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The binary protein-protein interaction landscape of Escherichia coli

Abstract

Efforts to map the Escherichia coli interactome have identified several hundred macromolecular complexes, but direct binary protein-protein interactions (PPIs) have not been surveyed on a large scale. Here we performed yeast two-hybrid screens of 3,305 baits against 3,606 preys (70% of the E. coli proteome) in duplicate to generate a map of 2,234 interactions, which approximately doubles the number of known binary PPIs in E. coli. Integration of binary PPI and genetic-interaction data revealed functional dependencies among components involved in cellular processes, including envelope integrity, flagellum assembly and protein quality control. Many of the binary interactions that we could map in multiprotein complexes were informative regarding internal topology of complexes and indicated that interactions in complexes are substantially more conserved than those interactions connecting different complexes. This resource will be useful for inferring bacterial gene function and provides a draft reference of the basic physical wiring network of this evolutionarily important model microbe.

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Figure 1: Systematic identification of binary PPIs in E. coli.
Figure 2: Quality assessment and comparison of Y2H interactions with data from the literature.
Figure 3: Structural analysis of the E. coli binary interactome.
Figure 4: Comparison of the properties of binary and AP-MS interaction networks.
Figure 5: Integrative analyses on the PPI and GI data.
Figure 6: Conservation of physical interactions between and within protein complexes.

References

  1. Arifuzzaman, M. et al. Large-scale identification of protein-protein interaction of Escherichia coli K-12. Genome Res. 16, 686–691 (2006).

    Article  CAS  Google Scholar 

  2. Butland, G. et al. Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 433, 531–537 (2005).

    Article  CAS  Google Scholar 

  3. Hu, P. et al. Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins. PLoS Biol. 7, e96 (2009).

    Article  Google Scholar 

  4. Rual, J.F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173–1178 (2005).

    Article  CAS  Google Scholar 

  5. Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104–110 (2008).

    Article  CAS  Google Scholar 

  6. Titz, B. et al. The binary protein interactome of Treponema pallidum–the syphilis spirochete. PLoS ONE 3, e2292 (2008).

    Article  Google Scholar 

  7. Uetz, P. et al. Herpesviral protein networks and their interaction with the human proteome. Science 311, 239–242 (2006).

    Article  CAS  Google Scholar 

  8. Rajagopala, S.V. et al. The Escherichia coli K-12 ORFeome: a resource for comparative molecular microbiology. BMC Genomics 11, 470 (2010).

    Article  Google Scholar 

  9. Rajagopala, S.V. & Uetz, P. Analysis of protein-protein interactions using high-throughput yeast two-hybrid screens. Methods Mol. Biol. 781, 1–29 (2011).

    Article  CAS  Google Scholar 

  10. Goll, J. et al. MPIDB: the microbial protein interaction database. Bioinformatics 24, 1743–1744 (2008).

    Article  CAS  Google Scholar 

  11. Rajagopala, S.V. et al. MPI-LIT: a literature-curated dataset of microbial binary protein–protein interactions. Bioinformatics 24, 2622–2627 (2008).

    Article  CAS  Google Scholar 

  12. Braun, P. et al. An experimentally derived confidence score for binary protein-protein interactions. Nat. Methods 6, 91–97 (2009).

    Article  CAS  Google Scholar 

  13. Chen, Y.C., Rajagopala, S.V., Stellberger, T. & Uetz, P. Exhaustive benchmarking of the yeast two-hybrid system. Nat. Methods 7, 667–668 (2010).

    Article  CAS  Google Scholar 

  14. Vidalain, P.O., Boxem, M., Ge, H., Li, S. & Vidal, M. Increasing specificity in high-throughput yeast two-hybrid experiments. Methods 32, 363–370 (2004).

    Article  CAS  Google Scholar 

  15. Venkatesan, K. et al. An empirical framework for binary interactome mapping. Nat. Methods 6, 83–90 (2009).

    Article  CAS  Google Scholar 

  16. Barrios-Rodiles, M. et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science 307, 1621–1625 (2005).

    Article  CAS  Google Scholar 

  17. Rajagopala, S.V., Sikorski, P., Caufield, J.H., Tovchigrechko, A. & Uetz, P. Studying protein complexes by the yeast two-hybrid system. Methods 58, 392–399 (2012).

