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Sequencing-based methods and resources to study antimicrobial resistance

Abstract

Antimicrobial resistance extracts high morbidity, mortality and economic costs yearly by rendering bacteria immune to antibiotics. Identifying and understanding antimicrobial resistance are imperative for clinical practice to treat resistant infections and for public health efforts to limit the spread of resistance. Technologies such as next-generation sequencing are expanding our abilities to detect and study antimicrobial resistance. This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods. We focus on sequencing-based resistance discovery and discuss tools and databases used in antimicrobial resistance studies.

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Fig. 1: Antimicrobial targets and resistance mechanisms.
Fig. 2: Assembly versus read mapping.
Fig. 3: Functional metagenomics to interrogate acquired resistance genes in different environments and human pathogens.

References

  1. 1.

    Cosgrove, S. E. & Carmeli, Y. The impact of antimicrobial resistance on health and economic outcomes. Clin. Infect. Dis. 36, 1433–1437 (2003).

    Article  PubMed  Google Scholar 

  2. 2.

    Hawkey, P. M. The growing burden of antimicrobial resistance. J. Antimicrob. Chemother. 62 (Suppl. 1), i1–i9 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. 3.

    Acar, J. F. Consequences of bacterial resistance to antibiotics in medical practice. Clin. Infect. Dis. 24 (Suppl. 1), 17–18 (1997).

    Article  Google Scholar 

  4. 4.

    Cosgrove, S. E. The relationship between antimicrobial resistance and patient outcomes: mortality, length of hospital stay, and health care costs. Clin. Infect. Dis. 42 (Suppl. 2), 82–89 (2006).

    Article  Google Scholar 

  5. 5.

    Tillotson, G. S. & Zinner, S. H. Burden of antimicrobial resistance in an era of decreasing susceptibility. Expert Rev. Anti. Infect. Ther. 15, 663–676 (2017).

    Article  CAS  PubMed  Google Scholar 

  6. 6.

    Poirel, L. & Nordmann, P. Carbapenem resistance in Acinetobacter baumannii: mechanisms and epidemiology. Clin. Microbiol. Infect. 12, 826–836 (2006).

    Article  CAS  PubMed  Google Scholar 

  7. 7.

    Johnson, A. P. & Woodford, N. Global spread of antibiotic resistance: the example of New Delhi metallo-beta-lactamase (NDM)-mediated carbapenem resistance. J. Med. Microbiol. 62, 499–513 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. 8.

    Gupta, N., Limbago, B. M., Patel, J. B. & Kallen, A. J. Carbapenem-resistant enterobacteriaceae: epidemiology and prevention. Clin. Infect. Dis. 53, 60–67 (2011).

    Article  PubMed  Google Scholar 

  9. 9.

    Liu, Y. Y. et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological and molecular biological study. Lancet Infect. Dis. 16, 161–168 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. 10.

    Ventola, C. L. The antibiotic resistance crisis: part 1: causes and threats. P T 40, 277–283 (2015).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Centers for Disease Control and Prevention. Antibiotic/antimicrobial resistance (AR/AMR): biggest threats and data. CDC http://www.cdc.gov/drugresistance/threat-report-2013/ (updated 26 Nov 2018).

  12. 12.

    Smith, R. & Coast, J. The true cost of antimicrobial resistance. BMJ 346, f1493 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    O’Neill, J. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations (Review on Antimicrobial Resistance, 2016).

  14. 14.

    Cassini, A. et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. Lancet. Infect. Dis. 19, 56–66 (2019). This paper demonstrates the impact of antimicrobial resistance on the health-care system and identifies major priorities for future mitigation efforts.

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    World Health Organization. Global Action Plan on Antimicrobial Resistance 2015 (Report No. 9789241509763) (WHO, 2015).

  16. 16.

    Tacconelli, E. et al. Surveillance for control of antimicrobial resistance. Lancet. Infect. Dis. 18, e99–e106 (2018).

    Article  PubMed  Google Scholar 

  17. 17.

    Wernli, D. et al. Mapping global policy discourse on antimicrobial resistance. BMJ Global Health 2, e000378 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Didelot, X., Bowden, R., Wilson, D. J., Peto, T. E. & Crook, D. W. Transforming clinical microbiology with bacterial genome sequencing. Nat. Rev. Genet. 13, 601–612 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    D’Costa, V. M., McGrann, K. M., Hughes, D. W. & Wright, G. D. Sampling the antibiotic resistome. Science 311, 374–377 (2006). This article shows that soil bacteria are a reservoir for resistance determinants.

    Article  PubMed  Google Scholar 

  20. 20.

    Wang, R. et al. The global distribution and spread of the mobilized colistin resistance gene mcr-1. Nat. Commun. 9, 1179 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Nordmann, P., Naas, T. & Poirel, L. Global spread of carbapenemase-producing Enterobacteriaceae. Emerg. Infect. Dis. 17, 1791–1798 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Canton, R. et al. Rapid evolution and spread of carbapenemases among Enterobacteriaceae in Europe. Clin. Microbiol. Infect. 18, 413–431 (2012).

