The spread of genes encoding antibiotic resistance is often mediated by horizontal gene transfer (HGT). Many of these genes are associated with transposons, a type of mobile genetic element that can translocate between the chromosome and plasmids. It is widely accepted that the translocation of antibiotic resistance genes onto plasmids potentiates their spread by HGT. However, it is unclear how this process is modulated by environmental factors, especially antibiotic treatment. To address this issue, we asked whether antibiotic exposure would select for the transposition of resistance genes from chromosomes onto plasmids and, if so, whether antibiotic concentration could tune the distribution of resistance genes between chromosomes and plasmids. We addressed these questions by analysing the transposition dynamics of synthetic and natural transposons that encode resistance to different antibiotics. We found that stronger antibiotic selection leads to a higher fraction of cells carrying the resistance on plasmids because the increased copy number of resistance genes on multicopy plasmids leads to higher expression of those genes and thus higher cell survival when facing antibiotic selection. Once they have transposed to plasmids, antibiotic resistance genes are primed for rapid spread by HGT. Our results provide quantitative evidence for a mechanism by which antibiotic selection accelerates the spread of antibiotic resistance in microbial communities.
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This work was partially supported by the National Institutes of Health (grant nos. R01A1125604, R01GM110494 and R01EB029466 to L.Y.), the National Science Foundation (no. MCB-1937259 to L.Y.) and David and Lucile Packard Foundation (L.Y.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Extended Data Fig. 1 Transposition dynamics, in response to antibiotic selection, is robust to variation in transposition rates (ηT), half-maximal effective concentration (Ap) and growth rate (μp) of Sp.
All simulations here ensure that the core assumptions of our mechanism are satisfied: Sc grows faster than Sp in the absence of the antibiotic, but the latter grows faster at a sufficiently high antibiotic concentration. All parameters are kept the same as in Fig. 1, unless noted otherwise. a, Transposition dynamics for varying ηT: 0.01 (blue), 10−4 (green), and 10−6 (black). b, Transposition dynamics for varying Ap: 20 (blue), 10 (green), and 5 (black). c, Transposition dynamics for varying μp: 0.5 (blue), 0.4 (green), and 0.3 (black).
Extended Data Fig. 2 Transposition dynamics depends on how Sc and Sp are individually affected by the antibiotic treatment.
In Fig. 1, we considered the case where (1) Sc grows faster than Sp in the absence of an antibiotic and (2) the growth rate of Sc decreases fasters than does the growth rate of Sp with increasing antibiotic concentrations. These antibiotic-dose responses are described by a set of Hill terms (equation (3) in Methods section). The general trend described in Fig. 1 captured the dynamics for the experimental systems analyzed. In general, the responses of the two strains to antibiotics can be diverse. Here, numerical simulations are performed with 3 different sets of nc and np values, while keeping other parameters the same as in Fig. 1. a, The growth rates of the subpopulation with chromosomal transposons (Sc) and the subpopulation with plasmid-based transposons (Sp) under different antibiotic concentrations. b, The relative growth rates (△μ) of the subpopulation with chromosomal transposons Sc) and the subpopulation with plasmid-based transposons (Sp) under different antibiotic concentrations. Here, different parameter sets generate three different trends of △μ with increasing antibiotic concentrations: (Left) △μ increases and then decreases, (Middle) △μ increases, decreases and then increases; (Right) △μ decreases, increases, and then decreases. c, Simulated dependence of the fraction of Sp on the antibiotic concentration. Overall, Sp is the dominant subpopulation in the culture above a threshold antibiotic concentration. Under some conditions (middle column), there may exist additional thresholds where the cost of carrying the transposon on the plasmid eventually outweighs its benefit.
Extended Data Fig. 3 Measurement of the transposon on plasmid fraction before and after antibiotic treatment by plasmid extraction and transformation.
Plasmids were extracted from the inoculation culture (a) and cultures after different antibiotic concentration treatment (b), and the fraction containing the resistance transposon was measured by qPCR (result shown in Fig. 2c) and then plasmids were transformed into E. coli DH5α. Next, qPCR was performed on the transformed culture to re-estimate the fraction of plasmids containing the resistant transposon. This result confirms the qPCR results shown in Fig. 2c. (mean ± S.D., n = 4). For the boxplot (a), the top whisker represents the maximum value, the top of the box represents the 75 percentile, the center line represents the median; the bottom of the box represents the 25th percentile, and the bottom whisker represents the minimum value.
Extended Data Fig. 4 Experiments with K-12 MG1655 confirmed the generality of the transposition to plasmid mechanism across E. coli strains.
