Research Article

Quantifying antibiotic impact on within-patient dynamics of extended-spectrum beta-lactamase resistance

University of Oxford, United Kingdom
National Institute for Public Health and theEnvironment, Netherlands
University of Antwerp, Belgium
Tel-Aviv University, Israel
University of Tuebingen, Germany
Infectious Diseases, University of Verona, Italy
Clinical Centre of Serbia, Serbia
Matei Balş National Institute for Infectious Diseases, Romania

May 7, 2020

https://doi.org/10.7554/eLife.49206

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Version of Record published: May 7, 2020 (This version)
Accepted: March 22, 2020
Received: June 12, 2019

1. Of interest
Association between bisphosphonate use and COVID-19 related outcomes

Jeffrey Thompson, Yidi Wang ... Ulrich H von Andrian

Research Article Dec 1, 2023
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Abstract

Antibiotic-induced perturbation of the human gut flora is expected to play an important role in mediating the relationship between antibiotic use and the population prevalence of antibiotic resistance in bacteria, but little is known about how antibiotics affect within-host resistance dynamics. Here we develop a data-driven model of the within-host dynamics of extended-spectrum beta-lactamase (ESBL) producing Enterobacteriaceae. We use bla_CTX-M (the most widespread ESBL gene family) and 16S rRNA (a proxy for bacterial load) abundance data from 833 rectal swabs from 133 ESBL-positive patients followed up in a prospective cohort study in three European hospitals. We find that cefuroxime and ceftriaxone are associated with increased bla_CTX-M abundance during treatment (21% and 10% daily increase, respectively), while treatment with meropenem, piperacillin-tazobactam, and oral ciprofloxacin is associated with decreased bla_CTX-M (8% daily decrease for all). The model predicts that typical antibiotic exposures can have substantial long-term effects on bla_CTX-M carriage duration.

eLife digest

Bacteria that are resistant to antibiotics are a growing global health crisis. One type of antibiotic resistance arises when certain bacteria that can produce enzymes called extended-spectrum beta-lactamases (or ESBLs for short) become more common in the gut. These enzymes stop important antibiotics, like penicillin, from working. However, exactly which antibiotics and treatment durations contribute to the emergence of this antibiotic resistance remain unknown.

Now, Niehus et al. find certain antibiotics that are associated with an increase in the number of gut bacteria carrying antibiotic resistance genes for ESBL enzymes. First, rectal swabs collected from 133 patients from three European hospitals were analysed to measure the total gut bacteria and the number of genes for ESBL enzymes. These samples had been collected at several time points including when the patient was first admitted to hospital, then every two to three days during their stay, and finally when they were discharged.

Combining the analysis of the samples with details of the patients’ charts showed that treatment with two antibiotics: cefuroxime and ceftriaxone, was linked to an increase in ESBL genes in the gut bacteria. Other antibiotics – namely, meropenem, piperacillin-tazobactam and oral ciprofloxacin – were associated with a decrease in the number of bacteria with ESBL genes. Niehus et al. then performed further analysis to see if different treatment regimens affected how long patients were carrying gut bacteria with ESBL genes. This predicted that a longer course of meropenem, 14 days rather than 5 days, would shorten the length of time patients carried ESBL-resistant bacteria in their guts by 70%, although this effect will likely depend on the location of the hospital and the local prevalence of other types of antibiotic resistance.

This analysis reveals new details about how antibiotic treatment can affect ESBL resistance genes. More studies are needed to understand how antibiotics affect other antibiotic resistance genes and how resistant bacteria spread. This will help scientists understand how much specific antibiotic regimens contribute to antibiotic resistance. It may also help scientists develop new antibiotic treatment strategies that reduce antibiotic resistance.

Introduction

Antibiotic use can increase resistance prevalence in a host population through multiple pathways (Lipsitch and Samore, 2002). It may: (i) affect the duration of resistance carriage and hence transmission potential; (ii) increase bacterial load of resistant organisms and thus increase transmission; or (iii) selectively suppress host microbial flora where resistance is lacking, which may reduce the potential for transmission of sensitive organisms and also render hosts more susceptible to acquiring resistant bacteria. As well as being important for understanding population dynamics, levels of intestinal resistance are also likely to be important from an individual patient perspective. It has been shown, for instance, that the digestive tract is the primary source of enterobacteria causing bloodstream infections in haematological patients, and a high abundance of beta-lactam resistant enterobacteria in the gut flora is predictive of a high risk of a corresponding drug-resistant bloodstream infection (Woerther et al., 2015). Moreover, colonization with extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae amongst patients receiving cephalosporin-based prophylaxis prior to colorectal surgery is associated with a more than two-fold increase in risk of surgical site infection (Dubinsky-Pertzov et al., 2019). Therefore, quantifying within-host selection dynamics should lead to a better understanding of both individual patient-level and population-level risks and benefits of antibiotic use.

Here we focus on Enterobacteriaceae, a bacterial family that is commonly found in the healthy mammalian gut microbiome (Donnenberg, 1979). Some member genus-species—Klebsiella pneumoniae, Escherichia coli, Enterobacter spp.—are important opportunistic human pathogens that can cause urinary tract, bloodstream, and intra-abdominal infections, as well as hospital-acquired respiratory tract infections. A major concern is the global increase in extended-spectrum beta-lactamase-producing organisms in this family (Coque et al., 2008; Tacconelli et al., 2018; Valverde et al., 2004). ESBL genes – of which the most important and globally widespread is the bla_CTX-M gene family – confer resistance to clinically important broad-spectrum antimicrobials, such as third generation cephalosporins (Paterson, 2000). These genes commonly reside on large conjugative plasmids (Bonnet, 2004), and are co-carried with other antibiotic resistance determinants, making them a good marker for multi-drug resistance (MDR) in strains of Enterobacteriaceae (Schwaber et al., 2005). Because Enterobacteriaceae have their main biological niche in the gut microbiome (Masci, 2005), these bacteria are exposed to substantial collateral selection from antibiotics used to treat or prevent infections with other organisms (‘bystander selection’ [Tedijanto et al., 2018]). Quantifying the effects of antibiotic therapy on within-host resistance dynamics will help us to better understand the potential for selection of drug-resistant Enterobacteriaceae associated with different patterns of antibiotic usage.

In this work, we analysed sequential rectal swabs (n = 833) from 133 ESBL positive hospitalised patients from three hospitals (Italy, Romania, Serbia) to study the dynamics of antibiotic resistance gene abundance. Both bla_CTX-M gene and, as a proxy for total bacterial load, 16S rRNA gene abundance were determined using quantitative polymerase chain reaction (qPCR). Previously, using a subset of these data, Meletiadis et al. demonstrated a statistical association between exposure to ceftriaxone and increases in bla_CTX-M normalised by total bacterial load. Here, we addressed some broader questions. We studied the effects of a range of different antibiotics on the abundance of bla_CTX-M and of 16S rRNA, and we aimed to fully characterise the within and between host variation of bla_CTX-M and 16S rRNA and their within-host dynamics. For this purpose we developed a novel dynamic model, a state-space model that we fit to fine-grained patient-level measurements and antibiotic exposure data. By incorporating hidden-state dynamics our model allowed us to dissect and quantify different types of data variability, such as noise from qPCR measurement or from the DNA extraction process, and to separate this from the within-host processes. In this way we directly estimated ecologically important parameters such as strength of resistance amplification during antibiotic treatment or the rate of decline of bla_CTX-M. We then used our model to make counterfactual predictions about how alternative choices of treatment would impact bla_CTX-M carriage duration. The development of this data-driven within-host model and its use in exploring the impact of antibiotic treatment on amplification and loss of resistance is an important step in furthering our quantitative mechanistic understanding of how antibiotic use drives changes in the prevalence of resistance in a population.

Results

Patient cohort and treatment

The study enrolled a total of 1102 patients who were screened positive for ESBL producing Enterobacteriaceae at admission, and 133 patients (12%) gave consent to be included in the study: 51 (38%) from Romania; 52 (39%) from Serbia; and 30 (23%) from Italy. The median age was 59 years (range of 23–88), and 46% were female. The median length of hospital stay was 15 days (maximum of 53 days). All patients apart from one had two or more rectal swabs taken, with a median of five swabs per patient (range of 1–15). 114 out of 133 (86%) enrolled patients received antibiotics during their stay and 85 of these 114 (75%) received two or more different antibiotics, which were given both in mono- and combination therapy (see Figure 1). A total of 3993 patient days were observed, of which 2686 (67%) were days with antibiotic therapy (mono- or combination therapy). Table 1 summarises important details of the study. Note that the antibiotics that we considered in this study were exclusively antibacterial drugs, and we ignored treatment with anti-tuberculosis drugs (pyrazinamide and isoniazid), which occurred only in two patients.

Figure 1 with 1 supplement see all

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Time series plots demonstrating the diverse range of dynamical patterns of bla_CTX-M resistance gene abundance across the 132 patients with two or more samples.

The x-axis scale is identical across panels, the length of one week is given for scale in the top-left corner. Timelines are ordered by length. The y-axis scale differs between panels, with the space between vertical grey lines representing a 10-fold change in the absolute bla_CTX-M gene abundance (measured in copy numbers). The left-hand side shows patients who received antibiotic treatment (n = 113), and the two right-hand side columns are patients without antibiotic treatment (n = 19). For clarity, we show only the twelve most frequently used antibiotics in distinct colours and other antibiotics in light grey.

Table 1

Summary of the study.

Number of participating hospitals	3 (Serbia, Italy, Romania)
Study duration	2 years (Jan 2011 - Dec 2012)
Inclusion criteria	Inpatients of medical and surgical wards, adults, non-pregnant, ESBL producting Enterobacteriaceae carriers (at admission)
Number of patients followed up	133 (including one with a single swab taken)
Intervals between rectal swabs	two to three days
qPCR targets	bla_CTX-M (ESBL resistance gene), 16S rRNA (total bacterial load)
Number of different antibiotics used	35

This study (registration number NCT01208519) was conducted by the SATURN consortium, supported by the European Commission under the 7th Framework Program.

The different antibiotic classes, ranked by proportion of treatment days, were cephalosporins 25% ), fluoroquinolones (18%), penicillins (9%), nitroimidazole derivatives (metronidazole) (9%), glycopeptides (8%), carbapenems (5%), and others (26%). Two thirds of antibiotic treatment days were from intravenously administered antibiotics and one third from oral administration. Details on individual antibiotics are given in Table 2.

Table 2

Overview of antibiotic treatments showing the ten most used antibiotics in this patient cohort.

Intravenous (iv), oral (or), and intramuscular (im) route administration is given in percent of treatment days.

