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Within-patient evolution of plasmid-mediated antimicrobial resistance

Abstract

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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Fig. 1: pOXA-48 PVs tested in E. coli J53.
Fig. 2: Screening the within-patient evolution of pOXA-48-mediated AMR.
Fig. 3: Characterization of the in vivo evolution of plasmid-mediated AMR.

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Data availability

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.

Code availability

The code generated during the study can be found at https://github.com/LaboraTORIbio/within_patient_evolution.

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Acknowledgements

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).

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Authors and Affiliations

Authors

Contributions

A.S.M. and J.D.F. conceptualized the study. J.D.F., L.C., D.B., J.R.-B. and A.S.M. designed the methodology. L.T.-C., J.D.F. and R.L.-S. analysed the genomic data. C.C., A.S.-L., A.A.V., J.R.-B. and J.D.F. performed the experiments and contributed to data analysis. R.C. designed and supervised sampling and the collection of bacterial isolates. M.H.-G. collected the bacterial isolates. J.D.F. and A.S.M. analysed the data and prepared the original draft of the manuscript and undertook the reviewing and editing. All authors supervised and approved the final version of the manuscript. A.S.M. was responsible for funding acquisition and supervision.

Corresponding authors

Correspondence to Javier DelaFuente or Alvaro San Millan.

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Extended data

Extended Data Fig. 1 Enterobacteria carrying pOXA-48 recovered during the R-GNOSIS study.

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.

Extended Data Fig. 2 Growth dynamics of E. coli J53 carrying different PVs.

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.

Extended Data Fig. 4

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.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Data 1

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).

Supplementary Data 2

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.

Supplementary Data 3

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.

Supplementary Data 4

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.

Supplementary Data 5

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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