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22
MEROPENEM-INDUCED REDUCTION IN COLISTIN SUSCEPTIBILITY IN PSEUDOMONAS AERUGINOSA STRAIN ATCC 27853
Savinova TA ^ Bocharova YuA, Chaplin AV, Korostin DO, Shamina OV, Mayansky NA, Chebotar IV Pirogov Russian National Research Medical University, Moscow, Russia
Antibiotic-resistant strains of Pseudomonas aeruginosa are a global threat to public health. The knowledge of mechanisms underlying antibiotic resistance is essential to counter P. aeruginosa infections. This study describes the phenomenon of meropenem-induced cross-resistance to colistin in the ATCC 27853 strain of P. aeruginosa. The study was conducted in the specimens of P. aeruginosa grown from the reference ATCC 27853 strain in the medium containing meropenem gradients. Susceptibility of the isolates to carbapenems and colistin was assessed using the agar dilution method; susceptibly to colistin was assessed using the broth microdilution method. A total of 93 P. Aeruginosa isolates were analyzed; of them two demonstrated reduced susceptibility to carbapenems (meropenem, imipenem) and colistin. Whole-genome sequencing of the isolates was performed on a MGISEQ-2000 platform. Missense mutations in the oprD and mexD genes and a nonsense mutation in the phoQ gene were detected. We conclude that exposure of P. aeruginosa to meropenem can lead to cross-resistance to colistin, a last resort drug for P. aeruginosa infections.
Keywords: antibiotic resistance, Pseudomonas aeruginosa, meropenem, colistin Funding: the study was supported by the Russian Science Foundation (Project 20-15-00235).
Acknowledgements: the authors thank the Center of Precision Genome Editing and Genetic Technologies for Biomedicine of Pirogov Russian National Research Medical University for their advice on the methodology of the study.
Author contribution: Savinova TA — formal analysis of sequencing data, manuscript preparation; Bocharova YuA — methodology, formal analysis; Chaplin AV — formal analysis of sequencing data; Korostin DO — methodology, data validation; Shamina OV — methodology; Mayansky NA, Chebotar IV — concept; manuscript editing.
[X] Correspondence should be addressed: Tatiana A. Savinova
Ostrovityanova, 1, Moscow, 117997, Russia; taniasavinova@gmail.com
Received: 27.12.2021 Accepted: 10.01.2022 Published online: 19.01.2022
DOI: 10.24075/brsmu.2022.001
МЕРОПЕНЕМ-ИНДУЦИРОВАННОЕ СНИЖЕНИЕ ЧУВСТВИТЕЛЬНОСТИ К КОЛИСТИНУ У PSEUDOMONAS AERUGINOSA ATCC 27853
Т. А. Савинова Ю. А. Бочарова, А. В. Чаплин, Д. О. Коростин, О. В. Шамина, Н. А. Маянский, И. В. Чеботарь
Российский национальный исследовательский медицинский университет имени Н. И. Пирогова, Москва, Россия
Нечувствительные к антибиотикам штаммы Pseudomonas aeruginosa представляют собой глобальную проблему в здравоохранении. Исследование механизмов возникновения резистентности лежит в основе разработки способов борьбы с P. aeruginosa. Целью работы было исследовать возникновение кросс-резистентности у P. aeruginosa в процессе адаптации к популярному антибиотику меропенему. Объектами исследования были образцы P. aeruginosa, полученные при росте референтного штамма P. aeruginosa ATCC 27853 на среде с возрастающей концентрацией меропенема. Чувствительность изолятов к карбапенемам и колистину определяли при помощи разведения в агаре, чувствительность к колистину оценивали методом серийных разведений. Было получено 93 изолята P. aeruginosa, два из которых имели сниженную чувствительность одновременно к карбапенемам (меропенем, имипенем) и колистину. Геномы изолятов секвенировали на полногеномном секвенаторе MGISEQ-2000; обнаружены миссенс-мутации в генах oprD и mexD и нонсенс-мутация в phoQ. Получнные результаты показывают, что при воздействии меропенема на штаммы P. aeruginosa может развиваться кросс-резистентность к колистину — препарату резерва для лечения синегнойной инфекции.
Ключевые слова: антибиотики, резистентность, Pseudomonas aeruginosa, меропенем, колистин
Финансирование: работа выполнена при поддержке гранта Российского научного фонда (проект № 20-15-00235).
