Antimicrobial resistance (AMR) is a major threat to human health worldwide, and the rapid detection and quantification of resistance, combined with antimicrobial stewardship, are key interventions to combat the spread and emergence of AMR. Antimicrobial susceptibility testing (AST) systems are the collective set of diagnostic processes that facilitate the phenotypic and genotypic assessment of AMR and antibiotic susceptibility. Over the past 30 years, only a few high-throughput AST methods have been developed and widely implemented. By contrast, several studies have established proof of principle for various innovative AST methods, including both molecular-based and genome-based methods, which await clinical trials and regulatory review. In this Review, we discuss the current state of AST systems in the broadest technical, translational and implementation-related scope.
Subscribe to Journal
Get full journal access for 1 year
only $22.08 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Theuretzbacher, U. et al. Analysis of the clinical antibacterial and antituberculosis pipeline. Lancet Infect. Dis. 19, e40–e50 (2019). Excellent analysis of the state of the art in antibiotic discovery and codification, with shortcomings in development routes succinctly described.
Bartlett, J. G., Gilbert, D. N. & Spellberg, B. Seven ways to preserve the miracle of antibiotics. Clin. Infect. Dis. 56, 1445–1450 (2013).
Nathwani, D. et al. Value of hospital antimicrobial stewardship programs [ASPs]: a systematic review. Antimicrob. Resist. Infect. Control 8, 35 (2019).
Okeke, I. N. et al. Diagnostics as essential tools for containing antibacterial resistance. Drug Resist. Updat. 14, 95–106 (2011).
Kjeldgaard, J. S. Results from the MIC Survey 2017 (DTU Food, National Food Institute, Denmark, 2018).
Bertrand, R. L. Lag phase is a dynamic, organized, adaptive, and evolvable period that prepares bacteria for cell division. J. Bacteriol. 201, e00697–18 (2019).
Sommer, M. O. A., Munck, C., Toft-Kehler, R. V. & Andersson, D. I. Prediction of antibiotic resistance: time for a new preclinical paradigm? Nat. Rev. Microbiol. 15, 689–696 (2017).
Nicoloff, H., Hjort, K., Levin, B. R. & Andersson, D. I. The high prevalence of antibiotic heteroresistance in pathogenic bacteria is mainly caused by gene amplification. Nat. Microbiol. 4, 504–514 (2019).
Knopp, M. et al. De novo emergence of peptides that confer antibiotic resistance. mBio 10, e00837–19 (2019).
Brauner, A., Fridman, O., Gefen, O. & Balaban, N. Q. Distinguishing between resistance, tolerance and persistence to antibiotic treatment. Nat. Rev. Microbiol. 14, 320–330 (2016).
Brauner, A., Shoresh, N., Fridman, O. & Balaban, N. Q. An experimental framework for quantifying bacterial tolerance. Biophys. J. 112, 2664–2671 (2017).
Bakshi, S. et al. Nonperturbative imaging of nucleoid morphology in live bacterial cells during an antimicrobial peptide attack. Appl. Environ. Microbiol. 80, 4977–4986 (2016).
Veloo, A. C. M., Baas, W. H., Haan, F. J., Coco, J. & Rossen, J. W. Prevalence of antimicrobial resistance genes in Bacteroides spp. and Prevotella spp. Dutch clinical isolates. Clin. Microbiol. Infect. 25, S1198–S1743 (2019).
Veloo, A. C. M., Chlebowicz, M., Winter, H. L. J., Bathoorn, D. & Rossen, J. W. A. Three metronidazole-resistant Prevotella bivia strains harbour a mobile element, encoding a novel nim gene, nimK, and an efflux small MDR transporter. J. Antimicrob. Chemother. 73, 2687–2690 (2018).
Van Belkum, A. et al. Developmental roadmap for antimicrobial susceptibility testing systems. Nat. Rev. Microbiol. 17, 51–62 (2019).
Blair, J. M., Webber, M. A., Baylay, A. J., Ogbolu, D. O. & Piddock, L. J. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 13, 42–51 (2015).
Mason, A. et al. Accuracy of different bioinformatics methods in detecting antibiotic resistance and virulence factors from Staphylococcus aureus whole-genome sequences. J. Clin. Microbiol. 56, 1815–1817 (2018).
