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Evolution of Resilience in Protein Interactomes Across The Evolution of resilience in protein interactomes across the tree of life Marinka Zitnika, Rok Sosicˇa, Marcus W. Feldmanb,1, and Jure Leskoveca,c,1 aDepartment of Computer Science, Stanford University, Stanford, CA 94305; bDepartment of Biology, Stanford University, Stanford, CA 94305; and cChan Zuckerberg Biohub, San Francisco, CA 94158 Contributed by Marcus W. Feldman, December 18, 2018 (sent for review October 19, 2018; reviewed by Edoardo Airoldi and Aviv Bergman) Phenotype robustness to environmental fluctuations is a common physical interactions between 1,450,633 proteins from 1,840 biological phenomenon. Although most phenotypes involve mul- species, encompassing all current protein interaction infor- tiple proteins that interact with each other, the basic principles of mation at a cross-species scale (SI Appendix, section S1 and how such interactome networks respond to environmental unpre- Table S4). We group these interactions by species and repre- dictability and change during evolution are largely unknown. sent each species with a separate interactome network, in which Here we study interactomes of 1,840 species across the tree of nodes indicate a species’ proteins and edges indicate exper- life involving a total of 8,762,166 protein–protein interactions. imentally documented physical interactions, including direct Our study focuses on the resilience of interactomes to network biophysical protein–protein interactions, regulatory protein– failures and finds that interactomes become more resilient during DNA interactions, metabolic pathway interactions, and kinase– evolution, meaning that interactomes become more robust to net- substrate interactions measured in that species. We integrate work failures over time. In bacteria, we find that a more resilient into the dataset (22) the evolutionary history of species pro- interactome is in turn associated with the greater ability of the vided by the tree of life constructed from small subunit ribosomal organism to survive in a more complex, variable, and competi- RNA gene sequence information (12) (SI Appendix, section tive environment. We find that at the protein family level proteins S2). Using network science, we study the network organiza- exhibit a coordinated rewiring of interactions over time and that tion of each interactome, in particular its resilience to net- a resilient interactome arises through gradual change of the net- work failures, a critical factor determining the function of the work topology. Our findings have implications for understanding interactome (23–26). We identify the relationship between the EVOLUTION molecular network structure in the context of both evolution and resilience of an interactome and evolution and use this resilience environment. to uncover relationships with natural environments in which organisms live. Although the interactomes are incomplete and protein–protein interaction networks j molecular evolution j ecology j biased toward much-studied proteins and model species (SI network resilience j network rewiring Appendix, section S1 and Fig. S7), our analyses give results that are consistent across taxonomic groups, that are not sen- he enormous diversity of life shows a fundamental ability of sitive to network data quality or network size change (SI Torganisms to adapt their phenotypes to changing environ- Appendix, section S8 and Fig. S8), and indicate that our conclu- ments (1). Most phenotypes are the result of an interplay of many sions will still hold when more protein interaction data become molecular components that interact with each other and the envi- available. ronment (2–5). The study of life’s diversity has a long history and extensive phylogenetic studies have demonstrated evolution at Significance the DNA sequence level (6–8). While studies based on sequence data alone have demonstrated evolution of genomes, mecha- nistic insights into how evolution shapes interactions between The interactome network of protein–protein interactions cap- proteins in an organism remain elusive (9, 10). tures the structure of molecular machinery that underlies DNA sequence information has been used to associate genes organismal complexity. The resilience to network failures is with their functions (11), determine properties of ancestral life a critical property of the interactome as the breakdown of (12, 13), and understand how the environment affects genomes interactions may lead to cell death or disease. By study- (14). Despite these advances in understanding DNA sequence ing interactomes from 1,840 species across the tree of life, evolution, little is known about basic principles that govern we find that evolution leads to more resilient interactomes, the evolution of interactions between proteins. In particular, providing evidence for a longstanding hypothesis that inter- evolution of DNA and amino acid sequences could lead to per- actomes evolve favoring robustness against network failures. vasive rewiring of protein–protein interactions and create or We find that a highly resilient interactome has a beneficial destroy the ability of the interactions to perform their biological impact on the organism’s survival in complex, variable, and functions. competitive habitats. Our findings reveal how interactomes The importance of protein–protein interactions has spurred change through evolution and how these changes affect their experimental efforts to map all interactions between proteins response to environmental unpredictability. in a particular organism, its interactome, namely the com- Author contributions: M.Z., M.W.F., and J.L. designed research; M.Z., R.S., and J.L. per- plex network of protein–protein interactions in that organ- formed research; M.Z., R.S., M.W.F., and J.L. analyzed data; and M.Z., R.S., M.W.F., and ism. A large number of high-throughput experiments have J.L. wrote the paper.y reported high-quality interactomes in a number of organisms Reviewers: E.A., Harvard University; and A.B., Albert Einstein College Of Medicine. y (15–19). Because interactomes underlie all living organisms, The authors declare no conflict of interest.y it is critical to understand how these networks change dur- This open access article is distributed under Creative Commons Attribution-NonCommercial- ing evolution (20, 21) and elucidate key principles of their NoDerivatives License 4.0 (CC BY-NC-ND).y structure. 1 To whom correspondence may be addressed. Email: [email protected] or jure@ Here, we use protein interactions measured by these large- cs.stanford.edu.y scale interactome mapping experiments and study the evolu- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. tionary dynamics of the interactomes across the tree of life. 1073/pnas.1818013116/-/DCSupplemental.y Our protein interaction dataset contains a total of 8,762,166 www.pnas.org/cgi/doi/10.1073/pnas.1818013116 PNAS Latest Articles j 1 of 8 Downloaded by guest on September 29, 2021 Results indicating that the interactome is not resilient to network Modeling Resilience of the Interactome. Natural selection has failures. influenced many features of living organisms, both at the level To fully characterize the interactome resilience of a species we of individual genes (27) and at the level of whole organisms (13). measure fragmentation of the species’ interactome across all pos- To determine how natural selection influences the structure of sible network failure rates (SI Appendix, Fig. S3). Consider the interactomes, we study the resilience of interactomes to network interactome of the pathogenic bacterium Haemophilus influenzae failures (23, 25, 26). Resilience is a critical property of an inter- and the interactome of humans, which have different resilience actome as the breakdown of proteins can fundamentally affect (Fig. 1C). In the H. influenzae interactome, on removing small the exchange of any biological information between proteins in a fractions of all nodes many network components of varying sizes cell (Fig. 1A). Network failure could occur through the removal appear, producing a quickly increasing Shannon diversity. In of a protein (e.g., by a nonsense mutation) or the disruption of a contrast, the human interactome fragments into a few small com- protein–protein interaction (e.g., by environmental factors, such ponents and one large component whose size slowly decreases as as availability of resources). The removal of even a small number small components break off, resulting in Shannon diversity that of proteins can completely fragment the interactome and lead increases linearly with the network failure rate (Fig. 1C). Thus, to cell death and disease (4, 5) (SI Appendix, section S5.1 and unlike the fragmentation of the H. influenzae interactome, the Table S3). Disruptions of interactions can thus affect the inter- human interactome stays together as a large component for very actome to the extent that its connectivity can be completely lost high network failure rates, providing evidence for the topological and the interactome loses its biological function and increases stability of the interactome. In general, the calculation of mod- the risk of disease (5). ified Shannon diversity over all possible network failure rates f We formally characterize the resilience of an interactome of a yields a monotonically increasing function that reaches its min- species by measuring how fragmented the interactome becomes imum value of 0 at f = 0 (i.e., a connected interactome) and its when all interactions involving a fraction f of the proteins
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