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Record W2015044744 · doi:10.1139/w06-001

Lateral gene transfer in <i>Mycobacterium avium</i> subspecies <i>paratuberculosis</i>

2006· article· en· W2015044744 on OpenAlexafffundvenue
Pradeep Reddy Marri, John P. Bannantine, Michael L. Paustian, G. Brian Golding

Bibliographic record

VenueCanadian Journal of Microbiology · 2006
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsParatuberculosisBiologyHorizontal gene transferGeneMycobacterium avium subspecies paratuberculosisMycobacteriumGenomeActinobacteriaBacteriaGeneticsMicrobiologyProteobacteria

Abstract

fetched live from OpenAlex

Lateral gene transfer is an integral part of genome evolution in most bacteria. Bacteria can readily change the contents of their genomes to increase adaptability to ever-changing surroundings and to generate evolutionary novelty. Here, we report instances of lateral gene transfer in Mycobacterium avium subsp. paratuberculosis, a pathogenic bacteria that causes Johne's disease in cattle. A set of 275 genes are identified that are likely to have been recently acquired by lateral gene transfer. The analysis indicated that 53 of the 275 genes were acquired after the divergence of M. avium subsp. paratuberculosis from M. avium subsp. avium, whereas the remaining 222 genes were possibly acquired by a common ancestor of M. avium subsp. paratuberculosis and M. avium subsp. avium after its divergence from the ancestor of M. tuberculosis complex. Many of the acquired genes were from proteobacteria or soil dwelling actinobacteria. Prominent among the predicted laterally transferred genes is the gene rsbR, a possible regulator of sigma factor, and the genes designated MAP3614 and MAP3757, which are similar to genes in eukaryotes. The results of this study suggest that like most other bacteria, lateral gene transfers seem to be a common feature in M. avium subsp. paratuberculosis and that the proteobacteria contribute most of these genetic exchanges.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.222
Teacher spread0.212 · 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

Citations29
Published2006
Admission routes3
Has abstractyes

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