Abstract
Purpose :
Herpes simplex virus type 1 causes is the leading cause of infectious blindness in the United States. Animal studies have shown that the severity of HSV-1 ocular disease is influenced by three main factors; innate resistance, host immune response and viral strain. We previously showed that mixed infection with two avirulent HSV-1 strains (OD4 and CJ994) resulted in recombinants that exhibited a range of disease phenotypes from severe to avirulent, suggesting epistatic interactions were involved. The goal of this study was to develop a quantitative trait locus (QTL) analysis of HSV-1 ocular virulence determinants and to identify virulence associated SNPs.
Methods :
Blepharitis, stromal keratitis, and neovascularization quantitative scores were characterized for 40 OD4:CJ994 recombinants and viral titers in the eye were also measured. Virulence quantitative trait locus mapping (vQTLmap) was performed using the Lasso, Random Forest, and Ridge regression methods to identify significant phenotypically meaningful regions for each ocular disease parameter. The best-fit Ridge regression model identified several SNPs for blepharitis, vascularization and stromal keratitis.
Results :
Notably, phenotypically meaningful nonsynonymous variations were detected in the UL24, UL29 (ICP8), UL41 (VHS), UL53 (gK), UL54 (ICP27), UL56, ICP4, US1 (ICP22), US3 and gG genes. Network analysis revealed that many of these genes were in viral regulatory (IE genes) networks and viral genes that affect innate resistance mechanisms. For the first time QTL based analysis has been used on HSV-1 genomes to identify ocular virulence gene networks. Several genes previously implicated in virulence were identified, while other genes were novel. Several novel polymorphisms were identified in these genes.
Conclusions :
This approach provides a framework that will be useful for identifying virulence genes in other pathogenic viruses and to resolve protein-protein interactions and epistatic effects that affect HSV-1 ocular virulence.
This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.