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Record W2567515096 · doi:10.1155/2016/6815894

Distribution of Suicin Gene Clusters in<i> Streptococcus suis</i> Serotype 2 Belonging to Sequence Types 25 and 28

2016· article· en· W2567515096 on OpenAlexafffund
Taryn Athey, Katy Vaillancourt, Michel Frenette, Nahuel Fittipaldi, Marcelo Gottschalk, Daniel Grenier

Bibliographic record

VenueBioMed Research International · 2016
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsUniversité de MontréalFonds de Recherche du Québec - SantéUniversité LavalUniversity of TorontoPublic Health Ontario
FundersImperial College LondonMinistère de l'Agriculture, des Pêcheries et de l'AlimentationWellcome TrustWellcome
KeywordsGeneGene clusterBiologySerotypeStreptococcus suisSequence analysisGeneticsGenomeLantibioticsWhole genome sequencingMicrobiologyBacteriocinBacteriaVirulence

Abstract

fetched live from OpenAlex

Recently, we reported the purification and characterization of three distinct lantibiotics (named suicin 90-1330, suicin 3908, and suicin 65) produced by Streptococcus suis . In this study, we investigated the distribution of the three suicin lantibiotic gene clusters among serotype 2 S. suis strains belonging to sequence type (ST) 25 and ST28, the two dominant STs identified in North America. The genomes of 102 strains were interrogated for the presence of suicin gene clusters encoding suicins 90-1330, 3908, and 65. The gene cluster encoding suicin 65 was the most prevalent and mainly found among ST25 strains. In contrast, none of the genes related to suicin 90-1330 production were identified in 51 ST25 strains nor in 35/51 ST28 strains. However, the complete suicin 90-1330 gene cluster was found in ten ST28 strains, although some genes in the cluster were truncated in three of these isolates. The vast majority (101/102) of S. suis strains did not possess any of the genes encoding suicin 3908. In conclusion, this study indicates heterogeneous distribution of suicin genes in S. suis .

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.212

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 head0.076
GPT teacher head0.410
Teacher spread0.334 · 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 designObservational
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

Citations8
Published2016
Admission routes2
Has abstractyes

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