Antibiotic	Number of treated patients (total n=133)	Route
Ceftriaxone	64	( 98% iv , 2% im )
Ciprofloxacin	34	( 67% iv , 33% or )
Metronidazole	20	( 50% iv , 50% or )
Cefuroxime	13	( 100% iv , 0% or )
Vancomycin	13	( 86% iv , 14% or )
Meropenem	10	( 100% iv , 0% or )
Amikacin	9	( 100% iv , 0% or )
Amoxicillin-clavulanic acid	9	( 57% iv , 43% or )
Piperacillin-tazobactam	7	( 100% iv , 0% or )
Imipenem	5	( 100% iv , 0% or )

Resistance dynamics

The time-varying bla_CTX-M abundance exhibits a diverse range of dynamic patterns, including monotonic increases and decreases, as well as highly variable non-monotonic behaviour (Figure 1). Qualitatively similar fluctuations in bla_CTX-M abundance were seen both in the presence and absence of antibiotic treatment. To determine whether this high level of dynamic variation contained a meaningful biological signal, we first studied temporal autocorrelation. If the observed variability is driven by observation uncertainty – for instance through the swab procedure, DNA extraction, or qPCR process – we expect autocorrelation close to zero in the time series. Conversely, if the observed fluctuations reflect true within-host dynamics in carriage levels, we would generally expect to see positive autocorrelation. We found a clear signal of first-order autocorrelation for both the bla_CTX-M and the 16S rRNA gene time series, though autocorrelation was substantially stronger for the bla_CTX-M data (Figure 1—figure supplement 1a and b). Using a Bayesian state-space model that decomposes the time series data into an observation component (representing noise due to variability in qPCR runs, and in the procedure of swab taking and sample processing) and a process component (due to the within-host dynamics), we estimated that much of the variability in bla_CTX-M and 16S rRNA outcomes was due to measurement error associated with the swab procedure (median estimate of the proportion of total abundance variability attributable to swab error [90% credible interval [CrI]] of 54% [44%, 57%] and 73% [68%, 77%], respectively) (see Figure 1—figure supplement 1c).

However, the bla_CTX-M data in particular were found to also contain a strong process component signal, indicating that a median estimate of 36% (90% CrI 30%, 43%) of the variability in the qPCR outcomes was due to underlying within-host dynamics (Figure 1—figure supplement 1c). To further investigate the determinants of bla_CTX-M gene variation, we explored how much the bla_CTX-M gene load varied between different patients or, over time, within the same patient. Using a Bayesian state-space model (see Methods and Materials) we found 16S rRNA gene abundance to be two orders of magnitude higher than bla_CTX-M (median ratio 16S / bla_CTX-M [90% CrI] 158 [88, 181]), with an estimated coefficient of variation (ratio of standard deviation to the mean) of 5.5 for 16S rRNA and 32.1 for bla_CTX-M. Between-patient abundance of bla_CTX-M showed substantially more variability than within-patient abundance (median ratio [90% CrI] 134 [18, 1422]). In contrast, 16S rRNA gene abundance had similar between-patient and within-patient variability (median ratio [90% CrI] 0.8 [0.4,1.7]) (Figure 2). We noted that the rank plots (Figure 1—figure supplement 1a) indicate some convergence problems of $σ_{b i o, 16 S}$ , but several independent runs of the MCMC algorithm with different initial values consistently arrived at the same mean and standard deviation of the posterior estimate.

Figure 2 with 1 supplement see all

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Variability of 16S abundance and bla_CTX-M abundance within and between patient time series using a Bayesian hierarchical model.

In this model, abundance is distributed around individual patient intercepts, which are distributed around a common population intercept. The plot shows individual patient intercepts given as mean posterior estimates (coloured dots) together with posterior predictions for sequence abundance for each patient (grey bars show 80% central quantiles).

Associating resistance and antibiotic treatment

The change in relative resistance between samples, measured as bla_CTX-M abundance divided by 16S rRNA gene abundance, was only slightly elevated in time intervals where antibiotics were given compared to those where they were not (Figure 3a).

Figure 3

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Association of antibiotic use with change in relative resistance (abundance of bla_CTX-M divided by abundance of 16S rRNA).

The upper panels show the change in relative resistance between all neighbouring timepoints (black dots), dashed horizontal lines in grey indicate the region of no change. Pairs of violin scatter plots (with the mean values shown as red bars) contrast different treatment that occurred between those timepoints. ‘Yes’ indicates treatment with specified antibiotics and ‘No’ means treatment with other antibiotics or no treatment. The lower three panels show the distribution of mean differences of the change in relative resistance between treatment groups generated through treatment-label permutation (areas in darker grey show 80% central quantiles). The distributions are overlaid with the observed difference (red vertical line). Panel (a) compares treatment with any antibiotic versus no antibiotic (number of intervals without treatment/number of intervals with treatment are 251(N)/449(Y)). Panel (b) compares treatment with antibiotics with activity against *Enterobacteriaceae* and to which bla_CTX-M does not confer resistance (colistin, meropenem, ertapenem, imipenem, amoxicillin-clavulanic acid, ampicillin-sulbactam, piperacillin-tazobactam, gentamicin, amikacin, ciprofloxacin, ofloxacin, levofloxacin, tigecycline, doxicycline) with all other treatment, including no treatment (445(N)/255(Y)). Finally, in panel (c) we consider antibiotics with broad-spectrum activity but to which bla_CTX-M does confer resistance (cefepime, ceftazidime, ceftriaxone, cefotaxime, cefuroxime, amoxicillin, ampicillin) (513(N)/187(Y)).

However, use of antibiotics with activity against Enterobacteriaceae to which carriage of bla_CTX-M does not confer resistance (colistin, meropenem, ertapenem, imipenem, amoxicillin-clavulanic acid, ampicillin-sulbactam, piperacillin-tazobactam, gentamicin, amikacin, ciprofloxacin, ofloxacin, levofloxacin, tigecycline, doxicycline) was associated with a modest decrease in bla_CTX-M abundance (Figure 3b). In contrast, the use of antibiotics with broad spectrum killing activity and to which carriage of bla_CTX-M does confer resistance (cefepime, ceftazidime, ceftriaxone, cefotaxime, cefuroxime, amoxicillin, ampicillin) was associated with substantially higher increases in relative bla_CTX-M abundance (Figure 3c).

Dynamic antibiotic effect model

Fitting a dynamic model of bla_CTX-M abundance and 16S rRNA abundance to the data (133 patients, 833 swabs, 3361 qPCR measurements), we found that cefuroxime and ceftriaxone were associated with increases in both absolute bla_CTX-M abundance (mean daily increase [90% CrI] 21% [1%, 42%] and 10% [4%, 17%], respectively) and relative bla_CTX-M abundance (14% [-1%, 30%] and 11% [5%, 17%], respectively) (Figure 4). Piperacillin-tazobactam, meropenem and ciprofloxacin (when given orally) were negatively associated with both bla_CTX-M (−8% [−18%, 2%], −8% [−17%, 1%], and −8% [−17%, 2%], respectively) and 16S rRNA gene abundance (−3% [−8%, 1%], −3% [−7%, 1%], and −1% [−6%, 3%], respectively), although uncertainty was large (Figure 4). Their effect on relative resistance (bla_CTX-M/16S rRNA) also appeared to be negative (−5% [−14%, 5%] for piperacillin-tazobactam, −5% [−14%, 4%] for meropenem, −7% [−15%, 3%] for oral ciprofloxacin). Intravenously administered ciprofloxacin did not show these effects. Imipenem and meropenem had similar effects on bla_CTX-M abundance, while no clear effects were evident for amikacin, metronidazole, and amoxicillin-clavulanic acid (Figure 4). The out of sample prediction accuracy using approximated leave-one-out cross-validation (loo-cv) (Watanabe, 2010) (see Materials and methods) for the dynamic model with antibiotic effects is higher than the accuracy of the model without antibiotic effects (Δloo-cv = 4.2 ).

Figure 4 with 3 supplements see all

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Estimated effects of different antibiotics on within-host dynamics from a multivariable model.

The bars show estimated daily effects of individual antibiotics on the absolute bla_CTX-M abundance (red) and 16S rRNA abundance (light blue) indicating the 80% and 95% highest posterior density intervals (thick and thin horizontal bars, respectively). The model also gives the antibiotic effect on the bla_CTX-M/16S relative resistance shown as arrows on the right-hand side. Arrows are in grey for antibiotics with mean effect estimates between −10% and +10%, otherwise they are coloured red (positive selection) and green (negative selection). Route of antibiotic administration (intravenous, iv; oral, or) is indicated in parenthesis.

With the dynamic model we are able to make predictions about the time required for the bla_CTX-M gene to fall below detection levels. To achieve this, we added to our stochastic model a threshold below which the bla_CTX-M gene cannot be detected (see Materials and mthods). Below this threshold the gene may either be lost from the bacterial community, or it may exist in very small reservoirs for example in persister cells (Balaban et al., 2019). The predictions of detectable carriage duration show a high degree of uncertainty, visible as long-tailed predictive distributions (Figure 5). Because of the skew, we report here the median instead of the mean together with 80% credible intervals. We chose the duration of different antimicrobial therapies according to clinical guidelines. Assuming that the estimated antibiotic associations represent causal effects, we find that a single eight day course of cefuroxime or a 14 day course of ceftriaxone substantially prolongs carriage of bla_CTX-M, by a median estimate of 147% (80% CrI 13.4%, 577%) for cefuroxime and 120% (80% CrI −8.6%, 492%) for ceftriaxone versus no exposure (Figure 5). Addition of oral ciprofloxacin to a course of amoxicillin-clavulanic acid or ceftriaxone reduces bla_CTX-M carriage duration (by approximately 51% [80% CrI −115%, 89%] and 48% [80% CrI −71.1%, 86%]) (Figure 5). A typical 14 day course of meropenem or a 8 day course of piperacillin-tazobactam reduce bla_CTX-M carriage duration relative to no treatment (by approximately 42% [80% CrI −25%, 75%] and 41% [80% CrI −45%, 71%], respectively), and each reduces bla_CTX-M carriage even more relative to a 7 day course of combined ceftriaxone plus amikacin (by approximately 69% [80% CrI 20%, 89%] and 66% [80% CrI −7%, 88%], respectively) (Figure 5). Finally, a 14 day course of meropenem reduces bla_CTX-M resistance carriage relative to a shorter 5 day course (by approximately 69% [80% CrI 20%, 89%]) (Figure 5).

Figure 5

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Simulated predictions of bla_CTX-M carriage duration under different antibiotic treatments.

