1. Computational and Systems Biology
Download icon

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
  • Cited 0
  • Views 360
  • Annotations
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.

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)

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.

Metrics

  • 360
    Page views
  • 66
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    2. Developmental Biology
    Sebastian S James et al.
    Short Report

    Brain development relies on an interplay between genetic specification and self-organization. Striking examples of this relationship can be found in the somatosensory brainstem, thalamus, and cortex of rats and mice, where the arrangement of the facial whiskers is preserved in the arrangement of cell aggregates to form precise somatotopic maps. We show in simulation how realistic whisker maps can self-organize, by assuming that information is exchanged between adjacent cells only, under the guidance of gene expression gradients. The resulting model provides a simple account of how patterns of gene expression can constrain spontaneous pattern formation to faithfully reproduce functional maps in subsequent brain structures.

    1. Computational and Systems Biology
    2. Neuroscience
    Chen Chen et al.
    Research Article

    While animals track or search for targets, sensory organs make small unexplained movements on top of the primary task-related motions. While multiple theories for these movements exist—in that they support infotaxis, gain adaptation, spectral whitening, and high-pass filtering—predicted trajectories show poor fit to measured trajectories. We propose a new theory for these movements called energy-constrained proportional betting, where the probability of moving to a location is proportional to an expectation of how informative it will be balanced against the movement’s predicted energetic cost. Trajectories generated in this way show good agreement with measured trajectories of fish tracking an object using electrosense, a mammal and an insect localizing an odor source, and a moth tracking a flower using vision. Our theory unifies the metabolic cost of motion with information theory. It predicts sense organ movements in animals and can prescribe sensor motion for robots to enhance performance.