Heyckendorf, J. et al. What is resistance? Impact of phenotypic versus molecular drug resistance testing on therapy for multi- and extensively drug-resistant tuberculosis. Antimicrob. Agents Chemother. 62, e01550–17 (2018). This report clearly describes the discrepancies between phenotypic and genotypic assessments of antibiotic susceptibility and suggests useful solutions to some of these discrepancies.
Schumacher, A., Vranken, T., Malhotra, A., Arts, J. J. C. & Habibovic, P. In vitro antimicrobial susceptibility testing methods: agar dilution to 3D tissue-engineered models. Eur. J. Clin. Microbiol. Infect. Dis. 37, 187–208 (2018).
Shin, D. J., Andini, N., Hsieh, K., Yang, S. & Wang, T. H. Emerging analytical techniques for rapid pathogen identification and susceptibility testing. Annu. Rev. Anal. Chem. 12, 41–67 (2019).
Leonard, H., Colodner, R., Halachmi, S. & Segal, E. Recent advances in the race to design a rapid diagnostic test for antimicrobial resistance. ACS Sens. 3, 2202–2217 (2018).
Idelevich, E. A. & Becker, K. How to accelerate antimicrobial susceptibility testing. Clin. Microbiol. Infect. 25, 1347–1355 (2019).
Hombach, M. et al. Rapid detection of ESBL, carbapenemases, MRSA and other important resistance phenotypes within 6–8 h by automated disc diffusion antibiotic susceptibility testing. J. Antimicrob. Chemother. 72, 3063–3069 (2017). This study shows that simple add-ons to classic AST systems can still lead to substantial gains in turn-around time.
Junkins, A. D. et al. BD Phoenix and Vitek 2 detection of mecA-mediated resistance in Staphylococcus aureus with cefoxitin. J. Clin. Microbiol. 47, 2879–2882 (2009).
Isenberg, H. D., Reichler, A. & Wiseman, D. Prototype of a fully automated device for determination of bacterial antibiotic susceptibility in the clinical laboratory. Appl. Microbiol. 22, 980–986 (1971).
Cantón, R. et al. Validation of the VITEK2 and the advance expert system with a collection of Enterobacteriaceae harboring extended spectrum or inhibitor resistant β-lactamases. Diagn. Microbiol. Infect. Dis. 41, 65–70 (2001).
Rockland, M. et al. Implementation of semiautomated antimicrobial susceptibility interpretation hardware for nontuberculous mycobacteria may overestimate susceptibility. J. Clin. Microbiol. 57, e01756–18 (2019).
Reller, L. B., Weinstein, M., Jorgensen, J. H. & Ferraro, M. J. Antimicrobial susceptibility testing: a review of general principles and contemporary practices. Clin. Infect. Dis. 49, 1749–1755 (2009).
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).
Pancholi, P. et al. Multicenter evaluation of the accelerate phenotest BC kit for rapid identification and phenotypic antimicrobial susceptibility testing using morphokinetic cellular analysis. J. Clin. Microbiol. 56, e01329–17 (2018).
Wang, H. Y., Uh, Y., Kim, S., Shim, T. S. & Lee, H. Evaluation of the Quantamatrix Multiplexed Assay Platform system for simultaneous detection of Mycobacterium tuberculosis and the rifampicin resistance gene using culture-positive mycobacteria. Int. J. Infect. Dis. 61, 107–113 (2017).
Kim, J. H. et al. Prospective evaluation of a rapid antimicrobial susceptibility test (QMAC-dRAST) for selecting optimal targeted antibiotics in positive blood culture. J. Antimicrob. Chemother. 74, 2255–2260 (2019).
Bailey, A. L., Ledeboer, N. & Burnham, C. D. Clinical microbiology is growing up: the total laboratory automation revolution. Clin. Chem. 65, 634–643 (2019).
Lainhart, W. & Burnham, C. A. Enhanced recovery of fastidious organisms from urine culture in the setting of total laboratory automation. J. Clin. Microbiol. 56, e00546–18 (2018).
Eyre, D. W. et al. WGS to predict antibiotic MICs for Neisseria gonorrhoeae. J. Antimicrob. Chemother. 72, 1937–1947 (2017).
Alonso, C. A. et al. Antibiogramj: a tool for analysing images from disk diffusion tests. Comput. Methods Prog. Biomed. 143, 159–169 (2017).
Wood, C. S. et al. Taking connected mobile-health diagnostics of infectious diseases to the field. Nature 566, 467–474 (2019).