    Article  CAS  Google Scholar 

  18. Berman, H.M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  Google Scholar 

  19. Barabasi, A.L. Scale-free networks: a decade and beyond. Science 325, 412–413 (2009).

    Article  CAS  Google Scholar 

  20. Collins, S.R. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806–810 (2007).

    Article  CAS  Google Scholar 

  21. Wang, J., Du, Z., Payattakool, R., Yu, P.S. & Chen, C.J. A new method to measure the semantic similarity of GO terms. Bioinformatics 23, 1274–1281 (2007).

    Article  CAS  Google Scholar 

  22. Aloy, P. et al. Structure-based assembly of protein complexes in yeast. Science 303, 2026–2029 (2004).

    Article  CAS  Google Scholar 

  23. Lasker, K. et al. Integrative structure modeling of macromolecular assemblies from proteomics data. Mol. Cell. Proteomics 9, 1689–1702 (2010).

    Article  CAS  Google Scholar 

  24. Babu, M. et al. Genetic interaction maps in Escherichia coli reveal functional crosstalk among cell envelope biogenesis pathways. PLoS Genet. 7, e1002377 (2011).

    Article  CAS  Google Scholar 

  25. Bandyopadhyay, S., Kelley, R., Krogan, N.J. & Ideker, T. Functional maps of protein complexes from quantitative genetic interaction data. PLOS Comput. Biol. 4, e1000065 (2008).

    Article  Google Scholar 

  26. Beltrao, P., Cagney, G. & Krogan, N.J. Quantitative genetic interactions reveal biological modularity. Cell 141, 739–745 (2010).

    Article  CAS  Google Scholar 

  27. Boone, C., Bussey, H. & Andrews, B.J. Exploring genetic interactions and networks with yeast. Nat. Rev. Genet. 8, 437–449 (2007).

    Article  CAS  Google Scholar 

  28. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    Article  CAS  Google Scholar 

  29. Butland, G. et al. eSGA: E. coli synthetic genetic array analysis. Nat. Methods 5, 789–795 (2008).

    Article  CAS  Google Scholar 

  30. Brohee, S. & van Helden, J. Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7, 488 (2006).

    Article  Google Scholar 

  31. Babu, M. Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli. PLoS Genet. (in the press).

  32. Oliver, D.B. & Beckwith, J. E. coli mutant pleiotropically defective in the export of secreted proteins. Cell 25, 765–772 (1981).

    Article  CAS  Google Scholar 

  33. Rajagopala, S.V. et al. The protein network of bacterial motility. Mol. Syst. Biol. 3, 128 (2007).

    Article  Google Scholar 

  34. Bershtein, S., Mu, W., Serohijos, A.W., Zhou, J. & Shakhnovich, E.I. Protein quality control acts on folding intermediates to shape the effects of mutations on organismal fitness. Mol. Cell 49, 133–144 (2013).

    Article  CAS  Google Scholar 

  35. Dixon, S.J., Costanzo, M., Baryshnikova, A., Andrews, B. & Boone, C. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).

    Article  CAS  Google Scholar 

  36. Typas, A. et al. Regulation of peptidoglycan synthesis by outer-membrane proteins. Cell 143, 1097–1109 (2010).

    Article  CAS  Google Scholar 

  37. Matthews, L.R. et al. Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or “interologs”. Genome Res. 11, 2120–2126 (2001).

    Article  CAS  Google Scholar 

  38. Sharan, R. et al. Conserved patterns of protein interaction in multiple species. Proc. Natl. Acad. Sci. USA 102, 1974–1979 (2005).

    Article  CAS  Google Scholar 

  39. Singleton, M.R., Dillingham, M.S., Gaudier, M., Kowalczykowski, S.C. & Wigley, D.B. Crystal structure of RecBCD enzyme reveals a machine for processing DNA breaks. Nature 432, 187–193 (2004).

    Article  CAS  Google Scholar 

  40. Cingolani, G. & Duncan, T.M. Structure of the ATP synthase catalytic complex (F(1)) from Escherichia coli in an autoinhibited conformation. Nat. Struct. Mol. Biol. 18, 701–707 (2011).

