Antimicrobial resistance (AMR) in bacteria is a major threat to public health; one of the key elements in the spread and evolution of AMR in clinical pathogens is the transfer of conjugative plasmids. The drivers of AMR evolution have been studied extensively in vitro but the evolution of plasmid-mediated AMR in vivo remains poorly explored. Here, we tracked the evolution of the clinically relevant plasmid pOXA-48, which confers resistance to the last-resort antibiotics carbapenems, in a large collection of enterobacterial clones isolated from the gut of hospitalized patients. Combining genomic and experimental approaches, we first characterized plasmid diversity and the genotypic and phenotypic effects of multiple plasmid mutations on a common genetic background. Second, using cutting-edge genomic editing in wild-type multidrug-resistant enterobacteria, we dissected three cases of within-patient plasmid-mediated AMR evolution. Our results revealed compensatory evolution of plasmid-associated fitness cost and the evolution of enhanced plasmid-mediated AMR in bacteria evolving in the gut of hospitalized patients. Crucially, we observed that the evolution of pOXA-48-mediated AMR in vivo involves a pivotal trade-off between resistance levels and bacterial fitness. This study highlights the need to develop new evolution-informed approaches to tackle plasmid-mediated AMR dissemination.
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The sequence data supporting the findings of this study are available at the National Center for Biotechnology Information Database with accession no. PRJNA838107 (https://www.ncbi.nlm.nih.gov/bioproject/838107). The raw data obtained in this study are available as Supplementary Data 5. The remaining R-GNOSIS sequences can be found in León‐Sampedro et al.23.
The code generated during the study can be found at https://github.com/LaboraTORIbio/within_patient_evolution.
Murray, C. J. et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 399, 629–655 (2022).
Vincent, J.-L. et al. International study of the prevalence and outcomes of infection in intensive care units. JAMA 302, 2323–2329 (2009).
van Schaik, W. The human gut resistome. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140087 (2015).
Partridge, S. R., Kwong, S. M., Firth, N. & Jensen, S. O. Mobile genetic elements associated with antimicrobial resistance. Clin. Microbiol. Rev. 31, e00088-17 (2018).
Dimitriu, T., Matthews, A. C. & Buckling, A. Increased copy number couples the evolution of plasmid horizontal transmission and plasmid-encoded antibiotic resistance. Proc. Natl Acad. Sci. USA 118, e2107818118 (2021).
San Millan, A., Escudero, J. A., Gifford, D. R., Mazel, D. & MacLean, R. C. Multicopy plasmids potentiate the evolution of antibiotic resistance in bacteria. Nat. Ecol. Evol. 1, 10 (2016).
Wheatley, R. et al. Rapid evolution and host immunity drive the rise and fall of carbapenem resistance during an acute Pseudomonas aeruginosa infection. Nat. Commun. 12, 2460 (2021).
Fröhlich, C. et al. Cryptic β-lactamase evolution is driven by low β-lactam concentrations. mSphere 6, e00108-21 (2021).
Souque, C., Escudero, J. A. & MacLean, R. C. Integron activity accelerates the evolution of antibiotic resistance. eLife 10, e62474 (2021).
Martínez-García, L., González-Alba, J. M., Baquero, F., Cantón, R. & Galán, J. C. Ceftazidime is the key diversification and selection driver of VIM-type carbapenemases. mBio 9, e02109-17 (2018).
Bottery, M. J., Wood, A. J. & Brockhurst, M. A. Adaptive modulation of antibiotic resistance through intragenomic coevolution. Nat. Ecol. Evol. 1, 1364–1369 (2017).
Vogwill, T. & MacLean, R. C. The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evol. Appl. 8, 284–295 (2015).
Brockhurst, M. A. & Harrison, E. Ecological and evolutionary solutions to the plasmid paradox. Trends Microbiol. 30, 534–543 (2022).
Loftie-Eaton, W. et al. Compensatory mutations improve general permissiveness to antibiotic resistance plasmids. Nat. Ecol. Evol. 1, 1354–1363 (2017).
Hall, J. P. J. et al. Plasmid fitness costs are caused by specific genetic conflicts enabling resolution by compensatory mutation. PLoS Biol. 19, e3001225 (2021).
Rajer, F. & Sandegren, L. The role of antibiotic resistance genes in the fitness cost of multiresistance plasmids. mBio 13, e0355221 (2022).
