An introduction to EpiPol (Epigenetic affecting Polymorphism) concept with an in silico identification of CpG-affecting SNPs in the upstream regulatory sequences of human AHR gene

Advancing personalized medicine in neurodegenerative diseases: The role of epigenetics and pharmacoepigenomics in pharmacotherapy

About 80 % of brain disorders have a genetic basis. The pathogenesis of most neurodegenerative diseases is associated with a myriad of genetic defects, epigenetic alterations (DNA methylation, histone/chromatin remodeling, miRNA dysregulation), and environmental factors. The emergence of new sequencing technologies and tools to study the epigenome has led to identifying predictive biomarkers for earlier diagnosis, opening up the possibility of prophylactical interventions. As a result, advances in pharmacogenetics and pharmacoepigenomics now allow for personalized treatments based on the profile of each patient and the specific genetic and epigenetic mechanisms involved. This Review highlights the complexity of neurodegenerative diseases and the variability in patient responses to pharmacotherapy, emphasizing the influence of genetic polymorphisms on the pharmacokinetics and pharmacodynamics of drugs used to treat those conditions. We specifically discuss the potential modulatory effect of several genetic polymorphisms associated with an increased risk of developing different neurodegenerative diseases. We explore genetic and genomic technologies and the potential of analyzing individual-specific drug metabolism to predict and influence drug response and associated clinical outcomes. We also provide insights into the mechanism of action of the drugs under investigation and their potential impact on disease-modifying pathways. Finally, the Review underscores the great potential of this field to enhance the effectiveness and safety of drug treatments through personalized medicine.

Refinement of coding SNPs in the human aryl hydrocarbon receptor gene using ISNPranker: An integrative-SNP ranking web-tool

Different bioinformatic methods apply various approaches to predict how much the effect of a SNP could be deleterious and therefore their results may differ significantly. However, variation studies often need to consider an integrated prediction result to analyze the effect of SNPs. To address this problem, we used an algorithm to map ordinal predictions to a numeral space and averaging them, and based on it we developed the ISNPranker web-tool (http://isnpranker.semilab.ir/). It takes heterogonous outputs of different predictors and generates integrated numerical predictions and ranks SNPs based on them. Afterward, we used ISNPranker to identify the most deleterious coding SNPs (cSNPs) of the human aryl hydrocarbon receptor (AHR) gene. AHR is a ligand-activated transcription factor that governs many molecular and cellular mechanisms and cSNPs may affect its structure, interactions, and function. Forty validated cSNPs of AHR were initially analyzed using 16 publicly available SNP analyzers and the results were introduced to the ISNPranker and integrated predictions were obtained. The cSNPs were ranked in 34 levels of danger and rs200257782 in the ARNT dimerization domain (ADD_121-289) of AHR was identified as the most deleterious cSNP. The rs148360742, which affect ADD_40-79 and Hsp90 binding domain (HBD_27-79) was in the second rank and the third and fourth ranks were occupied by ADD_121-289-located variations rs571123681 and rs141667112 respectively. In conclusion, we introduced ISNPranker, which is a web-tool for integrative ranking of SNPs, and we showed that AHR structure and function may be highly sensitive to the cSNPs in the ARNT dimerization domain.