A computational exploration of resilience and evolvability of protein–protein interaction networks

Klein, Brennan and Holmér, Ludvig and Smith, Keith M. and Johnson, Mackenzie M. and Swain, Anshuman and Stolp, Laura and Teufel, Ashley I. and Kleppe, April S. (2021) A computational exploration of resilience and evolvability of protein–protein interaction networks. Communications Biology, 4. 1352. ISSN 2399-3642 (https://doi.org/10.1038/s42003-021-02867-8)

[thumbnail of Klein-etal-CB-2021-A-computational-exploration-of-resilience-and-evolvability-of-protein–protein-interaction-networks]
Preview
 Text. Filename: Klein-etal-CB-2021-A-computational-exploration-of-resilience-and-evolvability-of-protein_protein-interaction-networks.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (3MB)| Preview

Abstract

Protein–protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks’ resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

ORCID iDs

Klein, Brennan, Holmér, Ludvig, Smith, Keith M. ORCID logoORCID: https://orcid.org/0000-0002-4615-9020, Johnson, Mackenzie M., Swain, Anshuman, Stolp, Laura, Teufel, Ashley I. and Kleppe, April S.;
  • Item type:Article
    ID code:87331
    Dates:
    Date
    Event
    2 December 2021
    Published
    3 November 2021
    Accepted
    9 November 2020
    Submitted
    Notes:Lynch, M. The cellular, developmental and population-genetic determinants of mutation-rate evolution. Genetics 180, 933–943 (2008). Article PubMed PubMed Central Google Scholar Ohno, S. Evolution is condemned to rely upon variations of the same theme: the one ancestral sequence for genes and spacers. Perspect. Biol. Med. 25, 559–572 (1982). Article PubMed CAS Google Scholar Ohno, S., Wolf, U. & Atkin, N. B. Evolution from fish to mammals by gene duplication. Hereditas 59, 169–187 (1968). Article PubMed CAS Google Scholar Wolfe, K. H. & Shields, D. C. Molecular evidence for an ancient duplication of the entire yeast genome. Nature 387, 708–713 (1997). Article PubMed CAS Google Scholar Carvunis, A.-R. et al. Proto-genes and de novo gene birth. Nature 487, 370–374 (2012). Article PubMed PubMed Central CAS Google Scholar Reinhardt, J. A. et al. De novo ORFs in Drosophila are important to organismal fitness and evolved rapidly from previously non-coding sequences. PLoS Genet. 9, e1003860 (2013). Article PubMed PubMed Central Google Scholar Levy, A. How evolution builds genes from scratch. Nature 574, 314–316 (2019). Article PubMed CAS Google Scholar Van Oss, S. B. & Carvunis, A.-R. De novo gene birth. PLoS Genet. 15, e1008160 (2019). Begun, D. J., Lindfors, H. A., Kern, A. D. & Jones, C. D. Evidence for de Novo evolution of testis-expressed genes in the Drosophila yakuba/Drosophila erecta Clade. Genetics 176, 1131–1137 (2007). Article PubMed PubMed Central CAS Google Scholar Wilson, B. A., Foy, S. G., Neme, R. & Masel, J. Young genes are highly disordered as predicted by the preadaptation hypothesis of de novo gene birth. Nat. Ecol. Evol. 1, 0146 (2017). Article PubMed PubMed Central Google Scholar Klasberg, S., Bitard-Feildel, T., Callebaut, I. & Bornberg-Bauer, E. Origins and structural properties of novel and de novo protein domains during insect evolution. FEBS J. 285, 2605–2625 (2018). Article PubMed CAS Google Scholar Bornberg-Bauer, E., Schmitz, J. & Heberlein, M. Emergence of de novo proteins from ‘dark genomic matter’ by ‘grow slow and moult’. Biochemical Soc. Trans. 43, 867–873 (2015). Article CAS Google Scholar Toll-Riera, M. & Albà, M. M. Emergence of novel domains in proteins. BMC Evol. Biol. 13, 47 (2013). Article PubMed PubMed Central CAS Google Scholar Abrusán, G. Integration of new genes into cellular networks, and their structural maturation. Genetics 195, 1407–1417 (2013). Article PubMed PubMed Central Google Scholar Toll-Riera, M., Radó-Trilla, N., Martys, F. & Albà, M. M. Role of low-complexity sequences in the formation of novel protein coding sequences. Mol. Biol. Evol. 29, 883–886 (2012). Article PubMed CAS Google Scholar Huttener, R. et al. GC content of vertebrate exome landscapes reveal areas of accelerated protein evolution. BMC Evol. Biol. 19, 144 (2019). Article PubMed PubMed Central CAS Google Scholar Teufel, A. I., Ritchie, A. M., Wilke, C. O. & Liberles, D. A. Using the mutation-selection framework to characterize selection on protein sequences. Genes (Basel) 9, 409 (2018). Komar, A. A. The Yin and Yang of codon usage. Hum. Mol. Genet. 