Genome analysis should allow the discovery of interdependent loci that together cause antibiotic resistance. In practice, however, the vast number of possible epistatic interactions erodes statistical power. Here, we extend an approach that has been successfully used to identify epistatic residues in proteins to infer genomic loci that are strongly coupled. This approach reduces the number of tests required for an epistatic genome-wide association study of antibiotic resistance and increases the likelihood of identifying causal epistasis. We discovered 38 loci and 240 epistatic pairs that influence the minimum inhibitory concentrations of 5 different antibiotics in 1,102 isolates of Neisseria gonorrhoeae that were confirmed in a second dataset of 495 isolates. Many known resistance-affecting loci were recovered; however, the majority of associations occurred in unreported genes, such as murE. About half of the discovered epistasis involved at least one locus previously associated with antibiotic resistance, including interactions between gyrA and parC. Still, many combinations involved unreported loci and genes. While most variation in minimum inhibitory concentrations could be explained by identified loci, epistasis substantially increased explained phenotypic variance. Our work provides a systematic identification of epistasis affecting antibiotic resistance in N. gonorrhoeae and a generalizable approach for epistatic genome-wide association studies.
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Measured MICs and NCBI SRA identifiers for the raw sequencing data can be found in Supplementary Table 1.
Palmer, A. C. & Kishony, R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat. Rev. Genet. 14, 243–248 (2013).
de Sousa, J. M., Balbontín, R., Durão, P. & Gordo, I. Multidrug-resistant bacteria compensate for the epistasis between resistances. PLoS Biol. 15, e2001741 (2017).
Unemo, M. & Shafer, W. M. Antimicrobial resistance in Neisseria gonorrhoeae in the 21st century: past, evolution, and future. Clin. Microbiol. Rev. 27, 587–613 (2014).
Cui, Y. et al. Epidemic clones, oceanic gene pools, and eco-LD in the free living marine pathogen Vibrio parahaemolyticus. Mol. Biol. Evol. 32, 1396–1410 (2015).
Coll, F. et al. Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis. Nat. Genet. 50, 307–316 (2018).
Emily, M., Mailund, T., Hein, J., Schauser, L. & Schierup, M. H. Using biological networks to search for interacting loci in genome-wide association studies. Eur. J. Hum. Genet. 17, 1231–1240 (2009).
Marks, D. S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6, e28766 (2011).
Lapedes, A. S., Giraud, B., Liu, L. & Stormo, G. D. Correlated mutations in models of protein sequences: phylogenetic and structural effects. Lect. Notes Monogr. Ser. 33, 236–256 (1999).
Qin, C. & Colwell, L. J. Power law tails in phylogenetic systems. Proc. Natl Acad. Sci. USA 115, 690–695 (2018).
Cocco, S., Monasson, R. & Weigt, M. From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction. PLoS Comput. Biol. 9, e1003176 (2013).
Weinreb, C. et al. 3D RNA and functional interactions from evolutionary couplings. Cell 165, 963–975 (2016).
Hopf, T. A. et al. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 3, e03430 (2014).
Hopft, T. A. et al. Mutation effects predicted from sequence co-variation. Nat. Biotechnol. 35, 128–135 (2017).
Skwark, M. J. et al. Interacting networks of resistance, virulence and core machinery genes identified by genome-wide epistasis analysis. PLoS Genet. 13, e1006508 (2017).
Puranen, S. SuperDCA for genome-wide epistasis analysis. Microb. Genom. 4, e000184 (2018).
Grad, Y. H. et al. Genomic epidemiology of gonococcal resistance to extended-spectrum cephalosporins, macrolides, and fluoroquinolones in the United States, 2000–2013. J. Infect. Dis. 214, 1579–1587 (2016).
Demczuk, W. et al. Whole-genome phylogenomic heterogeneity of Neisseria gonorrhoeae isolates with decreased cephalosporin susceptibility collected in Canada between 1989 and 2013. J. Clin. Microbiol. 53, 191–200 (2015).