    Article  CAS  PubMed  Google Scholar 

  23. 23.

    Potter, R. F., D’Souza, A. W. & Dantas, G. The rapid spread of carbapenem-resistant Enterobacteriaceae. Drug Resist. Updat. 29, 30–46 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Pesesky, M. W. et al. KPC and NDM-1 genes in related Enterobacteriaceae strains and plasmids from Pakistan and the United States. Emerg. Infect. Dis. 21, 1034–1037 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Pehrsson, E. C. et al. Interconnected microbiomes and resistomes in low-income human habitats. Nature 533, 212–216 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Gibson, M. K., Forsberg, K. J. & Dantas, G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 9, 207–216 (2015). This paper describes the creation of a profile HMM-based resistance database and presents an application of this database showing that environmental-based and human-based samples have different resistance profiles.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Zerbino, D. R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Simpson, J. T. et al. ABySS: a parallel assembler for short read sequence data. Genome Res. 19, 1117–1123 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Compeau, P. E., Pevzner, P. A. & Tesler, G. How to apply de Bruijn graphs to genome assembly. Nat. Biotechnol. 29, 987–991 (2011). This short paper explains how DBGs are used in genome assembly.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Ghurye, J. S., Cepeda-Espinoza, V. & Pop, M. Metagenomic assembly: overview, challenges and applications. Yale J. Biol. Med. 89, 353–362 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Miller, J. R., Koren, S. & Sutton, G. Assembly algorithms for next-generation sequencing data. Genomics 95, 315–327 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Antipov, D. et al. plasmidSPAdes: assembling plasmids from whole genome sequencing data. Bioinformatics 32, 3380–3387 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. 35.

    Rozov, R. et al. Recycler: an algorithm for detecting plasmids from de novo assembly graphs. Bioinformatics 33, 475–482 (2017).

    CAS  PubMed  Google Scholar 

  36. 36.

    Roosaare, M., Puustusmaa, M., Mols, M., Vaher, M. & Remm, M. PlasmidSeeker: identification of known plasmids from bacterial whole genome sequencing reads. PeerJ 6, e4588 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Lanza, V. F. et al. Plasmid flux in Escherichia coli ST131 sublineages, analyzed by plasmid constellation network (PLACNET), a new method for plasmid reconstruction from whole genome sequences. PLOS Genet. 10, e1004766 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Peng, Y., Leung, H. C., Yiu, S. M. & Chin, F. Y. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. 40.

    Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Namiki, T., Hachiya, T., Tanaka, H. & Sakakibara, Y. MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res. 40, e155 (2012). Peng et al. (2012), Li et al. (2015), Nurk et al. (2017) and Namiki et al. (2012) are method papers of metagenomic assemblers developed to assemble complex metagenomics data sets with uneven sequencing depths.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Sczyrba, A. et al. Critical assessment of metagenome interpretation-a benchmark of metagenomics software. Nat. Methods 14, 1063–1071 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Bremges, A. & McHardy, A. C. Critical assessment of metagenome interpretation enters the second round. mSystems 3, e00103-18 (2018). Together with Sczyrba et al., this paper describes the CAMI project designed to evaluate the differences between different metagenomics tools for metagenomic assembly, taxonomic classification and assembled contig binning.

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  Article  Google Scholar 

  46. 46.

    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Article  CAS  PubMed  Google Scholar 

  47. 47.

    Werner, J. J. et al. Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys. ISME J. 6, 94–103 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. 48.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Inouye, M. et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 6, 90 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Clausen, P. T., Zankari, E., Aarestrup, F. M. & Lund, O. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. J. Antimicrob. Chemother. 71, 2484–2488 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. 52.

    Hunt, M. et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb. Genom. 3, e000131 (2017).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Rowe, W. et al. Search engine for antimicrobial resistance: a cloud compatible pipeline and web interface for rapidly detecting antimicrobial resistance genes directly from sequence data. PLOS ONE 10, e0133492 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Munk, P. et al. A sampling and metagenomic sequencing-based methodology for monitoring antimicrobial resistance in swine herds. J. Antimicrob. Chemother. 72, 385–392 (2017).

    Article  CAS  PubMed  Google Scholar 

  56. 56.

    Rowe, W. P. M. & Winn, M. D. Indexed variation graphs for efficient and accurate resistome profiling. Bioinformatics 34, 3601–3608 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  58. 58.

    Henson, J., Tischler, G. & Ning, Z. Next-generation sequencing and large genome assemblies. Pharmacogenomics 13, 901–915 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Carr, R. & Borenstein, E. Comparative analysis of functional metagenomic annotation and the mappability of short reads. PLOS ONE 9, e105776 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J. & Segata, N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844 (2017). This detailed review discusses the best strategies used in shotgun metagenomics studies.

    Article  CAS  PubMed  Google Scholar 

  61. 61.