25, R77–R85 (2016). Article PubMed PubMed Central CAS Google Scholar de Oliveira, J. L. et al. Inferring adaptive codon preference to understand sources of selection shaping codon usage bias. Mol. Biol. Evol. 38, 3247–3266 (2021). Rancati, G., Moffat, J., Typas, A. & Pavelka, N. Emerging and evolving concepts in gene essentiality. Nat. Rev. Genet. 19, 34 (2018). Article PubMed CAS Google Scholar Cody, J. D. The consequences of abnormal gene dosage: lessons from chromosome 18. Trends Genet. 36, 764–776 (2020). Article PubMed CAS Google Scholar Teufel, A. I., Liu, L.-Z. & Liberles, D. A. Models for gene duplication when dosage balance works as a transition state to subsequent neo-or sub- functionalization. BMC Evol. Biol. 16, 1–8 (2016). Article Google Scholar Chen, S. et al. An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders. Nat. Genet. 50, 1032–40 (2018). Article PubMed PubMed Central CAS Google Scholar Fragoza, R. et al. Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations. Nat. Commun. 10, 4141 (2019). Article PubMed PubMed Central Google Scholar Ogbunugafor, C. B., Wylie, C. S., Diakite, I., Weinreich, D. M. & Hartl, D. L. Adaptive landscape by environment interactions dictate evolutionary dynamics in models of drug resistance. PLoS Comput. Biol. 12, e1004710 (2016). Article PubMed PubMed Central Google Scholar Weinreich, D. M., Delaney, N. F., DePristo, M. A. & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006). Article PubMed CAS Google Scholar Wagner, A. Robustness and evolvability: A paradox resolved. Proc. R. Soc. B: Biol. Sci. 275, 91–100 (2007). Article Google Scholar Liu, C. et al. Computational network biology: data, models, and applications. Phys. Rep. 846, 1–66 (2020). Article Google Scholar Kafri, R., Dahan, O., Levy, J. & Pilpel, Y. Preferential protection of protein interaction network hubs in yeast: Evolved functionality of genetic redundancy. Proc. Natl Acad. Sci. USA 105, 1243–1248 (2008). Article PubMed PubMed Central CAS Google Scholar Klein, B. & Hoel, E. The emergence of informative higher scales in complex networks. Complexity https://doi.org/10.1155/2020/8932526 (2020). Zitnik, M., Sosič, R., Feldman, M. W. & Leskovec, J. Evolution of resilience in protein interactomes across the tree of life. Proc. Natl Acad. Sci. USA 116, 4426–4433 (2019). Article PubMed PubMed Central CAS Google Scholar Drummond, D. A., Bloom, J. D., Adami, C., Wilke, C. O. & Arnold, F. H. Why highly expressed proteins evolve slowly. Proc. Natl Acad. Sci. USA 102, 14338–14343 (2005). Article PubMed PubMed Central CAS Google Scholar Drummond, D. A. & Wilke, C. O. Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134, 341–352 (2008). Article PubMed PubMed Central CAS Google Scholar Razban, R. M. Protein melting temperature cannot fully assess whether protein folding free energy underlies the universal abundance-evolutionary rate correlation seen in proteins. Mol. Biol. Evol. 36, 1955–1963 (2019). Article PubMed PubMed Central CAS Google Scholar Plata, G. & Vitkup, D. Protein stability and avoidance of toxic misfolding do not explain the sequence constraints of highly expressed proteins. Mol. Biol. Evol. 35, 700–703 (2017). Article Google Scholar Heo, M., Maslov, S. & Shakhnovich, E. Topology of protein interaction network shapes protein abundances and strengths of their functional and nonspecific interactions. Proc. Natl Acad. Sci. USA 108, 4258–4263 (2011). Article PubMed PubMed Central CAS Google Scholar Leskovec, J. & Krevl, A. SNAP datasets: Stanford large network dataset collection. http://snap.stanford.edu/tree-of-life/ (2014). Kanehisa, M., Sato, Y., Furumichi, M., Morishima, K. & Tanabe, M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 47, D590–D595 (2018). Article PubMed Central Google Scholar Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582 (2014). Article PubMed CAS Google Scholar Edfors, F. et al. Gene-specific correlation of RNA and protein levels in human cells and tissues. Mol. Syst. Biol. 12, 883 (2016). Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019). Article PubMed CAS Google Scholar Edgar, R., Domrachev, M. & Lash, A. E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002). Article PubMed PubMed Central CAS Google Scholar Spealman, P. et al. Conserved non-AUG uORFs revealed by a novel regression analysis of ribosome profiling data. Genome Res. 28, 214–222 (2018). Article PubMed PubMed Central CAS Google Scholar Petryszak, R. et al. Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res. 44, D746–D752 (2016). Article PubMed CAS Google Scholar Consortium, G. