integration and Excision of Genomic Islands Mobilized by Integrating Conjugative Elements of the SXT/R391 Family

2012· article· en· W2133920592 on OpenAlexaff
Aurélie Daccord, Mathias Mursell, Dominic Poulin‐Laprade, Vincent Burrus

Bibliographic record

VenueJournal of Bacteriology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsIntegraseBiologyIntegrasesGeneticsChromosomeGeneRecombinationHorizontal gene transferComputational biologyGenome

Abstract

fetched live from OpenAlex

Mobilizable genomic islands (MGIs) are small genomic islands that are mobilizable by SXT/R391 integrating conjugative elements (ICEs) due to similar origins of transfer. Their site-specific integration and excision are catalyzed by the integrase that they encode, but their conjugative transfer entirely depends upon the conjugative machinery of SXT/R391 ICEs. In this study, we report the mechanisms that control the excision and integration processes of MGIs. We found that while the MGI-encoded integrase Int(MGI) is sufficient to promote MGI integration, efficient excision from the host chromosome requires the combined action of Int(MGI) and of a novel recombination directionality factor, RdfM. We determined that the genes encoding these proteins are activated by SetCD, the main transcriptional activators of SXT/R391 ICEs. Although they share the same regulators, we found that unlike rdfM, int(MGI) has a basal level of expression in the absence of SetCD. These findings explain how an MGI can integrate into the chromosome of a new host in the absence of a coresident ICE and shed new light on the cross talk that can occur between mobilizable and mobilizing elements that mobilize them, helping us to understand part of the rules that dictate horizontal transfer mechanisms.

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 score0.272
Threshold uncertainty score0.204

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 head0.262
Teacher spread0.257 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

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