De Silva, D. et al. Whole-genome sequencing to determine transmission of Neisseria gonorrhoeae: an observational study. Lancet Infect. Dis. 16, 1295–1303 (2016).
Breakpoint Tables for Interpretation of MICs and Zone Diameters, Version 7.1 (European Committee on Antimicrobial Susceptibility Testing, 2017); http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_7.1_Breakpoint_Tables.pdf
Remmele, C. W. et al. Transcriptional landscape and essential genes of Neisseria gonorrhoeae. Nucleic Acids Res. 42, 10579–10595 (2014).
Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M. & Aurell, E. Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. Phys. Rev. E 87, 012707 (2013).
Harrison, O. B. et al. Genomic analyses of Neisseria gonorrhoeae reveal an association of the gonococcal genetic island with antimicrobial resistance. J. Infect. 73, 578–587 (2016).
Griffiss, J. M., Lammel, C. J., Wang, J., Dekker, N. P. & Brooks, G. Neisseria gonorrhoeae coordinately uses Pili and Opa to activate HEC-1-B cell microvilli, which causes engulfment of the gonococci. Infect. Immun. 67, 3469–3480 (1999).
Ronpirin, C., Jerse, A. E. & Cornelissen, C. N. Gonococcal genes encoding transferrin-binding proteins A and B are arranged in a bicistronic operon but are subject to differential expression. Infect. Immun. 69, 6336–6347 (2001).
Krell, T. et al. Insight into the structure and function of the transferrin receptor from Neisseria meningitidis using microcalorimetric techniques. J. Biol. Chem. 278, 14712–14722 (2003).
Tønjum, T. & Koomey, M. The pilus colonization factor of pathogenic neisserial species: organelle biogenesis and structure/function relationships—a review. Gene 192, 155–163 (1997).
Heckels, J. E. Structure and function of pili of pathogenic Neisseria species. Clin. Microbiol. Rev. 2, S66–S73 (1989).
Szklarczyk, D. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362–D368 (2017).
Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).
Sul, J. H. et al. Accounting for population structure in gene-by-environment interactions in genome-wide association studies using mixed models. PLoS Genet. 12, e1005849 (2016).
Tonkin-Hill, G., Lees, J. A., Bentley, S. D., Frost, S. D. W. & Corander, J. RhierBAPS: an R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res. 3, 93 (2018).
Cheng, L., Connor, T. R., Sirén, J., Aanensen, D. M. & Corander, J. Hierarchical and spatially explicit clustering of DNA sequences with BAPS software. Mol. Biol. Evol. 30, 1224–1228 (2013).
Seib, K. L. et al. Defenses against oxidative stress in Neisseria gonorrhoeae: a system tailored for a challenging environment. Microbiol. Mol. Biol. Rev. 70, 344–361 (2006).
Kohanski, M. A., Dwyer, D. J. & Collins, J. J. How antibiotics kill bacteria: from targets to networks. Nat. Rev. Microbiol. 8, 423–435 (2010).
Unemo, M. & Nicholas, R. A. Emergence of multidrug-resistant, extensively drug-resistant and untreatable gonorrhea. Future Microbiol. 7, 1401–1422 (2012).
Todorova, K. et al. Transfer of penicillin resistance from Streptococcus oralis to Streptococcus pneumoniae identifies murE as resistance determinant. Mol. Microbiol. 97, 866–880 (2015).
Redgrave, L. S., Sutton, S. B., Webber, M. A. & Piddock, L. J. Fluoroquinolone resistance: mechanisms, impact on bacteria, and role in evolutionary success. Trends Microbiol. 22, 438–445 (2014).
Rozen, D. E., McGee, L., Levin, B. R. & Klugman, K. P. Fitness costs of fluoroquinolone resistance in Streptococcus pneumoniae. Antimicrob. Agents Chemother. 51, 412–416 (2007).
