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Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions

Author

Listed:
  • Jordan Currie

    (University of Colorado School of Medicine)

  • Vyshnavi Manda

    (University of Colorado School of Medicine)

  • Sean K. Robinson

    (University of Colorado School of Medicine)

  • Celine Lai

    (Stanford University)

  • Vertica Agnihotri

    (City of Hope Comprehensive Cancer Center)

  • Veronica Hidalgo

    (University of Colorado School of Medicine)

  • R. W. Ludwig

    (University of Colorado School of Medicine)

  • Kai Zhang

    (Beckman Research Institute, City of Hope National Medical Center)

  • Jay Pavelka

    (University of Colorado School of Medicine)

  • Zhao V. Wang

    (Beckman Research Institute, City of Hope National Medical Center)

  • June-Wha Rhee

    (City of Hope Comprehensive Cancer Center)

  • Maggie P. Y. Lam

    (University of Colorado School of Medicine
    University of Colorado School of Medicine
    University of Colorado School of Medicine)

  • Edward Lau

    (University of Colorado School of Medicine
    University of Colorado School of Medicine)

Abstract

The spatial and temporal distributions of proteins are critical to protein function, but cannot be directly assessed by measuring protein bundance. Here we describe a mass spectrometry-based proteomics strategy, Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently protein turnover rates and subcellular localization in the same experiment. Applying the method, we find that unfolded protein response (UPR) has different effects on protein turnover dependent on their subcellular location in human AC16 cells, with proteome-wide slowdown but acceleration among stress response proteins in the ER and Golgi. In parallel, UPR triggers broad differential localization of proteins including RNA-binding proteins and amino acid transporters. Moreover, we observe newly synthesized proteins including EGFR that show a differential localization under stress than the existing protein pools, reminiscent of protein trafficking disruptions. We next applied SPLAT to an induced pluripotent stem cell derived cardiomyocyte (iPSC-CM) model of cancer drug cardiotoxicity upon treatment with the proteasome inhibitor carfilzomib. Paradoxically, carfilzomib has little effect on global average protein half-life, but may instead selectively disrupt sarcomere protein homeostasis. This study provides a view into the interactions of protein spatial and temporal dynamics and demonstrates a method to examine protein homeostasis regulations in stress and drug response.

Suggested Citation

  • Jordan Currie & Vyshnavi Manda & Sean K. Robinson & Celine Lai & Vertica Agnihotri & Veronica Hidalgo & R. W. Ludwig & Kai Zhang & Jay Pavelka & Zhao V. Wang & June-Wha Rhee & Maggie P. Y. Lam & Edwar, 2024. "Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46600-5
    DOI: 10.1038/s41467-024-46600-5
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    References listed on IDEAS

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    1. Oliver M. Crook & Colin T. R. Davies & Lisa M. Breckels & Josie A. Christopher & Laurent Gatto & Paul D. W. Kirk & Kathryn S. Lilley, 2022. "Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    2. Justin W. Chartron & Katherine C. L. Hunt & Judith Frydman, 2016. "Cotranslational signal-independent SRP preloading during membrane targeting," Nature, Nature, vol. 536(7615), pages 224-228, August.
    3. Thomas G. Martin & Valerie D. Myers & Praveen Dubey & Shubham Dubey & Edith Perez & Christine S. Moravec & Monte S. Willis & Arthur M. Feldman & Jonathan A. Kirk, 2021. "Cardiomyocyte contractile impairment in heart failure results from reduced BAG3-mediated sarcomeric protein turnover," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    4. Oliver M Crook & Claire M Mulvey & Paul D W Kirk & Kathryn S Lilley & Laurent Gatto, 2018. "A Bayesian mixture modelling approach for spatial proteomics," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-29, November.
    5. Aikaterini Geladaki & Nina Kočevar Britovšek & Lisa M. Breckels & Tom S. Smith & Owen L. Vennard & Claire M. Mulvey & Oliver M. Crook & Laurent Gatto & Kathryn S. Lilley, 2019. "Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics," Nature Communications, Nature, vol. 10(1), pages 1-15, December.
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