    Kaminski, J. et al. High-specificity targeted functional profiling in microbial communities with ShortBRED. PLOS Comput. Biol. 11, e1004557 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Gibson, M. K. et al. Developmental dynamics of the preterm infant gut microbiota and antibiotic resistome. Nat. Microbiol. 1, 16024 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Tsukayama, P. et al. Characterization of wild and captive baboon gut microbiota and their antibiotic resistomes. mSystems 3, e00016-18 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Hsu, T. et al. Urban transit system microbial communities differ by surface type and interaction with humans and the environment. mSystems 1, e00018-16 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Liu, B. & Pop, M. ARDB—antibiotic resistance genes database. Nucleic Acids Res 37, D443–D447 (2009). ARDB was one of the first general antimicrobial resistance gene databases, and this paper spawned several other efforts to compile resistance gene information across drug classes and bacterial species.

    Article  CAS  PubMed  Google Scholar 

  66. 66.

    Gupta, S. K. et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob. Agents Chemother. 58, 212–220 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Jia, B. et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45, D566–D573 (2017). This paper describes recent updates to the CARD and tools that are associated with the database.

    Article  CAS  PubMed  Google Scholar 

  68. 68.

    Thai, Q. K. & Pleiss, J. SHV lactamase engineering database: a reconciliation tool for SHV beta-lactamases in public databases. BMC Genomics 11, 563 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Thai, Q. K., Bos, F. & Pleiss, J. The lactamase engineering database: a critical survey of TEM sequences in public databases. BMC Genomics 10, 390 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Bush, K. & Jacoby, G. A. Updated functional classification of β-lactamases. Antimicrob. Agents Chemother. 54, 969–976 (2010).

    Article  CAS  PubMed  Google Scholar 

  71. 71.

    Srivastava, A., Singhal, N., Goel, M., Virdi, J. S. & Kumar, M. CBMAR: a comprehensive beta-lactamase molecular annotation resource. Database (Oxford) 2014, bau111 (2014).

    Article  CAS  Google Scholar 

  72. 72.

    Zankari, E. et al. Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 67, 2640–2644 (2012). This article describes Resfinder, a widely used tool for the identification of acquired antimicrobial resistance genes in whole-genome data.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Zankari, E. et al. PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. J. Antimicrob. Chemother. 72, 2764–2768 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Yin, X. et al. ARGs-OAP v2.0 with an expanded SARG database and hidden Markov models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes. Bioinformatics 34, 2263–2270 (2018).

    Article  CAS  PubMed  Google Scholar 

  75. 75.

    Sandgren, A. et al. Tuberculosis drug resistance mutation database. PLOS Med. 6, e2 (2009).

    Article  CAS  PubMed  Google Scholar 

  76. 76.

    Flandrois, J. P., Lina, G. & Dumitrescu, O. MUBII-TB-DB: a database of mutations associated with antibiotic resistance in Mycobacterium tuberculosis. BMC Bioinformatics 15, 107 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Cox, G. & Wright, G. D. Intrinsic antibiotic resistance: mechanisms, origins, challenges and solutions. Int. J. Med. Microbiol. 303, 287–292 (2013).

    Article  CAS  PubMed  Google Scholar 

  78. 78.

    Gygli, S. M., Borrell, S., Trauner, A. & Gagneux, S. Antimicrobial resistance in Mycobacterium tuberculosis: mechanistic and evolutionary perspectives. FEMS Microbiol. Rev. 41, 354–373 (2017).

    Article  CAS  PubMed  Google Scholar 

  79. 79.

    Allix-Beguec, C. et al. Prediction of susceptibility to first-line tuberculosis drugs by DNA sequencing. N. Engl. J. Med. 379, 1403–1415 (2018). This paper shows the effectiveness of a sequencing approach to phenotypic antimicrobial resistance predictions in M. tuberculosis.

    Article  CAS  PubMed  Google Scholar 

  80. 80.

    McArthur, A. G. & Tsang, K. K. Antimicrobial resistance surveillance in the genomic age. Ann. NY Acad. Sci. 1388, 78–91 (2017).

    Article  PubMed  Google Scholar 

  81. 81.

    Yelin, I. & Kishony, R. Antibiotic resistance. Cell 172, 1136–1136 (2018).

    Article  CAS  PubMed  Google Scholar 

  82. 82.

    Eddy, S. R. Profile hidden Markov models. Bioinformatics 14, 755–763 (1998).

    Article  CAS  PubMed  Google Scholar 

  83. 83.

    Wallace, J. C., Port, J. A., Smith, M. N. & Faustman, E. M. FARME DB: a functional antibiotic resistance element database. Database (Oxford) 2017, baw165 (2017). This paper compiles putative resistance determinants from functional antimicrobial selections in public databases to identify resistance determinants that are not well represented in databases built primarily from clinical bacterial isolates.

    Article  CAS  Google Scholar 

  84. 84.

