Single-nucleotide variations in cardiac arrhythmias: prospects for genomics and proteomics based biomarker discovery

Ayman Abunimer1, Krista Smith2, Tsung-Jung Wu3

  • 1Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA. aabunimer115@gmail.com.

Genes
|April 8, 2014
PubMed

Insights

Researchers identified 75 genetic variations linked to cardiac arrhythmias, including 10 that alter proteins. Further analysis revealed how these genetic changes affect S-nitrosylation, impacting pathways like blood coagulation and muscle contraction, aiding arrhythmia susceptibility prediction.

Area of Science:

  • Genetics and Cardiovascular Research
  • Proteomics and Molecular Biology

Background:

  • Cardiovascular diseases are a leading cause of premature death globally.
  • Cardiac arrhythmias, like sudden cardiac death and atrial fibrillation, arise from abnormal heart electrical activity.
  • Genome-wide association studies (GWAS) identify genetic variations (SNVs) linked to acquired arrhythmias.

Purpose of the Study:

  • To compile a comprehensive list of SNVs associated with cardiac arrhythmias.
  • To investigate the impact of non-synonymous SNVs on protein function, specifically S-nitrosylation.
  • To identify novel genetic and proteomic factors contributing to arrhythmia susceptibility.

Main Methods:

  • Manual curation of published GWAS data to identify 75 arrhythmia-associated SNVs.
  • Analysis of S-nitrosylation sites affected by non-synonymous SNVs (nsSNVs).
  • Bioinformatic analysis (pathway and Gene Ontology analysis) of predicted S-nitrosylation sites.

Main Results:

  • A curated list of 75 SNVs associated with cardiac arrhythmias was established.
  • Ten SNVs were found to cause amino acid changes, enabling proteomic detection.
  • Analysis revealed loss of 7 known S-nitrosylation sites and modification of 1429 proteins due to nsSNVs.
  • Over-representation of blood coagulation, muscle contraction, and cytoskeletal activity pathways was observed.

Conclusions:

  • This study provides a valuable resource of SNVs and their proteomic context for arrhythmia research.
  • Understanding the interplay between genetic variation, S-nitrosylation, and cardiac function can improve arrhythmia susceptibility prediction.
  • Elucidating these mechanisms offers a new parameter for predicting predisposition to cardiac diseases.

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