1. Evolutionary Biology
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An adjunctive therapy administered with an antibiotic prevents enrichment of antibiotic-resistant clones of a colonizing opportunistic pathogen

  1. Valerie J Morley  Is a corresponding author
  2. Clare L Kinnear
  3. Derek G Sim
  4. Samantha N Olson
  5. Lindsey M Jackson
  6. Elsa Hansen
  7. Grace A Usher
  8. Scott A Showalter
  9. Manjunath P Pai
  10. Robert J Woods
  11. Andrew F Read
  1. Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, United States
  2. Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, United States
  3. Department of Biochemistry and Molecular Biology, The Pennsylvania State University, United States
  4. Department of Chemistry, The Pennsylvania State University, United States
  5. Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, United States
  6. Huck Institutes for the Life Sciences, The Pennsylvania State University, United States
  7. Department of Entomology, The Pennsylvania State University, United States
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Cite this article as: eLife 2020;9:e58147 doi: 10.7554/eLife.58147

Abstract

A key challenge in antibiotic stewardship is figuring out how to use antibiotics therapeutically without promoting the evolution of antibiotic resistance. Here, we demonstrate proof of concept for an adjunctive therapy that allows intravenous antibiotic treatment without driving the evolution and onward transmission of resistance. We repurposed the FDA-approved bile acid sequestrant cholestyramine, which we show binds the antibiotic daptomycin, as an ‘anti-antibiotic’ to disable systemically-administered daptomycin reaching the gut. We hypothesized that adjunctive cholestyramine could enable therapeutic daptomycin treatment in the bloodstream, while preventing transmissible resistance emergence in opportunistic pathogens colonizing the gastrointestinal tract. We tested this idea in a mouse model of Enterococcus faecium gastrointestinal tract colonization. In mice treated with daptomycin, adjunctive cholestyramine therapy reduced the fecal shedding of daptomycin-resistant E. faecium by up to 80-fold. These results provide proof of concept for an approach that could reduce the spread of antibiotic resistance for important hospital pathogens.

eLife digest

Antibiotics are essential for treating infections. But their use can inadvertently lead to the emergence of antibiotic-resistant bacteria that do not respond to antibiotic drugs, making infections with these bacteria difficult or impossible to treat. Finding ways to prevent antibiotic resistance is critical to preserving the effectiveness of antibiotics.

Many bacteria that cause infections in hospitals live in the intestines, where they are harmless. But these bacteria can cause life-threatening infections when they get into the bloodstream. When patients with bloodstream infections receive antibiotics, the bacteria in their intestines are also exposed to the drugs. This can kill off all antibiotic-susceptible bacteria, leaving behind only bacteria that have mutations that allow them to survive the drugs. These drug-resistant bacteria can then spread to other patients causing hard-to-treat infections.

To stop this cycle of antibiotic treatment and antibiotic resistance, Morley et al. tested whether giving a drug called cholestyramine with intravenous antibiotics could protect the gut bacteria. In the experiments, mice were treated systemically with an antibiotic called daptomycin, which caused the growth of daptomycin-resistant strains of bacteria in the mice’s intestines. In the laboratory, Morley et al. discovered that cholestyramine can inactivate daptomycin.

Giving the mice cholestyramine and daptomycin together prevented the growth of antibiotic-resistant bacteria in the mice's intestines. Moreover, cholestyramine is taken orally and is not absorbed into the blood. It therefore only inactivates the antibiotic in the gut, but not in the blood.

The experiments provide preliminary evidence that giving cholestyramine with antibiotics might help prevent the spread of drug resistance. Cholestyramine is already used to lower cholesterol levels in people. More studies are needed to determine if cholestyramine can protect gut bacteria and prevent antibiotic resistance in people.

Introduction

Vancomycin-resistant Enterococcus faecium (VR E. faecium) is an important cause of antibiotic-resistant infections in healthcare settings (Arias and Murray, 2012; García-Solache and Rice, 2019; O'Driscoll and Crank, 2015). The antibiotic daptomycin is one of the few remaining first-line therapies for VRE infection (O'Driscoll and Crank, 2015), but daptomycin-resistance is spreading in VRE populations (Judge et al., 2012; Kamboj et al., 2011; Kinnear et al., 2019; Woods et al., 2018). Therapeutic daptomycin use is thought to be a key driver of resistance (Kinnear et al., 2020; Woods et al., 2018). Managing the evolution of daptomycin-resistance in healthcare settings is crucial to future control of VRE infections.

E. faecium is an opportunistic pathogen that colonizes the human GI tract asymptomatically, spreads via fecal-oral transmission, and causes symptomatic infections when introduced to sites like the bloodstream or the urinary tract (Arias and Murray, 2012). E. faecium colonizing the gut may be exposed to daptomycin during therapeutic use, potentially contributing to the transmission of daptomycin-resistant E. faecium. Daptomycin is administered intravenously to treat infections caused by pathogens including VRE and Staphylococcus aureus. Daptomycin is primarily eliminated by the kidneys, but 5–10% of the dose enters the intestines through biliary excretion (Woodworth et al., 1992). We hypothesize that this therapeutically unnecessary intestinal daptomycin exposure could drive resistance evolution in E. faecium colonizing the gut. Increased resistance in colonizing populations is important, because gut E. faecium populations are sources for nosocomial infections and transmission between patients (Alevizakos et al., 2017; Olivier et al., 2008).

If unintended intestinal daptomycin exposure drives resistance evolution in E. faecium, this offers an opportunity to intervene. The opportunity emerges from a key feature of this system—the bacteria causing infection are physically separated from the population contributing to transmission. If daptomycin could be inactivated in the intestine without altering plasma concentrations, daptomycin could be used to kill bacteria at the target infection site without driving resistance in off-target populations. Preventing resistance evolution in these reservoir populations could protect patients from acquiring resistant infections, and it could limit the shedding of resistant strains and so onward transmission to other patients. We hypothesized that co-administering an oral adjuvant that reduces daptomycin activity would prevent selection for daptomycin-resistance in the gut during systemic daptomycin treatment. We tested this strategy using the adjuvant cholestyramine in a mouse VR E. faecium gut colonization model.

Results

Generation of daptomycin-resistant VR E. faecium in the mouse GI tract

To directly test the proposition that systemic daptomycin treatment could select for resistance in the GI tract, and to generate daptomycin-resistant VR E. faecium mutants for subsequent experiments, we inoculated mice orally with daptomycin-susceptible VR E. faecium strains. Beginning one day after E. faecium inoculation, mice received daily doses of either subcutaneous daptomycin (50, 100, or 400 mg/kg), oral daptomycin (5, 50, 100, or 400 mg/kg), or a control mock injection for 5 days. We used a range of doses and routes of administration to maximize the likelihood of observing resistance emergence in at least one of the mice. The 50 and 100 mg/kg subcutaneous doses were selected to generate pharmacokinetics similar to clinical human doses (Mortin et al., 2007; Samonis et al., 2008), and the 5 mg/kg oral approximates the 5–10% of a daptomycin dose that is secreted into the intestines during standard intravenous treatment (Woodworth et al., 1992). We used two susceptible VR E. faecium strains, BL00239-1 (MICc = 2.0 (Minimum Inhibitory Concentration computed, see Methods)) and PR00708-14 (MICc = 2.7), which were originally isolated at the University of Michigan Hospital from a clinical bloodstream infection and a different patient’s clinical perirectal swab, respectively. Mouse fecal samples were collected to quantify VR E. faecium shedding and determine daptomycin susceptibility of isolated E. faecium clones.

