1. Computational and Systems Biology
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A large accessory protein interactome is rewired across environments

  1. Zhimin Liu
  2. Darach Miller
  3. Fangfei Li
  4. Xianan Liu
  5. Sasha F Levy  Is a corresponding author
  1. Stony Brook University, United States
  2. Stanford University, United States
  3. SLAC National Accelerator Laboratory, United States [US]
Research Article
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Cite this article as: eLife 2020;9:e62365 doi: 10.7554/eLife.62365

Abstract

To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across 9 growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a 3-fold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.

Data availability

Raw barcode sequencing data are available from the NIH Sequence Read Archive as accession PRJNA630095 (https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP259652). Barcode sequences, counts, fitness values, and PPI calls are available in the Supplementary Tables (https://osf.io/jmhrb/). Additional data to make figures are available in Mendeley data (https://data.mendeley.com/datasets/9ygwhk5cs3/2) and Open Science Framework (https://osf.io/7yt59/) as detailed in code repository README files. Analysis scripts are written in R and Python. All code used to analyze data, perform statistical analyses, and generate figures is available at Github (https://github.com/sashaflevy/PPiSeq).

The following data sets were generated

Article and author information

Author details

  1. Zhimin Liu

    Department of Biochemistry, Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9333-8101
  2. Darach Miller

    Department of Genetics, Stanford University, Palo Alto, United States
    Competing interests
    No competing interests declared.
  3. Fangfei Li

    Department of Applied Mathematics and Statistics, Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, United States
    Competing interests
    No competing interests declared.
  4. Xianan Liu

    Department of Biochemistry, Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, United States
    Competing interests
    Xianan Liu, S.F.L. and X.L have filed a patent application (WO2017075529A1) on the double barcoding platform used in this manuscript..
  5. Sasha F Levy

    Joint Initiative for Metrology in Biology, SLAC National Accelerator Laboratory, Menlo Park, United States [US]
    For correspondence
    sflevy@stanford.edu
    Competing interests
    Sasha F Levy, S.F.L. and X.L have filed a patent application (WO2017075529A1) on the double barcoding platform used in this manuscript..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0923-1636

Funding

National Institutes of Health (R01HG008354)

  • Sasha F Levy

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Christian R Landry, Université Laval, Canada

Publication history

  1. Received: August 21, 2020
  2. Accepted: September 4, 2020
  3. Accepted Manuscript published: September 14, 2020 (version 1)
  4. Version of Record published: October 21, 2020 (version 2)

Copyright

© 2020, Liu et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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