    Munk, P. et al. Abundance and diversity of the faecal resistome in slaughter pigs and broilers in nine European countries. Nat. Microbiol. 3, 898–908 (2018). This article presents a new technique for identifying antimicrobial resistance determinants by including 3D information.

    Article  CAS  PubMed  Google Scholar 

  85. 85.

    Ruppe, E. et al. Prediction of the intestinal resistome by a three-dimensional structure-based method. Nat. Microbiol. 4, 112–123 (2019).

    Article  CAS  PubMed  Google Scholar 

  86. 86.

    Xavier, B. B. et al. Consolidating and exploring antibiotic resistance gene data resources. J. Clin. Microbiol. 54, 851–859 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Hall, R. M. & Schwarz, S. Resistance gene naming and numbering: is it a new gene or not? J. Antimicrob. Chemother. 71, 569–571 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Carnevali, C. et al. Occurrence of mcr-1 in colistin-resistant salmonella enterica isolates recovered from humans and animals in Italy, 2012 to 2015. Antimicrob. Agents Chemother. 60, 7532–7534 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Ortega-Paredes, D., Barba, P. & Zurita, J. Colistin-resistant Escherichia coli clinical isolate harbouring the mcr-1 gene in Ecuador. Epidemiol. Infect. 144, 2967–2970 (2016).

    Article  CAS  PubMed  Google Scholar 

  90. 90.

    Teo, J. Q. et al. mcr-1 in multidrug-resistant blaKPC-2-producing clinical enterobacteriaceae isolates in Singapore. Antimicrob. Agents Chemother. 60, 6435–6437 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Fernandes, M. R. et al. First report of the globally disseminated IncX4 plasmid carrying the mcr-1 gene in a colistin-resistant Escherichia coli sequence type 101 isolate from a human infection in Brazil. Antimicrob. Agents Chemother. 60, 6415–6417 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Delgado-Blas, J. F., Ovejero, C. M., Abadia-Patino, L. & Gonzalez-Zorn, B. Coexistence of mcr-1 and blaNDM-1 in Escherichia coli from Venezuela. Antimicrob. Agents Chemother. 60, 6356–6358 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Kline, K. E. et al. Investigation of first identified mcr-1 gene in an isolate from a U. S. patient - Pennsylvania, 2016. MMWR Morb. Mortal. Wkly. Rep. 65, 977–978 (2016).

    Article  PubMed  Google Scholar 

  94. 94.

    Wong, S. C. et al. Colistin-resistant enterobacteriaceae carrying the mcr-1 gene among patients in Hong Kong. Emerg. Infect. Dis. 22, 1667–1669 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Brauer, A. et al. Plasmid with colistin resistance gene mcr-1 in extended-spectrum-beta-lactamase-producing Escherichia coli strains isolated from pig slurry in Estonia. Antimicrob. Agents Chemother. 60, 6933–6936 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    von Wintersdorff, C. J. et al. Detection of the plasmid-mediated colistin-resistance gene mcr-1 in faecal metagenomes of Dutch travellers. J. Antimicrob. Chemother. 71, 3416–3419 (2016).

    Article  CAS  Google Scholar 

  97. 97.

    Crofts, T. S., Gasparrini, A. J. & Dantas, G. Next-generation approaches to understand and combat the antibiotic resistome. Nat. Rev. Microbiol. 15, 422–434 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Riesenfeld, C. S. et al. Uncultured soil bacteria are a reservoir of new antibiotic resistance genes. Environ. Microbiol. 6, 981–989 (2015). This is one of the initial studies to demonstrate the application of functional metagenomic selections for discovering novel antibiotic resistance genes.

    Article  CAS  Google Scholar 

  99. 99.

    Pehrsson, E. C., Forsberg, K. J., Gibson, M. K., Ahmadi, S. & Dantas, G. Novel resistance functions uncovered using functional metagenomic investigations of resistance reservoirs. Front. Microbiol 4, 145 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012). This paper applies a functional metagenomics approach and assembly pipeline to show evidence of resistance gene exchange between human pathogens and soil bacteria.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    de la Bastide, M. & McCombie, W. R. Assembling genomic DNA sequences with PHRAP. Curr. Protoc. Bioinformatics 17, 11.4.1–11.4.15 (2007).

    Google Scholar 

  102. 102.

    Zhu, W., Lomsadze, A. & Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 38, e132 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Torres-Cortes, G. et al. Characterization of novel antibiotic resistance genes identified by functional metagenomics on soil samples. Environ. Microbiol. 13, 1101–1114 (2011).

    Article  CAS  PubMed  Google Scholar 

  104. 104.

    Forsberg, K. J., Patel, S., Wencewicz, T. A. & Dantas, G. The tetracycline destructases: a novel family of tetracycline-inactivating enzymes. Chem. Biol. 22, 888–897 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Martinez, A. et al. Genetically modified bacterial strains and novel bacterial artificial chromosome shuttle vectors for constructing environmental libraries and detecting heterologous natural products in multiple expression hosts. Appl. Environ. Microbiol. 70, 2452–2463 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Dantas, G. & Sommer, M. O. Context matters - the complex interplay between resistome genotypes and resistance phenotypes. Curr. Opin. Microbiol. 15, 577–582 (2012). This article covers the importance of genomic context in understanding how genotypic resistance determinants result in varied phenotypic antimicrobial susceptibility profiles.