(a) qPCR and (b) DNA electrophoresis demonstrate that the fraction of cells with the transposon on the plasmid, Sp, increases as antibiotic concentrations increase (data shown as mean ± S.D., n = 3). The MG1655 strain is used as the genetic background, and the same genetic constructs that were used in DH5α for previous experiments (See Fig. 2), were inserted into the homologous genomic region (strain S127). The top arrow in the panel (b) indicates the plasmid with the transposon insertion, and the bottom arrow indicates the empty plasmid. Two independent repeated experiments were performed for the DNA electrophoresis experiments (b).
Extended Data Fig. 5 Extended data demonstrating the increase of the copy number of resistance transposons by antibiotic selection, regardless of promoter, resistance gene, or plasmid origin.
The qPCR results in Fig. 3 show increasing Sp fractions with increasing antibiotic selection at different promoters, resistance genes, and plasmid origins. Here, we picked several samples from Fig. 3, and performed extended verification by DNA electrophoresis (a-c) or plasmid extraction and transformation (d) results (mean ± S.D., n = 4). The top arrow on the gel panels (a-c) represents the plasmid with transposon insertion, while the bottom arrow represents the empty plasmid. Two independent repeated experiments were performed for the DNA electrophoresis experiments (a-c).
Extended Data Fig. 6 Experiments with K-12 MG1655 demonstrated the generality of the transposition to plasmid mechanism, across E. coli strains, and under the control of different promoters.
qPCR showed transposon copy numbers increased with increasing antibiotic concentrations for synthetic transposons with different basal expression levels of the tetA resistance gene (mean ± S.D., n = 3). qPCR was performed on MG1655 strains containing the same high-copy-number plasmid, but with chromosomal-based transposons under the control of different promoters: a weak promoter in strain S128 (a) or a medium promoter in strain S129 (b).
Measurement of the growth rates of the strains with a stable tetA resistance gene on the chromosome or on different plasmids (strain S04, S05 S130-135) without antibiotic selection. In general, Sp grew slower than Sc without selection, indicating a higher burden caused by transposons on the plasmids. (n = 4 for PUC origin, n=6 for PBR322 origin, n=8 for other origins, p value calculated by two-tailed Student’s t-test). For each boxplot, the top whisker represents the maximum value, the top of the box represents the 75 percentile, the center line represents the median; the bottom of the box represents the 25th percentile, and the bottom whisker represents the minimum value.
As native-derived transposons may not be as active as the synthetic miniTn5-derived transposons, we used serial inoculation experiments to select for higher transposon copy numbers. Cultures were inoculated into deep well plates with increasing tetracycline concentrations over time. The protocol enriched cells with plasmid-borne transposons, which were hard to detect using the protocol designed for miniTn5-transposons.
Extended Data Fig. 9 Tn10 carrying the resistance gene transposed from the F plasmid to high-copy number pUC plasmids in response to antibiotic treatment.
a, Schematic of experimental design. F plasmid has a copy number of ~1-2; pUC plasmid has a copy number of ~200. b, qPCR showed that distribution of native Tn10 transposons shifted from the F plasmid to a high-copy plasmid (mean ± S.D., n = 3) at high antibiotic concentrations (strain S136).
Extended Data Fig. 10 Schematic of the method used to determine the Sp fraction by plasmid extraction and transformation, and of the method used to determine the copy number of transposons and plasmids in qPCR.
a, We used the Tet+ transposon and Kan+ plasmid as an example (Extended Data Fig. 3b) to show how we calculate the fraction of transposons on the plasmids. Three kinds of plasmids were extracted from the culture: originally empty plasmids (Kan+), plasmids with transposon insertions outside the kanR gene region (Kan+Tet+), and plasmids with transposons insertion inside the kanR gene region (Tet+). After transformation, plates with different antibiotic combinations were used to determine the total number of different plasmids from the original mixture. The detailed calculation process is in the method section. b, We used the Tet+ transposon and Kan+ plasmid as an example (Fig. 2c) to show how we calculate the copy of transposons or plasmids. During the chromosome integration process, a cmR gene was inserted into the chromosome together with but outside the transposon, so the probes for cmR gene can be used to represent the copy of chromosome. The transposon and the plasmid were marked with tetA and kanR gene respectively. We also constructed a DNA fragment with all three markers at 1:1:1 ratio, so this ca be used as a control to calculate the relative copies of different genes. For details of these calculations, see the Methods section.
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Yao, Y., Maddamsetti, R., Weiss, A. et al. Intra- and interpopulation transposition of mobile genetic elements driven by antibiotic selection. Nat Ecol Evol 6, 555–564 (2022). https://doi.org/10.1038/s41559-022-01705-2
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