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Record W2061589412 · doi:10.1128/aem.04049-13

Low-Molecular-Weight Metabolites Secreted by Paenibacillus larvae as Potential Virulence Factors of American Foulbrood

2014· article· en· W2061589412 on OpenAlexfundno aff
H Schild, Sebastian W. Fuchs, Helge B. Bode, Bernd Grünewald

Bibliographic record

VenueApplied and Environmental Microbiology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsnot available
FundersDalhousie University
KeywordsAmerican foulbroodBiologyVirulenceMicrobiologyLarvaPaenibacillusBroodVirulence factorBacteriaSporeGeneZoologyBotanyGenetics16S ribosomal RNA

Abstract

fetched live from OpenAlex

The spore-forming bacterium Paenibacillus larvae causes a severe and highly infective bee disease, American foulbrood (AFB). Despite the large economic losses induced by AFB, the virulence factors produced by P. larvae are as yet unknown. To identify such virulence factors, we experimentally infected young, susceptible larvae of the honeybee, Apis mellifera carnica, with different P. larvae isolates. Honeybee larvae were reared in vitro in 24-well plates in the laboratory after isolation from the brood comb. We identified genotype-specific differences in the etiopathology of AFB between the tested isolates of P. larvae, which were revealed by differences in the median lethal times. Furthermore, we confirmed that extracts of P. larvae cultures contain low-molecular-weight compounds, which are toxic to honeybee larvae. Our data indicate that P. larvae secretes metabolites into the medium with a potent honeybee toxic activity pointing to a novel pathogenic factor(s) of P. larvae. Genome mining of P. larvae subsp. larvae BRL-230010 led to the identification of several biosynthesis gene clusters putatively involved in natural product biosynthesis, highlighting the potential of P. larvae to produce such compounds.

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 score0.022
Threshold uncertainty score0.691

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 studies0.0000.001
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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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