A rice protein interaction network reveals high centrality nodes and candidate pathogen effector targets

Area of Science:

• Plant pathology and molecular biology
• Network science and systems biology
• Bioinformatics and computational biology

Background:

• Network science is crucial for understanding complex biological systems, including host-pathogen interactions.
• Protein-protein interaction networks provide insights into cellular mechanisms and disease processes.
• Identifying pathogen effector targets is essential for developing disease resistance strategies in crops.

Purpose of the Study:

• To analyze the rice protein-protein interactome (RicePPInets) for scale-free network properties and identify influential nodes.
• To develop and implement an improved computational method for analyzing large-scale networks, including RicePPInets.
• To integrate RicePPInets with co-expression networks to create a comprehensive interactome (RIXIN) for rice-Xanthomonas interactions and identify pathogen effector targets.

Main Methods:

• Demonstrated scale-free network properties of RicePPInets.
• Improved computational code for weighted k-shell decomposition for large-scale network analysis.
• Integrated RicePPInets with co-expression networks to generate the RIce-Xanthomonas INteractome (RIXIN).
• Analyzed network topology to identify central players and modules involved in rice-Xanthomonas interactions.

Main Results:

• RicePPInets exhibit scale-free network properties with highly central nodes.
• Internal network layers of RicePPInets contain influential nodes for information spreading.
• Previously identified transcription activator-like (TAL) effector targets are enriched in highly connected nodes within RIXIN.
• These key nodes are involved in critical biological processes relevant to plant defense.

Conclusions:

• The study identified influential nodes within the rice interactome that are critical for host-pathogen interactions.
• The developed integrative network-based platform (RIXIN) effectively prioritizes candidate pathogen effector targets.
• This computational framework offers a translatable approach for studying other plant pathosystems and accelerating functional validation.