The distributions on the left-hand side shows model predictions with parameter uncertainty, but assuming deterministic dynamics. The right-hand side shows the predictions with parameter uncertainty as well as Markov process uncertainty. The darker grey areas shows the 50% credible intervals and the white lines show the median predictions. Each density distribution is overlaid with the density line of the no treatment case (dotted line) and its median prediction (dotted vertical line). In the first five rows, we compare predictions for treatment with amoxicillin-clavulanic acid (18 days) and ceftriaxone (14 days), and each in combination treatment with ciprofloxacin. In the next three rows, we compare treatment with ceftriaxone plus amikacin (7 days), meropenem (14 days), and piperacillin-tazobactam (8 days). In the final two rows, we compare a 14 day course of meropenem with a shortened course (5 days). AMC stands for amoxicillin-clavulanic acid.

Discussion

By fitting a dynamic model accounting for both observation noise and within-host dynamics to time series data from 133 patients, we quantified the association between antibiotic exposure and changes in rectal swab abundance of gut bacteria and bla_CTX-M resistance genes. The largest effects were found for exposures to the second and third generation cephalosporins, cefuroxime and ceftriaxone, both of which were associated with increases in bla_CTX-M abundance. Forward simulations indicated that if these associations are causal, exposure to typical courses of these antibiotics would be expected to more than double the carriage duration of bla_CTX-M. Both cefuroxime and ceftriaxone have broad-spectrum killing activity (Nahata and Barson, 1985; Neu and Fu, 1978), but have limited activity against ESBL-producing organisms (Livermore and Brown, 2001; Sorlózano et al., 2007). Therefore, a direct selective effect of these two antibiotics is biologically plausible to account for the above finding.

Though credible intervals were wide, meropenem, piperacillin-tazobactam, and oral ciprofloxacin – all common agents for treating hospital-acquired infections (Lautenbach et al., 2001; Masterton et al., 2003; Paterson, 2006) – were associated with reductions in bla_CTX-M abundance. All three are broad-spectrum antibiotics with activity against ESBL producers in the absence of specific co-resistance, suggesting that this association may at least in part be a causal effect. These antibiotics were also associated with a negative effect on relative resistance (bla_CTX-M divided by 16S rRNA gene abundance). This observation can be explained by a general reduction of bacterial biomass that leads the bla_CTX-M abundance to drop below detection levels. In line with this, our simulations suggested that a typical course of meropenem or of piperacillin-tazobactam would reduce bla_CTX-M carriage duration relative to no treatment by about 40%, and each course reduces bla_CTX-M carriage duration by about 70% relative to a combined course of ceftriaxone plus amikacin. Pharmacokinetic models suggest that bacteriocidal serum concentrations of ceftriaxone persist for relatively short time periods after treatment (typically between 1 hr and 4d, Garot et al., 2011). In contrast, our model predicts that treatment effects on the gut flora can be much longer lasting (on the order of weeks). Also, a 14 day course of meropenem is predicted to reduce ESBL resistance carriage duration relative to a shortened course (5 days) by approximately 70%. While these findings suggest suppression of ESBL producing bacteria by use of carbapenem as a measure to reduce the risk of infection in high-risk patients, such a measure clearly needs to be balanced against the considerable risk of selecting for carbapenem resistance. Finally, we also found that adding oral ciprofloxacin to amoxicillin-clavulanic acid or ceftriaxone was associated with reductions in predicted median carriage duration of ESBL-producing bacteria by approximately 50%. This is in line with recent work indicating that hospital monotherapy with cephalosporins is more strongly association with later ESBL carriage relative to combination therapy (Tacconelli et al., 2020). Administration of oral fluoroquinolone to reduce faecal load of ESBL-producers in asymptomatic carriers has been used in outbreak settings with ESBL E. coli and K. pneumoniae (Paterson et al., 2001), where risk for fluoroquinolones resistance was low. However, the wide variation in rates of ciprofloxacin resistance amongst ESBL-producing Enterobacteriaceae across settings (Winokur et al., 2001) is likely to limit the generalisability of this finding. Although oral ciprofloxacin showed an association with reduced bla_CTX-M abundance, intravenous ciprofloxacin showed near zero effect. Antibiotic selection for resistance due to antibiotics with different routes of administration has been previously explored in a mouse model, suggesting that, indeed, oral drug administration has stronger selective effect on resistance than intravenous administration (Zhang et al., 2013), but similar studies for humans are lacking. Delineating the relationship between the various routes of antibiotic administration and resistance selection will be important for a better understanding of advantages and disadvantages of different routes of administration.

There are are number of advantages to our modelling approach over more classical, associational methods used in related work (Meletiadis et al., 2017). First, because we use a mechanistic model it allows us to directly estimate ecologically important parameters such as strength of resistance amplification under antibiotic selection, which are of inherent interest and can inform further modelling work. Indeed, our predictions of resistance carriage duration (Figure 5) are good examples of the latter. Second, our model uses the variability present in the data to quantify different types of data variability due to the data collection (noise from the qPCR machine, noise from taking the swab and DNA extraction). This allows our model to fully propagate uncertainty to the final estimates. Finally, rather than using only aggregate data (which loses information), our analysis is designed to fully exploit the information available in the time series data. Our work also has a number of important limitations, aside from the obvious risks of confounding present in this observational dataset. Our analysis did not explicitly model changing antibiotic concentrations in the gut, nor did it attempt to explicitly model how antibiotics affect the ecology of the gut bacterial community. While it would have been straightforward to include a pharmacokinetic model of antibiotic concentrations (similar studies have been performed in mice [Jumbe et al., 2003] and pigs [Nguyen et al., 2014]), disentangling direct effects of antibiotic concentrations from indirect effects mediated by other components of the gut flora is far more challenging and beyond the scope of what we considered appropriate with the available data. Instead, our model assumed multiplicative antibiotic effects, which we considered a reasonable simplification of the underlying mechanisms. Multiplicative effects imply that antibiotics alter the daily total bacterial growth rate (16S abundance) and the growth of resistant bacteria relative to the average bacterium (bla_CTX-M/16S), but it does not allow for more complex fitness effects due to, for example, synergies between antibiotics (MacLean et al., 2010), or density-dependent effects, whereby antibiotic-mediated killing may depend on bacterial density (Udekwu et al., 2009). Further, many non-antibiotic drugs have been shown to have an impact on human gut bacteria (Maier et al., 2018), but only antibacterials were considered in our analysis. Lastly, all patients in this study were identified (and consenting) ESBL-carriers. Therefore, apart from the potential for selection bias, we assumed that all changes in bla_CTX-M abundance were due to within-host dynamics, neglecting the possibility of new acquisitions, which should be the scope for other modelling frameworks that integrate both within- and between-host dynamics.

Antibiotic impact on the human gut microbiome is likely to be an important mediator for the increase of bacterial resistance globally (Donskey, 2004; Relman and Lipsitch, 2018). A large body of theory has been developed that demonstrates the role of within-host processes for understanding population-wide selection of resistance through antibiotic use (Blanquart, 2019; Davies et al., 2019; Webb et al., 2005; Knight et al., 2018; Lipsitch et al., 2000; Lipsitch and Samore, 2002; Webb et al., 2005). However, surprisingly few studies have used data to quantify the effect of antibiotic treatment on resistance abundance within individual gut microbiomes. Two studies involving patients admitted to intensive care units looked at the effect of a preventative antibiotic cocktail (selective digestive decontamination) on gut microbiome resistance in patients, with one study (including n = 13 patients) finding no clear effect (Buelow et al., 2014), and the other (n = 10) showing increases of four different resistance genes associated with treatment (Buelow et al., 2017). Gibson et al. (2016) studied the faecal metagenomics of preterm infants (n = 84) over time and found that treatment with antibiotics was correlated with enrichment of both cognate and noncognate resistance markers. But none of these studies attempted to model resistance dynamics. The modelling framework we have developed enables testable predictions about the impact of different antibiotics and lengths of treatment on the duration of carriage of resistant determinants above detection threshold. Such understanding is an important step toward understanding the spread of antibiotic resistance.

The development of a mechanistic understanding of the relationship between antibiotic use in a population and the proportion of this population in whom resistance can be detected relies on quantifying the antibiotic effects in individual exposed patients, as we do here, but also on quantifying the knock-on effects on transmission to contacts. These indirect effects are likely to be considerable. A recent study in Dutch travellers returning to the Netherlands who had acquired ESBL-producing Enterobacteriaceae carriage overseas found that their new carriage status was associated with a 150% increase in the daily risk of non-carrying household members also becoming ESBL-positive (Arcilla et al., 2017). Developing mechanistic models for the spread of ESBLs and other resistance determinants within host populations accounting for direct and indirect antibiotic effects is an important priority for future research. Such models would help us to understand and predict how changes in antibiotic usage patterns affect the prevalence of antimicrobial resistance in a community and ultimately help to prioritise interventions to reduce the burden of antimicrobial resistance.

Materials and methods

Study participants and follow-up

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Participants were recruited as part of an observational, prospective, cohort study that included three hospitals (Italy, Serbia and Romania), with known high endemic prevalence of antibiotic resistance in bacterial infections. The hospitals were serving a general urban population. The study was conducted over two years from January 2011 to December 2012 as part of the multi-centre SATURN (‘Impact of Specific Antibiotic Therapies on the prevalence of hUman host ResistaNt bacteria’) project (NO241796; clinicaltrials.gov NTC01208519). The study enrolled adult (>18 y) inpatients of medical and surgical wards, excluding pregnant patients. Enrolled patients were screened at admission for carriage of ESBL-producing Enterobacteriaceae with rectal swabs (E swab, Copan, Italy). Patients who tested positive for ESBL producing Enterobacteriaceae carriage (details below) were included in the follow-up cohort. The target cohort size was calculated to be 400 patients, based on the number of patients that would be required to detect one log difference in resistance abundance with 90% certainty. However, patient recruitment we slower than anticipated. For all follow-up patients (n = 133) rectal swabs were taken every two to three days (as per study protocol) during hospitalisation, which includes one swab at admission and one at discharge. The swabs were stored at −80 degrees Celsius and sent to a central laboratory for processing. Using patient charts, the study also collected information on antibiotics treatment, including antibiotic type, duration, and route of administration. See Table 1 for an overview of the study details. Written informed consent and consent to publish was obtained from all patients before study enrolment. All collected data was entered de-identified into the central study database which sat in Tel Aviv in accordance with the local rules of personal data privacy protection. The study protocol was reviewed and approved by the Catholic University Ethics Commission in Rome, Italy (protocol P/291/CE/2010 approved on 6.4.2010) and the Clinical Center of Serbia Ethic Committee (protocol 451/34 approved on 18.03.2010). At the site in Romania patient screening for multidrug-resistant bacteria was considered, due to the local epidemiology, a quality improvement intervention and did not require institutional ethical approval.