Perry, J. D. A decade of development of chromogenic culture media for clinical microbiology in an era of molecular diagnostics. Clin. Microbiol. Rev. 30, 449–479 (2017). Perry et al. provide an excellent overview of combined classic microbial identification and targeted AST in the format of the oldest (growth-based) type of diagnostics in clinical bacteriology.
Park, M. J. et al. Performance of a novel fluorogenic chimeric analog for the detection of third-generation cephalosporin resistant bacteria. J. Microbiol. Methods 131, 161–165 (2016).
Smith, K. P., Richmond, D. L., Brennan-Krohn, T., Elliott, H. L. & Kirby, J. E. Development of MAST: a microscopy-based antimicrobial susceptibility testing platform. SLAS Technol. 22, 662–674 (2017).
Nordmann, P., Jayol, A. & Poirel, L. Rapid detection of polymyxin resistance in Enterobacteriaceae. Emerg. Infect. Dis. 22, 1038–1043 (2016).
Flentie, K. et al. Microplate-based surface area assay for rapid phenotypic antibiotic susceptibility testing. Sci. Rep. 9, 237 (2019).
Hayden, R. T. et al. Rapid antimicrobial susceptibility testing using forward laser light scatter technology. J. Clin. Microbiol. 54, 2701–2706 (2016).
Johnson, W. L., France, D. C., Rentz, N. S., Cordell, W. T. & Walls, F. L. Sensing bacterial vibrations and early response to antibiotics with phase noise of a resonant crystal. Sci. Rep. 7, 12138 (2017).
Cowger, T. A. et al. Protein-adsorbed magnetic-nanoparticle-mediated assay for rapid detection of bacterial antibiotic resistance. Bioconjug. Chem. 28, 890–896 (2017).
Shi, X., Kadiyala, U., VanEpps, J. S. & Yau, S. T. Culture-free bacterial detection and identification from blood with rapid, phenotypic, antibiotic susceptibility testing. Sci. Rep. 8, 3416 (2018).
Malmberg, C. et al. A novel microfluidic assay for rapid phenotypic antibiotic susceptibility testing of bacteria detected in clinical blood cultures. PLoS One 11, e0167356 (2016).
Baltekin, Ö., 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).
Li, H. et al. Adaptable microfluidic system for single-cell pathogen classification and antimicrobial susceptibility testing. Proc. Natl Acad. Sci. USA 116, 201819569 (2019).
Kittel, M. et al. Rapid susceptibility testing of multi-drug resistant Escherichia coli and Klebsiella by glucose metabolization monitoring. Clin. Chem. Lab. Med. 57, 1271–1279 (2019).
Chung, C. Y., Wang, J. C. & Chuang, H. S. Rapid bead-based antimicrobial susceptibility testing by optical diffusometry. PLoS One 11, e0148864 (2016).
Iriya, R. et al. Real-time detection of antibiotic activity by measuring nanometer-scale bacterial deformation. J. Biomed. Opt. 22, 1–9 (2017).
Sparbier, K., Schubert, S. & Kostrzewa, M. MBT-ASTRA: A suitable tool for fast antibiotic susceptibility testing? Methods 104, 48–54 (2016).
Ibarlucea, B. et al. Nanowire sensors monitor bacterial growth kinetics and response to antibiotics. Lab Chip 17, 4283–4293 (2017).
Kuss, S. et al. Versatile electrochemical sensing platform for bacteria. Anal. Chem. 91, 4317–4322 (2019).
Idelevich, E. A. et al. Rapid phenotypic detection of microbial resistance in Gram-positive bacteria by a real-time laser scattering method. Front. Microbiol. 8, 1064 (2017).
Stupar, P. et al. Nanomechanical sensor applied to blood culture pellets: a fast approach to determine the antibiotic susceptibility against agents of bloodstream infections. Clin. Microbiol. Infect. 23, 400–405 (2017).
Fonseca, E. et al. Evaluation of rapid colistin susceptibility directly from positive blood cultures using a flow cytometry assay. Int. J. Antimicrob. Agents 54, 820–823 (2019).
Novelli-Rousseau, A. et al. Culture-free antibiotic-susceptibility determination from single-bacterium Raman spectra. Sci. Rep. 8, 3957 (2018).