Only very high daptomycin doses (400 mg/kg subcutaneous, ≥50 mg/kg oral) consistently reduced fecal VR E. faecium below the level of detection during treatment; with lower doses, VR E. faecium shedding was often detectable throughout treatment (Figure 1A). For Strain BL00239-1, E. faecium clones with increased daptomycin-resistance were isolated from two of three mice following treatment with 100 mg/kg subcutaneous daptomycin (Figure 1B–C). We chose one of these daptomycin-resistant clones to use in subsequent experiments (strain BL00239-1-R, MICc = 8.6). Sequencing of the core genome showed that the resistant strain acquired a mutation in the major cardiolipin synthase clsA gene (R211L, CGA→CTA), which has been previously described in association with daptomycin-resistance (Adams et al., 2015), and a transposon insertion into the methionine sulfoxide reductase msrA gene (Zhao et al., 2010). For the second strain (PR00708-14), mice were treated with subcutaneous daptomycin (50, 100, or 200 mg/kg) or a mock injection. We screened for the emergence of increased resistance by plating mouse fecal suspensions on daptomycin-supplemented agar. Samples from two mice treated with 200 mg/kg daptomycin produced colonies on daptomycin-supplemented plates, and we isolated three clones from each of these samples. These isolated clones had increased daptomycin MICc relative to PR00708-14 by broth microdilution (MICc = 13.5, 11.5, and 8.0 from one mouse; MICc = 12.0, 29.8, and 12.0 from the second mouse).We chose one of these isolated clones for use in subsequent experiments (PR00708-14-R, MICc = 12.0). Genome sequencing revealed that PR00708-14 and PR00708-14-R differed by two mutations, which to our knowledge have not previously been associated with daptomycin-resistance. We identified mutations in a TerC family integral membrane protein (locus tag HMPREF0351_10759 in D0 E. faecium reference genome, I22T, CGA→CTA) and in a hypothetical protein (locus tag HMPREF0351_12146 in D0 E. faecium reference genome, frameshift, deleted G at 190nt). These experiments show that daptomycin-resistance can emerge de novo in E. faecium colonizing the GI tract following systemic daptomycin treatment.

Emergence of daptomycin-resistant VR E. faecium in mouse GI tracts following subcutaneous daptomycin treatment.

(A) VR E. faecium densities in fecal samples during and after daptomycin treatment (strain BL00239-1). Each line represents VR E. faecium densities from an individual mouse (N = 3 per treatment). The pink shaded region indicates days of daptomycin therapy. The dotted line marks the detection limit. Red dots indicate time points where clones were isolated for analysis shown in Panel B. The 400 mg/kg subcutaneous treatment was discontinued after 4 days due to apparent toxicity, and one mouse in this treatment was euthanized at Day 4. (B) Following daptomycin treatment, three VR E. faecium clones were isolated from the feces of each mouse. Filled points show the mean of triplicate daptomycin susceptibility (MICC) measurements for each clone, and open points show the individual measurements. Point shape indicates the mouse of origin. The dashed line marks the clinical breakpoint for daptomycin susceptibility. The ancestral clone (BL00239-1) used to inoculate mice is also shown. (C) For the 100 mg/kg subcutaneous treatment, VR E. faecium clones were isolated from each mouse at multiple time points. Filled points show the mean of triplicate daptomycin susceptibility (MICC) measurements for each clone, and open points show individual measurements. Color indicates mouse of origin. The dotted line marks the clinical breakpoint for daptomycin susceptibility. The resistant clone used in subsequent experiments (BL00239-1-R) is circled in red.

Daptomycin treatment enriches for daptomycin-resistant VR E. faecium in the GI tract

We used the de novo resistant mutants isolated above (Figure 1) to test whether daptomycin therapy selects for daptomycin-resistance in intestinal VR E. faecium populations when a resistant mutant is already present. We orally inoculated mice with a 1:20 mixture of the experimentally generated daptomycin-resistant and susceptible VR E. faecium strains (BL00239-1-R and BL00239-1). Note that this approach – seeding inocula with known numbers of resistant bacteria – allows the response to selection to be measured while avoiding the experimental noise introduced by mutation waiting times. Mice were treated with subcutaneous daptomycin (50, 75, 100, or 200 mg/kg) for five or ten days after VRE inoculation. Control mice received either a mock saline injection or no injection. Fecal samples from Days 8 and 14 post-inoculation were plated in triplicate to quantify total VR E. faecium density, and samples were also plated on daptomycin-supplemented agar to estimate the proportion of VR E. faecium that were daptomycin-resistant. Control populations remained susceptible to daptomycin, but all doses and durations of daptomycin dramatically enriched for resistance in the GI tract (Figure 2A–B). At both time points, controls had significantly lower proportions of resistant bacteria than daptomycin-treated mice (Figure 2A–B; mixed effects negative binomial regression, p<0.01, see Model one in Supplementary file 1). The effect sizes (Cohen’s d) at Days 8 and 14 were 5.90 (95% CI 4.94, 6.86) and 2.34 (95% CI 1.82, 2.87), respectively. The absolute numbers of VRE enumerated in fecal samples did not vary significantly between treatments (mixed effects negative binomial regression, Model two in Supplementary file 1; Figure 2—figure supplement 1).

Figure 2 with 1 supplement see all
Subcutaneous daptomycin treatment selects for resistance in the GI tract.

(A-B) Mouse fecal suspensions were plated on Enterococcus-selective plates with daptomycin (+ DAP) and without daptomycin (- DAP) at Day 8 (A) and Day 14 (B). Each filled point represents the mean of triplicate measures from a single mouse sample, and open points show individual measurements. Mice were treated with daptomycin for 5 days (triangles) or 10 days (squares) at the doses listed at times denoted (gray crosses in C; N = 5 mice per treatment). Samples with VR E. faecium density <3×103 CFU/10 mg feces were not plated for this assay due to insufficient bacterial density. (C) Recovered fecal daptomycin measured by LC-MS for a subset of mice. Each line tracks daptomycin measurements from a single mouse sampled at Days 2, 6, and 8. (D) For the subset of fecal samples analyzed by LC-MS, fecal daptomycin plotted against fecal VR E. faecium densities (samples from all available treatments and time points plotted together). Dotted line indicates the MIC of the susceptible strain BL00239-1 (MIC = 2 µg/mL or 2 µg/g).

The dramatic enrichment for daptomycin-resistant VR E. faecium in treated mice showed that subcutaneously-administered daptomycin produced GI tract concentrations high enough to select for resistance. To quantify fecal daptomycin concentrations, we analyzed fecal samples from a subset of daptomycin-treated mice by liquid chromatography-mass spectrometry (LC-MS) (Figure 2C). Samples from all time points tested (Days 2, 6, and 8) contained detectable daptomycin, and concentrations generally peaked at the end of treatment (Day 6). Higher daptomycin doses generally corresponded to higher fecal concentrations, but concentrations were highly variable and overlapped between treatments. While fecal VR E. faecium densities correlated poorly with the daptomycin dose administered (Figure 2—figure supplement 1), fecal VR E. faecium densities correlated with the amount of daptomycin recovered in feces (Figure 2D). These data confirmed that subcutaneously-administered daptomycin at our experimental doses generated a range of daptomycin concentrations in the GI tract that included inhibitory concentrations for the susceptible VR E. faecium strain.

We ran two additional experiments to further investigate the competitive dynamics between this susceptible and resistant pair (BL00239-1 and BL00239-1-R) in the presence and absence of daptomycin treatment. First, we tested whether susceptible bacteria competitively suppressed resistant bacteria in the GI tract. We inoculated mice with either a mixture of 108 CFU susceptible + 103 CFU-resistant VR E. faecium, or with a resistant-only inoculum at one of two inoculum sizes (108 CFU or 103 CFU). Mice received 5 days of subcutaneous daptomycin injections at 100 mg/kg or control saline injections. Shedding of resistant and susceptible bacteria were quantified at time points throughout the experiment by plating (Figure 3A). In the absence of daptomycin treatment, the daptomycin-susceptible strain remained the most prevalent in mixed populations. When mixed populations were exposed to daptomycin, resistance increased to high frequency in three populations, and the population size fell dramatically in the remaining two populations. In mice inoculated with only 103 CFU-resistant bacteria, the resistant clone was able to grow to high numbers with or without daptomycin. These data were consistent with the competitive suppression of the resistant strain by the susceptible strain in the absence of daptomycin treatment, and competitive release of the resistant strain during daptomycin treatment (Day et al., 2015; Wargo et al., 2007).

Competitive dynamics between daptomycin-resistant and susceptible VRE faecium.

(A) Each panel shows VR E. faecium counts on plates with daptomycin (+DAP, red line) and without daptomycin (-DAP, blue line) for a single mouse over time. Labels show initial inocula. Red shading indicates days of daptomycin treatment (100 mg/kg daily subcutaneous injections). (B) In a second experiment, mice were inoculated with a mix of susceptible and resistant bacteria (S+R, 20% R), resistant bacteria only (R), or susceptible bacteria only (S). Mice received no drug treatment. At Day 14, fecal suspensions were plated on plates with daptomycin (+DAP) and without daptomycin (-DAP) to determine whether the resistant strain had decreased in frequency. The starting inoculum dose is shown in red, and Day 14 samples from each mouse are shown in gray. Filled points show means of replicate measurements for a sample, and open circles show individual measurements. Note that resistant bacteria do not form colonies at 100% efficiency on +DAP plates.

