bioRxivGeneticsPreprint
Insights into the genetic architecture of resistance to viral haemorrhagic septicaemia virus in rainbow trout from a genome-wide association study to in vitro CRISPR-Cas9 functional evaluation
Valentin ThomasiD, Bertrand ColletiD, Edwige Quillet, Michaël Marchand, François HuetziD, Pierre BoudinotiD, Florence PhocasiD, Delphine LalliasiD
Abstract
Viral haemorrhagic septicaemia (VHS) is a severe disease affecting rainbow trout (Oncorhynchus mykiss) and a wide range of wild freshwater and marine fish species. VHSV threatens rainbow trout aquaculture, as it may cause 100% mortality in fry. Previous studies identified a quantitative trait locus (QTL) on chromosome 3 associated with resistance to VHSV waterborne challenge and reduced viral replication in fin explants, although these findings were obtained using limited genetic diversity. The objective of this study was to validate and extend the identification of genomic regions associated with resistance to VHSV in the genetically diverse rainbow trout line designated "synthetic." A genome-wide association study (GWAS) was conducted using whole-genome sequences from parents of progeny classified as resistant or susceptible to a VHSV waterborne challenge. While the QTL on chromosome 3 was not validated in the synthetic line, four novel suggestive SNPs associated with survival following VHSV waterborne challenge were identified on chromosomes 6, 8, 17, and 32. Notably, one SNP on chromosome 17 was located within a gene potentially involved in antiviral defence, a paralog of lrp1 (low-density lipoprotein receptor-related protein 1). To further investigate its role, lrp1 function was analysed in vitro using CRISPR-Cas9 genome editing. Three independent lrp1-/- CHSE-EC cell lines were generated and challenged with VHSV. The results showed that lrp1 is not essential for viral entry but may modulate the inflammatory response during VHSV infection in epithelial cell lines.
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- Citations
- 0
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