Tannert, A., Grohs, R., Popp, J. & Neugebauer, U. Phenotypic antibiotic susceptibility testing of pathogenic bacteria using photonic readout methods: recent achievements and impact. Appl. Microbiol. Biotechnol. 103, 549–566 (2019).
Gao, J. et al. Nanotube assisted microwave electroporation for single cell pathogen identification and antimicrobial susceptibility testing. Nanomedicine 17, 246–253 (2019).
Pitruzzello, G. et al. Multiparameter antibiotic resistance detection based on hydrodynamic trapping of individual E. coli. Lab Chip 19, 1417–1426 (2019).
Yu, H. et al. Phenotypic antimicrobial susceptibility testing with deep learning video microscopy. Anal. Chem. 90, 6314–6322 (2018).
Machen, A., Drake, T. & Wang, Y. F. Same day identification and full panel antimicrobial susceptibility testing of bacteria from positive blood culture bottles made possible by a combined lysis-filtration method with MALDI-TOF VITEK mass spectrometry and the VITEK2 system. PLoS One 9, e87870 (2014).
Correa-Martínez, C. L., Idelevich, E. A., Sparbier, K., Kostrzewa, M. & Becker, K. rapid detection of extended-spectrum β-lactamases (ESBL) and AmpC β-lactamases in Enterobacterales: development of a screening panel using the MALDI-TOF MS-based direct-on-target microdroplet growth assay. Front. Microbiol. 10, 13 (2019).
Köck, R. et al. Implementation of short incubation MALDI-TOF MS identification from positive blood cultures in routine diagnostics and effects on empiric antimicrobial therapy. Antimicrob. Resist. Infect. Control 6, 12 (2017).
Ruppé, E. et al. Prediction of the intestinal resistome by a three-dimensional structure-based method. Nat. Microbiol. 4, 112–123 (2019). This study describes a novel software-mediated approach for resistomics, showing that resistance traits can be reliably defined without prior (selective) culture.
Hendriksen, R. S. et al. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat. Commun. 10, 1124 (2019). This analysis of resistance markers found in a global collection of sewage specimens underscores the depth of environmental penetration of antibiotic resistance.
Jin, N., Paraskevaidi, M., Semple, K. T., Martin, F. L. & Zhang, D. Infrared spectroscopy coupled with a dispersion model for quantifying the real-time dynamics of kanamycin resistance in artificial microbiota. Anal. Chem. 89, 9814–9821 (2017).
Fowler, P. W. et al. Robust prediction of resistance to trimethoprim in Staphylococcus aureus. Cell. Chem. Biol. 25, 339–349 (2018).
Chen, B. A. et al. Detection of multidrug-resistance proteins with protein array chips [Chinese]. Zhonghua Zhong Liu Za Zhi 27, 528–530 (2005).
Mertins, S. et al. Generation and selection of antibodies for a novel immunochromatographic lateral flow test to rapidly identify OXA-23-like-mediated carbapenem resistance in Acinetobacter baumannii. J. Med. Microbiol. 68, 1021–1032 (2019).
Tada, T. et al. Assessment of a newly developed immunochromatographic assay for NDM-type metallo-β-lactamase producing Gram-negative pathogens in Myanmar. BMC Infect. Dis. 28, 565 (2019).
Rösner, S. et al. Evaluation of a novel immunochromatographic lateral flow assay for rapid detection of OXA-48, NDM, KPC and VIM carbapenemases in multidrug-resistant Enterobacteriaceae. J. Med. Microbiol. 68, 379–381 (2019).
Volland, H. et al. Development and multicentric validation of a lateral flow immunoassay for rapid detection of MCR-1-producing Enterobacteriaceae. J. Clin. Microbiol. 57, e01454–18 (2019).
Pasteran, F. et al. Rapid identification of OXA-48 and OXA-163 subfamilies in carbapenem-resistant Gram-negative bacilli with a novel immunochromatographic lateral flow assay. J. Clin. Microbiol. 54, 2832–2836 (2016).
Charretier, Y. et al. Rapid bacterial identification, resistance, virulence and type profiling using selected reaction monitoring mass spectrometry. Sci. Rep. 5, 13944 (2015).
Liang, T. et al. Rapid microbial identification and antibiotic resistance detection by mass spectrometric analysis of membrane lipids. Anal. Chem. 91, 1286–1294 (2019).
Schulze, H., Rubtsova, M. & Bachmann, T. T. in Modern Techniques for Pathogen Detection (eds Popp, J. & Bauer, M.) 113–220 (Wiley, 2015).