Next, we tested whether the frequency of daptomycin-resistant VR E. faecium would decrease over time in the absence of daptomycin treatment, potentially indicating that daptomycin-resistance comes at a fitness cost. We inoculated mice with a 1:5 mixture of daptomycin-resistant and susceptible VR E. faecium. Mice received no daptomycin treatment. After 14 days, the proportion of resistant bacteria had declined (one sample t-test, t = −22.42, df = 9, p<0.01, Cohen’s d = 7.09), consistent with a competitive disadvantage (fitness cost) to the daptomycin-resistance mutation (Figure 3B). Control mice inoculated with only resistant or only susceptible bacteria did not have significantly different proportions of resistance between days 0 and 14 (resistant: t = −3.73, df = 2, p=0.06; susceptible: t = 1, df = 2, p=0.42).

In vitro characterization of cholestyramine as a potential adjuvant

If an orally-administered adjuvant could reduce daptomycin activity in the GI tract, this could prevent the emergence of daptomycin-resistant E. faecium in the gut, potentially reducing transmission of resistant bacteria without impacting the effectiveness of intravenous daptomycin therapy. We identified cholestyramine, an FDA-approved bile-acid sequestrant, as a potential adjuvant for daptomycin therapy. Cholestyramine is a high-molecular weight anion exchange resin that binds with bile acids, forming an insoluble complex that is excreted in the feces (Jacobson et al., 2007). Cholestyramine is known to interact with a number of co-administered drugs through the same mechanism, reducing their bioactivity (Jacobson et al., 2007). We hypothesized that cholestyramine would bind daptomycin based on their chemical structures.

In vitro tests were consistent with cholestyramine binding daptomycin. Daptomycin solutions were incubated with cholestyramine, and then the cholestyramine was removed by centrifugation. The resulting supernatants were analyzed for changes in daptomycin concentration and activity. Daptomycin concentrations can be measured directly by ultraviolet (UV) absorbance at 364 nm (Figure 4A). Daptomycin concentrations were reduced in supernatants after incubation with cholestyramine in a dose-dependent manner (Figure 4B). Additionally, daptomycin solutions incubated with cholestyramine had reduced antibiotic activity against E. faecium in broth microdilution (Figure 4C). Together, these results were consistent with cholestyramine removing daptomycin from solution, supporting cholestyramine as a candidate adjuvant for daptomycin therapy.

Cholestyramine captures daptomycin in vitro.

(A) Calibration curve (best fit linear regression) showing that daptomycin concentration can be measured by UV absorption at 364 nm (N = 3 per concentration). (B) Daptomycin concentration was reduced in solutions treated with cholestyramine (N = 3 per concentration). (C) Daptomycin solutions treated with cholestyramine had reduced biological activity against VRE in broth microdilutions (N = 3 per antibiotic treatment). Bacterial densities (OD600) following growth in the presence of daptomycin (DAP), daptomycin solution treated with cholestyramine (DAP + CHOL), or saline solution treated with cholestyramine (saline + CHOL) are shown. Concentrations are shown as the initial concentration of daptomycin in solution prior to cholestyramine treatment. Saline controls were constant across all listed concentrations. Horizontal line shows detection threshold.

Adjunctive cholestyramine therapy prevents emergence of daptomycin-resistance

We conducted four experiments to test whether adjunctive therapy with cholestyramine could prevent the emergence of daptomycin-resistant VR E. faecium in the mouse GI tract. In each experiment, mice were orally inoculated with a 1:20 mixture of daptomycin-resistant and susceptible VR E. faecium and then treated with subcutaneous daptomycin injections for 5 days. Densities of total VR E. faecium and daptomycin-resistant VR E. faecium were determined by plating (Figure 5, Figure 5—figure supplements 15). The experiments tested the evolutionary impact of oral cholestyramine in different mouse strains, with different VR E. faecium strains, and with different timing of cholestyramine administration. The design of the four experiments was as follows: (A) Swiss Webster mice with E. faecium strains BL00239-1 and BL00239-1-R, with cholestyramine started one day before daptomycin (Figure 5—figure supplement 1), (B) C57BL/6 mice with E. faecium strains BL00239-1 and BL00239-1-R, with cholestyramine started one day before daptomycin (Figure 5—figure supplement 2), (C) Swiss Webster mice with E. faecium strains PR00708-14 and PR00708-14-R, with cholestyramine started one day before daptomycin (Figure 5—figure supplement 3), and (D) Swiss Webster mice with E. faecium strains BL00239-1 and BL00239-1-R, with cholestyramine started the same day as daptomycin (Figure 5—figure supplement 4). Data from these experiments were analyzed together, with a block effect included in the models. Because bacterial densities were found not to correlate to daptomycin dose (Figure 2—figure supplement 1), all daptomycin doses were combined into a single group for analysis. Figure 5—figure supplements 14 show these data broken down by experiment and by daptomycin dose. For daptomycin-treated mice shedding detectable levels of VR E. faecium by our plating assay (at least 20 CFU/10 mg feces at a given time point), the cholestyramine-supplemented diet reduced the proportion of daptomycin-resistant VR E. faecium (mixed effects binomial regression, p<0.01, Model three in Supplementary file 1). The effect size (Cohen’s d) of cholestyramine diet on the proportion of resistant bacteria in daptomycin-treated mice was 0.41 (95% CI 0.01, 0.82) at Day 2, 0.56 (95% CI 0.13, 1.00) at Day 4, 0.90 (95% CI 0.45, 1.35) at Day 6, 1.08 (95% CI 0.63, 1.53) at Day 8, and 0.63 (95% CI 0.21, 1.05) at Day 14. In addition, to more accurately determine resistance proportions for Days 8 and 14, we plated an estimated 200 CFU from each sample in triplicate on plates with and without daptomycin (Figure 5—figure supplement 5). This second assay confirmed that cholestyramine reduced the proportion of resistant VR E. faecium at these time points (mixed effects binomial regression, p<0.01, Model four in Supplementary file 1; Cohen’s d 1.25 (95% CI 0.97, 1.53) at Day 8, 0.73 (95% CI 0.48, 0.98) at Day 14).

Figure 5 with 5 supplements see all
Adjunctive cholestyramine reduces enrichment of daptomycin-resistance in the GI tract.

(A) The proportion of fecal VR E. faecium that were daptomycin-resistant over time in mice. Proportions were determined by plating on agar with daptomycin (+DAP) and without daptomycin (-DAP). Data shown were combined from four experiments (for each diet N = 20 controls and N = 50 daptomycin-treated, mean + SEM shown). Proportions were not determined for samples with <20 CFU VR E. faecium per 10 mg feces, as these densities were below the limit of detection for this plating assay, and these samples were not included in Panel A. The pink shaded region indicates days of daptomycin therapy. (B) Total VR E. faecium densities corresponding to data shown in Panel A (for each diet N = 20 controls and N = 50 daptomycin-treated, mean + SEM shown). Dotted line shows total density (-DAP) and solid line shows the density of daptomycin-resistant VR E. faecium (+DAP). All samples, including those with low densities, were included in Panel B.

We also quantified absolute densities of daptomycin-resistant and susceptible VR E. faecium over time by plating samples from Days 0, 1, 2, 4, 6, 8, and 14 (Figure 5). These data showed that the cholestyramine-supplemented diet reduced fecal shedding of daptomycin-resistant VR E. faecium in daptomycin-treated mice (Antibiotic*Diet*Day interaction p<0.01, Model five in Supplementary file 1). The effect size was greatest in the days after daptomycin treatment. The effect size (Cohen’s d) of cholestyramine diet on shedding of resistant bacteria in daptomycin-treated mice was 0.43 (95% CI 0.02, 0.83) at Day 2, 0.08 (95% CI −0.32, 0.48) at Day 4, 0.12 (95% CI −0.28, 0.51) at Day 6, 0.36 (95% CI −0.04, 0.76) at Day 8, and 0.57 (95% CI 0.16, 0.98) at Day 14.