Благодарности: авторы благодарят Центр высокоточного редактирования и генетических технологий для биомедицины ФГАОУ ВО РНИМУ им. Н. И. Пирогова Минздрава РФ за консультации по методической части исследования.
Вклад авторов: Т. А. Савинова — формальный анализ данных секвенирования, подготовка рукописи; Ю. А. Бочарова — методология, формальный анализ; А. В. Чаплин — формальный анализ данных секвенирования; Д. О. Коростин — методология, валидация данных; О. В. Шамина — методология; Н. А. Маянский, И. В. Чеботарь — концептуализация, редактирование рукописи.
[23 Для корреспонденции: Татьяна Александровна Савинова
ул. Островитянова, д. 1, г. Москва, 117997, Россия; taniasavinova@gmail.com
Статья получена: 27.12.2021 Статья принята к печати: 10.01.2022 Опубликована онлайн: 19.01.2022 DOI: 10.24075/vrgmu.2022.001
Pseudomonas aeruginosa is a significant opportunistic pathogen and a serious burden to public health and economy
[1]. Especially dangerous are carbapenem-resistant strains of P. aeruginosa regarded by WHO as critical priority pathogens
[2]. This breeds the need for understanding mechanisms underlying bacterial resistance to carbapenems. Research into the molecular genetic underpinnings of carbapenem resistance focuses mostly on ß-lactamase-associated mechanisms that are determined by plasmid genes and therefore can be acquired through horizontal gene transfer. However, there
is another contributor whose role should not be overlooked: induced mutations in the core genome of P. aeruginosa resulting in high-level carbapenem resistance [3]. There are two approaches to the study of mutations conferring resistance to carbapenems. The first involves the analysis of drug-resistant isolates obtained from clinical, agricultural or environmental sources. In the second approach, the evolution of antibiotic resistance is modeled in vitro. For that, bacteria are grown in antibiotic concentration gradients. A smart method for studying mutational resistance has been proposed in [4]. Its authors
created a spatiotemporal model that enabled migration of Escherichia coli in trimethoprim and ciprofloxacin gradients and generated a variety of mutants for further analysis. Interestingly, some of the E. coli clones carried mutations that were not linked to trimethoprim or ciprofloxacin resistance [4]. So, we became curious to explore the direction and implications of such mutations. Specifically, we were interested in the clinically significant phenomenon of cross-resistance, in which a mutation induced by exposure to an antibiotic could confer resistance to other antibiotics [5, 6]. The aim of this study was to test the hypothesis that P. aeruginosa can develop cross-resistance to other antibiotics while adapting to meropenem.
METHODS
Bacteriological study
In our experiment, we used the spatiotemporal model of antibiotic resistance in motile bacteria [4]. The reference ATCC 27853 strain of P. aeruginosa was precultured in semi-solid LB agar (0.28% agarose) in a Petri dish at 37 °C for 24 h. After 24 h, the cells were harvested from the propagating colony front and seeded onto another Petri dish with semi-solid LB agar. The procedure was repeated 3 times. Then, 10 pl of the grown bacterial culture was picked up with an inoculation loop and introduced into the top layer (semi-solid agar) of the culture medium contained in a device shown in the Figure. The medium had a sandwich composition. The bottom layer was LB Miller broth (Becton Dickinson; USA) supplemented with 1.6% agarose, 30 pg/ml kanamycin sulfate, 100 pg/ml cycloheximide, and meropenem taken at one of the concentrations shown in the Figure. The optimum thickness of the bottom layer equaled three-fifths of the total medium thickness (~2.0 cm). The bottom layer was distributed into 5 isolated compartments of the dish containing different concentrations of meropenem. The middle layer (one-fifth of the total medium thickness) was LB Miller broth supplemented with 2.0% agarose, 30 pg/ml kanamycin sulfate, 100 pg/ml cycloheximide, and ink (4.0 ml per 1L culture medium) added as a contrasting background for photography purposes. The middle layer spread over the bottom layer was solid. The top layer (one-fifth of the total medium thickness) was semi-solid agar (Miller LB broth) with
0.3% agarose, 30 pg/ml kanamycin sulfate and 100 pg/ml cycloheximide.