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).
Schoepp, N. G. et al. Rapid pathogen-specific phenotypic antibiotic susceptibility testing using digital LAMP quantification in clinical samples. Sci. Transl Med. 9, eaal3693 (2017).
Athamanolap, P., Hsieh, K., Chen, L., Yang, S. & Wang, T. H. Integrated bacterial identification and antimicrobial susceptibility testing using PCR and high-resolution melt. Anal. Chem. 89, 11529–11536 (2017).
Dunne, W. M. Jr, Jaillard, M., Rochas, O. & Van Belkum, A. Microbial genomics and antimicrobial susceptibility testing. Expert Rev. Mol. Diagn. 17, 257–269 (2017).
Igarashi, Y. et al. Laboratory evaluation of the Anyplex™ II MTB/MDR and MTB/XDR tests based on multiplex real-time PCR and melting-temperature analysis to identify Mycobacterium tuberculosis and drug resistance. Diagn. Microbiol. Infect. Dis. 89, 276–281 (2017).
Özenci, V. & Rossolini, G. M. Rapid microbial identification and antimicrobial susceptibility testing to drive better patient care: an evolving scenario. J. Antimicrob. Chemother. 74, i2–i5 (2019).
Bishop, E. J. et al. Concurrent analysis of nose and groin swab specimens by the IDI-MRSA PCR assay is comparable to analysis by individual-specimen PCR and routine culture assays for detection of colonization by methicillin-resistant Staphylococcus aureus. J. Clin. Microbiol. 44, 2904–2908 (2006).
Jacqmin, H., Schuermans, A., Desmet, S., Verhaegen, J. & Saegeman, V. Performance of three generations of Xpert MRSA in routine practice: approaching the aim? Eur. J. Clin. Microbiol. Infect. Dis. 36, 1363–1365 (2017).
Holzknecht, B. J., Hansen, D. S., Nielsen, L., Kailow, A. & Jarløv, J. O. Screening for vancomycin-resistant enterococci with Xpert® vanA/vanB: diagnostic accuracy and impact on infection control decision making. New Microbes New Infect. 2, 54–59 (2017).
Cortegiani, A. et al. Use of Cepheid Xpert Carba-R® for rapid detection of carbapenemase-producing bacteria in abdominal septic patients admitted to intensive care unit. PLoS One 11, e0160643 (2016).
MacVane, S. H. & Nolte, F. S. Benefits of adding a rapid PCR-based blood culture identification panel to an established antimicrobial stewardship program. J. Clin. Microbiol. 54, 2455–2463 (2016).
Gadsby, N. J. et al. Comparison of Unyvero P55 pneumonia cartridge, in-house PCR and culture for the identification of respiratory pathogens and antibiotic resistance in bronchoalveolar lavage fluids in the critical care setting. Eur. J. Clin. Microbiol. Infect. Dis. 38, 1171–1178 (2019).
Hischebeth, G. T. et al. Unyvero i60 implant and tissue infection (ITI) multiplex PCR system in diagnosing periprosthetic joint infection. J. Microbiol. Methods 121, 27–32 (2016).
Felsenstein, S. et al. Impact of a rapid blood culture assay for Gram-positive identification and detection of resistance markers in a pediatric hospital. Arch. Pathol. Lab. Med. 140, 267–275 (2016).
Zboromyrska, Y. et al. Rapid detection of β-lactamases directly from positive blood cultures using a loop-mediated isothermal amplification (LAMP)-based assay. Int. J. Antimicrob. Agents 46, 355–356 (2015).
Schumacher, S. et al. Highly-integrated lab-on-chip system for point-of-care multiparameter analysis. Lab Chip 12, 464–473 (2012).
Leopold, S. R., Goering, R. V., Witten, A., Harmsen, D. & Mellmann, A. Bacterial whole-genome sequencing revisited: portable, scalable, and standardized analysis for typing and detection of virulence and antibiotic resistance genes. J. Clin. Microbiol. 52, 2365–2370 (2014).
Khaledi, A. et al. Transcriptome profiling of antimicrobial resistance in Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 60, 4722–4733 (2016).
Shendure, J. et al. DNA sequencing at 40: past, present and future. Nature 550, 345–353 (2017).
Hughes, E. E. et al. Clinical sensitivity of Cystic Fibrosis mutation panels in a diverse population. Hum. Mutat. 37, 201–208 (2016).