    Article  PubMed  Google Scholar 

  107. 107.

    Rishishwar, L., Petit, R. A. 3rd, Kraft, C. S. & Jordan, I. K. Genome sequence-based discriminator for vancomycin-intermediate Staphylococcus aureus. J. Bacteriol. 196, 940–948 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Bradley, P. et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat. Commun. 6, 10063 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Davis, J. J. et al. Antimicrobial resistance prediction in PATRIC and RAST. Sci. Rep. 6, 27930 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. 110.

    Arango-Argoty, G. et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6, 23 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  111. 111.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  CAS  Google Scholar 

  112. 112.

    Baroud, M. et al. Underlying mechanisms of carbapenem resistance in extended-spectrum beta-lactamase-producing Klebsiella pneumoniae and Escherichia coli isolates at a tertiary care centre in Lebanon: role of OXA-48 and NDM-1 carbapenemases. Int. J. Antimicrob. Agents 41, 75–79 (2013).

    Article  CAS  PubMed  Google Scholar 

  113. 113.

    Shigemura, K. et al. Association of overexpression of efflux pump genes with antibiotic resistance in Pseudomonas aeruginosa strains clinically isolated from urinary tract infection patients. J. Antibiot. 68, 568–572 (2015).

    Article  CAS  PubMed  Google Scholar 

  114. 114.

    Depardieu, F., Podglajen, I., Leclercq, R., Collatz, E. & Courvalin, P. Modes and modulations of antibiotic resistance gene expression. Clin. Microbiol. Rev. 20, 79–114 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Isenberg, H. D. Clinical microbiology: past, present, and future. J. Clin. Microbiol. 41, 917–918 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  116. 116.

    Bauer, A. W., Kirby, W. M., Sherris, J. C. & Turck, M. Antibiotic susceptibility testing by a standardized single disk method. Am. J. Clin. Pathol. 45, 493–496 (1966).

    Article  CAS  PubMed  Google Scholar 

  117. 117.

    Brown, D. F. & Brown, L. Evaluation of the E test, a novel method of quantifying antimicrobial activity. J. Antimicrob. Chemother. 27, 185–190 (1991).

    Article  CAS  PubMed  Google Scholar 

  118. 118.

    Jorgensen, J. H. & Ferraro, M. J. Antimicrobial susceptibility testing: a review of general principles and contemporary practices. Clin. Infect. Dis. 49, 1749–1755 (2009). This is a review of traditional microbiology techniques and of several automation innovations, including disc diffusion, microbroth dilution and a Vitek system.

    Article  CAS  PubMed  Google Scholar 

  119. 119.

    Seng, P. et al. Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin. Infect. Dis. 49, 543–551 (2009).

    Article  CAS  PubMed  Google Scholar 

  120. 120.

    Nagy, E., Maier, T., Urban, E., Terhes, G. & Kostrzewa, M. Species identification of clinical isolates of Bacteroides by matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry. Clin. Microbiol. Infect. 15, 796–802 (2009).

    Article  CAS  PubMed  Google Scholar 

  121. 121.

    Eigner, U. et al. Performance of a matrix-assisted laser desorption ionization-time-of-flight mass spectrometry system for the identification of bacterial isolates in the clinical routine laboratory. Clin. Lab. 55, 289–296 (2009).

    CAS  PubMed  Google Scholar 

  122. 122.

    Vrioni, G. et al. MALDI-TOF mass spectrometry technology for detecting biomarkers of antimicrobial resistance: current achievements and future perspectives. Ann. Transl Med. 6, 240 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Kostrzewa, M., Sparbier, K., Maier, T. & Schubert, S. MALDI-TOF MS: an upcoming tool for rapid detection of antibiotic resistance in microorganisms. Proteomics Clin. Appl. 7, 767–778 (2013).

    Article  CAS  PubMed  Google Scholar 

  124. 124.

    Sparbier, K., Schubert, S. & Kostrzewa, M. MBT-ASTRA: a suitable tool for fast antibiotic susceptibility testing? Methods 104, 48–54 (2016).

    Article  CAS  PubMed  Google Scholar 

  125. 125.

    Yilmaz, O. & Demiray, E. Clinical role and importance of fluorescence in situ hybridization method in diagnosis of H pylori infection and determination of clarithromycin resistance in H pylori eradication therapy. World J. Gastroenterol. 13, 671–675 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Moter, A. & Gobel, U. B. Fluorescence in situ hybridization (FISH) for direct visualization of microorganisms. J. Microbiol. Methods 41, 85–112 (2000).

    Article  CAS  PubMed  Google Scholar 

  127. 127.

    Juttner, S. et al. Reliable detection of macrolide-resistant Helicobacter pylori via fluorescence in situ hybridization in formalin-fixed tissue. Mod. Pathol. 17, 684–689 (2004).