The cells were incubated in air at 37 °C for 216 h. Every 12 h, P. aeruginosa samples were collected from the propagating colony front and reseeded on Mueller-Hinton agar (Becton Dickinson; USA) to obtain a sufficient amount of bacteria for the subsequent analysis of their phenotypic traits (antibiotic resistance profiles) and genomic changes.
Resistance to meropenem and imipenem was tested using the agar dilution method described in [7]. Resistance to colistin was assessed using the broth microdilution method following the guidelines of the Clinical and Laboratory Standards Institute (CLSI) [8].
Bacterial DNA was isolated from the 24-h culture of P. aeruginosa grown on Mueller-Hinton agar (Becton Dickinson; USA) using a QIAamp DNA Mini Kit (Qiagen; Germany) according to the manufacturer's protocol. The obtained DNA samples were stored at -20 °C.
To prepare genomic DNA libraries, 400 g of the isolated bacterial DNA was sheared in an ultrasonicator (Covaris; USA). The fragments were then end-repaired and ligated to MGI adapters (MGI; China). The libraries were purified on Agencourt AMPure XP beads (Beckman; USA). Concentrations of the bacterial DNA and DNA libraries were measured using a Qubit 4 fluorometer (Thermo Fisher Scientific; USA).
Whole-genome sequencing was performed using the MGISEQ-2000 platform (MGI; China). Read length was 250 bp.
The quality of the raw sequence data was tested in FASTQC; the reads were trimmed in Trimmomatic v.0.38. Bacterial genomes were assembled de novo using SPAdes 3.14 [9]. The assembled sequences were tested for contamination using Contest16S. The obtained genome assemblies were evaluated in QUAST 5.0 [10]. Genetic similarity between the assembled genomes was assessed in MUMmer [11]. The genomes were annotated using RAST [12] and Prokka software [13]. To detect the presence of single nucleotide polymorphisms (SNPs), the short reads were mapped to the reference genome in Snippy [14]. ATCC 27853 was used as a reference genome. The detected variants were annotated and their influence on the genes was predicted in SnpEff [15]. The search for antibiotic resistance genes in the genomes assembled de novo, their analysis and validation of the detected SNPs were all carried
Table. Characteristics of carbapenem-resistant strains of P. aeruginosa with reduced susceptibility to colistin
Methodology Characteristic P. aeruginosa ATCC 27853, reference P. aeruginosa, isolate E62 P. aeruginosa, isolate E74
Phenotyplc antibiotic susceptibility assessment Agar dilution method, susceptibility to meropenem 0.25 pg/ml, S 16 pg/ml, R 16 pg/ml, R
imipenem 0.001 pg/ml, S 128 pg/ml, R 256 pg/ml, R
Broth microdilution method, susceptibility to colistin 0.5 pg/ml, I* 2 pg/ml, I* 4 pg/ml R
Changes in genome Carbapenem resistance genes oprD wt Mutation resulting in G307D Mutation resulting in G307D
mexD wt Mutation resulting in E89K Mutation resulting in E89K
Colistin resistance gene phoQ wt Nonsense mutation resulting in Y290stop Nonsense mutation resulting in Y290stop
Polymorphism SNP — 134 177
Note: S — susceptible; I — intermediate; R — resistant; wt — wild type, matches the reference genome; SNP — single nucleotide polymorphism; * — according to the CLSI criteria, the term "susceptible" cannot be applied to describe the susceptibility of P. aeruginosa to colistin; all P. aeruginosa strains for which colistin MIC < 2 pg/ml are classified as susceptible at increased exposure.
2000 |ig/ml
Fig. A device (MEGA-plate, 40 x 20 cm) for spatiotemporal modeling of meropenem resistance in P. aeruginosa. The bottom of the device is divided into 5 sections separated by 2.5-cm walls. The culture medium in the MEGA plate has a sandwich composition. The bottom layer contains solid LB agar (the description is provided in the article) distributed into the sections. Each of the sections contains different concentrations of meropenem (shown on the right). The middle layer is LB agar without meropenem. The top layer is semi-solid LB agar (Miller). The arrow indicates the inoculation site and the direction in which P. aeruginosa propagates along the surface of semi-solid agar.
out in BLASTn. The analysis of resistance determinants was aided by ResFinder and the AMRFinderPlus algorithm included in the NCBI Pathogen Detection pipeline [16, 17]
RESULTS
A total of 93 P. aeruginosa mutants with various phenotypic traits (colony color, antibiotic resistance profile, mucoid/non-mucoid phenotype) were harvested during 216 h of incubation. Among those isolates, two strains (E62 obtained at 192 h of incubation and E74 obtained at 216 h of incubation) demonstrated significantly reduced (four- to eightfold) susceptibility to colistin and high resistance to meropenem and imipenem. Phenotypic and genotypic characteristics of these 2 strains are shown in the Table.