    Article  CAS  Google Scholar 

  41. Hallez, R., Letesson, J.J., Vandenhaute, J. & De Bolle, X. Gateway-based destination vectors for functional analyses of bacterial ORFeomes: application to the Min system in Brucella abortus. Appl. Environ. Microbiol. 73, 1375–1379 (2007).

    Article  CAS  Google Scholar 

  42. Gavin, A.C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    Article  CAS  Google Scholar 

  43. Faith, J.J. et al. Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata. Nucleic Acids Res. 36, D866–D870 (2008).

    Article  CAS  Google Scholar 

  44. Nichols, R.J. et al. Phenotypic landscape of a bacterial cell. Cell 144, 143–156 (2011).

    Article  CAS  Google Scholar 

  45. Hagberg, A.A. et al. Exploring network structure, dynamics, and function using NetworkX. Proceedings of the 7th Python in Science Conference 11–15 (2008).

  46. Xu, Z. & Hao, B. CVTree update: a newly designed phylogenetic study platform using composition vectors and whole genomes. Nucleic Acids Res. 37, W174–W178 (2009).

    Article  CAS  Google Scholar 

  47. Alix, B., Boubacar, D.A. & Vladimir, M.T.-R.E.X. a web server for inferring, validating and visualizing phylogenetic trees and networks. Nucleic Acids Res. 40, W573–W579 (2012).

    Article  CAS  Google Scholar 

  48. Remm, M., Storm, C.E. & Sonnhammer, E.L. Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. J. Mol. Biol. 314, 1041–1052 (2001).

    Article  CAS  Google Scholar 

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Acknowledgements

S.V.R. and P.U. were supported by US National Institutes of Health grant GM079710. P.U., R.H. and P.A. were supported by the Seventh Research Framework Programme of the European Union (AntiPathoGN; EU grant HEALTH-F3-2009-223101). M.B. was supported by the Discovery Grant from the Natural Sciences and Engineering Research Council (DG-20234). We thank X. De Bolle (University of Namur, Belgium) for providing pRH016 and pRH018 vectors.

Author information

Authors and Affiliations

Authors

Contributions

The project was conceived by S.V.R. and P.U. and directed by S.V.R. and R.P.; P.S., J.F.-K., R.H., G.S. and S.V.R. performed the experiments; S.V.R., A.K., R.M., S.B.P., J.V., R.A., S.P., A.C., S.W., P.A. and M.B. performed computational analysis; and A.E. provided support for computational analysis tools. S.V.R., P.A. and M.B. wrote the manuscript, with input from P.U.

Corresponding authors

Correspondence to Seesandra V Rajagopala or Mohan Babu.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results, Supplementary Figures 1–9, Supplementary Tables 3–4 and 8, Supplementary Note (PDF 14094 kb)

Supplementary Table 1

Literature curated E. coli binary PPIs supported by multiple publications or methods. (XLS 158 kb)

Supplementary Table 2

Escherichia coli protein-protein interactions indentified by Y2H screening. (XLS 248 kb)

Supplementary Table 5

Escherichia coli combined binary PPIs (obtained by combining Y2H data from this study with the manually curated literature binary interactions) (XLS 443 kb)

Supplementary Table 6

Literature binary interactions used for the mapping of the internal topology of complexes of 3+ components from (Hu et al.) (XLS 228 kb)

Supplementary Table 7

List of Y2H PPIs and their corresponding GI scores (XLS 49 kb)

Supplementary Table 9

Predicted protein complexes from Hu et al. (XLS 62 kb)

Supplementary Table 10

EcoCyc protein complexes (XLS 78 kb)

Supplementary Table 11

Ecoli interactions observed in experimental structures from the Protein Data Bank (XLS 1994 kb)

Supplementary Table 12

E. coli interactions from AP/MS studies with Socio-affinity score higher than 10 (XLS 34 kb)

Supplementary Table 13

Conserved PPIs experimentally characterized in other bacteria (interologs) (XLS 211 kb)

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Rajagopala, S., Sikorski, P., Kumar, A. et al. The binary protein-protein interaction landscape of Escherichia coli. Nat Biotechnol 32, 285–290 (2014). https://doi.org/10.1038/nbt.2831

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