Total VR E. faecium shedding was also influenced by the addition of cholestyramine (Antibiotic*Diet*Day interaction p<0.01, Model six in Supplementary file 1). Here the effect size is greatest during daptomycin treatment. The effect size (Cohen’s d) of cholestyramine diet on total shedding in daptomycin-treated mice was 0.84 (95% CI 0.43, 1.23) at Day 2, 0.36 (95% CI −0.04, 0.76) at Day 4, 0.31 (95% CI −0.09, 0.71) at Day 6, 0.04 (95% CI −0.36, 0.43) at Day 8, and 0.40 (95% CI 0.00, 0.80) at Day 14. If we consider only the control treatments, where there is no possibility of cholestyramine protecting against daptomycin killing, the addition of cholestyramine to the diet does not significantly influence total VR E. faecium counts alone (p=0.63, Model seven in Supplementary file 1) or in combination with day (p=0.18).

Discussion

Here we have shown proof of concept for an adjunctive therapy approach to prevent the emergence of daptomycin-resistant E. faecium in the GI tract during daptomycin therapy. Ideally, this approach would allow clinicians to treat bloodstream infections with intravenous daptomycin without fueling the hospital transmission of multidrug-resistant bacteria. This would be a novel approach because the desired outcome is reduced resistance evolution and reduced transmission of resistant pathogens. Colonization with VR E. faecium is a risk factor for future infection (Alevizakos et al., 2017; Olivier et al., 2008), presumably because GI tract populations are sources for infections. By maintaining daptomycin sensitivity GI tract populations, this strategy could reduce the risk of resistant infection for colonized patients, as well as reducing transmission of resistance between patients. With some optimization, this adjunctive approach could be implemented in hospitals. Cholestyramine is an inexpensive, FDA-approved drug with few side effects (Beckett and Wilhite, 2015; Jacobson et al., 2007). Cholestyramine has been used clinically for over 50 years, including in hospital patient populations that would be targets for adjunctive cholestyramine therapy (Ballantyne, 2009). Cholestyramine does have potential to interfere with other orally administered drugs, which could be a risk considered on a patient by patient basis (Scaldaferri et al., 2013). This drug–drug interaction potential is managed in practice by administering oral drugs an hour before or 4 to 6 hr after cholestyramine administration. Because the effects and risks of cholestyramine are well understood, testing cholestyramine as an adjuvant with daptomycin in human trials is appealingly low risk to patient health while offering large potential gains to hospital infection control.

While cholestyramine has the potential to be repurposed in hospitals, this study also revealed potential limitations of cholestyramine therapy and areas requiring further study prior to clinical trials. Cholestyramine therapy was most effective in a narrow range of conditions (intermediate daptomycin doses, cholestyramine started prior to daptomycin therapy) (Figure 5—figure supplements 15), and the daptomycin dose at which resistance was most likely to be enriched varied between E. faecium strains (Figure 5—figure supplements 15). The effect of cholestyramine was also variable among mice (Figure 5—figure supplements 15), possibly as a result of variable daptomycin concentrations in the intestines (Figure 2C–D). This is potentially a major limitation for translating this therapy, as daptomycin pharmacokinetics and pharmacodynamics also vary among human patients. Optimizing cholestyramine therapy will require detailed data on intestinal daptomycin pharmacokinetics, including temporal variation over the course of treatment and variation among individuals. From these data, one could estimate a maximum target for daptomycin capture, and optimize cholestyramine dosing accordingly. This therapeutic approach has the potential to be expanded to other pathogen-drug combinations, but there is likely no one size fits all adjunctive therapy; optimization may be required for each pathogen-drug combination.

In addition to binding antibiotics, cholestyramine sequesters bile acids in the intestine, which alters the GI tract environment. This means that cholestyramine treatment could indirectly affect bacteria in the GI tract through mechanisms other than direct interactions with drugs. For example, exposure to bile acids has implications for E. faecium phenotypes. Secondary bile acids trigger a morphotype switch in Enterococcus that facilitates intestinal colonization and biofilm formation (McKenney et al., 2019). In our experiments, we did not observe differences in colonization efficiency or VR E. faecium counts in mice treated with cholestyramine alone. In mouse models, cholestyramine has also been shown to reduce bile acid-mediated resistance to Clostridium difficile infection, which is a possible risk associated with cholestyramine treatment (Buffie et al., 2015).

Here we tested cholestyramine as an adjuvant to reduce selection for antibiotic resistance in the GI tract, but other adjuvants could be used or designed for the same purpose. At least two other drugs that reduce antibiotic activity in the GI tract are currently in development. DAV-132, a formulation of activated charcoal encased in zinc-pectinate beads, was shown to site-specifically bind antimicrobials in the gut in a stage I clinical trial (de Gunzburg et al., 2018; de Gunzburg et al., 2015). DAV-132 has been shown to reduce fecal concentrations of antibiotics by 99% without affecting plasma concentrations (Burdet et al., 2017; de Gunzburg et al., 2018; de Gunzburg et al., 2015; Khoder et al., 2010). Activated charcoal could adsorb antibiotics in the gut, and could be an effective adjuvant for a broad range of antibiotic classes. Adjuvants with specific activity against particular antibiotics could also be developed. This has been successfully demonstrated with orally-administered β-lactamases given with intravenous β-lactam antibiotics. β-lactamases enzymatically inactivate β-lactams. Under the name SYN-004, this β-lactamase treatment advanced to clinical trials in human subjects (Kaleko et al., 2016; Kokai-Kun et al., 2017; Pitout, 2009; Tarkkanen et al., 2009). Data from clinical trials show the drugs successfully inactivate β-lactams in the digestive tract without adversely affecting levels of antibiotic in plasma (Kaleko et al., 2016; Kokai-Kun et al., 2017; Pitout, 2009; Tarkkanen et al., 2009). These drugs have been developed with the goal of preventing infection with C. difficile after antibiotic therapy, but they could likely also prevent the emergence of antibiotic resistance in the GI tract. The orally-administered β-lactamase SYN-006 mitigated the enrichment of genes associated with antibiotic resistance in the microbiomes of pigs treated with a carbapenem antibiotic (Connelly et al., 2019). So far as we are aware, there is no direct experimental evidence analogous to ours that those drugs can prevent resistance emergence in colonizing opportunistic pathogens, but it seems likely they could.

Adjunctive therapies like the one proposed here could help manage resistance evolution in other important pathogens listed by Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) as major threats (CDC, 2019; WHO, 2017). Like VRE, many opportunistic pathogens experience substantial antibiotic exposure when they are not the targets of treatment. Opportunistic pathogens like Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, and Enterobacter cloacae colonize the gut asymptomatically, where they can be unintentionally exposed to antibiotics (Morley et al., 2019). According to one estimate, over 90% of the total antimicrobial exposure experienced by K. pneumoniae occurs when K. pneumoniae was not the target of treatment (Tedijanto et al., 2018). For H. influenzae, E. coli, and Staphylococcus aureus over 80% of total exposure to antibiotics was estimated to occur when the bacteria were bystanders (Tedijanto et al., 2018). These colonizing populations are sources for infections, so selection for resistance in bystander populations can contribute to rising rates of resistant infections (Morley et al., 2019). Adjunctive strategies that allow intravenous antibiotics to reach target sites while reducing off-target exposure could help stem the spread of resistant pathogens listed by the CDC as urgent or serious threats (CDC, 2019), including carbapenem-resistant Enterobacteriaceae (CRE), extended-spectrum beta-lactamase producing Enterobacteriaceae, vancomycin-resistant Enterococcus (VRE), methicillin-resistant Staphylococcus aureus (MRSA), and drug-resistant Staphylococcus aureus. The strategy presented here focuses on inactivating antibiotic in the gastrointestinal tract, the likely source of the bulk of the antimicrobial resistance in pathogens listed as top threats by the CDC, but similar strategies could be developed to shield microbiota at other sites, such as the skin and respiratory tract.

