Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning

The Association Between Thymidylate Synthase Gene Polymorphisms and the Risk of Ischemic Stroke in Chinese Han Population

Family history of hypertension, smoking, diabetes and alcohol consumption and atherosclerotic plaque were identified as common risk factors in IS. We aimed at investigating the relationship between Thymidylate Synthase (TS) gene polymorphisms and ischemic stroke (IS).This case-control research selected and genotyped three single nucleotide polymorphisms (SNPs)of TS( rs699517, rs2790, and rs151264360) with Sanger sequencing in Chinese Han population. We also adopted logistic regression analysis in genetic models for calculating odds ratios and 95% confidence intervals. Genotype-Tissue Expression(GTEx) database analyzed the tissue-specific expression and TS polymorphisms. The ischemic stroke patients showed higher low-density lipoprotein cholesterol and total homocysteine (tHcy). It was found that patients with the TT genotype of rs699517 and GG genotype of rs2790 had larger degrees of tHcy than those with CC + CT genotypes and AA + AG genotypes, respectively. The genotype distribution of the three SNPs did not deviate from Hardy-Weinberg equilibrium (HWE). Haplotype analysis showed that T-G-del was the major haplotype in IS, and C-A-ins was the major haplotype in controls. GTEx database indicated that the rs699517 and rs2790 increased the expression of TS in healthy human and associated with TS expression level in a single tissue. In conclusion: This study has shown that TS rs699517 and rs2790 were significantly related to ischemic stroke patients.

Are Ischemic Stroke and Alzheimer's Disease Genetically Consecutive Pathologies?

Complex diseases that affect the functioning of the central nervous system pose a major problem for modern society. Among these, ischemic stroke (IS) holds a special place as one of the most common causes of disability and mortality worldwide. Furthermore, Alzheimer's disease (AD) ranks first among neurodegenerative diseases, drastically reducing brain activity and overall life quality and duration. Recent studies have shown that AD and IS share several common risk and pathogenic factors, such as an overlapping genomic architecture and molecular signature. In this review, we will summarize the genomics and RNA biology studies of IS and AD, discussing the interconnected nature of these pathologies. Additionally, we highlight specific genomic points and RNA molecules that can serve as potential tools in predicting the risks of diseases and developing effective therapies in the future.

O’Brien T, Perucca P, Ge Z, Anderson A. Applications for Deep Learning in Epilepsy Genetic Research

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.