    Article  CAS  PubMed  Google Scholar 

  128. 128.

    Haas, M., Essig, A., Bartelt, E. & Poppert, S. Detection of resistance to macrolides in thermotolerant campylobacter species by fluorescence in situ hybridization. J. Clin. Microbiol. 46, 3842–3844 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Werner, G. et al. Detection of mutations conferring resistance to linezolid in Enterococcus spp. by fluorescence in situ hybridization. J. Clin. Microbiol. 45, 3421–3423 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Choi, J. et al. A rapid antimicrobial susceptibility test based on single-cell morphological analysis. Sci. Transl Med. 6, 267ra174 (2014).

    Article  CAS  PubMed  Google Scholar 

  131. 131.

    Kalashnikov, M. et al. Rapid phenotypic stress-based microfluidic antibiotic susceptibility testing of Gram-negative clinical isolates. Sci. Rep. 7, 8031 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. 132.

    Mohan, R. et al. A multiplexed microfluidic platform for rapid antibiotic susceptibility testing. Biosens. Bioelectron. 49, 118–125 (2013).

    Article  CAS  PubMed  Google Scholar 

  133. 133.

    Hou, H. W., Bhattacharyya, R. P., Hung, D. T. & Han, J. Direct detection and drug-resistance profiling of bacteremias using inertial microfluidics. Lab. Chip 15, 2297–2307 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. 134.

    Baltekin, O., Boucharin, A., Tano, E., Andersson, D. I. & Elf, J. Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging. Proc. Natl Acad. Sci. USA 114, 9170–9175 (2017).

    Article  CAS  PubMed  Google Scholar 

  135. 135.

    Choi, J. et al. Rapid drug susceptibility test of Mycobacterium tuberculosis using microscopic time-lapse imaging in an agarose matrix. Appl. Microbiol. Biotechnol. 100, 2355–2365 (2016).

    Article  CAS  PubMed  Google Scholar 

  136. 136.

    Seki, M., Kim, C. K., Hayakawa, S. & Mitarai, S. Recent advances in tuberculosis diagnostics in resource-limited settings. Eur. J. Clin. Microbiol. Infect. Dis. 37, 1405–1410 (2018).

    Article  PubMed  Google Scholar 

  137. 137.

    Wolfe, A. J. et al. Evidence of uncultivated bacteria in the adult female bladder. J. Clin. Microbiol. 50, 1376–1383 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  138. 138.

    Rudkjobing, V. B. et al. Comparing culture and molecular methods for the identification of microorganisms involved in necrotizing soft tissue infections. BMC Infect. Dis. 16, 652 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. 139.

    Fok, C. S. et al. Urinary symptoms are associated with certain urinary microbes in urogynecologic surgical patients. Int. Urogynecol. J. 29, 1765–1771 (2018).

    Article  PubMed  Google Scholar 

  140. 140.

    Mowat, A. Commentary on: urinary symptoms are associated with certain urinary microbes in urogynecologic surgical patients. Int. Urogynecol. J. 29, 1773 (2018).

    Article  PubMed  Google Scholar 

  141. 141.

    Patel, J. B., Tenover, F. C., Turnidge, J. D. & Jorgensen, J. H. in Manual of Clinical Microbiology 10th edn (eds Versalovic, J. et al.) (American Society for Microbiology, 2011).

  142. 142.

    Shetty, N., Hill, G. & Ridgway, G. L. The Vitek analyser for routine bacterial identification and susceptibility testing: protocols, problems, and pitfalls. J. Clin. Pathol. 51, 316–323 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. 143.

    Idelevich, E. A. et al. Evaluation of an automated system for reading and interpreting disk diffusion antimicrobial susceptibility testing of fastidious bacteria. PLOS ONE 11, e0159183 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. 144.

    Lutgring, J. D. et al. Evaluation of the accelerate pheno system: results from two academic medical centers. J. Clin. Microbiol. 56, e01672-17 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. 145.

    Marschal, M. et al. Evaluation of the accelerate pheno system for fast identification and antimicrobial susceptibility testing from positive blood cultures in bloodstream infections caused by gram-negative pathogens. J. Clin. Microbiol. 55, 2116–2126 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. 146.

    Florio, W., Morici, P., Ghelardi, E., Barnini, S. & Lupetti, A. Recent advances in the microbiological diagnosis of bloodstream infections. Crit. Rev. Microbiol. 44, 351–370 (2018).

    Article  PubMed  Google Scholar 

  147. 147.

    Peker, N., Couto, N., Sinha, B. & Rossen, J. W. Diagnosis of bloodstream infections from positive blood cultures and directly from blood samples: recent developments in molecular approaches. Clin. Microbiol. Infect. 24, 944–955 (2018).

    Article  CAS  PubMed  Google Scholar 

  148. 148.

