Aquaculture Europe 2023

September 18 - 21, 2023

Vienna,Austria

Add To Calendar 20/09/2023 11:30:0020/09/2023 11:45:00Europe/ViennaAquaculture Europe 2023THE COMPARISON OF THE EPITHELIAL SKIN TRANSCRIPTOME RESPONSE IN ATLANTIC SALMON S. salar INFESTED WITH SEA LICE C. rogercresseyi IN SEAWATER CAGES REVEALS DIFFERENTIAL BIOLOGICAL FUNCTIONS RELATED TO SEASONALITYStolz 2The European Aquaculture Societywebmaster@aquaeas.orgfalseDD/MM/YYYYaaVZHLXMfzTRLzDrHmAi181982

THE COMPARISON OF THE EPITHELIAL SKIN TRANSCRIPTOME RESPONSE IN ATLANTIC SALMON S. salar INFESTED WITH SEA LICE C. rogercresseyi IN SEAWATER CAGES REVEALS DIFFERENTIAL BIOLOGICAL FUNCTIONS RELATED TO SEASONALITY

F elipe E. Reyes-López1 *, Mabel Vidal1 , Silvana Guerra-Arredondo1, Merari Goldstein1, María J. Santillán-Araneda1, Eva Vallejos-Vidal1,2.

 

1 Fish health and integrative physiogenomics research team,  Centro de Biotecnología Acuícola, Facultad de Química y Biología,  Universidad de Santiago de Chile,  Alameda 3363, Edificio Eduardo Morales Santos, Piso 3,  9170002 Estación Central, Santiago, Chile.

2Núcleo de Investigación Aplicada en Ciencias Veterinarias y Agronómicas, Universidad de Las Américas, Facultad de Medicina Veterinaria y Agronomía, Walker Martínez 1360, Piso 3, 8250122 La Florida, Santiago, Chile

 

Correspondance : felipe.reyes.l@usach.cl

 



Introduction

The Atlantic salmon (S. salar) industry covers approximately 90% of the total salmonid culture, with an annual worldwide production of ~1,000,000 tonnes . Chile is the second largest Atlantic salmon worldwide producer, representing its second most important economic activity.  Among their challenges, the  industry has  to deal with sanitary issues, such as s ea lice (C. rogercresseyi ) infestations causing significant economic losses and social consequences for the aquaculture industry worldwide. Sea lice is an ectoparasite that infests the skin mucosa of Atlantic salmon in Chile. Despite its relevance , few studies have focused on evaluating the health status of the skin mucosa of Atlantic salmon reared in sea cage farms. In addition, e nvironmental fluctuations such as temperature, a parameter of particular relevance in fish due to their poikilothermic characteristics, have not been considered either. For this reason, in this study, we evaluated the epithelial skin mucosa transcriptome in summer (at the maximum seawater surface temperature) and autumn ( at the descending ramp temperature  between the highest temperature and the lowest temperature) of Atlantic salmon infested with C. rogercresseyi.

Materials and methods

 We performed a transcriptomic profile of the  epithelial  skin mucosa in Atlantic salmon infested and non-infested with sea lice  sampled  from the same seawater cage  of a farm located in the fjords of the Aysén Region (Chile). We conducted the sampling in two different seasons: summer (during the peak of maximum water temperature; 10 weeks after seawater transfer) and autumn (at the half-descending ramp temperature;23 weeks after seawater transfer) . For transcriptomic analysis (RNA-Seq), t otal RNA was obtained from the epithelial  skin  tissue. We used a pooling strategy for the non-infested and infested fish group (n= 3 pools per condition; n= 5 fish per pool).

 We verified the quality of each sequencing library with FastqQC (Andrews, 2010), a software package that estimates the number of uncallable and low-quality bases. We mapped  the skin mucosa transcriptome  to the Salmo salar reference genome (Ssal_v3.1) using STAR (Dobin, 2013), a high-performance community standard aligner. We used transcripts per million (TPM) values as gene expression levels for all the analyse s.  The differential gene expression analysis was based on the negative binomial distribution using  the  DESeq2 package (Love, 2014). Pathways enrichment analysis was performed using the Gene Ontology Consortium database (Gene Ontology Consortium, 2019) and STRING  database (Szklarczyk, 2019).

Results

 We mapped our data to a total of 14,831 genes. We observed a different expression profile between phenotypes (infested; non-infested) and seasons (summer; autumn). We identified only 19 differential expressed genes (DEGs) in the summer. By contrast, in autumn, the DEGs augmented to  102 DEGs.  The representation of common DEGs between summer and autumn was minimal, with o nly 3  DEGs. For the exclusive DEGs, the summer showed 11 upregulated and 6 downregulated DEGs. On the other hand, the autumn showed  66  upregulated and 33 downregulated DEGs.

We compared  the  functionality of the  upregulated and downregulated DEGs between the infested and non-infested Atlantic salmon.  The enrichment analysis showed the preference of the extracellular matrix process for the upregulated DEGs during summer. Conversely, the downregulated DEGs during summer showed no enrichment. In autumn,  we observed  upregulated  DEGs  associated with cellular response to corticotropin-releasing hormone stimulus, metabolic processes , cell proliferation, and nitric oxide biosynthetic process regulation , among others. On the other hand,  the  downregulated DEGs were associated with  processes like  riboflavin transport  and mitochondrion organization.

Conclusions

 Our analysis revealed that sea lice infestation does not appear to dominate the differential expression profile in summer, suggesting that both infested and non-infested samples are more concerned with seawater environmental adaptation . On the contrary ,  during autumn, the infested and non-infested Atlantic salmon show a differential expression profile and biological processes in response to sea lice. In this way ,  it stands out t he physiological response to stress orchestrated by the cellular response to glucocorticoid stimulus.

Acknowledgments

Fondecyt (1211841; 11221308) and DICYT-USACH (082344RL_Postdoc; 082344RL_Ayudante) grants.

References

Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data.

Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., ... & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15-21.

Gene Ontology Consortium. (2019). The gene ontology resource: 20 years and still GOing strong. Nucleic acids research, 47(D1), D330-D338.

Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 15(12), 1-21.

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., ... & Mering, C. V. (2019). STRING v11: proteinprotein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic acids research, 47(D1), D607-D613.