Su, M., Satola, S. W. & Read, T. D. Genome-based prediction of bacterial antibiotic resistance. J. Clin. Microbiol. 57, e01405–e01418 (2019).
Nguyen, M. et al. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Sci. Rep. 8, 421 (2018).
Bertrand, D. et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat. Biotechnol. 37, 937–944 (2019).
Khazaei, T., Barlow, J. T., Schoepp, N. G. & Ismagilov, R. F. RNA markers enable phenotypic test of antibiotic susceptibility in Neisseria gonorrhoeae after 10 minutes of ciprofloxacin exposure. Sci. Rep. 8, 11606 (2018).
Bhattacharyya, R. P. et al. Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination. Nat. Med. 25, 1858–1864 (2019). Bhattacharyya et al. have published one of the few studies in which phenotypic and genotypic analyses of the resistome are presented and in which the current gold standard methods are seriously challenged.
Guérillot, R. et al. Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq). Genome Med. 10, 63 (2018).
Ellington, M. J. et al. The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: report from the EUCAST Subcommittee. Clin. Microbiol. Infect. 23, 2–22 (2017).
Couto, N. et al. Critical steps in clinical shotgun metagenomics for the concomitant detection and typing of microbial pathogens. Sci. Rep. 8, 13767 (2018).
Lemon, J. K., Khil, P. P., Frank, K. M. & Dekker, J. P. Rapid nanopore sequencing of plasmids and resistance gene detection in clinical isolates. J. Clin. Microbiol. 55, 3530–3543 (2017).
Yang, Y. et al. Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics 34, 1666–1671 (2018).
Golparian, D. et al. Antimicrobial resistance prediction and phylogenetic analysis of Neisseria gonorrhoeae isolates using the Oxford Nanopore MinION sequencer. Sci. Rep. 8, 17596 (2018).
Rossen, J. W. A., Friedrich, A. W. & Moran-Gilad, J. ESCMID Study Group for Genomic and Molecular Diagnostics (ESGMD): practical issues in implementing whole-genome-sequencing in routine diagnostic microbiology. Clin. Microbiol. Infect. 24, 355–360 (2018).
Westblade, L. F. et al. Role of clinicogenomics in infectious disease diagnostics and public health microbiology. J. Clin. Microbiol. 54, 1686–1693 (2016).
Quainoo, S. et al. Whole-genome sequencing of bacterial pathogens: the future of nosocomial outbreak analysis. Clin. Microbiol. Rev. 30, 1015–1063 (2017).
Blauwkamp, T. A. et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat. Microbiol. 4, 663–674 (2019).
Angaali, N., Vemu, L., Padmasri, C., Mamidi, N. & Teja, V. D. Direct identification and susceptibility testing of Gram-negative bacilli from turbid urine samples using VITEK2. J. Lab. Physicians 10, 299–303 (2018).
Campigotto, A., Goneau, L. & Matukas, L. M. Direct identification and antimicrobial susceptibility testing of microorganisms from positive blood cultures following isolation by lysis-centrifugation. Diagn. Microbiol. Infect. Dis. 92, 189–193 (2018).
Kang, J. H. et al. An extracorporeal blood-cleansing device for sepsis therapy. Nat. Med. 20, 1211–1216 (2015).
Van Belkum, A. & Dunne, W. M. Jr. Next-generation antimicrobial susceptibility testing. J. Clin. Microbiol. 51, 2018–2024 (2013).
Pulido, M. R., García-Quintanilla, M., Martín-Peña, R., Cisneros, J. M. & McConnell, M. J. Progress on the development of rapid methods for antimicrobial susceptibility testing. J. Antimicrob. Chemother. 68, 2710–2717 (2013).
Behera, B. et al. Emerging technologies for antibiotic susceptibility testing. Biosens. Bioelectron. 142, 111552 (2019). This study provides excellent graphical illustrations of emerging technologies for AST.
Hays, J. P. et al. The successful uptake and sustainability of rapid infectious disease and antimicrobial resistance point-of-care testing requires a complex ‘mix-and-match’ implementation package. Eur. J. Clin. Microbiol. Infect. Dis. 38, 1015–1022 (2019).
Warhurst, G. et al. Rapid detection of health-care-associated bloodstream infection in critical care using multipathogen real-time polymerase chain reaction technology: a diagnostic accuracy study and systematic review. Health Technol. Assess. 19, 1–142 (2015).
