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Dysbiosis individualizes the fitness effect of antibiotic resistance in the mammalian gut


In the absence of antibiotics, it is essential that antibiotic resistance has a fitness cost for microorganisms if suspending antibiotics treatment is to be a useful strategy for reducing antibiotic resistance. However, the cost of antibiotic resistance within the complex ecosystem of the mammalian gut is not well understood. Here, using mice, we show that the same antibiotic resistance mutation can reduce fitness in one host, while being neutral or even increasing fitness in other hosts. Such antagonistic pleiotropy is shaped by the microbiota because resistance in germ-free mice is consistently costly across all hosts, and the host-specific effect on antibiotic resistance is reduced in hosts with similar microbiotas. Using an eco-evolutionary model of competition for resources, we identify a general mechanism that underlies between-host variation and predicts that the dynamics of compensatory evolution of resistant bacteria should be host specific, a prediction that was supported by experimental evolution in vivo. The microbiome of each human is close to unique, and our results suggest that the short-term cost of resistances and their long-term within-host evolution are also highly personalized, a finding that may contribute to the observed variable outcome of withdrawing antibiotics to reduce resistance levels.

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Fig. 1: Effect of microbiota on the fitness costs of resistances.
Fig. 2: Multi-species ecological model of pleiotropic AR mutations and the effect of a stable microbiome.
Fig. 3: A general ecological model predicts time-dependent and host-specific selection on AR after antibiotic treatment.
Fig. 4: Dynamics and genetic basis of compensatory evolution of AR strains across hosts.

Data availability

The fitness and sequencing data that support the findings of this study are available from the Dryad Digital Repository (

Code availability

The code of the simulations to produce the data of Figs. 2 and 3 are available from the Dryad Digital Repository (


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We thank the personnel of the IGC Rodent Facility, Genomic Facility and the Bioinformatics Unit for their assistance and A. Konrad for reading of the paper. L.L.C., P.D. and M.A. were supported by Fundação para a Ciência e Tecnologia (FCT) fellowships PD/BD/106003/2014, SFRH/BPD/118474/2016 and PD/BD/138735/2018, respectively. Research was supported by project JPIAMR/0001/2016-ERA NET and ONEIDA project (LISBOA-01-0145-FEDER-016417) cofunded by FEEI—Fundos Europeus Estruturais e de Investimento from Programa Operacional Regional Lisboa 2020, and by national funds from FCT.

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L.L.C. and P.D. performed the experiments. M.A. performed the modelling. All of the authors analysed the results. I.G. designed and coordinated the study. All of the authors contributed to writing the manuscript and provided final approval for publication.

Corresponding author

Correspondence to Isabel Gordo.

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Extended data

Extended Data Fig. 1 An indirect method for streptomycin detection in fecal samples.

a, Calibration curve to measure the concentration of streptomycin that provides a fitness advantage. Effect of different concentrations of streptomycin on fitness (+2SE) from pairwise competitions between E. coli MG1655 resistant to streptomycin (RpsLK43T or RpsLK43R single mutants or double mutant RpsLK43TRpoBH526Y, MIC above detection level > 8192 μg/ml) against a susceptible strain (MIC ≈ 2 μg/ml) in fecal medium supplemented with LB (n = 3 replicates per resistant mutant). Fecal samples of SPF mice not treated with antibiotic were resuspended (8 mL of PBS per gram of fecal content) and then filtered to remove the bacteria. To enrich the fecal medium with nutrients, 2 ml of LB was added per 8 mL of filtered medium. Increasing concentrations of antibiotic were added to this mixed fecal medium and pairwise competitions of 1:1 between resistant and susceptible strains were performed for 24 h at 37 °C. Fitness of the StrR mutants were measured as a ratio of Ln(Mut/WT) per generation. The method does not allow a reliable detection of streptomycin at concentrations below ≈2 µg/ml of streptomycin, as at these concentrations the mutants still show a fitness cost. b, The amount of streptomycin detected on the fecal samples of the single housed SPF mice (Fig. 1b), which were collected 4 h after the gavage of E. coli (20 h before the first time point in Fig. 1b). A sample of mice which receives continuous antibiotic treatment is shown as a red bar. Samples were filtered in 1 ml PBS and 250 μl LB added and competitions between the resistant (rpsLK43R) and susceptible strains were performed in triplicate. No antibiotic was detected in any sample using this assay (except the positive control).

Extended Data Fig. 2 Fitness effects of double and single resistances are individualized in the presence of a complex microbiome.

The fitness costs of the double mutant StrRRifR (DM) were measured against the single StrR (Mut) and against the single RifR in both in 3 single housed three specific-pathogen-free (SPF) mice carrying a complex microbiota (right panels, n = 3) and in germ-free devoid of microbiota (left panels, n = 3). Each mouse is represented by a different symbol (circles, squares and diamonds) and SPF mice have additional colors to highlight fitness effects differences (red and green represent cost or no cost of the AR mutation on that mouse, respectively). High variance of AR fitness effects across mice is observed when competing the double against the single resistances in the presence of a microbiota. This variance is observed even when both strains are resistant to streptomycin (StrRRifR vs StrR). To inquire if fitness effects were transitive, the fitness effects estimated from these competitions were compared to the values estimated from the competitions of the single mutants against the susceptible strain (Supplementary Table 6).

Extended Data Fig. 3 Effect of streptomycin perturbation on the microbiota composition shown as the relative frequencies of bacteria at the phylum level.

Microbiota composition was assayed through 16S amplicon sequencing, before and after antibiotic treatment, a, in individual caged mice and b, in co-housed mice. Each bar graph represents the composition in each mouse. In (b) the mice that were co-housed are shown by the same color (6 mice in green, 6 mice in blue and 6 mice in gray).

