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Network biology discovers pathogen contact points in host protein-protein interactomes

Author

Listed:
  • Hadia Ahmed

    (University of Alabama at Birmingham)

  • T. C. Howton

    (University of Alabama at Birmingham)

  • Yali Sun

    (University of Alabama at Birmingham)

  • Natascha Weinberger

    (Vienna Biocenter (VBC))

  • Youssef Belkhadir

    (Vienna Biocenter (VBC))

  • M. Shahid Mukhtar

    (University of Alabama at Birmingham
    University of Alabama at Birmingham)

Abstract

In all organisms, major biological processes are controlled by complex protein–protein interactions networks (interactomes), yet their structural complexity presents major analytical challenges. Here, we integrate a compendium of over 4300 phenotypes with Arabidopsis interactome (AI-1MAIN). We show that nodes with high connectivity and betweenness are enriched and depleted in conditional and essential phenotypes, respectively. Such nodes are located in the innermost layers of AI-1MAIN and are preferential targets of pathogen effectors. We extend these network-centric analyses to Cell Surface Interactome (CSILRR) and predict its 35 most influential nodes. To determine their biological relevance, we show that these proteins physically interact with pathogen effectors and modulate plant immunity. Overall, our findings contrast with centrality-lethality rule, discover fast information spreading nodes, and highlight the structural properties of pathogen targets in two different interactomes. Finally, this theoretical framework could possibly be applicable to other inter-species interactomes to reveal pathogen contact points.

Suggested Citation

  • Hadia Ahmed & T. C. Howton & Yali Sun & Natascha Weinberger & Youssef Belkhadir & M. Shahid Mukhtar, 2018. "Network biology discovers pathogen contact points in host protein-protein interactomes," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04632-8
    DOI: 10.1038/s41467-018-04632-8
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    Cited by:

    1. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    2. Wang, Longyun & Mou, Jianhong & Dai, Bitao & Tan, Suoyi & Cai, Mengsi & Chen, Huan & Jin, Zhen & Sun, Guiquan & Lu, Xin, 2024. "Influential nodes identification based on hierarchical structure," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).

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