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015). Article Google Scholar Meysman, P. et al. COLOMBOS v2.0: an ever expanding collection of bacterial expression compendia. Nucleic Acids Res. 42, D649–D653 (2013). Article PubMed PubMed Central Google Scholar Klinge, S., Voigts-Hoffmann, F., Marc, L. & Ban, N. Atomic structures of the eukaryotic ribosome. Trends Biochem. Sci. 37, 189–198 (2012). Article PubMed CAS Google Scholar Melnikov, S. et al. One core, two shells: bacterial and eukaryotic ribosomes. Nat. Struct. Mol. Biol. 19, 560–567 (2012). Article PubMed CAS Google Scholar Wilson, D. N. & Doudna Cate, J. H. The structure and function of the eukaryotic ribosome. Cold Spring Harb. Perspect. Biol. 4, a011536 (2012). Article PubMed PubMed Central Google Scholar Peña, C., Hurt, E. & Panse, V. G. Eukaryotic ribosome assembly, transport and quality control. Nat. Struct. Mol. Biol. 24, 689–699 (2017). Article PubMed Google Scholar Newman, M. E. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 1–15 (2004). Google Scholar Jensen, R. A. Enzyme recruitment in evolution of new function. Annu. Rev. Microbiol. 30, 409–425 (1976). Article PubMed CAS Google Scholar Smith, K. Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space. Sci. Rep. 11, 1976 (2021). Eldar, A. & Elowitz, M. B. Functional roles for noise in genetic circuits. Nature 467, 167 (2010). Article PubMed PubMed Central CAS Google Scholar Eling, N., Morgan, M. D. & Marioni, J. C. Challenges in measuring and understanding biological noise. Nat. Rev. Genet. 20, 536–548 (2019). Article PubMed PubMed Central CAS Google Scholar Lynch, M. The frailty of adaptive hypotheses for the origins of organismal complexity. Proc. Natl Acad. Sci. USA 104 Suppl 1, 8597–8604 (2007). Article PubMed Google Scholar Barroso, G. V., Puzovic, N. & Dutheil, J. Y. The evolution of gene-specific transcriptional noise is driven by selection at the pathway level. Genetics 208, 173–189 (2018). Article PubMed CAS Google Scholar Ciliberti, S., Martin, O. C. & Wagner, A. Robustness can evolve gradually in complex regulatory gene networks with varying topology. PLoS Comput. Biol. 3, e15 (2007). Article PubMed PubMed Central Google Scholar Nam, H. et al. Network context and selection in the evolution to enzyme specificity. Science 337, 1101–1104 (2012). Article PubMed PubMed Central CAS Google Scholar Timsit, Y., Sergeant-Perthuis, G. & Bennequin, D. Evolution of ribosomal protein network architectures. Sci. Rep. 11, 625 (2021). Article PubMed PubMed Central CAS Google Scholar Muñoz-Gómez, S. A., Bilolikar, G., Wideman, J. G. & Geiler-Samerotte, K. Constructive neutral evolution 20 years later. J. Mol. Evol. 89, 172–182 (2021). Article PubMed PubMed Central Google Scholar Bornberg-Bauer, E. & Heames, B. Becoming a de novo gene. Nat. Ecol. Evol. 3, 524–525 (2019). Article PubMed Google Scholar Starr, T. N., Flynn, J. M., Mishra, P., Bolon, D. N. & Thornton, J. W. Pervasive contingency and entrenchment in a billion years of Hsp90 evolution. Proc. Natl Acad. Sci. USA 115, 4453–4458 (2018). Article PubMed PubMed Central CAS Google Scholar Pollock, D. D., Thiltgen, G. & Goldstein, R. A. Amino acid coevolution induces an evolutionary Stokes shift. Proc. Natl Acad. Sci. USA 109, E1352–E1359 (2012). Article PubMed PubMed Central CAS Google Scholar Consortium, T. U. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019). Article Google Scholar Fortunato, S. & Hric, D. Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016). Article Google Scholar Lancichinetti, A. & Fortunato, S. Community detection algorithms: a comparative analysis. Phys. Rev. E 80, 1–11 (2009). Article Google Scholar Girvan, M. & Newman, M. E. Community structure in social and biological networks. Proc. Natl Acad. Sci. SA 99, 7821–6 (2002). Article CAS Google Scholar Schaub, M. T., Delvenne, J.-C., Rosvall, M. & Lambiotte, R. The many facets of community detection in complex networks. Appl. Netw. Sci. 2, 4 (2017). Delvenne, J.-C., Yaliraki, S. N. & Barahona, M. Stability of graph communities across time scales. Proc. Natl Acad. Sci. USA 107, 12755–12760 (2010). Article PubMed PubMed Central CAS Google Scholar Klein, B. jkbren/presilience: presilience, Version v1.0. 2021. https://doi.org/10.5281/zenodo.5507368 (2021).
    Subjects:Science > Natural history > Biology
    Science > Natural history > Genetics
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:15 Nov 2023 13:15
    Last modified:11 Nov 2024 14:08
    URI:https://strathprints.strath.ac.uk/id/eprint/87331
CORE (COnnecting REpositories)