Duckworth, B. P. et al. Bisubstrate adenylation inhibitors of biotin protein ligase from Mycobacterium tuberculosis. Chem. Biol. 18, 1432–1441 (2011).
Correia, S. et al. Comparative subproteomic analysis of clinically acquired fluoroquinolone resistance and ciprofloxacin stress in Salmonella Typhimurium DT104B. Proteomics Clin. Appl. 11, 1600107 (2017).
Ubukata, K. et al. Association of amino acid substitutions in penicillin-binding protein 3 with β-lactam resistance in β-lactamase-negative ampicillin-resistant Haemophilus influenzae. Antimicrob. Agents Chemother. 45, 1693–1699 (2001).
Morikawa, Y. et al. In vitro activities of piperacillin against β-lactamase-negative ampicillin-resistant Haemophilus influenzae. Antimicrob. Agents Chemother. 48, 1229–1234 (2004).
Vaara, M. Outer membrane permeability barrier to azithromycin, clarithromycin, and roxithromycin in gram-negative enteric bacteria. Antimicrob. Agents Chemother. 37, 354–356 (1993).
Delcour, A. H. Outer membrane permeability and antibiotic resistance. Biochim. Biophys. Acta 1794, 808–816 (2009).
Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).
Barber, M. F. & Elde, N. C. Escape from bacterial iron piracy through rapid evolution of transferrin. Science 346, 1362–1366 (2014).
Bradley, P. et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat. Commun. 6, 10063 (2015).
Deatherage, D. E. & Barrick, J. E. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol. Biol. 1151, 165–188 (2014).
Johns, N. I. et al. Metagenomic mining of regulatory elements enables programmable species-selective gene expression. Nat. Methods 15, 323–329 (2018).
Toth-Petroczy, A. et al. Structured states of disordered proteins from genomic sequences. Cell 167, 158–170 (2016).
Dunn, S. D., Wahl, L. M. & Gloor, G. B. Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics 24, 333–340 (2008).
Visscher, P. M., Hill, W. G. & Wray, N. R. Heritability in the genomics era—concepts and misconceptions. Nat. Rev. Genet. 9, 255–266 (2008).
Croucher, N. J. et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res. 43, e15 (2015).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
Yang, Z Computational Molecular Evolution (Oxford Univ. Press, Oxford, 2006).
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).
Letunic, I. & Bork, P. Interactive tree of life (iTOL)v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).
Faure, M. et al. Interaction between the lipoamide-containing H-protein and the lipoamide dehydrogenase (L-protein) of the glycine decarboxylase multienzyme system 2. Crystal structures of H- and L-proteins. Eur. J. Biochem. 267, 2890–2898 (2000).
Gordon, E. et al. Crystal structure of UDP-N-acetylmuramoyl-L-alanyl-D-glutamate: meso-diaminopimelate ligase from Escherichia coli. J. Biol. Chem. 276, 10999–11006 (2001).
We thank members of the Marks laboratory and C. Sander for his support during this research project.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–4.
Minimum inhibitory concentration of penicillin (PEN), tetracycline (TET), cefixime (CFX), and ciprofloxacin (CIPRO) measured in the exploratory (n = 1,102) and confirmatory dataset (n = 495) and their NCBI SRA identifiers.
Significant evolutionarily coupled loci of exploratory dataset (n = 1,102).
Evolutionarily coupled gene–gene interactions of the exploratory dataset (n = 1,102).
Entire single-locus GWAS analysis of the exploratory and confirmatory dataset.
Confirmed and Bonferroni-corrected epistatic associations.
Entire epistatic GWAS analysis of the exploratory and confirmatory datasets.
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Schubert, B., Maddamsetti, R., Nyman, J. et al. Genome-wide discovery of epistatic loci affecting antibiotic resistance in Neisseria gonorrhoeae using evolutionary couplings. Nat Microbiol 4, 328–338 (2019). https://doi.org/10.1038/s41564-018-0309-1
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