A multiplex SNaPshot assay for the detection of single nucleotide polymorphism associated with clinical severity of enterovirus infections.

ABSTRACT

Non-polio enterovirus infections are known to cause a variety of diseases and neurological complications. It is also known that the severity of these diseases largely differs among individuals with different genotypes and alleles. The Single Nucleotide Polymorphisms (SNPs) within specific genes have a considerable effect on the immune response to enteroviruses and on the outcome of disease, leading to variations in complications and infection susceptibility. Knowing the distribution of such SNPs can be valuable for individual case management and studying epidemiological parameters of enterovirus infections. In this feasibility study, a multiplex version of the primer extension-based technique called the SNaPshot Assay has been developed to examine SNPs in various relevant genes for predicting the clinical severity of enterovirus infections. It is already established that this technique is precise, consistent, scalable, and likely to exhibit high throughput. The multiplex SNaPshot can investigate multiple genetic susceptibility markers simultaneously, and the assay can be used to identify vulnerable populations, understand the epidemiology of infections, and manage the outbreaks of enteroviruses. Based on the literature, 15 SNPs were identified which are suspected for higher susceptibility to the worst outcomes after enterovirus infection and the assay was developed. Blood samples of 100 healthy volunteers were collected and tested for assay feasibility as well as to know the proportions of 15 selected SNPs. After the analysis, seven SNPs have been identified and suggested to be considered for future assays. Based on the pilot test results, it appears that positivity for any three out of the identified seven SNPs might indicate a higher risk, and future studies correlated with clinical studies among patients with and without severe diseases utilizing this assay will provide robust parameters to determine at-risk individuals more accurately.