While adjunctive therapies have potential to be used for a variety of pathogens, the genetic mechanism of resistance will influence the resulting evolutionary dynamics. The cholestyramine therapy described here prevents enrichment of resistant clones within a bacterial population consisting of both sensitive and resistant bacteria. Daptomycin-resistance in E. faecium emerges through point mutations in the genome, so patients colonized with daptomycin-susceptible E. faecium likely harbor low-frequency resistant clones due to spontaneous mutation. Other forms of resistance, like VanA-type vancomycin resistance in E. faecium, emerge through horizontal gene transfer rather than point mutations (Depardieu and Courvalin, 2017). For resistance to emerge in patient colonized with vancomycin-susceptible E. faecium, a horizontal transfer event would have to occur, or a new resistant colonizer would have to be introduced. If these events are rarer than spontaneous mutation, patients are less likely to be colonized with a mixture of vancomycin sensitive and resistant bacteria than with daptomycin-resistance, as studied here. However, gains and losses of transmissible resistance elements have been observed in the gut microbiomes of patients (Kinnear et al., 2020), and altering antibiotic pressure in the gut could reduce selection for resistant bacteria, even when that resistance is encoded by horizontally transferred genes.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional
information
Strain, strain background (mouse, female)Swiss Webster (CFW)Charles River
Strain, strain background (mouse, female)C57BL/6Charles River
Chemical compound, drugcholestyramineSigma-Aldrichcat #: C4650
Strain, strain background (E. faecium)BL00239-1This paperclinical bloodstream isolate, R. Woods lab, University of Michigan
Strain, strain background (E. faecium)PR00708-14This paperclinical perirectal swab isolate, R. Woods lab, University of Michigan
Strain, strain background (E. faecium)BL00239-1-RThis paperderived from evolution in mouse gut
Strain, strain background (E. faecium)PR00708-14-RThis paperderived from evolution in mouse gut

Mice and bacterial strains

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Unless otherwise specified, mice in all experiments were female Swiss Webster. In one experiment, inbred female C57BL/6 mice were used to ensure the results were not specific to one mouse strain. Mice were fed a standard diet (5001 Laboratory Rodent Diet) or a standard diet supplemented with 2% w/w cholestyramine resin. All mice were housed individually during experiments.

Daptomycin-susceptible VR E. faecium strains were isolated from different patients at the University of Michigan Hospital. Strain BL00239-1 was isolated from a bloodstream infection, and strain PR00708-14 was isolated from a perirectal swab. Additional strains were isolated during these experiments from mouse fecal samples (including BL00239-1-R and PR00708-14-R). These strains were isolated by streaking on agar plates for two rounds of clonal purification.

Daptomycin treatment experiments

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All mice were pretreated with ampicillin (0.5 g/L in drinking water) for 7 days before E. faecium inoculation. Ampicillin disrupts the natural gut flora and facilitates Enterococcus colonization (McKenney et al., 2019). Sample sizes for mouse experiments were chosen based on previous experience with similar experiments. Mice that were co-housed during ampicillin pre-treatment were evenly allocated among experimental treatment groups. E. faecium strains were plated from glycerol stocks and then grown overnight in liquid culture in Brain Heart Infusion broth. Mice were inoculated via oral gavage with 108 CFU E. faecium suspended in saline. E. faecium inoculum counts were confirmed by plating. Following E. faecium inoculation, mice were split into individual cages with untreated water and any experimental diets. Daptomycin doses were administered daily starting one day post-inoculation via subcutaneous injection or oral gavage. Daptomycin doses were based on an average mouse weight for each experiment. In some experiments, a cholestyramine-supplemented diet (2% w/w) was provided to mice starting one day prior to the first daptomycin dose (Experiments A-C) or starting the same day as the first daptomycin dose (Experiment D). Once started, mice were maintained on the cholestyramine diet for the duration of the experiment. For stool collection, mice were placed in clean plastic cups, and fresh stool was collected using a sterile toothpick. Stool samples were suspended in PBS (25 uL PBS/mg stool) and frozen with glycerol at −80°C for subsequent analysis.

In vitro tests of daptomycin interaction with cholestyramine

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For measurements of UV absorbance, solutions of 5 mg/mL daptomycin in phosphate-buffered saline (PBS) were combined with various concentrations of cholestyramine. These mixtures were vortexed for 30 s, then allowed to incubate for 5 min at room temperature. Following incubation, cholestyramine was removed by centrifugation. Supernatants were analyzed for absorbance at 364 nm on a NanoVue Plus Spectrophotometer. A calibration curve was used to determine daptomycin concentrations from A364 values.

For tests of daptomycin bioactivity, solutions of 1 mg/mL daptomycin were incubated with or without 12 mg/mL cholestyramine for 45 min at 37°C with shaking (N = 3 per treatment). The cholestyramine was removed by centrifugation, and the supernatant was used in broth microdilutions with E. faecium. Saline solution incubated with cholestyramine run as a control had no effect on cell growth.

Analysis of VRE in stool samples

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VR E. faecium were enumerated by plating diluted fecal suspensions on selective plates (Enterococcosel agar supplemented with 16 µg/mL vancomycin). Plates were incubated at 35°C for 40–48 hr, and colonies were counted. To quantify the proportion of these bacteria that were daptomycin-resistant, fecal suspensions were plated on calcium-supplemented Enterococcosel plates with 16 µg/mL vancomycin and 10 µg/mL daptomycin. Plates were incubated at 35°C for 40–48 hr, and colonies were counted. Serially-diluted fecal suspensions were each plated once on plates without daptomycin and once on plates containing daptomycin to estimate the proportion of daptomycin-resistant bacteria. An additional plating assay was performed to more accurately determine proportions of resistant bacteria for Days 8 and 14 in each experiment. For this assay, known sample densities were used to plate an estimated 200 CFU on plates with and without daptomycin in triplicate. This assay only included samples with high enough initial bacterial density to generate 200 CFU on each plate (3 × 103 CFU/10 mg in initial sample). Counters were not blinded to treatment groups.

In some experiments, E. faecium clones were isolated from fecal samples and analyzed by broth microdilution. Clones were purified by streaking twice on Enterococcosel agar with 16 µg/mL vancomycin, and were then stored in glycerol stocks at −80°C. Broth microdilutions were performed according to Clinical and Laboratory Standards Institute (CLSI) guidelines (CLSI, 2017). After incubation, cell densities were measured by OD600 absorbance in a plate reader. OD values were fitted to a Hill function curve to determine the computed MIC (MICc) as described previously (Kinnear et al., 2020).

Genome sequencing

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Whole genomic DNA preparations were submitted to the University of Michigan sequencing core for Illumina library preparation and paired end sequencing with Illumina NovaSeq 6000 (isolates PR00708-14 and PR00708-14-R) or Illumina HiSeq (isolates BL00239-1 and BL00239-1-R). Long read data was additionally generated for strain BL00239-1 using the Oxford Nanopore MinION. The nanopore library were prepared using the Nanopore Ligation Sequencing Kit (SQK-LSK109). Quality control of sequencing reads was performed using Trimmomatic (Bolger et al., 2014). De novo genome assembly was performed using SPAdes (Bankevich et al., 2012) and genomes were annotated using Prokka (Seemann, 2014). Trimmed reads from resistant isolates were mapped against corresponding susceptible reference genomes using Burrows-Wheeler Aligner (BWA) (Li and Durbin, 2009), and candidate variants were identified with The Genome Analysis Toolkit (GATK) (McKenna et al., 2010) or breseq (Deatherage and Barrick, 2014). Reads from the reference sample were aligned to the reference genome (aligned to self) to generate a list of background variants; these background variants were filtered out during variant calling. Remaining candidate variants were screened by visual inspection of alignments in Integrative Genomics Viewer (IGV) (Thorvaldsdottir et al., 2013).

Analysis of daptomycin concentrations

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Fecal daptomycin concentrations were measured via LC-MS at the University of Michigan Pharmacokinetics Core. A labeled daptomycin-d5 internal standard was used to generate calibration curves.

Statistical analysis

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Statistical analyses were run in R v1.2.1335 (Brooks et al., 2017) using the packages ‘nlme’ (Pinheiro et al., 2016) and ‘glmmTMB’ (Brooks et al., 2017). To analyze proportions of resistant bacteria, samples were plated on agar with and without daptomycin, resulting in a count of resistant bacteria and a count of total bacteria. Due to sampling, these proportions were not bounded by zero and one, so proportion data were normalized by dividing each value by the maximum value in the data set. Proportions of resistant bacteria were analyzed using mixed binomial regression models. Absolute VRE densities were analyzed using mixed models with an autoregressive error structure as previously described (Pollitt et al., 2012). Full model structures and output are shown in Supplementary file 1.