Van Belkum, A. & Rochas, O. Laboratory-based and point-of-care testing for MSSA/MRSA detection in the age of whole genome sequencing. Front. Microbiol. 9, 1437 (2018).
Peretz, A., Pastukh, N. & Nitzan, O. Is one colony enough? J. Clin. Microbiol. 54, 925 (2016). Peretz et al. develop a critical assessment of a long-lasting practical problem with potentially substantial diagnostic and financial consequences.
Boulund, F. et al. Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets. BMC Genomics 18, 682 (2017).
Humphries, R. M., Abbott, A. N. & Hindler, J. A. Understanding and addressing CLSI breakpoint revisions: a primer for clinical laboratories. J. Clin. Microbiol. 57, e00203–e00219 (2019). Breakpoints are subject to continuous discussion and evolution, with definitions being modified; the authors clearly describe the rationales for breakpoint management.
Roope, L. S. J. et al. The challenge of antimicrobial resistance: what economics can contribute. Science 364, eaau4679 (2019).
Legenza, L. et al. Geographic mapping of Escherichia coli susceptibility to develop a novel clinical decision support tool. Antimicrob. Agents Chemother. 63, e00048–19 (2019).
Yarygin, K. S. et al. ResistoMap — online visualization of human gut microbiota antibiotic resistome. Bioinformatics 33, 2205–2206 (2017).
McArthur, A. G. et al. The comprehensive antibiotic resistance database. Antimicrob. Agents Chemother. 57, 3348–3357 (2013).
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).
Kleinheinz, K. A., Joensen, K. G. & Larsen, M. V. Applying the ResFinder and VirulenceFinder web-services for easy identification of acquired antibiotic resistance and E. coli virulence genes in bacteriophage and prophage nucleotide sequences. Bacteriophage 4, e27943 (2014).
Galata, V. et al. Integrating culture-based antibiotic resistance profiles with whole-genome sequencing data for 11,087 clinical isolates. Genomics Proteomics Bioinformatics 17, 169–182 (2019).
Arango-Argoty, G. et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6, 23 (2018).
Keshri, V. et al. An integrative database of β-lactamase enzymes: sequences, structures, functions, and phylogenetic trees. Antimicrob. Agents Chemother. 63, e02319–18 (2019).
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).
Boolchandani, M., D’Souza, A. W. & Dantas, G. Sequencing-based methods and resources to study antimicrobial resistance. Nat. Rev. Genet. 20, 356–370 (2019).
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). This study underscores that bioinformatic pipelines are intrinsically different, with different systems sometimes predicting conflicting genotypes and hence phenotypes.
Xavier, B. B. et al. Consolidating and exploring antibiotic resistance gene data resources. J. Clin. Microbiol. 54, 851–859 (2016).
The CRyPTIC Consortium and the 100,000 Genomes Project. Prediction of susceptibility to first-line tuberculosis drugs by DNA sequencing. N. Engl. J. Med. 379, 1403–1415 (2018).
Gygli, S. M. et al. Whole-genome sequencing for drug resistance profile prediction in Mycobacterium tuberculosis. Antimicrob. Agents Chemother. 63, e02175–18 (2019).
Fowler, P. W. et al. Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis. Microbiology 164, 1522–1530 (2018).
Holt, K. E. et al. Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health. Proc. Natl Acad. Sci. USA 112, E3574–E3581 (2015).
Tamma, P. D. et al. Applying rapid whole-genome sequencing to predict phenotypic antimicrobial susceptibility testing results among carbapenem-resistant Klebsiella pneumoniae clinical isolates. Antimicrob. Agents Chemother. 63, e01923–18 (2018).
Peng, J. P. et al. A whole-genome sequencing analysis of Neisseria gonorrhoeae isolates in China: an observational study. EClinicalmedicine 7, P47–P54 (2019).
Do Nascimento, V. et al. Comparison of phenotypic and WGS-derived antimicrobial resistance profiles of enteroaggregative Escherichia coli isolated from cases of diarrhoeal disease in England, 2015–16. J. Antimicrob. Chemother. 72, 3288–3297 (2017).
Sadouki, Z. et al. Comparison of phenotypic and WGS-derived antimicrobial resistance profiles of Shigella sonnei isolated from cases of diarrhoeal disease in England and Wales, 2015. J. Antimicrob. Chemother. 72, 2496–2502 (2017).
