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Atlantic salmon scale explants in bacteria-host interaction studies: in vitro challenge model
Aleksei Krasnov1, Amritha Johny1, Elisabeth Ytteborg1
1The Norwegian Institute of Aquaculture, Nofima, 9291, Tromsø, Norway.
Fish & Shellfish Immunology
|May 30, 2025
Summary
Atlantic salmon scale explants (SE) offer a novel model for studying fish immune responses to bacterial pathogens. SE demonstrate robust antiviral, antibacterial, and inflammatory gene expression, acting as immune sentinels.
Area of Science:
- Aquaculture
- Immunology
- Fish Health
Background:
- Cell lines and primary cultures are common models for host-pathogen interactions.
- Live fish challenge trials are costly and raise ethical concerns.
- Atlantic salmon scale explants (SE) present a potential alternative model.
Purpose of the Study:
- To evaluate Atlantic salmon scale explants (SE) as a model for bacterial challenges.
- To compare immune gene expression in SE with other fish tissues.
- To investigate the immune response of SE to specific bacterial pathogens.
Main Methods:
- Transcriptomic analysis of SE compared to body map data.
- Exposure of SE to bacterial pathogens (Moritella viscosa, Tenacibaculum finnmarkense) and commensal bacteria.
- Analysis of immune gene expression, including chemokines, cytokines, and inflammatory markers.
Main Results:
- SE exhibit strong immune activity, ranking high in antiviral, antibacterial, and inflammatory gene expression.
- SE express high levels of skin-specific structural genes.
- Bacterial exposure upregulated a suite of immune genes in SE in a dose-dependent manner, indicating sentinel function.
- Distinct immune responses were observed between different bacterial species and in combination with commensal bacteria.
Conclusions:
- Atlantic salmon scale explants (SE) are a promising and ethically sound model for studying fish-bacterial interactions.
- SE function as immune sentinels, effectively mobilizing responses to bacterial encounters.
- Further research should include time-course studies and pathology detection for model refinement.