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Decision letter

  1. Vaughn S Cooper
    Reviewing Editor; University of Pittsburgh, United States
  2. Dominique Soldati-Favre
    Senior Editor; University of Geneva, Switzerland
  3. Vaughn S Cooper
    Reviewer; University of Pittsburgh, United States
  4. Luke McNally
    Reviewer; University of Edinburgh, United Kingdom

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This is a creative study that tests the theory that adjuvants can suppress resistance evolution in vivo. Specifically, the authors evaluate whether the adjuvant cholestyramine can suppress the evolution of resistance to daptomycin, an important part of the current arsenal, in a mouse VR E. faecium gut colonization model. The motivation is clear because daptomycin is given to treat sepsis, where resistance evolution would be a dead-end, but often also selects for resistance in the gut where it is transmissible.

Decision letter after peer review:

Thank you for submitting your article "An adjunctive therapy approach prevents antibiotic resistance emergence in opportunistic pathogens colonizing the gut" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Vaughn S Cooper as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Dominique Soldati-Favre as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Luke McNally (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.

Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife, either of which would be linked to the original paper.

Summary:

This is an elegant study designed to test if the adjuvant cholestyramine can suppress the evolution of resistance to daptomycin, an important part of the current arsenal, in a mouse VR E. faecium gut colonization model. The motivation is clear because daptomycin is given to treat sepsis, where resistance evolution would be a dead-end, but often also selects for resistance in the gut where it is transmissible. Overall, the premise is strong and the experimental design well described.

Revisions for this paper:

1) All reviewers found that the manuscript overstates the generalizability of the findings and the results are more inconsistent than acknowledged. The preventative effect they measured was limited to a narrow range of conditions (Figure 6) and the effect was inconsistent (Supplementary figure 2). First, in the absence of cholestyramine, a relative enrichment of the resistant strain was observed in 4 of the 6 dose-strain combinations (Figure 6A and C), but with an inconsistent dose-response (for Strain PR00708-14, in the absence of cholestyramine, relative enrichment was observed at a daptomycin dose of 100 mg/kg and 200 mg/kg, but not 150 mg/kg). There is significant variability in the pharmacokinetics and pharmacodynamics of antibiotics in humans too, so the narrow dose-effect range observed in this study may be limiting. Also, it may differ substantially for every bug-drug combination. The etiology of this narrow effect window is not known and was not investigated. The authors should discuss this finding as a possible major limitation of the model and potentially to the future application of this overall strategy.

2) Additionally, when cholestyramine was administered on the same day as daptomycin, no effect, or perhaps even the opposite effect, was observed (Supplementary figure 2). The data in Supplementary figure 2 is not discussed in the manuscript and is cited in the Materials and methods section with the sentence, "Preliminary experiments were preformed to determine the timing of experimental conditions (Supplementary figure 2)". However, the data presented in Supplementary figure 2 contradicts the main conclusion of the manuscript. The authors should provide further clarity because if one considers all of the data provided, the reproducibility of the major finding described in the manuscript might be significantly diminished.

3) The data in Supplementary figure 2 should be combined with the data in Figure 6, so that the reader can assess all of the data in aggregate. In fact, given bacterial densities were found to not correlate with daptomycin dose (Figure 2—figure supplement 1), it would be reasonable, for each bacterial strain, to combine all doses of daptomycin into a single group. This aggregate data should be presented in the main text and presented with a statistical evaluation of the effect size (comparing “standardl” vs. “chol”) at each time point. This evaluation of the data would be more logical and greatly simplify its presentation. The parsed-out data of individual daptomycin doses could still be presented in the supplemental data section.

4) The genome sequencing results are unacceptably vague and the requisite data do not appear to be available. You state "We confirmed that the isolated clone (PR00708-14-R) had an increased daptomycin MICc relative to PR00708-14 by broth microdilution (MICc = 12.0). Genome sequencing revealed that PR00708-14 and PR00708-14-R differed by several mutations in hypothetical proteins and noncoding regions, which to our knowledge have not previously been associated with daptomycin-resistance.” Please be specific. Further, this one limited example does not show that daptomycin-resistance evolved in vivo consistently. One could argue that the populations were uncontrolled at some concentrations and grew steadily to higher levels because of some physiological tolerance. Although this is unlikely, it would not be hard to pick a few more clones and confirm in fact that they evolved heritable resistance and identify the causative mutations, which you would report.

5) The distinction between preventing de novo emergence of a resistant pathogen and enrichment of a resistant colonizer is important and mechanistically distinct. For mechanisms of antibiotic resistance (such as daptomycin), where point mutations in the genome are causative, resistant clones within susceptible populations are likely to be present at very low frequencies simply due to spontaneous mutation. In this case, this approach does have promise because, in theory, all patients receiving daptomycin would be “at-risk” of developing de novo emergence of daptomycin-resistance. However, for mechanisms of antibiotic resistance that rely on horizontal gene transfer (HGT) or strains with intrinsic resistance, resistant clones do not arise spontaneously from a susceptible population. Instead, a HGT event must take place, or the patient must become newly colonized with that resistant strain. These are relatively rare events. For example, vancomycin resistance in Enterococcus (VRE) is caused by a van gene cassette. This multi-gene cassette does not arise spontaneously. Bacteria get this by HGT and patients get this by being inoculated with VRE in the hospital and, presumably, VRE colonization is sustained in the setting of repeated vancomycin exposures. Thus, a strategy for preventing enrichment of VRE:VSE would only work for patients that are already colonized with VRE. Consider drawing this distinction in the Discussion.

6) There is insufficient detail of the statistical models used by the authors to assess their validity. The full structure of all reported mixed effects models should be specified. In addition, only p-values for the statistical tests are reported. Effect sizes and their error should also be reported. Full tables of all estimated parameters, including for random effects, from the mixed effects models should be reported.

7) For analysis of time-series of VRE it is stated that interpolated density values were used. It needs to be clarified why and how this interpolation was carried out, and whether this has inflated the authors' sample size.

8) It is simply stated by the authors that model selection was carried out based on AIC. If model selection is to be carried out tables of AIC values should be reported for all fitted models and details of the model selection process need to be given. However, I don't believe model selection is appropriate here. It is well known that model selection based on AIC leads to inflated type I error rates via cryptic multiple hypothesis testing (https://link.springer.com/article/10.1007/s00265-010-1038-5). Statistical results should probably simply be reported from the full model.

9) The authors use negative binomial mixed effects models to analyse their data on proportions of resistant bacteria. This appears an odd choice, as a negative binomial distribution is typically used to model abundances. For analysing proportions given from counts a binomial model would be more appropriate. The authors either need to clarify exactly why they have used a negative binomial distribution or re-run these as binomial models.

Revisions expected in follow-up work:

1) In these experiments, the resistant clones persisted to day 14, so, further studies are needed to test whether this strategy can interrupt stable colonization, which may be a more relevant end-point than preventing enrichment. However, it should be mentioned in the Discussion that some studies have shown E. faecium outgrowth in the gut precedes clinical infections, as this is pertinent to this strategy and gives this study more merit and relevance.

2) Given the authors show they can generate de novo resistance in the gut and identify the resistant clones, this study would be greatly enhanced by testing whether cholestyramine could prevent the emergence of these de novo resistant clones when the mouse is inoculated with only a daptomycin-susceptible strain. This is a more relevant model for this particular bug-drug combination (see comment #4). The authors should briefly comment why they instead chose the mixed R+S inoculation strategy for the experimental design.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "An adjunctive therapy prevents enrichment of antibiotic-resistant clones of a colonizing opportunistic pathogen" for further consideration by eLife. Your revised article has been evaluated by Dominique Soldati-Favre (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

As reviewer 2 states clearly below, your handling of samples with low bacterial density needs to be clarified.

Reviewer #2:

The authors establish a mouse model of E. faecium gut colonization to test the following hypothesis: Administration of cholestyramine per os, given concurrently with parenteral daptomycin, prevents the enrichment of daptomycin-resistant E. faecium in the gut by sequestering daptomycin from the alimentary canal. This is a well-executed study that addresses a major driver of the antibiotic resistance crisis: the off-target effects of antibiotics.