For E62, meropenem and imipenem MICs were 16 pg/ml and 128 pg/ml, respectively; for E74, they were 16 pg/ml and 256 pg/ml, respectively, which satisfied the CLSI criteria for antibiotic resistance. According to CLSI criteria, the E62 isolate was characterized as susceptible to colistin at increased exposure; for this strain, colistin MIC was 4 times higher than for the baseline strain. According to the CLSI criteria, the E74 strain was characterized as resistant to colistin (MIC: 4 pg/ml).
Both strains carried a mutation in the porin gene (oprD) resulting in the substitution of glycine with aspartic acid at position 307 of the protein. Besides, both isolates had a missense mutation in the mexD gene (this gene encodes the subunit of the MexCD-OprJ efflux pump). Also, both E62 and E74 had a nonsense mutation in the phoQ gene resulting in the premature termination of protein synthesis (289 out of 448 amino acids).
DISCUSSION
P. aeruginosa strains with simultaneous resistance to carbapenems and polymyxins are not rare. For example, among multidrug resistant P. aeruginosa representatives, 22.2% of meropenem-resistant isolates were unsusceptible to colistin [18]. The evolution of such isolates is rarely described in the literature. It is possible that they acquire their resistance profiles through consecutive or simultaneous therapeutic exposure to carbapenems and colistin. The phenomenon observed in our study proves that P. aeruginosa can reduce their susceptibility
to colistin following exposure to meropenem. The hypothetical mechanisms underlying induction of cross-resistance to colistin by meropenem fall into the "all roads lead to resistance" concept, meaning that in P. aeruginosa any stressor causes hypermutability and leads to the emergence of multiple clones with novel properties [3]. Such a mutational explosion can lead to the emergence of persisting mutations disrupting synthesis of lipopolysaccharides, the primary target of colistin.
Genomes of the E62 and E74 isolates carried mutations that can explain their resistance to meropenem/imipenem and reduced susceptibility to colistin. The missense mutation in the oprD gene reported in this study may have caused a structural change in the OprD porin, which transports meropenem and imipenem inside the bacterial cell [19]. The search of GeneBank (https://www.ncbi.nlm.nih.gov/genbank) identified only one clinical isolate with a similar amino acid sequence of the OprD porin (GCA_003194245.1). Similar to our mutant, this isolate obtained in 2013 was also resistant to meropenem and imipenem (MIC > 32 pg/ml). Another mutation that could have reduced susceptibility to carbapenems was the missense mutation in the mexD gene encoding the subunit of the MexCD-OprJ efflux pump. The MexCD-OprJ system is involved in the efflux of p-lactams; its hyperexpression is correlated with carbapenem resistance in P. aeruginosa [20]. The phoQ gene codes for the sensor histidine kinase, which is part of the two-component regulatory PhoPQ system. Mutations in phoQ were reported to cause resistance to polymyxins in P. aeruginosa, including specimens isolated from patients with cystic fibrosis [21, 22].
Thus, all phenotypic characteristics of carbapenem-resistant isolates of P. aeruginosa with reduced susceptibility to colistin observed in our study were associated with mutations.
CONCLUSIONS
The phenomenon of cross-resistance described in this paper may be due to the fact that the rate of point mutations in P. aeruginosa, specifically in the genes implicated in antimicrobials resistance, increases under stress conditions. Our findings prove that exposure to meropenem can lead to resistance not only to other p-lactams but also to colistin used as a last resort drug for P. aeruginosa infections, which seriously complicates the treatment strategy and limits its options.
References
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4. Baym M, Lieberman TD, Kelsic ED, Chait R, Gross R, Yelin I, et al. Spatiotemporal microbial evolution on antibiotic landscapes. Science. 2016; 353 (6304): 1147-51. Available from: https://doi. org/10.1126/science.aag0822.
