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Record W4220815203 · doi:10.3389/fimmu.2022.804987

Interacting Effects of Sea Louse (Lepeophtheirus salmonis) Infection and Formalin-Killed Aeromonas salmonicida on Atlantic Salmon Skin Transcriptome

2022· article· en· W4220815203 on OpenAlexafffund
Albert Caballero‐Solares, Navaneethaiyer Umasuthan, Xi Xue, Tomer Katan, Surendra Kumar, Jillian D. Westcott, Zhiyu Chen, Mark D. Fast, Stanko Škugor, Richard G. Taylor, Matthew L. Rise

Bibliographic record

VenueFrontiers in Immunology · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of Prince Edward IslandMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundOcean Frontier InstituteMitacsGenome CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaGovernment of CanadaGenome AtlanticCompute CanadaNewfoundland and LabradorCargill
KeywordsLepeophtheirusBiologyTranscriptomeMicrobiologyAeromonas salmonicidaImmune systemZoologyGene expressionGeneImmunologyAquacultureFisheryFish <Actinopterygii>Genetics

Abstract

fetched live from OpenAlex

Lepeophtheirus salmonis (sea lice) and bacterial co-infection threatens wild and farmed Atlantic salmon performance and welfare. In the present study, pre-adult L. salmonis-infected and non-infected salmon were intraperitoneally injected with either formalin-killed Aeromonas salmonicida bacterin (ASAL) or phosphate-buffered saline (PBS). Dorsal skin samples from each injection/infection group (PBS/no lice, PBS/lice, ASAL/no lice, and ASAL/lice) were collected at 24 h post-injection and used for transcriptome profiling using a 44K salmonid microarray platform. Microarray results showed no clear inflammation gene expression signatures and revealed extensive gene repression effects by pre-adult lice (2,189 down and 345 up-regulated probes) in the PBS-injected salmon (PBS/lice vs. PBS/no lice), which involved basic cellular (e.g., RNA and protein metabolism) processes. Lice repressive effects were not observed within the group of ASAL-injected salmon (ASAL/lice vs. ASAL/no lice); on the contrary, the observed skin transcriptome changes –albeit of lesser magnitude (82 up and 1 down-regulated probes)– suggested the activation in key immune and wound healing processes (e.g., neutrophil degranulation, keratinocyte differentiation). The molecular skin response to ASAL was more intense in the lice-infected (ASAL/lice vs. PBS/lice; 272 up and 11 down-regulated probes) than in the non-infected fish (ASAL/no lice vs. PBS/no lice; 27 up-regulated probes). Regardless of lice infection, the skin’s response to ASAL was characterized by the putative activation of both antibacterial and wound healing pathways. The transcriptomic changes prompted by ASAL+lice co-stimulation (ASAL/lice vs. PBS/no lice; 1878 up and 3120 down-regulated probes) confirmed partial mitigation of lice repressive effects on fundamental cellular processes and the activation of pathways involved in innate (e.g., neutrophil degranulation) and adaptive immunity (e.g., antibody formation), as well as endothelial cell migration. The qPCR analyses evidenced immune-relevant genes co-stimulated by ASAL and lice in an additive (e.g., mbl2b, bcl6) and synergistic (e.g., hampa, il4r) manner. These results provided insight on the physiological response of the skin of L. salmonis-infected salmon 24 h after ASAL stimulation, which revealed immunostimulatory properties by the bacterin with potential applications in anti-lice treatments for aquaculture. As a simulated co-infection model, the present study also serves as a source of candidate gene biomarkers for sea lice and bacterial co-infection.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.206
Teacher spread0.202 · 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 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

Citations14
Published2022
Admission routes2
Has abstractyes

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