Identification of ESBL producing organism carriers

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Samples taken at admission were cultured on chromogenic agar (Brilliance ESBL, Oxoid, Basingstoke, UK) to test for ESBL producing Enterobacteriaceae. Characteristically coloured colonies for Enterobacteriaceae were isolated (single colony per colour), replated on blood agar and incubated overnight in air. ESBL status was then confirmed with the double disk diffusion method according to CLSI guidelines . These methods were performed in the laboratories on the respective hospitals (all laboratories were ISO accredited). The above methods are not specific to any single bacterial species, but instead identify ESBL producing specimens of the Enterobacteriaceae family, including Escherichia coli, Klebsiella pneumoniae, or Enterobacter cloacae and others. According to the standard definition by the Centers of Disease Control (CDC/NHSN, 2018), samples taken at admission identify community acquired organisms.

Quantitative PCR

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DNA was extracted from the swab samples using QIAampDNA Stool Mini Kit (Qiagen) and a fixed volume (4 µl) of DNA solution was used as a template for quantitative PCR (qPCR) assays. Two singleplex qPCR assays were conducted, one to assess quantity of bla_CTX-M gene family with primers CTX-M-A6 (TGGTRAYRTGGMTBAARGGCA) and CTX-M-A8 (TGGGTRAARTARGTSACCAGAA) (product length, 175 bp) and one targeting a conserved bacterial 16S rRNA gene region bacteria using the following primer set, 16S_E939F (GAATTGACGGGGGCCCGCACAAG) and 16S_1492R (TACGGYTACCTTGTTACGACTT) (product length, 597 bp) to assess total bacterial quantity. Each singleplex qPCR run targeted either 16S gene or bla_CTX-M, and included reaction tubes with negative controls and tubes containing a standard (16S or bla_CTX-M depending on the run) of different concentrations (eight different dilutions), which also served as a positive control. For details on reaction mix and cycling conditions see Lerner et al., 2013. All reactions were carried out in duplicates, sometimes triplicates, representing technical replicates. The qPCR was performed at the Laboratory of Medical Microbiology, University of Antwerp (ISO accredited). While we did not validate the 16S qPCR measurements through, for example, spiked standard DNA preparations, the ability of qPCR to quantify bacterial amounts in faecal samples has been shown previously (Rinttilä et al., 2004).

Time series autocorrelation

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We first transformed all qPCR measurements onto a log-scale. For all patients and each time point we then computed the mean of the qPCR duplicates (or triplicates) for bla_CTX-M and 16S rRNA. To get reliable estimates of autocorrelation, we selected only patients with more than five time points. Separately for the bla_CTX-M and 16S rRNA gene data, we computed the first-order autocorrelation (disregarding varying spacing between time points) for each patient, and we averaged these values across the patients. We then simulated serially uncorrelated ‘white noise’ time series, again separately for bla_CTX-M and 16S rRNA, with the same length as the patient data and with identical time series mean and variance. Similar to the real data, we computed mean autocorrelations for the simulated data and show their distribution for a large number of such simulations (n = 10,000) together with the observed autocorrelation (Figure 1—figure supplement 1a and b). We also computed the proportion of simulated datasets that showed an average autocorrelation equal to, or larger than, the observed data, and we show those numbers on the arrows in Figure 1—figure supplement 1a and b.

Estimating observation and process noise in time series

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To estimate the amount of observation noise and process noise in the time series we constructed a Bayesian state-space model that included qPCR noise, swab noise, and biological noise. This model is given through:

\begin{array}{ll} q_{i, j, k, g} & \sim N (s_{i, j, g}, σ_{q p c r}), \\ s_{i, j, g} & \sim N (x_{i, j, g}, σ_{s w a b_{g}}), \\ x_{i, j + 1, g} & \sim N (s_{i, j, g}, σ_{b i o_{g}}), \end{array}

where $i$ denotes a given patient, $j$ denotes a swab (one per time point), $k$ denotes a qPCR measurement (multiple repeats per swab), and $g$ denotes the genetic target, either bla_CTX-M or 16S rRNA. The term $q_{i, j, k, g}$ , represents the measured quantity of genetic target $g$ , of the kth qPCR replicate (on a log-scale) from patient $i$ , at time point $j$ . In addition, there are two hidden-state parameter vectors: $s_{i, j, g}$ is the underlying, true sequence abundance of genetic target $g$ that a qPCR assay with 100% efficiency could (in theory) measure at time point $j$ for patient $i$ , and $x_{i, j, g}$ is the actual gene abundance of genetic target $g$ , in the patient at time point $j$ for patient $i$ , before the added noise through the swab process and gene extraction. The unobserved variables of interest are $σ_{q p c r}$ , the qPCR machine error (assumed to be the same for bla_CTX-M and 16S rRNA), $σ_{s w a b_{g}}$ , the swab variation of the genetic target $g$ , and $σ_{b i o_{g}}$ , the variation of genetic target $g$ from biological processes. We assigned improper uniform (‘flat’) priors for the hidden-state parameters and generic weakly informative priors (half-normal, N⁺(0,1)) for the the noise parameters $σ_{q p c r}$ , $σ_{s w a b_{g}}$ , and $σ_{b i o_{g}}$ . We then fitted this model to the bla_CTX-M and 16S rRNA measurements. The posteriors of the three noise parameters are shown in Figure 1—figure supplement 1c, where we expressed each type of noise as a fraction of the total noise. The model was fitted using Stan software (v2.19.1) (Carpenter et al., 2017) and with additional analysis in R (R Development Core Team, 2016), and we sampled 80,000 samples from the posterior with four independent chains and a burn-in period of 20,000 samples. We assessed convergence by checking that R̂ (Gelman and Rubin, 1992) was low (<1.01) for all parameters, and visually by looking at the rank plots for all parameters (shown in Figure 2—figure supplement 1a). For rank plots, posterior draws are ranked across all chains and then ranks are plotted as histograms separately for each chain. A uniform shape of all histograms then indicates that all chains target the same posterior (Vehtari et al., 2019).

Between and within time series variance

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For the estimation of between and within time series variation we used a Bayesian hierarchical model, which accounted for unbalanced sampling between patients. This model used the mean posterior estimates of $x_{i, j}$ (actual gene abundance in time point $j$ for patient $i$ ) from the previous model, and it took the form

\begin{array}{ll} x_{i, j, g} & \sim N (μ_{i, g}, σ_{w i t h i n, g}), \\ μ_{i, g} & \sim N (μ_{g}, σ_{b e t w e e n, g}), \end{array}

where $m u_{i, g}$ is the mean abundance of genetic target $g$ (bla_CTX-M or 16S gene) and patient $i$ , around which the log-scaled measurements were assumed to be normally distributed with standard deviation $σ_{w i t h i n, g}$ , the within time series variation. The mean abundances were assumed to follow a normal distribution with a population mean µ and between-patient standard deviation $σ_{b e t w e e n}$ . We assigned improper uniform priors for the population and the patient means, and generic weakly informative priors for the standard deviations (N⁺(0,1)). We fitted the model using Stan (v2.19.1), with 80,000 posterior draws after a burn-in period of 500 iterations. The R̂ statistic (<1.01) and MCMC rank histogram plots (Figure 2—figure supplement 1b) were used to assess convergence. Model estimates are shown in Figure 2. To calculate the coefficient of variation for the non-log-scaled bla_CTX-M and 16S rRNA measurements, we use the transform described by Koopmans et al., 1964:

c_{v} = \sqrt{e^{s_{l n}^{2}} - 1},

where $s_{l n}$ is the estimated standard deviation of the log-scaled data.

Association of antibiotic treatment and changes in resistance

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To study the association between antibiotic treatment and resistance we looked at relative abundance of resistance (bla_CTX-M abundance/16S rRNA gene abundance) as a marker of natural selection. First, we computed the changes in relative resistance for every pair of adjacent time points and for each antibiotic we used a binary variable indicating whether or not a given antibiotic was administered between these time points. When an antibiotic treatment was on the same day as a swab, this treatment was allocated to the time interval between this day and the next swab. We first looked at how changes in relative resistance are associated with courses of any antibiotics, then with courses of anti-enterbacteriaceae antibiotics to which carriage of bla_CTX-M does not confer direct resistance (colistin, meropenem, ertapenem, imipenem, amoxicillin-clavulanic acid, ampicillin-sulbactam, piperacillin-tazobactam, gentamicin, amikacin, ciprofloxacin, ofloxacin, levofloxacin, tigecycline, doxicycline), and finally with antibiotics that have a broad-spectrum activity and to which bla_CTX-M does confer resistance (cefepime, ceftazidime, ceftriaxone, cefotaxime, cefuroxime, amoxicillin, ampicillin). Results are shown in Figure 3, upper panel. We evaluated how likely the observed differences between treatments are under the assumption of no association between treatment and resistance. For this we did a permutation or ‘reshuffling’ experiment: we randomly reassigned (without replacement) the antibiotic treatment labels to the data intervals. We compute the distribution of mean differences from 50,000 permutations and compare this to the observed difference (Figure 3, lower panel).

Dynamic within-host model

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We extended previous modelling approaches to extracting ecological parameters from microbial ecosystem dynamics (Faust and Raes, 2012; Stein et al., 2013) by applying a Bayesian hidden-state model, which featured two layers of hidden-state variables: the unobserved mean of the qPCR measurements, and the unobserved true abundances of bla_CTX-M or 16S rRNA in the gut. This model structure allowed us to separate process noise (stochastic effects impacting the gene abundance change from one day to the next) from observation noise (stochastic effects impacting the swab efficiency, DNA extraction or the qPCR measurements) and also to account for different spacing between measured time points.

We analysed antibiotic treatment separately by type and route of administration, only including treatments that occurred in five or more patients (amoxicillin-clavulanic acid (iv), piperacillin-tazobactam (iv), cefuroxime (iv), ceftriaxone (iv), meropenem (iv), imipenem (iv), ciprofloxacin (iv), ciprofloxacin (or), amikacin (iv), metronidazole (iv)). Since the bla_CTX-M and 16S rRNA measurements are expected to be proportional to the absolute abundance of the resistance gene and bacterial load respectively, the ratio bla_CTX-M/16S is a measure of the relative abundance of the bla_CTX-M gene in the gut microbiota. Positive or negative selective effects by antibiotics on bla_CTX-M mediated resistance are expected to cause shifts in bacteria carrying bla_CTX-M versus non-carriers. As a result they affect bla_CTX-M/16S, but quantifying their effects on absolute bla_CTX-M abundance is important for predicting extinction and persistence of the bla_CTX-M gene. Under the assumption that 16S rRNA gene abundance is independent of antibiotic treatment, variation in 16S rRNA would be caused mainly by the swab procedure and DNA extraction (and other steps in the protocol), and it could be used to normalise bla_CTX-M abundance. However, as we found in Figure 4, certain antibiotic treatments were associated with changes in 16S rRNA abundance. Thus, we used a dynamic model that explicitly modelled both antibiotic effects on 16S rRNA and on bla_CTX-M/16S, from which the effects on bla_CTX-M could then be computed.

