Proteomics and Machine Learning-Based Approach to Decipher Subcellular Proteome of Mouse Heart.

Journal: Molecular & cellular proteomics : MCP
PMID: 40113211

Abstract

Protein compartmentalization to distinctive subcellular niches is critical for cardiac function and homeostasis. Here, we employed a rapid and robust workflow based on differential centrifugal-based fractionation with mass spectrometry-based proteomics and bioinformatic analyses for systemic mapping of the subcellular proteome of mouse heart. Using supervised machine learning of 450 hallmark protein markers from 16 subcellular niches, we further refined the subcellular information of 2083 proteins with high confidence. Our data validation focused on specific subcellular niches such as mitochondria, cell surface, cardiac dyad, myofibril, and nuclear, unfolding dominant subcellular localization of proteins in their native environment of mouse heart. We further provide targeted nuclear enrichment and co-immunoprecipitation-based proteomic validation from the heart of nuclear-localizing protein networks. This study provides novel insights into the molecular landscape of different subcellular niches of the heart and serves as a draft map for heart subcellular proteome.

Authors

  • Haoyun Fang
    Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia.
  • Alin Rai
    Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Victoria, Australia.
  • Seyed Sadegh Eslami
    Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Victoria, Australia.
  • Kevin Huynh
    Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia.
  • Hsiao-Chi Liao
    School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia.
  • Agus Salim
    Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia; School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia.
  • David W Greening
    Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Victoria, Australia. Electronic address: david.greening@baker.edu.au.

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