The authors show that parenteral daptomycin selects for daptomycin-resistant E. faecium in the mouse gut and, separately, that cholestyramine can sequester daptomycin. Finally, the authors appear to have shown that cholestyramine administration per os can reduce the degree of enrichment of daptomycin-resistant E. faecium in the gut. This result provides a powerful proof-of-concept for this strategy with great potential. The authors now also appropriately discuss this general strategy and its major limitations.

The revised manuscript has addressed the major issues raised and I think this paper is much stronger now. However, the revised manuscript now has brand new details that need to be better explained:

1) "For daptomycin-treated mice shedding at least 3 x 103 CFU/10 mg feces at Days 8 and 14…". The statistical analysis included only mice with at least 3x103 CFU per 10mg feces at days 8 and 14. This added detail was not present in the original manuscript and it is not explained why data are being excluded now. There is a mention in the supplemental figure legends of "insufficient bacterial density", but insufficient for what exactly? Given the interpretation of these data relies on a robust statistical analysis and understanding of how the primary data were collected, the authors should more clearly explain the data handling procedure. If proportions were not calculated due to a threshold of <20 CFU/10mg (see #2 below), then what exactly is special about 3 x 103 CFU/10mg?

2) In the test and visualization of the Figure 5A data, the figure legend states data points with <20 CFU per 10mg feces were excluded from the plot, but no explanation is given for why this was done. Presumably this is due to an unacceptable amount of error in calculating proportions? Is <20 CFU also the raw plate count data? If so, this should be explicitly stated in the figure legend so the reader understands why these data were excluded and it can be distinguished from the threshold/rationale for #1 above.

3) Subsection “Adjunctive cholestyramine therapy prevents emergence of daptomycin-resistance”. Why are statistical tests only performed for days 8 and 14 for proportions but for all the days for densities? For example, the plotted proportion data look like an effect may begin to appear by day 4.

4) Subsection “Statistical analysis”; Re: model choice. The authors indicate the proportion values are not bounded by 0 and 1 and so cannot use a binomial model. I defer to the statisticians, but perhaps this can be solved by simply normalizing the data to values between 0 and 1? Basically, just divide all proportion values by the maximum value in the data set, then your values will be bounded by 0 and 1 and all the data will be treated equally in the process.

https://doi.org/10.7554/eLife.58147.sa1

Author response

Revisions for this paper:

1) All reviewers found that the manuscript overstates the generalizability of the findings and the results are more inconsistent than acknowledged. The preventative effect they measured was limited to a narrow range of conditions (Figure 6) and the effect was inconsistent (Supplementary figure 2). First, in the absence of cholestyramine, a relative enrichment of the resistant strain was observed in 4 of the 6 dose-strain combinations (Figure 6A and C), but with an inconsistent dose-response (for Strain PR00708-14, in the absence of cholestyramine, relative enrichment was observed at a daptomycin dose of 100 mg/kg and 200 mg/kg, but not 150 mg/kg). There is significant variability in the pharmacokinetics and pharmacodynamics of antibiotics in humans too, so the narrow dose-effect range observed in this study may be limiting. Also, it may differ substantially for every bug-drug combination. The etiology of this narrow effect window is not known and was not investigated. The authors should discuss this finding as a possible major limitation of the model and potentially to the future application of this overall strategy.

A paragraph addressing these limitations has been added to the Discussion. However, we note that analyzing the data together, as suggested in comment #3 shows that the overall result is robust to many of these details.

2) Additionally, when cholestyramine was administered on the same day as daptomycin, no effect, or perhaps even the opposite effect, was observed (Supplementary figure 2). The data in Supplementary figure 2 is not discussed in the manuscript and is cited in the Materials and methods section with the sentence, "Preliminary experiments were preformed to determine the timing of experimental conditions (Supplementary figure 2)". However, the data presented in Supplementary figure 2 contradicts the main conclusion of the manuscript. The authors should provide further clarity because if one considers all of the data provided, the reproducibility of the major finding described in the manuscript might be significantly diminished.

As suggested by reviewers, the experiment from Supplementary figure 2 has been moved to the main text and included for analysis with the data in Figure 5. Our main findings still hold true in this revised analysis. On the specific issue of timing of cholestyramine administration, we have yet to test that in a single experiment. Formally we have only a difference between Experiments A-C and D, and so cannot attribute that to timing, though that certainly is a factor we need to test going forward.

3) The data in Supplementary figure 2 should be combined with the data in Figure 6, so that the reader can assess all of the data in aggregate. In fact, given bacterial densities were found to not correlate with daptomycin dose (Figure 2—figure supplement 1), it would be reasonable, for each bacterial strain, to combine all doses of daptomycin into a single group. This aggregate data should be presented in the main text and presented with a statistical evaluation of the effect size (comparing “standardl” vs. “chol”) at each time point. This evaluation of the data would be more logical and greatly simplify its presentation. The parsed-out data of individual daptomycin doses could still be presented in the supplemental data section.

This figure (now Figure 5) and the corresponding analysis have been revised as suggested in subsection “Adjunctive cholestyramine therapy prevents emergence of daptomycin-resistance”. The parsed-out data are reported in Figure 5—figure supplements 1-5. This excellent suggestion has really simplified the overall presentation, thank you.

4) The genome sequencing results are unacceptably vague and the requisite data do not appear to be available. You state "We confirmed that the isolated clone (PR00708-14-R) had an increased daptomycin MICc relative to PR00708-14 by broth microdilution (MICc = 12.0). Genome sequencing revealed that PR00708-14 and PR00708-14-R differed by several mutations in hypothetical proteins and noncoding regions, which to our knowledge have not previously been associated with daptomycin-resistance.” Please be specific. Further, this one limited example does not show that daptomycin-resistance evolved in vivo consistently. One could argue that the populations were uncontrolled at some concentrations and grew steadily to higher levels because of some physiological tolerance. Although this is unlikely, it would not be hard to pick a few more clones and confirm in fact that they evolved heritable resistance and identify the causative mutations, which you would report.

Further details have been added to the sequencing results. We have reported MIC data for additional clones from the experiment with PR00708-14, as suggested (subsection “Generation of daptomycin-resistant VR E. faecium in the mouse GI tract”).

5) The distinction between preventing de novo emergence of a resistant pathogen and enrichment of a resistant colonizer is important and mechanistically distinct. For mechanisms of antibiotic resistance (such as daptomycin), where point mutations in the genome are causative, resistant clones within susceptible populations are likely to be present at very low frequencies simply due to spontaneous mutation. In this case, this approach does have promise because, in theory, all patients receiving daptomycin would be “at-risk” of developing de novo emergence of daptomycin-resistance. However, for mechanisms of antibiotic resistance that rely on horizontal gene transfer (HGT) or strains with intrinsic resistance, resistant clones do not arise spontaneously from a susceptible population. Instead, a HGT event must take place, or the patient must become newly colonized with that resistant strain. These are relatively rare events. For example, vancomycin resistance in Enterococcus (VRE) is caused by a van gene cassette. This multi-gene cassette does not arise spontaneously. Bacteria get this by HGT and patients get this by being inoculated with VRE in the hospital and, presumably, VRE colonization is sustained in the setting of repeated vancomycin exposures. Thus, a strategy for preventing enrichment of VRE:VSE would only work for patients that are already colonized with VRE. Consider drawing this distinction in the Discussion.

A paragraph addressing these issues has been added to the Discussion.

6) There is insufficient detail of the statistical models used by the authors to assess their validity. The full structure of all reported mixed effects models should be specified. In addition, only p-values for the statistical tests are reported. Effect sizes and their error should also be reported. Full tables of all estimated parameters, including for random effects, from the mixed effects models should be reported.

A new supplementary file (Supplementary file 1) shows the full structure and output for the reported models. In the main text, effect sizes and their error have been added.

7) For analysis of time-series of VRE it is stated that interpolated density values were used. It needs to be clarified why and how this interpolation was carried out, and whether this has inflated the authors' sample size.

For this revision, we have decided not to interpolate the bacterial density data analyzed in this manuscript. The data have been re-analyzed, and the main conclusions have not been affected. The Materials and methods section has been updated accordingly. We believe this simplifies the analysis and avoids inflating the sample size. Thanks for the suggestion.