Studying the standard deviation between qPCR measurement repeats as a function of the mean, we observed that qPCR variation remained relatively stable over five orders of magnitude of the mean measurement (from 1.5 to 6.5 on the log-scale), but it increased quickly for lower magnitudes (Figure 4—figure supplement 1). In the Bayesian model for different sources of variation described above, the parameter $σ_{q p c r}$ assumed that the qPCR uncertainty is the same across measurements. Here, we aimed to account for the fact that low measurements of gene copy numbers have higher uncertainty. We fitted a smooth spline (choosing five degrees of freedom) to the qPCR measurements (red line in Figure 4—figure supplement 1). This let us assign an estimated qPCR standard deviation to every set of qPCR repeats. We provided those estimates as data to the Bayesian model. This allowed us to use all qPCR measurements, including extremely low values, without removing any data points from the analysis. Our model then took the form:

\begin{array}{ll} q_{i, j, g, k} & \sim N (s_{i, j, g}, σ_{q p c r_{i, j, g}}), \\ s_{i, j, g = 16 S} & \sim N (x_{i, j, b = 16 S}, σ_{s w a b}), \\ x_{i, j, b = r a t i o} & = s_{i, j, g = C T X - M} - s_{i, j, g = 16 S}, \\ x_{i, j + 1, b} & \sim N (x_{i, j, b} + f (a b x)_{i, j, b}, \sqrt{t_{j + 1} - t_{j}} σ_{b i o, b}), \\ f (a b x)_{i, j, b} & = \sum_{t_{j}}^{t_{j + 1} - 1} (a_{b} + \sum_{z = 1}^{n_{z}} c_{z, b} y_{z, t}), \end{array}

where $g$ denotes either bla_CTX-M or 16S rRNA, and $b$ denotes either relative resistance (bla_CTX-M/16S rRNA) or 16S rRNA. Then, $q_{i, j, g, k}$ is the $k$ -th qPCR result (log-scaled) of patient $i$ , measured in the sample with index $j$ , and genetic target $g$ (bla_CTX-M or 16S rRNA). The qPCR standard deviation for sample $j$ and patient $i$ is given through $σ_{q p c r_{i, j, g}}$ , and it is estimated as described above. $s_{i, j, g}$ is the mean of the qPCR measurements in sample $j$ of patient $i$ , and genetic target $g$ , and $x_{i, j, b = 16 S}$ is the 16S sequence abundance that is actually present at time point $j$ in the gut of patient $i$ . The error introduced by the swab procedure, DNA extraction etc. is given through $σ_{s w a b}$ , and we assume that this error causes the same perturbations to bla_CTX-M and to 16S, such that this error cancels out when computing the ratio of bla_CTX-M and 16S rRNA $x_{i, j, b = r a t i o}$ , which on a log-scale is computed as the difference ( $s_{i, j, g = C T X - M} - s_{i, j, g = 16 S}$ ). Further, $t_{j}$ denotes the calendar day of sample $j$ in patient $i$ , and $t_{j + 1}$ denotes the calendar day of the following sample in the same patient $i$ . The daily biological variability of the bla_CTX-M/16S ratio and of 16S are given through $σ_{b i o, b = r a t i o}$ and $σ_{b i o, b = 16 S}$ , respectively, with $\sqrt{t_{j + 1} - t_{j}}$ adjusting the expected random walk variation by the number of days between observations (Lemons and Langevin, 2002). The ecological dynamics are modelled with the function $f {(a b x)}_{i, j, b}$ , which is the change in the expected value of $x_{i, j, b}$ between sample $j$ and $j + 1$ . In the definition of $f {(a b x)}_{i, j, b}$ in line 5 of Equation 4, $a_{b}$ denotes the neutral growth or loss of $g$ , $c_{z, b}$ denotes the effect of antibiotic $z$ on $b$ , and $y_{z, t}$ is a boolean variable indicating whether or not antibiotic $z$ was given on day $t$ . The term inside the bracket takes, for a single calendar day $t$ , the neutral growth/loss term ( $a_{b}$ ) and adds to this the summed effect of all antibiotics given on day $t$ . This is computed for each calendar day from $t_{j}$ until a day before the subsequent sample ( $t_{j + 1} - 1$ ). The effects of all of these days are then summed up. Note, that $x_{i, j, b}$ denotes the abundance of 16S rRNA, or the relative abundance of bla_CTX-M/16S rRNA, on the log-scale. Exponentiating this variable gives the copy numbers (or copy number ratio) on the real scale. Therefore, summing all effects on the scale of $x_{i, j, b}$ is equivalent to multiplying the exponentiated effects on the scale of copy numbers. For example, consider that genetic target $b = 16 S$ in patient $i = 1$ has at the time of sample $j = 1$ an abundance of $10^{x_{i = 1, j = 1, b = 16 S}} = 100$ copy numbers and a neutral trend of $a_{b = 16 S} = - 0.5$ . Suppose on the day of this sample ( $t_{i = 1, j = 1}$ ) two antibiotics $z = 1$ and $z = 2$ are given with effects $c_{z = 1, b = 16 S} = + 0.5$ and $c_{z = 2, b = 16 S} = + 0.1$ , then one day after this sample the genetic target abundance has an expected abundance of $100 * 10^{- 0.5} * 10^{+ 0.5} * 10^{+ 0.1} = 126$ .

In this model, $q_{i, j, g, k}$ and $y_{z, t}$ correspond to measured data, $σ_{q p c r_{i, j, g}}$ is computed from this data (see above). All other parameters are estimated: the hidden-state variables are $s_{i, j, g}$ , $x_{i, j, g = 16 S}$ , and $x_{i, j, g = r a t i o}$ , the noise variables are $σ_{s w a b}$ , $σ_{b i o, g = 16 S}$ , and $σ_{b i o, g = r a t i o}$ , and finally the variables describing the ecology are $a_{g}$ , $c_{z, g}$ . The model has three likelihood functions. The first (line 1) applies to each single qPCR result and it relates repeat qPCR measurements to their variability and underlying mean. The second (line 2) relates qPCR means of 16S rRNA to their variability and the underlying 16S rRNA gene abundance. The third likelihood (line 4) applies to all sample pairs where a previous and following sample exists from the same patient. This likelihood relates changes in underlying 16S rRNA abundance or underlying bla_CTX-M/16S rRNA to the parameters $a_{b}$ and $c_{z, b}$ , and $σ_{b i o}$ . All parameters (including all hidden-states) are estimated simultaneously. We can express the posterior distribution over all estimated parameters ( $Θ$ ), which is conditional on the set of all data ( $D$ ), and the prior over the parameter space $p (Θ)$ . For readability, here we only keep necessary subscripts:

\begin{array}{ll} p (Θ | D) & = p (s, x, a, c, σ_{s w a b}, σ_{b i o} | q, σ_{q p c r}, y), \\ p (Θ | D) & \propto p (Θ) \prod_{i, j, g, k} p (q_{i, j, g, k} | s_{i, j, g}, σ_{q p c r_{i, j, g}}) \prod_{i, j} p (s_{i, j, g = 16 S} | x_{i, j, b = 16 S}, σ_{s w a b}) \\ \prod_{i, j, b} p (x_{i, j + 1, b} | c_{b}, a_{b}, σ_{b i o, b}, x_{i, j, b}, y) . \end{array}

On the hidden-state variables we assigned improper uniform priors, on the standard deviations describing swab and biological variability we assigned standard weakly informative priors (N⁺(0,1)), and on the antibiotic effects ( $c$ ) we assigned conservative priors of the form N(0, 0.1). We fitted the model using Stan software (v2.19.1), and we sampled 80,000 samples from the posterior with four independent chains after a burn-in phase of 10,000 samples. The marginal posterior draws of the $c_{z, g}$ parameters are exponentiated to be on the scale of gene counts. Subtracting one allows us to express daily effects as percent of increase or decrease relative to the previous day (Figure 4). We also show marginal posterior distributions together with prior distributions for the $c_{z, g}$ parameters (Figure 4—figure supplement 2). Diagnostic plots for the MCMC sampling of the $c_{z, g}$ parameters are shown in Figure 4—figure supplement 3.

We compared our model as given through model definition four to a model without antibiotic effects (all $c$ parameters set to zero). The number of patients treated with the same antibiotic is too small to perform cross-validation (iteratively fitting the model to all time series while leaving out data from one patient, which is then used to assess model predictions). Therefore, we used an efficient approximation of Bayesian leave-one-out cross-validation using Pareto-smoothed importance sampling (Vehtari et al., 2017).

We forward simulated bla_CTX-M data using the dynamic model above and the posterior distributions from the model fit. We added to the model a threshold below which the bla_CTX-M gene becomes extinct or at least undetectable. According to a study of returning European travelers to Southeast Asia, ESBL carriers lose detectable resistant bacteria after a median of 30 days (Arcilla et al., 2017). Accordingly, we simulated bla_CTX-M time series without antibiotic treatment and chose an extinction threshold (0.25 bla_CTX-M copy numbers) that achieved the same median extinction time. We then used this model to repeatedly (2000 times) simulate bla_CTX-M carriage durations, with each simulation using a new draw from the parameter posterior. The resulting distribution of carriage times contains both the uncertainty in the parameter estimates and uncertainty from the Markov process (Figure 5, right-hand side). We also draw a set of parameter values from the posterior to simulate bla_CTX-M carriage durations repeatedly (200 times) with the same parameters, and taking the median carriage duration to remove Markov process uncertainty. We repeated this for 300 draws of parameters (Figure 5, left-hand side). We used both of the above methods to simulate carriage time under different alternative antibiotic treatments. The resulting distributions are shown in Figure 5.

Data availability

The Bayesian model code in Stan, the R code to fit the model, and the data in Rdata format as well as csv format are deposited in Dryad under https://doi.org/10.5061/dryad.8vf034t.

