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Evidence for dynamically organized modularity in the yeast protein–protein interaction network

Author

Listed:
  • Jing-Dong J. Han

    (Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School)

  • Nicolas Bertin

    (Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School)

  • Tong Hao

    (Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School)

  • Debra S. Goldberg

    (Harvard Medical School)

  • Gabriel F. Berriz

    (Harvard Medical School)

  • Lan V. Zhang

    (Harvard Medical School)

  • Denis Dupuy

    (Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School)

  • Albertha J. M. Walhout

    (Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School
    University of Massachusetts Medical School)

  • Michael E. Cusick

    (Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School)

  • Frederick P. Roth

    (Harvard Medical School)

  • Marc Vidal

    (Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School)

Abstract

In apparently scale-free protein–protein interaction networks, or ‘interactome’ networks1,2, most proteins interact with few partners, whereas a small but significant proportion of proteins, the ‘hubs’, interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs1. A link between the potential scale-free topology of interactome networks and genetic robustness3,4 seems to exist, because knockouts of yeast genes5,6 encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs1. Here we investigate how hubs might contribute to robustness and other cellular properties for protein–protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: ‘party’ hubs, which interact with most of their partners simultaneously, and ‘date’ hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes—or modules7 —to each other, whereas party hubs function inside modules.

Suggested Citation

  • Jing-Dong J. Han & Nicolas Bertin & Tong Hao & Debra S. Goldberg & Gabriel F. Berriz & Lan V. Zhang & Denis Dupuy & Albertha J. M. Walhout & Michael E. Cusick & Frederick P. Roth & Marc Vidal, 2004. "Evidence for dynamically organized modularity in the yeast protein–protein interaction network," Nature, Nature, vol. 430(6995), pages 88-93, July.
    • Handle: RePEc:nat:nature:v:430:y:2004:i:6995:d:10.1038_nature02555
    DOI: 10.1038/nature02555
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    Citations

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    Cited by:

    1. Luis P Fernandes & Alessia Annibale & Jens Kleinjung & Anthony C C Coolen & Franca Fraternali, 2010. "Protein Networks Reveal Detection Bias and Species Consistency When Analysed by Information-Theoretic Methods," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-14, August.
    2. Franke, R., 2016. "CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 384-408.
    3. Changki Hong & Jeewon Hwang & Kwang-Hyun Cho & Insik Shin, 2015. "An Efficient Steady-State Analysis Method for Large Boolean Networks with High Maximum Node Connectivity," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-19, December.
    4. Weijiang Li & Hiroyuki Kurata, 2008. "Visualizing Global Properties of Large Complex Networks," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-4, July.
    5. Seyed Yahya Anvar & Allan Tucker & Veronica Vinciotti & Andrea Venema & Gert-Jan B van Ommen & Silvere M van der Maarel & Vered Raz & Peter A C ‘t Hoen, 2011. "Interspecies Translation of Disease Networks Increases Robustness and Predictive Accuracy," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-14, November.
    6. Yau-Hua Yu & Hsu-Ko Kuo & Kuo-Wei Chang, 2008. "The Evolving Transcriptome of Head and Neck Squamous Cell Carcinoma: A Systematic Review," PLOS ONE, Public Library of Science, vol. 3(9), pages 1-11, September.
    7. Seah Choon Sen & Shahreen Kasim & Mohd Farhan Md Fudzee & Rusli Abdullah & Rodziah Atan, 2017. "Random Walk From Different Perspective," Acta Electronica Malaysia (AEM), Zibeline International Publishing, vol. 1(2), pages 26-27, November.
    8. Amir Lakizadeh & Saeed Jalili, 2016. "BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-16, July.
    9. Gabor I Simko & Peter Csermely, 2013. "Nodes Having a Major Influence to Break Cooperation Define a Novel Centrality Measure: Game Centrality," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
    10. Peter Langfelder & Paul S Mischel & Steve Horvath, 2013. "When Is Hub Gene Selection Better than Standard Meta-Analysis?," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-16, April.
    11. Sun, Yeran & Mburu, Lucy & Wang, Shaohua, 2016. "Analysis of community properties and node properties to understand the structure of the bus transport network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 523-530.
    12. Shiwei Lu & Yaping Huang & Zhiyuan Zhao & Xiping Yang, 2018. "Exploring the Hierarchical Structure of China’s Railway Network from 2008 to 2017," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    13. Hou, Bonan & Yao, Yiping & Liao, Dongsheng, 2012. "Identifying all-around nodes for spreading dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4012-4017.
    14. Pan-Jun Kim & Nathan D Price, 2011. "Genetic Co-Occurrence Network across Sequenced Microbes," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-9, December.
    15. Patrick C F Buchholz & Catharina Zeil & Jürgen Pleiss, 2018. "The scale-free nature of protein sequence space," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-14, August.
    16. Zhang, Yuerong & Marshall, Stephen & Manley, Ed, 2021. "Understanding the roles of rail stations: Insights from network approaches in the London metropolitan area," Journal of Transport Geography, Elsevier, vol. 94(C).
    17. Chrysafis Vogiatzis & Mustafa Can Camur, 2019. "Identification of Essential Proteins Using Induced Stars in Protein–Protein Interaction Networks," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 703-718, October.
    18. Fabio Cumbo & Paola Paci & Daniele Santoni & Luisa Di Paola & Alessandro Giuliani, 2014. "GIANT: A Cytoscape Plugin for Modular Networks," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-7, October.

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