    Fredborg, M. et al. Rapid antimicrobial susceptibility testing of clinical isolates by digital time-lapse microscopy. Eur. J. Clin. Microbiol. Infect. Dis. 34, 2385–2394 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. 149.

    Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Hakim, O. & Misteli, T. SnapShot: chromosome confirmation capture. Cell 148, 1068–1068.e2 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Trussart, M. et al. Defined chromosome structure in the genome-reduced bacterium Mycoplasma pneumoniae. Nat. Commun. 8, 14665 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  152. 152.

    Yildirim, A. & Feig, M. High-resolution 3D models of Caulobacter crescentus chromosome reveal genome structural variability and organization. Nucleic Acids Res. 46, 3937–3952 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. 153.

    Marbouty, M., Baudry, L., Cournac, A. & Koszul, R. Scaffolding bacterial genomes and probing host-virus interactions in gut microbiome by proximity ligation (chromosome capture) assay. Sci. Adv. 3, e1602105 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. 154.

    Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat. Commun. 9, 870 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. 155.

    Press, M. O. et al. Hi-C deconvolution of a human gut microbiome yields high-quality draft genomes and reveals plasmid-genome interactions. Preprint at bioRxiv https://doi.org/10.1101/198713 (2017).

    Article  Google Scholar 

  156. 156.

    Eid, J. et al. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009).

    Article  CAS  Google Scholar 

  157. 157.

    Clarke, J. et al. Continuous base identification for single-molecule nanopore DNA sequencing. Nat. Nanotechnol. 4, 265–270 (2009).

    Article  CAS  PubMed  Google Scholar 

  158. 158.

    Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Completing bacterial genome assemblies with multiplex MinION sequencing. Microb. Genom. 3, e000132 (2017).

    PubMed  PubMed Central  Google Scholar 

  159. 159.

    Liao, Y. C., Lin, S. H. & Lin, H. H. Completing bacterial genome assemblies: strategy and performance comparisons. Sci. Rep. 5, 8747 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. 160.

    Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLOS Comput. Biol. 13, e1005595 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. 161.

    Frank, J. A. et al. Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data. Sci. Rep. 6, 25373 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. 162.

    Driscoll, C. B., Otten, T. G., Brown, N. M. & Dreher, T. W. Towards long-read metagenomics: complete assembly of three novel genomes from bacteria dependent on a diazotrophic cyanobacterium in a freshwater lake co-culture. Stand. Genomic. Sci. 12, 9 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. 163.

    Beaulaurier, J. et al. Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation. Nat. Biotechnol. 36, 61–69 (2018).

    Article  CAS  PubMed  Google Scholar 

  164. 164.

    Bertrand, D. et al. Nanopore sequencing enables high-resolution analysis of resistance determinants and mobile elements in the human gut microbiome. Preprint at bioRxiv https://doi.org/10.1101/456905 (2018).

    Article  Google Scholar 

  165. 165.

    Břinda, K. et al. Lineage calling can identify antibiotic resistant clones within minutes. Preprint at bioRxiv https://doi.org/10.1101/403204 (2018).

    Article  Google Scholar 

  166. 166.

    Croucher, N. J. & Thomson, N. R. Studying bacterial transcriptomes using RNA-seq. Curr. Opin. Microbiol. 13, 619–624 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. 167.

    Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. 168.

    Dersch, P., Khan, M. A., Muhlen, S. & Gorke, B. Roles of regulatory RNAs for antibiotic resistance in bacteria and their potential value as novel drug targets. Front. Microbiol. 8, 803 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  169. 169.

    Khaledi, A. et al. Transcriptome profiling of antimicrobial resistance in Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 60, 4722–4733 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  170. 170.

    Suzuki, S., Horinouchi, T. & Furusawa, C. Prediction of antibiotic resistance by gene expression profiles. Nat. Commun. 5, 5792 (2014). This paper uses expression profiles to help predict phenotypic resistance from genotypic data, showing the power of combining multiple omics techniques.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. 171.

    Qin, H. et al. Comparative transcriptomics of multidrug-resistant Acinetobacter baumannii in response to antibiotic treatments. Sci. Rep. 8, 3515 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. 172.

    Low, Y. M. et al. Elucidating the survival and response of carbapenem resistant Klebsiella pneumoniae after exposure to imipenem at sub-lethal concentrations. Pathog. Glob. Health 112, 378–386 (2018).

    Article  CAS  PubMed  Google Scholar 

  173. 173.

    Cho, H. & Kim, K. S. Escherichia coli OxyS RNA triggers cephalothin resistance by modulating the expression of CRP-associated genes. Biochem. Biophys. Res. Commun. 506, 66–72 (2018).

    Article  CAS  PubMed  Google Scholar 

  174. 174.

    Schniederjans, M., Koska, M. & Häussler, S. Transcriptional and mutational profiling of an aminoglycoside-resistant Pseudomonas aeruginosa small-colony variant. Antimicrob. Agents Chemother. 61, e01178-17 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. 175.

    Felden, B. & Cattoir, V. Bacterial adaptation to antibiotics through regulatory RNAs. Antimicrob. Agents Chemother. 62, e02503-17 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. 176.