The following data sets were generated

(2020) Dryad Digital Repository
Quantifying antibiotic impact on within-host dynamics of extended-spectrum beta-lactamase resistance in hospitalized patients.
https://doi.org/10.5061/dryad.8vf034t

References

(2017) Import and spread of extended-spectrum β-lactamase-producing Enterobacteriaceae by international travellers (COMBAT study): a prospective, multicentre cohort study
The Lancet Infectious Diseases 17:78–85.
https://doi.org/10.1016/S1473-3099(16)30319-X
- PubMed
- Google Scholar
1. Balaban NQ
2. Helaine S
3. Lewis K
4. Ackermann M
5. Aldridge B
6. Andersson DI
7. Brynildsen MP
8. Bumann D
9. Camilli A
10. Collins JJ
11. Dehio C
12. Fortune S
13. Ghigo JM
14. Hardt WD
15. Harms A
16. Heinemann M
17. Hung DT
18. Jenal U
19. Levin BR
20. Michiels J
21. Storz G
22. Tan MW
23. Tenson T
24. Van Melderen L
25. Zinkernagel A
(2019) Definitions and guidelines for research on antibiotic persistence
Nature Reviews Microbiology 17:441–448.
https://doi.org/10.1038/s41579-019-0196-3
- PubMed
- Google Scholar
1. Blanquart F
(2019) Evolutionary epidemiology models to predict the dynamics of antibiotic resistance
Evolutionary Applications 12:365–383.
https://doi.org/10.1111/eva.12753
- PubMed
- Google Scholar
1. Bonnet R
(2004) Growing group of Extended-Spectrum -Lactamases: the CTX-M Enzymes
Antimicrobial Agents and Chemotherapy 48:1–14.
https://doi.org/10.1128/AAC.48.1.1-14.2004
- Google Scholar
1. Buelow E
2. Gonzalez TB
3. Versluis D
4. Oostdijk EA
5. Ogilvie LA
6. van Mourik MS
7. Oosterink E
8. van Passel MW
9. Smidt H
10. D'Andrea MM
11. de Been M
12. Jones BV
13. Willems RJ
14. Bonten MJ
15. van Schaik W
(2014) Effects of selective digestive decontamination (SDD) on the gut resistome
Journal of Antimicrobial Chemotherapy 69:2215–2223.
https://doi.org/10.1093/jac/dku092
- PubMed
- Google Scholar
(2017) Comparative gut Microbiota and resistome profiling of intensive care patients receiving selective digestive tract decontamination and healthy subjects
Microbiome 5:88.
https://doi.org/10.1186/s40168-017-0309-z
- PubMed
- Google Scholar
1. Carpenter B
2. Gelman A
3. Hoffman MD
4. Lee D
5. Goodrich B
6. Betancourt M
7. Brubaker M
8. Guo J
9. Li P
10. Riddell A
(2017) Stan : a probabilistic programming language
Journal of Statistical Software 76:1.
https://doi.org/10.18637/jss.v076.i01
- Google Scholar
Report
1. CDC/NHSN
(2018) Surveillance Definitions for Specific Types of Infections
CDC.

https://www.cdc.gov/nhsn/pdfs/pscmanual/17pscnosinfdef_current.pdf
- Google Scholar
(2008)
Increasing prevalence of ESBL-producing Enterobacteriaceae in Europe

Euro Surveillance : Bulletin Europeen Sur Les Maladies Transmissibles = European Communicable Disease Bulletin 13:19044.
- PubMed
- Google Scholar
1. Davies NG
2. Flasche S
3. Jit M
4. Atkins KE
(2019) Within-host dynamics shape antibiotic resistance in commensal bacteria
Nature Ecology & Evolution 3:440–449.
https://doi.org/10.1038/s41559-018-0786-x
- Google Scholar
Book
1. Donnenberg MS
(1979)
Enterobacteriaceae

In: Bennett M. D, editors. Principles and Practice of Infectious Diseases. Elsevier. pp. 2503–2517.
- Google Scholar
1. Donskey CJ
(2004) The role of the intestinal tract as a reservoir and source for transmission of nosocomial pathogens
Clinical Infectious Diseases 39:219–226.
https://doi.org/10.1086/422002
- PubMed
- Google Scholar
(2019) Carriage of Extended-spectrum Beta-lactamase-producing Enterobacteriaceae and the risk of surgical site infection after colorectal surgery: a prospective cohort study
Clinical Infectious Diseases 68:1699–1704.
https://doi.org/10.1093/cid/ciy768
- PubMed
- Google Scholar
1. Faust K
2. Raes J
(2012) Microbial interactions: from networks to models
Nature Reviews Microbiology 10:538–550.
https://doi.org/10.1038/nrmicro2832
- PubMed
- Google Scholar
1. Garot D
2. Respaud R
3. Lanotte P
4. Simon N
5. Mercier E
6. Ehrmann S
7. Perrotin D
8. Dequin PF
9. Le Guellec C
(2011) Population pharmacokinetics of ceftriaxone in critically ill septic patients: a reappraisal
British Journal of Clinical Pharmacology 72:758–767.
https://doi.org/10.1111/j.1365-2125.2011.04005.x
- PubMed
- Google Scholar
1. Gelman A
2. Rubin DB
(1992) Inference from iterative simulation using multiple sequences
Statistical Science 7:457–472.
https://doi.org/10.1214/ss/1177011136
- Google Scholar
1. Gibson MK
2. Wang B
3. Ahmadi S
4. Burnham CA
5. Tarr PI
6. Warner BB
7. Dantas G
(2016) Developmental dynamics of the preterm infant gut Microbiota and antibiotic resistome
Nature Microbiology 1:16024.
https://doi.org/10.1038/nmicrobiol.2016.24
- PubMed
- Google Scholar
1. Jumbe N
2. Louie A
3. Leary R
4. Liu W
5. Deziel MR
6. Tam VH
7. Bachhawat R
8. Freeman C
9. Kahn JB
10. Bush K
11. Dudley MN
12. Miller MH
13. Drusano GL
(2003) Application of a mathematical model to prevent in vivo amplification of antibiotic-resistant bacterial populations during therapy
Journal of Clinical Investigation 112:275–285.
https://doi.org/10.1172/JCI200316814
- PubMed
- Google Scholar
1. Knight GM
2. Costelloe C
3. Deeny SR
4. Moore LSP
5. Hopkins S
6. Johnson AP
7. Robotham JV
8. Holmes AH
(2018) Quantifying where human acquisition of antibiotic resistance occurs: a mathematical modelling study
BMC Medicine 16:137.
https://doi.org/10.1186/s12916-018-1121-8
- PubMed
- Google Scholar
(1964) Confidence intervals for the coefficient of variation for the normal and log normal distributions
Biometrika 51:25–32.
https://doi.org/10.1093/biomet/51.1-2.25
- Google Scholar
(2001) Epidemiological investigation of fluoroquinolone resistance in infections due to extended-spectrum beta-lactamase-producing Escherichia coli and Klebsiella pneumoniae
Clinical Infectious Diseases : An Official Publication of the Infectious Diseases Society of America 33:1288–1294.
https://doi.org/10.1086/322667
- PubMed
- Google Scholar
Book
1. Lemons DS
2. Langevin P
(2002)
An Introduction to Stochastic Processes in Physics

JHU Press.
- Google Scholar
(2013) Rectal swabs are suitable for quantifying the carriage load of KPC-producing carbapenem-resistant Enterobacteriaceae
Antimicrobial Agents and Chemotherapy 57:1474–1479.
https://doi.org/10.1128/AAC.01275-12
- PubMed
- Google Scholar
(2000) The epidemiology of antibiotic resistance in hospitals: Paradoxes and prescriptions
PNAS 97:1938–1943.
https://doi.org/10.1073/pnas.97.4.1938
- PubMed
- Google Scholar
1. Lipsitch M
2. Samore MH
(2002) Antimicrobial use and antimicrobial resistance: a population perspective
Emerging Infectious Diseases 8:347–354.
https://doi.org/10.3201/eid0804.010312
- PubMed
- Google Scholar
1. Livermore DM
2. Brown DF
(2001) Detection of beta-lactamase-mediated resistance
Journal of Antimicrobial Chemotherapy 48 Suppl 1:59–64.
https://doi.org/10.1093/jac/48.suppl_1.59
- PubMed
- Google Scholar
(2010) The population genetics of antibiotic resistance: integrating molecular mechanisms and treatment contexts
Nature Reviews Genetics 11:405–414.
https://doi.org/10.1038/nrg2778
- PubMed
- Google Scholar
1. Maier L
2. Pruteanu M
3. Kuhn M
4. Zeller G
5. Telzerow A
6. Anderson EE
7. Brochado AR
8. Fernandez KC
9. Dose H
10. Mori H
11. Patil KR
12. Bork P
13. Typas A
(2018) Extensive impact of non-antibiotic drugs on human gut Bacteria
Nature 555:623–628.
https://doi.org/10.1038/nature25979
- PubMed
- Google Scholar
1. Masci JR
(2005)
Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Diseases

Clinical Infectious Diseases , Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Diseases, An Official Publication of the Infectious Diseases Society of America.
- Google Scholar
(2003) Appropriate antimicrobial treatment in nosocomial infections-the clinical challenges
Journal of Hospital Infection 55 Suppl 1:1–12.
https://doi.org/10.1016/S0195-6701(03)00294-9
- PubMed
- Google Scholar
(2017) Amplification of antimicrobial resistance in gut flora of patients treated with ceftriaxone
Antimicrobial Agents and Chemotherapy 61:e00473-17.
https://doi.org/10.1128/AAC.00473-17
- Google Scholar
1. Nahata MC
2. Barson WJ
(1985) Ceftriaxone: a third-generation cephalosporin
Drug Intelligence & Clinical Pharmacy 19:900–906.
https://doi.org/10.1177/106002808501901203
- PubMed
- Google Scholar
1. Neu HC
2. Fu KP
(1978) Cefuroxime, a Beta-Lactamase-Resistant cephalosporin with a broad spectrum of Gram-Positive and -Negative activity
Antimicrobial Agents and Chemotherapy 13:657–664.
https://doi.org/10.1128/AAC.13.4.657
- Google Scholar
(2014) Mathematical modeling of bacterial kinetics to predict the impact of antibiotic colonic exposure and treatment duration on the amount of resistant enterobacteria excreted
PLOS Computational Biology 10:e1003840.
https://doi.org/10.1371/journal.pcbi.1003840
- PubMed
- Google Scholar
1. Paterson DL
(2000) Recommendation for treatment of severe infections caused by Enterobacteriaceae producing extended-spectrum beta-lactamases (ESBLs)
Clinical Microbiology and Infection 6:460–463.
https://doi.org/10.1046/j.1469-0691.2000.00107.x
- PubMed
- Google Scholar
1. Paterson DL
2. Singh N
3. Rihs JD
4. Squier C
5. Rihs BL
6. Muder RR
(2001) Control of an outbreak of infection due to extended-spectrum beta-lactamase--producing Escherichia coli in a liver transplantation unit
Clinical Infectious Diseases : An Official Publication of the Infectious Diseases Society of America 33:126–128.
https://doi.org/10.1086/320882
- PubMed
- Google Scholar
1. Paterson DL
(2006) Resistance in gram-negative Bacteria: enterobacteriaceae
The American Journal of Medicine 119:S20–S28.
https://doi.org/10.1016/j.amjmed.2006.03.013
- PubMed
- Google Scholar
Software
1. R Development Core Team
(2016) R: A Language and Environment for Statistical Computing
R Foundation for Statistical Computing, Vienna, Austria.

