Abstract
Single-nucleotide polymorphisms (SNPs) are single genetic code variations considered one of the most common forms of nucleotide modifications. Such SNPs can be located in genes associated to immune response and, therefore, they may have direct implications over the phenotype of susceptibility to infections affecting the productive sector. In this study, a set of immune-related genes (cc motif chemokine 19 precursor [ccl19], integrin β2 (itβ2, also named cd18), glutathione transferase omega-1 [gsto-1], heat shock 70 KDa protein [hsp70], major histocompatibility complex class I [mhc-I]) were analyzed to identify SNPs by data mining. These genes were chosen based on their previously reported expression on infectious pancreatic necrosis virus (IPNV)-infected Atlantic salmon phenotype. The available EST sequences for these genes were obtained from the Unigene database. Twenty-eight SNPs were found in the genes evaluated and identified most of them as transition base changes. The effect of the SNPs located on the 5’-untranslated region (UTR) or 3’-UTR upon transcription factor binding sites and alternative splicing regulatory motifs was assessed and ranked with a low-medium predicted FASTSNP score risk. Synonymous SNPs were found on itβ2 (c.2275G > A), gsto-1 (c.558G > A), and hsp70 (c.1950C > T) with low FASTSNP predicted score risk. The difference in the relative synonymous codon usage (RSCU) value between the variant codons and the wild-type codon (ΔRSCU) showed one negative (hsp70 c.1950C > T) and two positive ΔRSCU values (itβ2 c.2275G > A; gsto-1 c.558G > A), suggesting that these synonymous SNPs (sSNPs) may be associated to differences in the local rate of elongation. Nonsynonymous SNPs (nsSNPs) in the gsto-1 translatable gene region were ranked, using SIFT and POLYPHEN web-tools, with the second highest (c.205A > G; c484T > C) and the highest (c.499T > C; c.769A > C) predicted score risk possible. Using homology modeling to predict the effect of these nonsynonymous SNPs, the most relevant nucleotide changes for gsto-1 were observed for the nsSNPs c.205A > G, c484T > C, and c.769A > C. Molecular dynamics was assessed to analyze if these GSTO-1 variants have significant differences in their conformational dynamics, suggesting these SNPs could have allosteric effects modulating its catalysis. Altogether, these results suggest that candidate SNPs identified may play a crucial potential role in the immune response of Atlantic salmon.
Highlights
Genetic variation occurs within and among populations, leading to polymorphisms that could be associated with genetic trait or a phenotype in the presence of an environmental stimulus (Brookes, 1999; Rebbeck et al, 2004; Hirschhorn and Daly, 2005)
A total of 310 expressed sequence tag (EST) sequences obtained from the Unigene database (NCBI) were analyzed to identify single-nucleotide polymorphism (SNP) on ccl19, itb2, gsto-1, hsp70, and mhc-I
We identified a set of SNP located for a group of immune-related genes with differential gene expression in Salmo salar challenged with infectious pancreatic necrosis virus (IPNV) (Cepeda et al, 2011; Cepeda et al, 2012; Reyes-López et al, 2015)
Summary
Genetic variation occurs within and among populations, leading to polymorphisms that could be associated with genetic trait or a phenotype in the presence of an environmental stimulus (Brookes, 1999; Rebbeck et al, 2004; Hirschhorn and Daly, 2005). The SNPs are the most common form of variation in the genome and they are extensively used to study genetic differences between individuals and populations. These SNPs may contribute to changes in the genomic sequence, either in the coding (exons), intergenic, or noncoding (introns) region (Dijk et al, 2014; Ahmad et al, 2018). SNPs are considered the most useful biomarkers for disease diagnosis or prognosis due to their common frequency, ease of analysis, low genotyping costs, and the possibility to carry out association studies based on statistical and bioinformatics tools (Srinivasan et al, 2016). On the past decade it has been seen an enormous progress in identifying hundreds of thousands SNPs to identify associations with complex clinical conditions and phenotypic traits associated with hundreds of common diseases (Welter et al, 2014; Wijmenga and Zhernakova, 2018)
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