    Antonopoulos, D. A. et al. PATRIC as a unique resource for studying antimicrobial resistance. Brief. Bioinform. https://doi.org/10.1093/bib/bbx083 (2017).

    Article  PubMed  Google Scholar 

  177. 177.

    de Man, T. J. & Limbago, B. M. SSTAR, a stand-alone easy-to-use antimicrobial resistance gene predictor. mSphere 1, e00050-15 (2016).

    PubMed  PubMed Central  Google Scholar 

  178. 178.

    Lakin, S. M. et al. MEGARes: an antimicrobial resistance database for high throughput sequencing. Nucleic Acids Res. 45, D574–D580 (2017).

    Article  CAS  PubMed  Google Scholar 

  179. 179.

    Naas, T. et al. Beta-lactamase database (BLDB) - structure and function. J. Enzyme Inhib. Med. Chem. 32, 917–919 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. 180.

    Saha, S. B., Uttam, V. & Verma, V. u-CARE: user-friendly Comprehensive Antibiotic resistance Repository of Escherichia coli. J. Clin. Pathol. 68, 648–651 (2015).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank K. Sukhum and M. Pandey for reading through a draft of this paper. This work was supported in part by awards to G.D. through the National Institute of Allergy and Infectious Diseases (NIAID), the Eunice Kennedy Shriver National Institute of Child Health & Human Development and the National Center for Complementary and Integrative Health of the US National Institutes of Health (NIH) under award numbers R01AI123394, R01HD092414 and R01AT009741, respectively. A.W.D. received support from the Institutional Program Unifying Population and Laboratory-Based Sciences Burroughs Wellcome Fund Grant to Washington University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

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Nature Reviews Genetics thanks J. Parkhill, E. Ruppé and other anonymous reviewer(s) for their contribution to the peer review of this work.

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M.B. and A.W.D. researched the literature and wrote the article. All authors substantially contributed to discussions of the content and reviewed and/or edited the manuscript before submission.

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Correspondence to Gautam Dantas.

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Glossary

Antimicrobial resistance

Bacterial ability to survive or grow in otherwise lethal or inhibitory antimicrobial concentrations.

Antimicrobial susceptibility testing

(AST). Challenge of bacteria with antimicrobials to determine whether they have phenotypic antimicrobial resistance.

Horizontal gene transfer

(HGT). Passage of resistance genes from one bacterium to another when neither bacteria is the parent or daughter cell. This process usually occurs through transduction, conjugation or transformation.

Metagenomes

Collections of genes from all organisms of a given habitat or sample.

Resistance exchange networks

Interconnected groups of environments or bacteria that transfer resistance genes with each other.

Phylogeny

The evolutionary ancestral relationships between organisms.

Contigs

Contiguous sequences assembled from sequencing reads.

De Bruijn graph

(DBG). Directional graphing algorithm commonly used for short-read assembly.

Euler’s path

A walk through a directed graph that crosses each edge in the graph only once. Euler’s path is used to reconstruct genome sequences from De Bruijn graphs.

Isolate assembly

Gathering of sequencing reads from a bacterial isolate into longer contiguous sequences representative of their state within the bacterium.

Resistome

All antimicrobial resistance genes within a given sample of bacteria.

Annotation

Identification and labelling of genes within a genome.

Burrows–Wheeler transform

A reversible data transformation algorithm to organize text with repeated sequences for efficient compression. This algorithm is implemented in bioinformatics software owing to frequent repeated sequences in biological data.

Metagenomic assembly

Deconvolution and assembly of sequencing reads from a metagenomic sample.

Hidden Markov model

(HMM). A probabilistic model of antimicrobial resistance process where hidden states emit observable outputs. These models are commonly used for sequence annotation.

Microbiota

A community of microorganisms from a given habitat or sample.

Functional metagenomics

A biological assay in which a metagenomic library of DNA is expressed in a naive host and then the host is exposed to a selection pressure to select for DNA that confers a fitness advantage against the selection pressure.

Biocuration

The collection and organization of biological data in a data structure useful for future analysis.

Carbapenem resistance

Resistance against the broad-spectrum carbapenem class of β-lactam antimicrobials, which are often used as drugs of last resort.

Methicillin resistance

Resistance against methicillin, a narrow-spectrum penicillin derivative. Methicillin resistance is often seen in the context of methicillin-resistant Staphylococcus aureus (MRSA), a common human pathogen. This resistance is commonly gained by horizontal transfer of a modified target protein (see Fig. 1b and 1c).

Deep learning

An extension of representational machine learning methods where the algorithm uses multiple transformation layers between raw data and output rather than one layer. This often improves results for more complex machine learning tasks.

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Boolchandani, M., D’Souza, A.W. & Dantas, G. Sequencing-based methods and resources to study antimicrobial resistance. Nat Rev Genet 20, 356–370 (2019). https://doi.org/10.1038/s41576-019-0108-4

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