http://www.r-project.org
1. Relman DA
2. Lipsitch M
(2018) Microbiome as a tool and a target in the effort to address antimicrobial resistance
PNAS 115:12902–12910.
https://doi.org/10.1073/pnas.1717163115
- PubMed
- Google Scholar
(2004) Development of an extensive set of 16S rDNA-targeted primers for quantification of pathogenic and indigenous Bacteria in faecal samples by real-time PCR
Journal of Applied Microbiology 97:1166–1177.
https://doi.org/10.1111/j.1365-2672.2004.02409.x
- PubMed
- Google Scholar
(2005) High levels of antimicrobial coresistance among Extended-Spectrum- -Lactamase-Producing Enterobacteriaceae
Antimicrobial Agents and Chemotherapy 49:2137–2139.
https://doi.org/10.1128/AAC.49.5.2137-2139.2005
- Google Scholar
(2007) Activity in vitro of twelve antibiotics against clinical isolates of extended-spectrum beta-lactamase producing Escherichia coli
Journal of Basic Microbiology 47:413–416.
https://doi.org/10.1002/jobm.200710318
- PubMed
- Google Scholar
1. Stein RR
2. Bucci V
3. Toussaint NC
4. Buffie CG
5. Rätsch G
6. Pamer EG
7. Sander C
8. Xavier JB
(2013) Ecological modeling from time-series inference: insight into dynamics and stability of intestinal Microbiota
PLOS Computational Biology 9:e1003388.
https://doi.org/10.1371/journal.pcbi.1003388
- PubMed
- Google Scholar
(2018) Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant Bacteria and tuberculosis
The Lancet Infectious Diseases 18:318–327.
https://doi.org/10.1016/S1473-3099(17)30753-3
- PubMed
- Google Scholar
1. Tacconelli E
2. Górska A
3. De Angelis G
4. Lammens C
5. Restuccia G
6. Schrenzel J
7. Huson DH
8. Carević B
9. Preoţescu L
10. Carmeli Y
11. Kazma M
12. Spanu T
13. Carrara E
14. Malhotra-Kumar S
15. Gladstone BP
(2020) Estimating the association between antibiotic exposure and colonization with extended-spectrum β-lactamase-producing Gram-negative Bacteria using machine learning methods: a multicentre, prospective cohort study
Clinical Microbiology and Infection 26:87–94.
https://doi.org/10.1016/j.cmi.2019.05.013
- PubMed
- Google Scholar
(2018) Estimating the proportion of bystander selection for antibiotic resistance among potentially pathogenic bacterial flora
PNAS 115:E11988–E11995.
https://doi.org/10.1073/pnas.1810840115
- PubMed
- Google Scholar
1. Udekwu KI
2. Parrish N
3. Ankomah P
4. Baquero F
5. Levin BR
(2009) Functional relationship between bacterial cell density and the efficacy of antibiotics
Journal of Antimicrobial Chemotherapy 63:745–757.
https://doi.org/10.1093/jac/dkn554
- PubMed
- Google Scholar
(2004) Dramatic increase in prevalence of fecal carriage of extended-spectrum beta-lactamase-producing Enterobacteriaceae during nonoutbreak situations in Spain
Journal of Clinical Microbiology 42:4769–4775.
https://doi.org/10.1128/JCM.42.10.4769-4775.2004
- PubMed
- Google Scholar
(2017) Practical bayesian model evaluation using leave-one-out cross-validation and WAIC
Statistics and Computing 27:1413–1432.
https://doi.org/10.1007/s11222-016-9696-4
- Google Scholar
Preprint
(2019) Rank-normalization, folding, and localization: an improved R̂ for assessing convergence of MCMC
arXiv.

https://arxiv.org/pdf/1903.08008.pdf
- Google Scholar
1. Watanabe S
(2010)
Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory

Journal of Machine Learning Research 11:3571–3594.
- Google Scholar
1. Webb GF
2. D'Agata EM
3. Magal P
4. Ruan S
(2005) A model of antibiotic-resistant bacterial epidemics in hospitals
PNAS 102:13343–13348.
https://doi.org/10.1073/pnas.0504053102
- PubMed
- Google Scholar
(2001) Variations in the prevalence of strains expressing an extended-spectrum beta-lactamase phenotype and Characterization of isolates from Europe, the Americas, and the western pacific region
Clinical Infectious Diseases 32 Suppl 2:S94–S103.
https://doi.org/10.1086/320182
- PubMed
- Google Scholar
1. Woerther PL
2. Micol JB
3. Angebault C
4. Pasquier F
5. Pilorge S
6. Bourhis JH
7. de Botton S
8. Gachot B
9. Chachaty E
(2015) Monitoring antibiotic-resistant enterobacteria faecal levels is helpful in predicting antibiotic susceptibility of Bacteraemia isolates in patients with haematological malignancies
Journal of Medical Microbiology 64:676–681.
https://doi.org/10.1099/jmm.0.000078
- PubMed
- Google Scholar
1. Zhang L
2. Huang Y
3. Zhou Y
4. Buckley T
5. Wang HH
(2013) Antibiotic administration routes significantly influence the levels of antibiotic resistance in gut Microbiota
Antimicrobial Agents and Chemotherapy 57:3659–3666.
https://doi.org/10.1128/AAC.00670-13
- Google Scholar

Decision letter

Wendy S Garrett

Senior Editor; Harvard T.H. Chan School of Public Health, United States
Caroline Colijn

Reviewing Editor; Simon Fraser University, Canada
Amy Mathers

Reviewer
Lulla Opatowski

Reviewer; Institut Pasteur, France

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

Acceptance summary:

The dynamics of resistance in response to antibiotic treatment is of central importance in understanding resistance emergence more broadly. This paper develops a data-driven model to describe the within-host dynamics of Enterobacteriaceae that produce extended-spectrum β-lactamase. Supported by the model, the authors compare different antibiotics' effects on resistance.

Decision letter after peer review:

Thank you for submitting your article "Quantifying antibiotic impact on within-patient dynamics of extended-spectrum β-lactamase resistance" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Amy Mathers (Reviewer #2); Lulla Opatowski (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.

Summary:

This work asks what the dynamic consequences of antibiotic perturbations are to the human gut flora, and addresses this question with a novel mathematical model that is fit to data. The authors use the model to estimate the impact of antibiotic treatment on the abundance of resistance (through bla_CTX-M) and on the duration of carriage of resistance. Overall, the manuscript represents a potentially important contribution to the field. However, there is some T data overlap in a previous study by the authors. and we have identified a few essential revisions to resolve this issue and that need to be addressed as well.

Essential revisions:

The reviewers brought up the previous publication, Meletiadis, J. et al., 2017, in which a good portion of the cases were reported, and where some of the same conclusions were reached.

The authors should clarify the novel contributions of this work in light of the previous manuscript. It seems that the key claimed results here are (1) variability, Figures 1, 2; (2) effect of treatment on relative resistance; (3) effect of treatment on abundance and (4) the model with fitting, and consequently the estimates re duration of carriage. Each has limitations.

Please set these results, with the novelty and limitations, in context.

The modelling is clearly new and I think it is important. In that direction, the clarity should be improved. I particularly noted the very long paragraph in subsection “Dynamic within-host model”, and the benefit that explaining the model and its assumptions (and limitations) for non-statisticians, non-modellers would likely bring.

Please clarify re interpretation of 16s, abundance as noted by reviewers.

Please improve Figures 1 and 2. While "hieroglyphics" may go a bit far, I agree that Figure 1 is visually striking but hard to see and interpret. Perhaps in Figure 1 you could group the time curves by similarity, or present an expanded view of some representative curves, with the full set in an appendix. Or something like that.

Reviewer #1:

The motivation for this study and its goals are certainly important and timely. The collateral effects of antibiotic treatment on the distribution of bacteria species and strains in the enteric flora (microbiome) of humans and the incidence of antibiotic resistance genes and resistance encoding plasmids are subjects of considerable importance, epidemiologically as well as clinically. In their Introduction, the authors do a fine job of presenting this and particularly so for the extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae and the blaCTX family of resistance genes, that are the focus of their study.

This investigation certainly has the virtue of being extensive; fecal samples were taken sequentially from 133 hospitalized patients from three countries (Romania, Serbia and Italy) for a median of five samples from each patient. This virtue does have a downside; these patients were hospitalized for different reasons and treated orally of intravenously with 10 different antibiotics. The results presented suggest that some antibiotics are more likely than others to affect the distribution and abundance of these enteric bacteria and the bla_CRX-M genes, their study is correlative rather than mechanistic. From the results presented, it's not clear how the different modes and purpose of treatment the within-patient dynamics ESBL resistance. From what they say in their Discussion, the authors are very much aware of this limitation of this study.

Although their observation some antibiotics are more likely than others to affect the enteric microbiome and the frequency of bla_CRX-M genes could have epidemiological and clinical implication, they don't really consider these potential implications of their results. It's also not clear how this information can be used. Presumably the choice of antibiotics, mode and frequency of administration in these hospitalized patients is based on the nature of the infection and information about the susceptibility of the target bacteria to the drugs employed for treatment. While in an ideal world, consideration should to the collateral effects of treatment; we are far from that ideal world. This is particularly so for hospitalized patients. It would be of some interest to do an analogous study for the treatment of community-acquired infections (about 90% of antibiotic use in humans), where information about the collateral effects of treatment with different antibiotics may have broader and implications that could be implemented.

