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Record W4394683146 · doi:10.1128/aem.02349-23

Redefining piscine lactococcosis

2024· article· en· W4394683146 on OpenAlexaff
Taylor I. Heckman, Zeinab Yazdi, Caitlin E. Older, Matt J. Griffin, Geoffrey C. Waldbieser, Alexander M. Chow, Isabella Medina Silva, Kelsey M. Anenson, Julio C. García, Benjamin R. LaFrentz, Ðurđa Slavić, Kathy Toohey-Kurth, Paula Yant, Heather Fritz, Eileen E. Henderson, Rebeccah McDowall, Hugh Y. Cai, Mark Adkison, Esteban Soto

Bibliographic record

VenueApplied and Environmental Microbiology · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of Guelph
FundersAgricultural Research ServiceCalifornia Department of Fish and WildlifeUniversity of California, DavisMississippi State UniversityU.S. Department of Agriculture
KeywordsBiologyRibotypingMicrobiologyPathogenTypingAntimicrobialGeneGeneticsPolymerase chain reaction

Abstract

fetched live from OpenAlex

ABSTRACT Piscine lactococcosis is a significant threat to cultured and wild fish populations worldwide. The disease typically presents as a per-acute to acute hemorrhagic septicemia causing high morbidity and mortality, recalcitrant to antimicrobial treatment or management interventions. Historically, the disease was attributed to the gram-positive pathogen Lactococcus garvieae . However, recent work has revealed three distinct lactococcosis-causing bacteria (LCB)— L. garvieae, L. petauri, and L. formosensis— which are phenotypically and genetically similar, leading to widespread misidentification. An update on our understanding of lactococcosis and improved methods for identification are urgently needed. To this end, we used representative isolates from each of the three LCB species to compare currently available and recently developed molecular and phenotypic typing assays, including whole-genome sequencing (WGS), end-point and quantitative PCR (qPCR) assays, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), API 20 Strep and Biolog systems, fatty acid methyl ester analysis (FAME), and Sensititre antimicrobial profiling. Apart from WGS, sequencing of the gyrB gene was the only method capable of consistent and accurate identification to the species and strain level. A qPCR assay based on a putative glycosyltransferase gene was also able to distinguish L. petauri from L. garvieae/formosensis . Biochemical tests and MALDI-TOF MS showed some species-specific patterns in sugar and fatty acid metabolism or protein profiles but should be complemented by additional analyses. The LCB demonstrated overlap in host and geographic range, but there were relevant differences in host specificity, regional prevalence, and antimicrobial susceptibility impacting disease treatment and prevention. IMPORTANCE Lactococcosis affects a broad range of host species, including fish from cold, temperate, and warm freshwater or marine environments, as well as several terrestrial animals, including humans. As such, lactococcosis is a disease of concern for animal and ecosystem health. The disease is endemic in European and Asian aquaculture but is rapidly encroaching on ecologically and economically important fish populations across the Americas. Piscine lactococcosis is difficult to manage, with issues of vaccine escape, ineffective antimicrobial treatment, and the development of carrier fish or biofilms leading to recurrent outbreaks. Our understanding of the disease is also widely outdated. The accepted etiologic agent of lactococcosis is Lactococcus garvieae . However, historical misidentification has masked contributions from two additional species, L. petauri and L. formosensis , which are indistinguishable from L. garvieae by common diagnostic methods. This work is the first comprehensive characterization of all three agents and provides direct recommendations for species-specific diagnosis and management.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.180
Teacher spread0.175 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations34
Published2024
Admission routes1
Has abstractyes

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