Humphrey, B. et al. Fitness of Escherichia coli strains carrying expressed and partially silent IncN and IncP1 plasmids. BMC Microbiol. 12, 53 (2012).
Andersson, D. I. & Hughes, D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8, 260–271 (2010).
Basra, P. et al. Fitness tradeoffs of antibiotic resistance in extraintestinal pathogenic Escherichia coli. Genome Biol. Evol. 10, 667–679 (2018).
Bonomo, R. A. et al. Carbapenemase-producing organisms: a global scourge. Clin. Infect. Dis. 66, 1290–1297 (2018).
David, S. et al. Epidemic of carbapenem-resistant Klebsiella pneumoniae in Europe is driven by nosocomial spread. Nat. Microbiol. 4, 1919–1929 (2019).
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).
León-Sampedro, R. et al. Pervasive transmission of a carbapenem resistance plasmid in the gut microbiota of hospitalized patients. Nat. Microbiol. 6, 606–616 (2021).
Alonso-Del Valle, A. et al. Variability of plasmid fitness effects contributes to plasmid persistence in bacterial communities. Nat. Commun. 12, 2653 (2021).
Hernández-García, M. et al. Characterization of carbapenemase-producing Enterobacteriaceae from colonized patients in a university hospital in Madrid, Spain, during the R-GNOSIS project depicts increased clonal diversity over time with maintenance of high-risk clones. J. Antimicrob. Chemother. 73, 3039–3043 (2018).
Matsumura, Y., Peirano, G. & Pitout, J. D. D. Complete genome sequence of Escherichia coli J53, an azide-resistant laboratory strain used for conjugation experiments. Genome Announc. 6, e00433–18 (2018).
Yurtsev, E. A., Chao, H. X., Datta, M. S., Artemova, T. & Gore, J. Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids. Mol. Syst. Biol. 9, 683 (2013).
zur Wiesch, P. A., Kouyos, R., Engelstädter, J., Regoes, R. R. & Bonhoeffer, S. Population biological principles of drug-resistance evolution in infectious diseases. Lancet Infect. Dis. 11, 236–247 (2011).
Nguyen, T. N., Phan, Q. G., Duong, L. P., Bertrand, K. P. & Lenski, R. E. Effects of carriage and expression of the Tn10 tetracycline-resistance operon on the fitness of Escherichia coli K12. Mol. Biol. Evol. 6, 213–225 (1989).
Hall, J. P. J., Wood, A. J., Harrison, E. & Brockhurst, M. A. Source-sink plasmid transfer dynamics maintain gene mobility in soil bacterial communities. Proc. Natl Acad. Sci. USA 113, 8260–8265 (2016).
Stracy, M. et al. Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections. Science 375, 889–894 (2022).
Williams, D. et al. Divergent, coexisting Pseudomonas aeruginosa lineages in chronic cystic fibrosis lung infections. Am. J. Respir. Crit. Care Med. 191, 775–785 (2015).
Mowat, E. et al. Pseudomonas aeruginosa population diversity and turnover in cystic fibrosis chronic infections. Am. J. Respir. Crit. Care Med. 183, 1674–1679 (2011).
Williams, D. et al. Transmission and lineage displacement drive rapid population genomic flux in cystic fibrosis airway infections of a Pseudomonas aeruginosa epidemic strain. Microb. Genom. 4, e000167 (2018).
Yang, J. et al. High-efficiency scarless genetic modification in Escherichia coli by using lambda red recombination and I-SceI cleavage. Appl. Environ. Microbiol. 80, 3826–3834 (2014).
Goldberg, G. W., Jiang, W., Bikard, D. & Marraffini, L. A. Conditional tolerance of temperate phages via transcription-dependent CRISPR–Cas targeting. Nature 514, 633–637 (2014).
Jiang, Y. et al. Multigene editing in the Escherichia coli genome via the CRISPR–Cas9 system. Appl. Environ. Microbiol. 81, 2506–2514 (2015).
Engler, C., Kandzia, R. & Marillonnet, S. A one pot, one step, precision cloning method with high throughput capability. PLoS ONE 3, e3647 (2008).
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).
Alikhan, N. F., Petty, N. K., Ben Zakour, N. L. & Beatson, S. A. BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons. BMC Genomics 12, 402 (2011).
Fournet-Fayard, S., Joly, B. & Forestier, C. Transformation of wild type Klebsiella pneumoniae with plasmid DNA by electroporation. J. Microbiol. Methods 24, 49–54 (1995).
Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A. & Korobeynikov, A. Using SPAdes de novo assembler. Curr. Protoc. Bioinformatics 70, e102 (2020).
Mikheenko, A., Prjibelski, A., Saveliev, V., Antipov, D. & Gurevich, A. Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 34, i142–i150 (2018).
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).
Deatherage, D. E. & Barrick, J. E. Identification of mutations in laboratory evolved microbes from next-generation sequencing data using breseq. Methods Mol. Biol. 1151, 165–188 (2014).
Carattoli, A. et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob. Agents Chemother. 58, 3895–3903 (2014).
Zankari, E. et al. Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 67, 2640–2644 (2012).
Garcillán-Barcia, M. P. & de la Cruz, F. Why is entry exclusion an essential feature of conjugative plasmids? Plasmid 60, 1–18 (2008).
DelaFuente, J., Rodriguez-Beltran, J. & San Millan, A. Methods to study fitness and compensatory adaptation in plasmid-carrying bacteria. Methods Mol. Biol. 2075, 371–382 (2020).
San Millan, A. S. et al. Small-plasmid-mediated antibiotic resistance is enhanced by increases in plasmid copy number and bacterial fitness. Antimicrob. Agents Chemother. 59, 3335–3341 (2015).
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).
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).
Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).
Hunt, M. et al. Circlator: automated circularization of genome assemblies using long sequencing reads. Genome Biol. 16, 294 (2015).
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).
Tatusova, T. et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 44, 6614–6624 (2016).
Croucher, N. J. et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res. 43, e15 (2015).
Page, A. J. et al. SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments. Microb. Genom. 2, e000056 (2016).
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).
We thank the technical support of L. Jaraba Soto. We also thank C. MacLean, J. Penadés, J. Antonio Escudero and D. Padfield for constructive comments. This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC grant no. 757440-PLASREVOLUTION) and by the Instituto de Salud Carlos III (PI19/00749) cofunded by the European Development Regional Fund ‘A way to achieve Europe’. The R-GNOSIS project received financial support from the European Commission (grant no. R-GNOSIS-FP7-HEALTH-F3-2011-282512). A.S.-L. is supported by the European Commission (nos. H2020-MSCA-IF-2019, 895671-REPLAY) and by the European Society of Clinical Microbiology and Infectious Diseases (Research Grant 2022). J.R.-B. acknowledges financial support from a Miguel Servet contract from Instituto de Salud Carlos III (ISCIII) (grant no. CP20/00154), cofunded by the European Social Fund, ‘Investing in your future’, Centro de Investigación Biológica en Red de Enfermedades Infecciosas (CIBERINFEC), cofunded with European Regional Development Fund funds, and project no. PI21/01363, funded by Instituto de Salud Carlos III (ISCIII) and cofunded by the European Union. J.R.-B., R.C. and M.H.-G. are supported by CIBERINFEC (no. CB21/13/00084).
The authors declare no competing interests.
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a) Frequency of clinical isolates by species. Numbers on top of the bars indicate the number of isolates. b) Distribution of PVs from the collection by count and species (colours). c) Frequency of isolates of K. pneumoniae or other enterobacteria carrying the most common pOXA-48 variant, PV-I. Colours correspond to the PVs variant and the number within the bars correspond to the isolate count.
a) Growth curves of E. coli J53 carrying different PVs. Vertical axis shows the optical density at 600 nm (OD600) and the horizontal axis time in minutes. Each PV is indicated in the top label. B) Linear correlation of relative fitness (w) calculated by competition assays or by area under the growth curves (Pearson’s product- moment correlation t = 9.6665, df=13, P < 0.001, cor 0.936). Lines indicate the propagated standard error of the mean and points indicate the mean values for each genotype.
Extended Data Fig. 3 Plasmid copy number (PCN) and conjugation rates do not correlate with plasmid fitness costs.
Correlation between relative fitness (w) and a) PCN, or b) log10 conjugation rate of E. coli J53 carrying different PVs relative to the plasmid-free strain. In each panel individual dots correspond to the median values of E. coli J53 carrying different PVs. Spearman’s rank correlation rho and p-value (p) for each case are indicated in the figure. The red dashed line indicates the regression and the gray-shaded zone covers the 95% confidence interval.