Kos, V. N. et al. The resistome of Pseudomonas aeruginosa in relationship to phenotypic susceptibility. Antimicrob. Agents Chemother. 59, 427–436 (2015).
Van Belkum, A. et al. Phylogenetic distribution of CRISPR–Cas systems in antibiotic-resistant Pseudomonas aeruginosa. MBio 6, e01796–15 (2015).
Jaillard, M. et al. Correlation between phenotypic antibiotic susceptibility and the resistome in Pseudomonas aeruginosa. Int. J. Antimicrob. Agents 50, 210–218 (2017).
Chiu, C. Y. & Miller, S. A. Clinical metagenomics. Nat. Rev. Genet. 20, 341–355 (2019).
Asante, J. & Osei Sekyere, J. Understanding antimicrobial discovery and resistance from a metagenomic and metatranscriptomic perspective: advances and applications. Environ. Microbiol. Rep. 11, 62–86 (2019).
Yan, Q. et al. Evaluation of the CosmosID bioinformatics platform for prosthetic joint-associated sonicate fluid shotgun metagenomic data analysis. J. Clin. Microbiol. 57, e01182–18 (2019).
Lakin, S. M. et al. MEGARes: an antimicrobial resistance database for high throughput sequencing. Nucleic Acids Res. 45, D574–D580 (2017).
Palleja, A. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 3, 1255–1265 (2018).
Rahman, S. F., Olm, M. R., Morowitz, M. J. & Banfield, J. F. Machine learning leveraging genomes from metagenomes identifies influential antibiotic resistance genes in the infant gut microbiome. mSystems 3, e00123–17 (2018).
Cangelosi, G. A. & Meschke, J. S. Dead or alive: molecular assessment of microbial viability. Appl. Environ. Microbiol. 80, 5884–5891 (2014).
Smith, K. P. & Kirby, J. E. The inoculum effect in the era of multidrug resistance: minor differences in inoculum have dramatic effect on MIC determination. Antimicrob. Agents Chemother. 62, e00433–18 (2018).
van Dorp, L. et al. Rapid phenotypic evolution in multidrug-resistant Klebsiella pneumoniae hospital outbreak strains. Microb. Genom. 5, 263 (2019).
The authors gratefully acknowledge A. Hemmert (BioFire, Salt Lake City, UT, USA) for his insightful review of the text.
A.v.B., F.M. and O.R. are employees at bioMérieux, a company that designs, markets and sells antimicrobial susceptibility testing tools and systems. C.A.B. has received research support from bioMérieux, BioFire, Cepheid, Accelerate Diagnostics, Luminex, Bio-Rad Laboratories, Thermo Fisher and SeLux.
Peer review information
Nature Reviews Microbiology thanks R. Cantón, J. O’Grady, M. Fernandez Suarez and M. Sanguinetti for their contribution to the peer review of this work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Comprehensive Antibiotic Resistance Database (CARD): https://card.mcmaster.ca
EUCAST standardized rapid-AST protocols: www.eucast.org/rapid_ast_in_blood_cultures
- Minimal inhibitory concentration
(MIC). The lowest concentration of an antimicrobial agent that prevents visible growth of a bacterium species or isolate. The MIC is defined by combined activities of the microorganism, the affected patient and the antimicrobial agent itself.
- Lag phase
The temporary period in which microorganisms are adapting to a new environment, avoiding threats and metabolizing, and increasing in cell size but not yet actively dividing and multiplying. During this period, cells are synthesizing enzymes and other factors needed for actual cell division under the new environmental conditions.
The microbial ability to resist being killed by antimicrobials. This ability is distinct from (multi)drug resistance and is not caused by mutant microorganisms, but rather by cells existing in a dormant, non-dividing state.
- Zone of inhibition
If bacteria are grown as layers on solid growth media and an antibiotic stops the bacteria from growing or kills them, there will be an area around the place where the antibiotic has been positioned (usually in a well or on a paper disc) where the bacteria have not grown enough to be visible. The radius of such a region of growth inhibition is correlated with the level of antibiotic susceptibility of the strain being tested.
About this article
Cite this article
van Belkum, A., Burnham, C.D., Rossen, J.W.A. et al. Innovative and rapid antimicrobial susceptibility testing systems. Nat Rev Microbiol (2020). https://doi.org/10.1038/s41579-020-0327-x