8) It is simply stated by the authors that model selection was carried out based on AIC. If model selection is to be carried out tables of AIC values should be reported for all fitted models and details of the model selection process need to be given. However, I don't believe model selection is appropriate here. It is well known that model selection based on AIC leads to inflated type I error rates via cryptic multiple hypothesis testing (https://link.springer.com/article/10.1007/s00265-010-1038-5). Statistical results should probably simply be reported from the full model.

As suggested, in this revision we report the full models without model selection based on AIC. The models are reported in Supplementary file 1, and the Materials and methods have been updated.

9) The authors use negative binomial mixed effects models to analyse their data on proportions of resistant bacteria. This appears an odd choice, as a negative binomial distribution is typically used to model abundances. For analysing proportions given from counts a binomial model would be more appropriate. The authors either need to clarify exactly why they have used a negative binomial distribution or re-run these as binomial models.

We have updated the Materials and methods section to better explain our choice of model, and we have added the full model structures to Supplementary file 1. In brief, our proportions were determined by plating samples on agar with and without daptomycin. This gives a count for the total density, and a count for the resistant density. Due to sampling, the resulting proportions were not be bounded by 0 and 1, as required for a binomial distribution. Instead, we used a neg binomial distribution to model the count of resistant bacteria, with total count as an offset.

Revisions expected in follow-up work:

1) In these experiments, the resistant clones persisted to day 14, so, further studies are needed to test whether this strategy can interrupt stable colonization, which may be a more relevant end-point than preventing enrichment. However, it should be mentioned in the Discussion that some studies have shown E. faecium outgrowth in the gut precedes clinical infections, as this is pertinent to this strategy and gives this study more merit and relevance.

We have added to the Discussion that colonization often precedes infection. This point is also made in the Introduction.

2) Given the authors show they can generate de novo resistance in the gut and identify the resistant clones, this study would be greatly enhanced by testing whether cholestyramine could prevent the emergence of these de novo resistant clones when the mouse is inoculated with only a daptomycin-susceptible strain. This is a more relevant model for this particular bug-drug combination (see comment #4). The authors should briefly comment why they instead chose the mixed R+S inoculation strategy for the experimental design.

We have added a comment (subsection “Daptomycin treatment enriches for daptomycin-resistant VR E. faecium in the GI tract”) explaining our choice of experimental design.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Reviewer #2:

The authors establish a mouse model of E. faecium gut colonization to test the following hypothesis: Administration of cholestyramine per os, given concurrently with parenteral daptomycin, prevents the enrichment of daptomycin-resistant E. faecium in the gut by sequestering daptomycin from the alimentary canal. This is a well-executed study that addresses a major driver of the antibiotic resistance crisis: the off-target effects of antibiotics.

The authors show that parenteral daptomycin selects for daptomycin-resistant E. faecium in the mouse gut and, separately, that cholestyramine can sequester daptomycin. Finally, the authors appear to have shown that cholestyramine administration per os can reduce the degree of enrichment of daptomycin-resistant E. faecium in the gut. This result provides a powerful proof-of-concept for this strategy with great potential. The authors now also appropriately discuss this general strategy and its major limitations.

The revised manuscript has addressed the major issues raised and I think this paper is much stronger now. However, the revised manuscript now has brand new details that need to be better explained:

1) "For daptomycin-treated mice shedding at least 3 x 103 CFU/10 mg feces at Days 8 and 14…". The statistical analysis included only mice with at least 3x103 CFU per 10mg feces at days 8 and 14. This added detail was not present in the original manuscript and it is not explained why data are being excluded now. There is a mention in the supplemental figure legends of "insufficient bacterial density", but insufficient for what exactly? Given the interpretation of these data relies on a robust statistical analysis and understanding of how the primary data were collected, the authors should more clearly explain the data handling procedure. If proportions were not calculated due to a threshold of <20 CFU/10mg (see #2 below), then what exactly is special about 3 x 103 CFU/10mg?

We have clarified the reasoning for these cutoffs in our figure legends (Figure 5 and Figure 5—figure supplements 1-5), in the Materials and methods, and in the Results. In brief, two different plating methods are presented in this paper, which are now described more clearly in the Materials and methods. 1) Each fecal sample was plated in serial dilution on plates with and without daptomycin to determine densities of total and resistant bacteria, and our detection limit for this plating was 20 CFU/10mg feces. 2) We ran a second assay to more accurately estimate proportions for samples from days 8 and 14. For this assay, 200 CFU from each sample was plated in triplicate on plates with and without daptomycin based on previously determined bacterial densities. This assay required high enough density in the initial sample to prepare six plates at 200 CFU. To satisfy this, we used only samples that had an initial starting density of at least 3 x 103 CFU/10mg feces. Thank you for bringing our attention to this area of confusion, these are important details that are now better communicated.

2) In the test and visualization of the Figure 5A data, the figure legend states data points with <20 CFU per 10mg feces were excluded from the plot, but no explanation is given for why this was done. Presumably this is due to an unacceptable amount of error in calculating proportions? Is <20 CFU also the raw plate count data? If so, this should be explicitly stated in the figure legend so the reader understands why these data were excluded and it can be distinguished from the threshold/rationale for #1 above.

Please see #1. This has been clarified in the revised manuscript.

3) Subsection “Adjunctive cholestyramine therapy prevents emergence of daptomycin-resistance”. Why are statistical tests only performed for days 8 and 14 for proportions but for all the days for densities? For example, the plotted proportion data look like an effect may begin to appear by day 4.

We now include an analysis of proportions across all time points (Model 3, L293-302). An effect does appear by Day 4, and this is now captured in the Results. In addition, we include a separate analysis of Days 8 and 14, for which a different type of plating assay was conducted as described in comment #1 (Model 4, L302-308).

4) Subsection “Statistical analysis”; Re: model choice. The authors indicate the proportion values are not bounded by 0 and 1 and so cannot use a binomial model. I defer to the statisticians, but perhaps this can be solved by simply normalizing the data to values between 0 and 1? Basically, just divide all proportion values by the maximum value in the data set, then your values will be bounded by 0 and 1 and all the data will be treated equally in the process.

Thank you for this suggestion. We have re-run the models as suggested, and we find the results are consistent with our main findings. We have updated the Materials and methods and the models in Supplementary file 1 (Models 3 and 4) accordingly.

https://doi.org/10.7554/eLife.58147.sa2

Article and author information

Author details

  1. Valerie J Morley

    Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    vum84@psu.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6805-7562
  2. Clare L Kinnear

    Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, United States
    Contribution
    Conceptualization, Resources, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4126-7540
  3. Derek G Sim

    Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, United States
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Samantha N Olson

    Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, United States
    Contribution
    Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Lindsey M Jackson

    Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, United States
    Contribution
    Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0559-9433
  6. Elsa Hansen

    Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, United States
    Contribution
    Software, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Grace A Usher

    Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, United States
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8303-9992
  8. Scott A Showalter

    1. Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, United States
    2. Department of Chemistry, The Pennsylvania State University, University Park, United States
    Contribution
    Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  9. Manjunath P Pai

    Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, United States
    Contribution
    Formal analysis, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Robert J Woods

    Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Methodology, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  11. Andrew F Read

    1. Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, United States
    2. Huck Institutes for the Life Sciences, The Pennsylvania State University, University Park, United States
    3. Department of Entomology, The Pennsylvania State University, University Park, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7604-7903

Funding

Eberly College of Science, Penn State

  • Andrew F Read

Eberly Family Trust

  • Andrew F Read

NIH (R01 AI143852)

  • Robert J Woods

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Kevin Tracy for assistance in generating the nanopore sequencing and analysis pipeline. We are grateful to Vaughn Cooper, Luke McNally, and an anonymous reviewer for comments that greatly improved this manuscript.

Ethics

Animal experimentation: All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols of the Pennsylvania State University (approved IACUC protocol #47581).

Senior Editor

  1. Dominique Soldati-Favre, University of Geneva, Switzerland

Reviewing Editor

  1. Vaughn S Cooper, University of Pittsburgh, United States

Reviewers

  1. Vaughn S Cooper, University of Pittsburgh, United States
  2. Luke McNally, University of Edinburgh, United Kingdom

Publication history

  1. Received: April 22, 2020
  2. Accepted: October 29, 2020
  3. Version of Record published: December 1, 2020 (version 1)

Copyright

© 2020, Morley et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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