Poster Presentation 26th Annual Lorne Proteomics Symposium 2021

Automated proteomics workflows for translational medicine: myocardial infarction and early origins of heart disease (#120)

Selvam Paramasivan 1 , Roberto Barrero Gumiel 2 , Paul Millls 1 , Janna Morrison 3 , Pawel Sadowski 4
  1. University of Queensland, Brisbane, QLD, Australia
  2. Division of Research and Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
  3. UniSA clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
  4. Central Analytical Research Facility, Queensland University of Technology, Brisbane, QUEENSLAND, Australia

Myocardial infraction (MI) is a leading cause of cardiovascular disease-related deaths globally. In Australia, more than 500,000 persons suffer from heart attack annually. Studying sheep (Ovis aries) model, where the heart structure and development closely resemble that of humans, can contribute to a  better understanding of cardiac repair mechanisms and factors associated with increased risk of heart failure. We have previously introduced a high-throughput and fully automated proteomics workflows which allowed us to accelerate spectral library generation and SWATH-MS (Sequential Window Acquisition of all Theoretical Mass Spectra)-based protein quantitation in bovids. The pipeline utilizes an advanced robotic sample preparation system (PerkinElmer JANUS G3) to enable protein digestion, desalting and fractionation in a 96 format. Here we have deployed this pipeline to process sheep heart tissue samples from control and animals with failing hearts to generate highly comprehensive proteome maps of heart disease. Quantified proteins allowed us for a clear distinction between healthy and infarct tissue, and revealed pathways associated with cardiomyopathy, abnormal cardiovascular system physiology, dilated cardiomyopathy and ventricular arrhythmia. Furthermore, using bioinformatics predictions, we have correlated this next generation proteomics dataset with miRNA profiling to better understand the early origins of a heart disease.