5. Pal C, Papp B, Lazar V. Collateral sensitivity of antibiotic-resistant microbes. Trends Microbiol. 2015; 23 (7): 401-40. Available from: https://doi.org/10.1016/j.tim.2015.02.009.
6. Gnanadhas DP, Marathe SA, Chakravortty D. Biocides-resistance, cross-resistance mechanisms and assessment. Expert Opin Investig Drugs, 2013; 22 (2): 191-06. Available from: https:// doi.org/10.1517/13543784.2013.748035.
7. European Committee for Antimicrobial Susceptibility Testing (EUCAST) of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID). Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by agar dilution. Clin Microbiol Infect. 2000; 6 (9): 509-15. Available from: https://doi.org/10.1046/j.1469-0691.2000.00142.x.
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10. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013; 29 (8): 1072-5. Available from: https://doi.org/10.1093/ bioinformatics/btt086.
11. Marcais G, Delcher AL, Phillippy AM, Coston R, Salzberg SL, Zimin A. MUMmer4: A fast and versatile genome alignment system. PLoS Comput Biol. 2018; 14 (1): e1005944. Available from: https://doi.org/10.1371/journal.pcbi.1005944.
12. Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014; 42:
D206-14. Available from: https://doi.org/10.1093/nar/gkt1226.
13. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014; 30 (14): 2068-9. Available from: https://doi. org/10.1093/bioinformatics/btu153.
14. Seemann T. Snippy: fast bacterial variant calling from NGS reads. GitHub. 2015. Available from: https://github.com/tseemann/ snippy (accessed November 2021).
15. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012; 6 (2): 80-92. Available from: https://doi.org/10.4161/fly.19695.
16. Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ, Tolstoy I, et al. Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates. Antimicrob Agents Chemother. 2019; 63 (11): e00483-19. Available from: https://doi.org/10.1128/AAC.00483-19.
17. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother. 2020; 75 (12): 3491-500. Available from: https://doi.org/10.1093/jac/dkaa345.
18. Sader HS, Huband MD, Castanheira M, Flamm RK. Pseudomonas aeruginosa antimicrobial susceptibility results from four years (2012 to 2015) of the international network for optimal resistance monitoring program in the United States. Antimicrob Agents Chemother. 2017; 61 (3): e02252-16. Available from: https://doi. org/10.1128/AAC.02252-16.
19. Chevalier S, Bouffartigues E, Bodilis J, Maillot O, Lesouhaitier O, Feuilloley MGJ, et al. Structure, function and regulation of Pseudomonas aeruginosa porins. FEMS Microbiol Rev. 2017; 41 (5): 698-722. Available from: https://doi.org/10.1093/femsre/fux020.
20. Zahedi Bialvaei A, Rahbar M, Hamidi-Farahani R, Asgari A, Esmailkhani A, Mardani Dashti Y, et al. Expression of RND efflux pumps mediated antibiotic resistance in Pseudomonas aeruginosa clinical strains. Microbial Pathog. 2021; 153: 104789. Available from: https://doi.org/10.1016/j.micpath.2021.104789.
21. Barrow K, Kwon DH. Alterations in two-component regulatory systems of phoPQ and pmrAB are associated with polymyxin B resistance in clinical isolates of Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2009; 53 (12): 5150-4. Available from: https://doi.org/10.1128/AAC.00893-09 .
22. Miller AK, Brannon MK, Stevens L, Johansen HK, Selgrade SE, Miller SI, et al. PhoQ mutations promote lipid A modification and polymyxin resistance of Pseudomonas aeruginosa found in colistin-treated cystic fibrosis patients. Antimicrob Agents Chemother. 2011; 55 (12): 5761-9. Available from: https://doi. org/10.1128/AAC.05391-11.
Литература
1. Bou R, Lorente L, Aguilar A, Perpinan J, Ramos P, Peris M, et al. Hospital economic impact of an outbreak of Pseudomonas aeruginosa infections. J Hosp Infect. 2009; 71 (2): 138-42. Available from: https://doi.org/10.1016/jjhin.2008.07.018.
2. World Health Organization (WHO). Global Priority List of Antibiotic-Geneva, Switzerland: 2017. Available at: http://www.who.int/ medicines/publications/WHO-PPL-Short_Summary_25Feb-ET_ NM_WHO.pdf (accessed Novemder 2021).
