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Record W1993016819 · doi:10.4161/mge.20498

Diversity of integrating conjugative elements in actinobacteria

2012· article· en· W1993016819 on OpenAlexafffund
Éric Bordeleau, Mariana Gabriela Ghinet, Vincent Burrus

Bibliographic record

VenueMobile Genetic Elements · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsUniversité de Sherbrooke
FundersAcademy of FinlandNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsActinobacteriaPlasmidBiologyBacterial conjugationDNABacteriaStreptomycesGeneticsHorizontal gene transferGeneSecretionBacterial geneticsComputational biologyMicrobiologyEscherichia coliBiochemistryGenome

Abstract

fetched live from OpenAlex

Conjugation is certainly the most widespread and promiscuous mechanism of horizontal gene transfer in bacteria. During conjugation, DNA translocation across membranes of two cells forming a mating pair is mediated by two types of mobile genetic elements: conjugative plasmids and integrating conjugative elements (ICEs). The vast majority of conjugative plasmids and ICEs employ a sophisticated protein secretion apparatus called type IV secretion system to transfer to a recipient cell. Yet another type of conjugative DNA translocation machinery exists and to date appears to be unique to conjugative plasmids and ICEs of the Actinomycetales order, a sub-group of high G + C Gram-positive bacteria. This conjugative system is reminiscent of the machinery that allows segregation of chromosomal DNA during bacterial cell division and sporulation, and relies on a single FtsK-homolog protein to translocate double-stranded DNA molecules to the recipient cell. Recent thorough sequence analyses reveal that while this latter strategy appears to be used by the majority of ICEs in Actinomycetales, the former is also predicted to be important in exchange of genetic material in actinobacteria.

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 score0.168
Threshold uncertainty score0.681

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 science0.0000.001
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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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 routes2
Has abstractyes

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