Danna R. Gifford, Ernesto Berríos-Caro, Christine Joerres, Marc Suñé, Jessica H. Forsyth, Anish Bhattacharyya, Tobias Galla, Christopher G. Knight
Antibiotic combination therapies are an approach used to counter the evolution of resistance; their purported benefit is they can stop the successive emergence of independent resistance mutations in the same genome. Here, we show that bacterial populations with ‘mutators’, organisms with defects in DNA repair, readily evolve resistance to combination antibiotic treatment when there is a delay in reaching inhibitory concentrations of antibiotic—under conditions where purely wild-type populations cannot. In populations of Escherichia coli subjected to combination treatment, we detected a diverse array of acquired mutations, including multiple alleles in the canonical targets of resistance for the two drugs, as well as mutations in multi-drug efflux pumps and genes involved in DNA replication and repair.
Rising rates of resistance and declines in antimicrobial discovery have lead to an emerging public health crisis. Consequently, there is an urgent need for strategies that suppress resistance evolution to preserve existing antimicrobials. There has been sustained interest in the use of ‘combination therapy’ to prevent the development of resistance in infectious diseases. [1–3] and cancer . Combination therapy uses multiple drugs as part of the same treatment, an approach that has proved successful in various settings [5–9]. There is significant interest in expanding the use of combination therapy to tackle the global burden of antimicrobial resistance [10–12]. Considerable attention has been given to characterising how combinations inhibit bacterial growth [13–15], especially through exploiting non-additive effects (i.e. synergy and antagonism [3, 13, 16–20]) and higher-order interactions . However, there is currently conflicting evidence that combining therapies can stop the emergence of resistance, with combinations performing no better than monotherapy in some contexts [22, 23]. Determining what governs the resilience of combinations against resistance evolution therefore remains an open question.
Materials and method
Strains and media
Selection experiments involved ‘wild-type’ E. coli str. K-12 substr. BW25113 [F-, Δ(araD-araB)567, ΔlacZ4787(::rrnB-3), λ-, rph-1, Δ(rhaD-rhaB)568, hsdR514] , and a ‘mutator’ strain ΔmutS (as above, but with ΔmutS738::kan, indicating ΔmutS replacement with kanamycin resistance). The kanamycin resistance cassette has not previously been observed to affect resistance to the antibiotics we have considered here [24, 57]. Both strains were obtained from Dharmacon, Horizon Discovery Group, UK. Relative to the published reference genome , whole genome resequencing revealed no pre-existing mutations in the wild-type BW25113 background, and a single point mutation in the ΔmutS strain (1,985,889 G>A, resulting in an amino acid substitution in pgsA A137V), which does not have a known association with resistance.
Multi-resistance evolves in both single-drug and combination treatments when mutators are present
To determine the conditions under which multi-resistance evolves, we performed experimental evolution using four mutator frequency treatments (none, low, intermediate, high) and four selection regimes (antibiotic-free, single-drug with either rifampicin or nalidixic acid, combination with both antibiotics). We employed ramping selection, where antibiotic concentrations were doubled daily over six days (from 0.625 mg/l to 20 mg/l of each drug, where 10 mg/l of either is sufficient to inhibit wild-type growth). This concentration range is physiologically relevant for these antibiotics [49, 50]. We conducted daily assays to detect resistance, which is defined as the ability for a random sample of the population to grow on selective media containing the antibiotic(s) at concentrations above the minimum inhibitory concentration (MIC) of the wild-type strain.
A major motivation behind using antibiotic combination therapy is its presumed resilience against resistance evolution. We find that combination treatment can be effective at suppressing resistance evolution. However, our results question its resilience under two common scenarios that occur in clinic and nature: delayed inhibition and the presence of mutators. Multi-resistance evolved under both single-drug and combination treatments when mutators were introduced at frequencies often found in infection (Fig 1), despite conferring no clear advantage in single-drug environments. Increased mutation rate brought on by defective mismatch repair was sufficient to explain the emergence of multi-resistance via sequential acquisition of independent resistance mutations. Although other evolutionary mechanisms could be relevant in other contexts, they need not be invoked here, e.g. differences in birth and death rates affecting the emergence of resistance , acquiring fitter resistance alleles through clonal interference , differential supply of compensatory mutations , genomic variation influencing MIC , or co-evolution between genome and resistance mechanisms . Our findings raise concerns about the effectiveness of combination treatment in combating the evolution of drug resistance.
A vast number of combinations can be generated from existing antibiotics , which makes combination therapy an enticing approach for countering the rise of drug-resistant infections. While combinations treatments are indeed useful for reducing resistance evolution [40, 41], our results suggest that the potential for multi-resistance evolution needs thoughtful consideration in the design and application of such treatments. As direct assessment of all possible combinations is likely an insurmountable task , the experimental and modelling approaches developed here can serve as a framework for predicting whether particular combinations can suppress resistance evolution.
Citation: Gifford DR, Berríos-Caro E, Joerres C, Suñé M, Forsyth JH, Bhattacharyya A, et al. (2023) Mutators can drive the evolution of multi-resistance to antibiotics. PLoS Genet 19(6): e1010791. https://doi.org/10.1371/journal.pgen.1010791
Editor: Ivan Matic, Institut Cochin, FRANCE
Received: December 2, 2022; Accepted: May 18, 2023; Published: June 13, 2023
Copyright: © 2023 Gifford et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data, R scripts, and source code are freely available on GitHub (https://github.com/dannagifford/multi-resistance/), and were available during the review process.
Funding: This project was supported by the BBSRC (DRG and CGK: BB/M020975/1, CJ: BB/M011208/1), a UKRI Innovation/Rutherford Fund Fellowship (DRG: MR/R024936/1), the Academy of Medical Sciences (DRG: SBF007\100096), a University of Manchester Presidential Scholarship (EBC), a Postdoctoral Seed Award from Earth and Environmental Sciences, The University of Manchester (DRG), and a Wellcome Trust Institutional Strategic Support Fund award (DRG, MS, TG and CGK: part of 204796/Z/16/Z). TG acknowledges support from The Maria de Maeztu program for Units of Excellence in R&D (MDM-2017-0711). MS acknowledges the support of the Swedish Research Council (Grant No. 638-2013-9243). The funders had no role in study design, data collection and analysis, preparation of, or decision to publish the manuscript.
Competing interests: The authors have declared that no competing interests exist.