Extended Data Fig. 4 Co-housed animals share a more similar microbiota.

Principal coordinate Analysis (PCoA) from Unweighted UniFrac diversity measure of the microbiotas after antibiotic treatment (Day 1 of the competition in Figs. 1b and 1c). Co-Housed animals (green circles (6 mice where the fitness effect of StrR was measured), blue circles (6 mice where the fitness effect of RifR was measured), and grey circles (5 mice where the fitness effect of the double resistant was measured)) cluster accordingly to the housing and show reduced variance when compared with the singly housed animals (white circles). Variance analysis was conducted via the pairwise distances from the centroids of each group by using permutational ANOVA on the dispersion test (betadisper). The ellipses represent the standard deviation of point scores with a 95% confidence limit for each group.

Extended Data Fig. 5 Fitness effects and level of epistasis in vitro differ from the in vivo ones.

a, Fitness effects of each resistance mutation - StrR (rpsLK43T, in green), RifR (rpoBH526Y, in blue), StrRRifR (rpsLK43TrpoBH526Y, in grey) – in two commonly used in vitro environments: rich Luria Broth (LB) media and minimal media supplemented with 0.4% glucose (MM)- and in germ free (GF). Fitness effects in GF (shown in full bars darker color) from Fig. 1d (n = 3 mice, 5-day competition). Fitness effects in each in vitro media (LB, shown in full bars light color, and MM, shown in slashed bars, n = 3, 1 day-competition) with the same strains used in the GF mice. Error bars represent 2SE. b, Level of epistasis (ε) was measured assuming an additive model and its error calculated by error propagation1. Epistasis in LB and MM is shown in full grey bars and slashed bars, respectively. No epistasis is detected in GF (see Supplementary Table 6). The p-values of a 2-sided t-test are shown.

Extended Data Fig. 6 Pairwise correlation between the fitness effect of mutations and the Weighted UniFrac principal coordinates (PCoAs).

PCoAs from Weighted UniFrac diversity measure of the microbiotas after antibiotic treatment (Day 1 of the competition) grouped by mutation background a, StrR, b, RifR and c, StrRRifR). a-b, Both the fitness effects of StrR and RifR mutations correlate significantly with the PC2 (Pearson’s R and p-value shown).

Extended Data Fig. 7 Resistance mutations alter metabolism on specific carbon sources.

Growth curves were measured in minimal media supplemented either with 0.4% Glucose or 0.4% Mannose. a, Maximum growth rates in either of the sugars is shown for each of strains (susceptible in black, StrR in green, RifR in red and StrRRifR in gray). b, Carrying capacity measured as final OD600nm in both sugars. G and M stand for significant changes (P < 0.05, after correction for multiple testing) in glucose and mannose, respectively (N = 4, for each strain in each carbon source, Mann-Whitney test for comparison of medians between each resistance and the susceptible).

Extended Data Fig. 8 Selection is time-dependent and depends on the level of pleiotropy of the mutation.

a, Long-term dynamics of mutant x with low pleiotropy (left panel) and y with high pleiotropy (right panel) in each ecosystem (100 simulated microbiomes). b, Quantification of the selection coefficient (slope) from panel (a). Mutant x, (defined in Fig. 2), that represents a mutation conserving the trait ratio, shows similar fitness effect across the perturbed microbiomes, implying that pleiotropy is a necessary condition for the cost of resistance to be host-specific. Consistently mutant y shows dependence on the host microbiome. Over time the average cost of mutant y tends to the analytical prediction, which has a small value since for this mutation the presence of other species at equilibrium buffers its effect (Fig. 2).

Extended Data Fig. 9 Evidence for a time-dependent cost of AR in long-term competitions is consistent with model predictions.

The dynamics of Ln(Mut/WT) were followed for two weeks in 4 mice colonized with a mix of StrRRifR and RifR strains a, a mix of RifR and WT strains b, and a mix of StrRRifR and WT strains c, d. A long-term cost of resistance is observed (slopes (per day), after day 5 of the competitions: −0.2, p = 0.01; −0.37 p = 0.007; −0.37, p = 0.04; −0.27, p = 0.001; for (a), (b), (c) and (d), respectively, linear regression shown as black lines) even when no cost or even an increase in frequency of the resistance is detected in the short term (slopes (per day), during the initial days of the competitions: +0.3, p = 0.006; −0.1, p = 0.5; +0.43, p = 0.1; −0.26, p = 0.15; for (a), (b), (c) and (d), respectively, linear regression shown as red lines), as predicted by the model of resource competition. Fit of all the time points of the competition between StrRRifR and RifR strains to a quadratic model (indicating variable selection over time) is also shown in (a) (p = 0.0003, R2 = 0.93, quadratic model shown as green lines).

Extended Data Fig. 10 Loads of the resistant strains of E. coli colonizing the mice at weeks 3 and 6 of the evolution experiment.

The number of CFUs per feces gram are shown (pink measured at week 3 and red at week 6) for each mouse and each resistance background (x-axis). No significant correlation is found between the mean population size and the number of mutations accumulated (Spearman Correlation on the Log of the loads of E. coli with the number of mutations detected at week 6: Rs = 0.46, P = 0.056).

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Supplementary Figs. 1–4, Tables 1–7 and equations.

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Leónidas Cardoso, L., Durão, P., Amicone, M. et al. Dysbiosis individualizes the fitness effect of antibiotic resistance in the mammalian gut. Nat Ecol Evol 4, 1268–1278 (2020).

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