Workflow used to explore within-patient AMR evolution.a) PVs curing from clinical isolates; b) re-introduction of different PVs into the clinical isolates; c) evaluation of the plasmid-cost and the resistance profile of each plasmid-carrying bacteria combination; d) relative fitness (w) calculation; and E) calculation of plasmid copy number (PCN) for each PV.
Extended Data Fig. 5 Growth dynamics of the clinical bacteria carrying different PVs isolated from the three patients under different antibiotic treatments.
a) Growth curves of HKH* carrying different PVs (indicated by different colours, see legend). Vertical axis shows the OD600 and horizontal axis the time in minutes. Each antibiotic concentration is indicated in the top label (ERT stands for ertapenem; MER for meropenem and NO_AB for no antibiotic treatment, the number indicates the concentration in mg/L). n = 18 for each genotype and treatment. b) Growth of different HKH* carrying different PVs (as in A), using the values of the area under the curve (AUC in vertical axis, t = 1500 minutes). Individual points indicate individual values (n = 18 for each genotype and treatment) and horizontal lines indicate the median value of the replicates. c) Growth curves of JWC* carrying different PVs (as in A). AMC stands for amoxicillin + clavulanic acid. d) Growth of different JWC* carrying different PVs using the values of the area under the curve (as in B). e) Growth curves of WDV* carrying different PVs (as in A and C). f) Growth of different WDV* carrying different PVs using the values of the area under the curve (as in B and D). In panels a–f, Plasmid_free stands for pOXA-48-free.
Information of enterobacteria carrying pOXA-48-like plasmids. a, Enterobacteria collection with a description of the isolates (species, ST, isolation date) indicating each PV (including the mutation, positions and sequencing technologies). b, SNP features in pOXA-48. SNP information, including SNP type for the PVs (note that only PVs with SNPs are included).
Genomic information of Escherichia coli J53 carrying different PVs. a, Genome information and strain designation used for Illumina sequencing. b, Replicons and AMR in J53. The number of plasmids and the AMR genes identified in the different J53/PVs. c, SNP pOXA-48 in J53 ref. K8. SNPs detected in the PVs for each J53 carrying different PVs. d, SNP chrom in J53 ref. J53. SNPs in the chromosome of each J53 carrying different PVs.
Genomic information of clinical enterobacteria carrying different PVs involved in the within-patient pOXA-48 evolution. a, Genome information. Codes used (and available) for short and long sequencing. b, Predicted mutations. SNPs and indels for each comparison. RA, read alignment evidence (SNPs); MC, missing coverage evidence; JC, new junction evidence; MCJC, large deletions. In ‘predicted mutations’, 0 indicates absence of SNP or evidence, numbers >0 indicate total read depth of the SNP or near the event and ‘yes’ indicates presence of the event. Black letters indicate chromosome mutations (contig 1) and blue letters indicate pOXA-48 mutations (other contigs). c, Unassigned MC. Missing coverage evidences for each comparison. 0 indicates absence and 1 represents presence of the event. d, Unassigned JC. Detected new junction evidences for each comparison. In ‘unassigned JC’, 0 indicates absence and >0 indicates the frequency of the event as calculated by breseq.
Plasmids and primers used in this study. See Methods for more information. a, Plasmids for molecular biology. Description of synthetic plasmids used in this study. b, Primers. Description of the sequence and usage of the primers used in this study.
a, Raw data used for J53 PCN. qPCR data obtained and used to calculate plasmid copy number in E. coli J53. b, J53 IC90. Data of AMR susceptibility in E. coli J53 (IC90 values in Ertapenem in mg l−1). c, J53 competition. Data obtained from competition assays and used to calculate the relative fitness (w) in E. coli J53 carrying different PVs. d, J53 growth curves. Raw growth data obtained from E. coli J53 carrying different PVs. e, J53 mating assays. Data used to calculate the plasmid transfer rates of different PVs in E. coli J53. f, Within-patient PCN. qPCR data obtained and used to calculate plasmid copy number of different PVs in clinical bacteria. g, Within-patient IC90. Data of AMR susceptibility to ertapenem in terms of IC90 in mg l−1 in clinical bacteria carrying different PVs. h, Within-patient growth curves. Raw growth data obtained from clinical bacteria carrying different PVs.
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DelaFuente, J., Toribio-Celestino, L., Santos-Lopez, A. et al. Within-patient evolution of plasmid-mediated antimicrobial resistance. Nat Ecol Evol 6, 1980–1991 (2022). https://doi.org/10.1038/s41559-022-01908-7
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