Evolution of resilience in protein interactomes across the tree of life

By Marinka Zitnik, Rok Sosic, Michal Feldman, Jure Leskovec. 2018

99 citations recorded by the source corpus.

Significance The interactome network of protein–protein interactions captures the structure of molecular machinery that underlies organismal complexity. The resilience to network failures is a critical property of the interactome as the breakdown of interactions may lead to cell death or disease. By studying interactomes from 1,840 species across the tree of life, we find that evolution leads to more resilient interactomes, providing evidence for a longstanding hypothesis that interactomes evolve favoring robustness against network failures. We find that a highly resilient interactome has a beneficial impact on the organism’s survival in complex, variable, and competitive habitats. Our findings reveal how interactomes change through evolution and how these changes affect their response to environmental unpredictability. Phenotype robustness to environmental fluctuations is a common biological phenomenon. Although most phenotypes involve multiple proteins that interact with each other, the basic principles of how such interactome networks respond to environmental unpredictability and change during evolution are largely unknown. Here we study interactomes of 1,840 species across the tree of life involving a total of 8,762,166 protein–protein interactions. Our study focuses on the resilience of interactomes to network failures and finds that interactomes become more resilient during evolution, meaning that interactomes become more robust to network failures over time. In bacteria, we find that a more resilient interactome is in turn associated with the greater ability of the organism to survive in a more complex, variable, and competitive environment. We find that at the protein family level proteins exhibit a coordinated rewiring of interactions over time and that a resilient interactome arises through gradual change of the network topology. Our findings have implications for understanding molecular network structure in the context of both evolution and environment.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Biology & Bioinformatics, Network Science & Complex Systems

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Citations in the corpus

Strategies for Pre-training Graph Neural Networks (2019, methodology): “A dataset containing protein subgraphs from 50 species is used (Zitnik et al., 2019).”
Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, methodology): “To construct the neighborhoods, we randomly selected 100 proteins from each species and constructed 2-hop protein association neighborhoods centered on each of the selected proteins (Zitnik et al., 2019).”
Graph Representation Learning in Biomedicine and Healthcare (2021, unknown): “Fortunately, deep learning on graphs is rooted on organizing principles identified in the past two decades in systems biology and medicine 12–15 .”
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (2021, background): “…and meaningful motifs/patterns of temporal networks; CAW-N may also be generalized to predict high-order structures ( e.g. , triangles) that correspond to some function units of temporal networks from different domains (Benson et al., 2016; 2018; Zitnik et al., 2019). feedback on the abstract.”
Robustness and resilience of complex networks (2024, background): “Similarly, the robustness of protein– protein interaction networks to internal errors or external disruptions is strictly related to the function of a cell and its resilience across the tree of life 44 .”
Hyperbolic Graph Convolutional Neural Networks (2019, background): “However, many real-world graphs, such as protein interaction networks and social networks, often exhibit scale-free or hierarchical structure [7, 50] and Euclidean embeddings, used by existing GCNs, have a high distortion when embedding such graphs [6, 32].”