PLoS Biology (Apr 2021)

Quantitative proteome comparison of human hearts with those of model organisms.

  • Nora Linscheid,
  • Alberto Santos,
  • Pi Camilla Poulsen,
  • Robert W Mills,
  • Kirstine Calloe,
  • Ulrike Leurs,
  • Johan Z Ye,
  • Christian Stolte,
  • Morten B Thomsen,
  • Bo H Bentzen,
  • Pia R Lundegaard,
  • Morten S Olesen,
  • Lars J Jensen,
  • Jesper V Olsen,
  • Alicia Lundby

DOI
https://doi.org/10.1371/journal.pbio.3001144
Journal volume & issue
Vol. 19, no. 4
p. e3001144

Abstract

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Delineating human cardiac pathologies and their basic molecular mechanisms relies on research conducted in model organisms. Yet translating findings from preclinical models to humans present a significant challenge, in part due to differences in cardiac protein expression between humans and model organisms. Proteins immediately determine cellular function, yet their large-scale investigation in hearts has lagged behind those of genes and transcripts. Here, we set out to bridge this knowledge gap: By analyzing protein profiles in humans and commonly used model organisms across cardiac chambers, we determine their commonalities and regional differences. We analyzed cardiac tissue from each chamber of human, pig, horse, rat, mouse, and zebrafish in biological replicates. Using mass spectrometry-based proteomics workflows, we measured and evaluated the abundance of approximately 7,000 proteins in each species. The resulting knowledgebase of cardiac protein signatures is accessible through an online database: atlas.cardiacproteomics.com. Our combined analysis allows for quantitative evaluation of protein abundances across cardiac chambers, as well as comparisons of cardiac protein profiles across model organisms. Up to a quarter of proteins with differential abundances between atria and ventricles showed opposite chamber-specific enrichment between species; these included numerous proteins implicated in cardiac disease. The generated proteomics resource facilitates translational prospects of cardiac studies from model organisms to humans by comparisons of disease-linked protein networks across species.