3. Breidenstein EB, de la Fuente-Nunez C, Hancock RE. Pseudomonas aeruginosa: all roads lead to resistance. Trends Microbiol. 2011; 19 (8): 419-26. Available from: https://doi. org/10.1016/j.tim.2011.04.005
4. Baym M, Lieberman TD, Kelsic ED, Chait R, Gross R, Yelin I, et al. Spatiotemporal microbial evolution on antibiotic landscapes. Science. 2016; 353 (6304): 1147-51. Available from: https://doi. org/10.1126/science.aag0822.
5. Pal C, Papp B, Lazar V. Collateral sensitivity of antibiotic-resistant microbes. Trends Microbiol. 2015; 23 (7): 401-40. Available from: https://doi.org/10.1016/j.tim.2015.02.009.
6. Gnanadhas DP, Marathe SA, Chakravortty D. Biocides-resistance, cross-resistance mechanisms and assessment. Expert Opin Investig Drugs, 2013; 22 (2): 191-06. Available from: https://doi. org/10.1517/13543784.2013.748035.
7. European Committee for Antimicrobial Susceptibility Testing (EUCAST) of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID). Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by agar dilution. Clin Microbiol Infect. 2000; 6 (9): 509-15. Available from: https://doi.org/10.1046/j.1469-0691.2000.00142.x.
8. Clinical and Laboratory Standards Institute (CLSI). Performance Standards for Antimicrobial Susceptibility Testing. Available from: http://em100.edaptivedocs.net/dashboard.aspx (accessed November 2021).
9. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012; 19: 455-77. Available from: https://doi.org/10.1089/cmb.2012.0021.
10. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality
assessment tool for genome assemblies. Bioinformatics. 2013; 29 (8): 1072-5. Available from: https://doi.org/10.1093/ bioinformatics/btt086.
11. Marcais G, Delcher AL, Phillippy AM, Coston R, Salzberg SL, Zimin A. MUMmer4: A fast and versatile genome alignment system. PLoS Comput Biol. 2018; 14 (1): e1005944. Available from: https://doi.org/10.1371/journal.pcbi.1005944.
12. Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014; 42: D206-14. Available from: https://doi.org/10.1093/nar/gkt1226.
13. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014; 30 (14): 2068-9. Available from: https://doi. org/10.1093/bioinformatics/btu153.
14. Seemann T. Snippy: fast bacterial variant calling from NGS reads. GitHub. 2015. Available from: https://github.com/tseemann/ snippy (accessed November 2021).
15. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012; 6 (2): 80-92. Available from: https://doi.org/10.4161/fly.19695.
16. Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ, Tolstoy I, et al. Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates. Antimicrob Agents Chemother. 2019; 63 (11): e00483-19. Available from: https://doi.org/10.1128/AAC.00483-19.
17. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V et
al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother. 2020; 75 (12): 3491-500. Available from: https://doi.org/10.1093/jac/dkaa345.
18. Sader HS, Huband MD, Castanheira M, Flamm RK. Pseudomonas aeruginosa antimicrobial susceptibility results from four years (2012 to 2015) of the international network for optimal resistance monitoring program in the United States. Antimicrob Agents Chemother. 2017; 61 (3): e02252-16. Available from: https://doi. org/10.1128/AAC.02252-16.
19. Chevalier S, Bouffartigues E, Bodilis J, Maillot O, Lesouhaitier O, Feuilloley MGJ, et al. Structure, function and regulation of Pseudomonas aeruginosa porins. FEMS Microbiol Rev. 2017; 41 (5): 698-722. Available from: https://doi.org/10.1093/femsre/fux020.
20. Zahedi Bialvaei A, Rahbar M, Hamidi-Farahani R, Asgari A, Esmailkhani A, Mardani Dashti Y, et al. Expression of RND efflux pumps mediated antibiotic resistance in Pseudomonas aeruginosa clinical strains. Microbial Pathog. 2021; 153: 104789. Available from: https://doi.org/10.1016/j.micpath.2021.104789.
21. Barrow K, Kwon DH. Alterations in two-component regulatory systems of phoPQ and pmrAB are associated with polymyxin B resistance in clinical isolates of Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2009; 53 (12): 5150-4. Available from: https://doi.org/10.1128/AAC.00893-09 .
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