Reviewer #2:

This is an interesting and important piece of work demonstrating the correlation between certain antimicrobial exposure in patients and associated increase in abundance of bla_CTX-M. The gene increase is then normalized to 16s abundance for overall internal QC for extraction efficiency and bacterial content. The work appears to be very well done and the methods and approach to analysis appear to support the conclusions of the authors. The figures are easy to interpret and additive to the manuscript (not sure if Figure 1 is critical but it is interesting to look at the variability across the data set and would favor keeping). Also the discussion includes a portion focused on relative biomass and bacterial presence in the gut of patients on antimicrobials but it is not clear they would be able to infer this information from the variability in swab collection and extraction efficiency. Would favor revising the discussion to address this issue.

One concern is the amount this manuscript overlaps with another previously published manuscript (Meletiadis, J. et al., 2017). The authors state in the Introduction section that they used a subset of the data but in reviewing the other manuscript it appears that there are 133 patients in this analysis and 122 from the other description. They also used very similar methodology by normalizing the bla_CTX-M abundance with 16s. I think it would be helpful for the authors to further explain how this differs from prior work and is additive to the literature. The conclusions from that manuscript were almost identical as well. Would favor a re-write to address this existing work and contrast the submitted manuscript.

Reviewer #3:

Niehus and colleagues present an original study in which they investigate the impact of different antibiotics on ESBL resistance within-host in Enterobacteriaceae carriers. The data comes from 133 patients from Romania, Serbia and Italy and consists in longitudinal series of rectal swabs (with a median of 5 swabs per patient). Swabs were analysed in order to quantify the abundance of bla_CTX-M and 16SrRNA. For both indicators, the within- and between-host variabilities, and the within-host dynamics are analysed using Bayesian state-space models. Parameters associated to the selection exerted by different classes of antibiotics on the dynamics of bla_CTX-M, 16SrRNA and bla_CTX-M/16SrRNA are estimated.

This is a very exciting and well written article. This is, to my knowledge, the first study attempting to analyse, using hypothesis-driven mechanistic models, the dynamics of within-host resistance genes and the impact of various classes of antibiotics. I believe that the study and results presented here represent a major contribution to the field.

– It would be good for the reader to detail and clarify how each indicator should be interpreted. My understanding is for example that a decrease in bla_CTX-M/16SrRNA would more or less be interpreted as a decrease in resistance rate. However, it is not clear to me how important is the contribution of Enterobacteriaceae species to the global quantity of 16SrRNA.

– My main comment is related to the simulation study. It would be good to provide a validation of the model before running simulations, for example by doing out of fit assessment? Is that possible? Would you have enough power?

– It would be interesting to estimate whether the antibiotic classes have different delays of impact.

– I am not an expert but my understanding is that the aptitude to induce degradation of the flora may not be fully characterized by the "broad spectrum" characteristic. Other characteristics define whether the antibiotic destroys or not non-pathogenic (and anaerobic?) bacteria. It would be good to discuss more that aspect.

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

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

Summary:

Antimicrobial resistance is recognised by the World Health Organization and others as one of the most pressing threats to global health. However, our understanding of the ecological processes that link antimicrobial use to the emergence and spread of resistance is limited. In particular (and surprisingly for such a fundamental question), a quantitative understanding of the within-host processes relating clinical antibiotic exposure to within-patient resistance abundance is lacking. For bacteria that colonize the human gut, such within-host processes are likely to play a major role in mediating the relationship between antimicrobial use and the prevalence of resistance in the wider population. This paper focuses on resistance in Enterobacteriaceae conferred by extended-spectrum β-lactamase (ESBL) production. Such ESBL-producing organisms are responsible for a high and increasing burden of disease globally. The authors aimed to determine the effects of typical antimicrobial exposures in hospitalized patients on the dynamics of resistance gene abundance and total bacterial load. The authors asked: if such effects exist, how do they vary between different treatment regimens, and what is the predicted impact of antibiotic exposure on persistence of patient colonization with ESBL-producing organisms?

Essential revisions:

The manuscript remains of high interest because of the conceptual innovation of this work related to the model.

However, reviewers still do not fully understand the dynamical model, which in our view was one of the key points of novelty of this work because it makes the link between mechanistic processes and the noisy observations. Accordingly, further revisions of this section should be completed before the paper can be accepted for publication.

It would be helpful to link the data to the notation so we know which symbols correspond to data. For clarity, even after doing this please note which are the hidden-state variables (in the dynamic model; it's listed in the observation/process noise model).

Technically, the notation a, b refers to a closed interval so infinity should not be listed in this form. We would suggest simply the phrase "improper uniform prior".

Equation 4 and text: it appears that you haven't "added them on a log scale" because the sum that you have computed goes into the mean of the normal distributions in Equation 4.

What does x_t=1 = x_t=1a₀c₁c₂ have to do with any of the terms in (4) ? The text in remains inscrutable compared to the equations and notation. There is mention of the model "looping" through time points – how does this work? Please expand the text and be explicit about the likelihood at each stage, what the looping steps are, whether the terms are added to the mean or are multiplicative, and so on. Please define x_i,j,g=ratio, x_i,j,g=16S and all notation.

When you say that you "fitted the model with STAN" please give the posterior decomposition, and the likelihood. State which parameters are estimated. The notation of the model should be connected to Figure 4 and 5 where the model's inferences are shown.

Despite the response to review where you agree that it will be difficult to assess "abundance", you then include the following statement. It seems that the authors agree with the limitations around abundance in the response to review but then do not fully incorporate into the discussion. Please consider revising. Example: “Surprisingly, despite the relatively broad antibacterial spectrum of cefuroxime and ceftriaxone, there was no evidence that exposure to these antibiotics reduced 16S rRNA abundance (although we note that a "broad" spectrum is not defined in relation to the microbiota but to bacterial species of clinical importance).”

https://doi.org/10.7554/eLife.49206.sa1

Author response

Essential revisions:

The reviewers brought up the previous publication, Meletiadis, J. et al., 2017, in which a good portion of the cases were reported, and where some of the same conclusions were reached.

The authors should clarify the novel contributions of this work in light of the previous manuscript. It seems that the key claimed results here are (1) variability, Figures 1, 2; (2) effect of treatment on relative resistance; (3) effect of treatment on abundance and (4) the model with fitting, and consequently the estimates re duration of carriage. Each has limitations.

Please set these results, with the novelty and limitations, in context.

This is an important point. As we mention in our Introduction, a subset of the data we use were also used in a publication by Meletiadis et al., in 2017. Their work used standard statistical methods (Fisher exact test, classification regression tree, stepwise logistic regression) on collapsed data (each patient’s time series was converted to a zero or one) to ask, is there a statistical association between ceftriaxone treatment and bla_CTX-M normalised by total bacteria? Our study addresses a set of related but different questions and makes use of the full, uncollapsed data. First we are concerned with the differential effects of a range of antibiotics (not only one) both on bla_CTX-M and on total bacterial load. Second, our focus is on developing a mechanistic understanding of the impact of antibiotics on the dynamics of both bla_CTX-M and 16S rRNA, rather than considering static statistical associations. Third, through formulating and fitting a dynamic model to the data we aimed to estimate ecologically important parameters (strength of selection, speed of resistance waning) which are of interest in themselves. Finally, we sought to make predictions about the impact of different antibiotic treatment regimens on persistence of bla_CTX-M. We have now changed the final paragraph of our Introduction to clarify how our goals are different from the previous work. We also added a new section in the Discussion that highlights the methodological novelties and advantages where we previously only stated the limitations.

The modelling is clearly new and I think it is important. In that direction, the clarity should be improved. I particularly noted the very long paragraph in subsection “Dynamic within-host model”, and the benefit that explaining the model and its assumptions (and limitations) for non-statisticians, non-modellers would likely bring.

We very much agree with this point. In response, we have rewritten the “Dynamic within-host model” section by breaking it into clearer sections, and by more clearly explaining the novelty of the model and more clearly listing the model assumptions. We also make sure to use language that is accessible for non-statisticians and by explaining some mathematical terminology.

Please clarify re interpretation of 16s, abundance as noted by reviewers.

As noted by reviewer 1, it would be interesting to validate 16S as an appropriate measure of cell density. Our study did not include an internal validation of the 16S rRNA measurements, however other studies have previously shown the ability of qPCR to quantify bacterial amounts in fecal samples (Rinttilä, 2004). We now mention this previous validation and the limitation that we do not include an internal validation in the “Quantitative PCR” section. We further added in our Discussion a comment on newer methods that quantify specifically viable bacterial cells (van Frankenhuyzen et al., 2013), which may guide future studies like ours.

Related to this, reviewer 2 notes that it may be difficult to draw conclusions about antibiotic effects on 16S rRNA given variability in DNA extraction efficiency. We agree that the variability of DNA extraction is expected to be large. Therefore our model was specifically designed to directly estimate variability that affects repeat measurements (through noise in the swab efficiency and DNA extraction) and to separate this variability from real effects on the underlying true 16S rRNA quantity. We realised that in our previous manuscript we only mentioned the swab procedure as a source of noise affecting the DNA concentration in the qPCR machine. We correct this now by being more explicit in various places for our new manuscript:

· “noise through the swab process and gene extraction”

· “observation noise (stochastic effects impacting the swab efficiency, DNA extraction or the qPCR measurements)”

· “variation in 16S rRNA would be caused mainly by the swab procedure and DNA extraction (and other steps in the protocol)”

· “error introduced by the swab procedure, DNA extraction etc.”

· “noise from qPCR measurement or from the DNA extraction process” “observation uncertainty – for instance through the swab procedure, DNA extraction, or qPCR process”

Reviewer 2 also states that one would anticipate antibiotics to cause shifts in relative species abundances as opposed to reductions in overall cell numbers. We fully share this expectation, in particular for antibiotics that target only a small subset of species present in the microbiome. Indeed, almost all of our inferred 16S effects support this hypothesis. We now take this up more explicitly in our Discussion by saying: The stable 16S abundance could be due to rapid overgrowth of non-susceptible strains which replace bacteria killed by these antibiotics (Hildebrand et al., 2019), leading to shifts in species composition rather than total species abundance.

Please improve Figures 1 and 2. While "hieroglyphics" may go a bit far, I agree that Figure 1 is visually striking but hard to see and interpret. Perhaps in Figure 1 you could group the time curves by similarity, or present an expanded view of some representative curves, with the full set in an appendix. Or something like that.

We have now changed Figures 1 and 2 to make them easier to interpret. In particular, we followed reviewer 1’s comment on Figure 2 and made it more concise by removing the additional bars of the lower part that indicated between and within patient time series variability. They were confusing as they showed a variability on the same scale as the sequence abundance, and between and within patient variability can easily be summarised and reported as numbers, which is what we do now.

For Figure 1, we have now produced a new version that contains a random selection of 50% of the study patients only. We additionally improved the previous figure with all patients by choosing a colourblind-friendly colour scheme and by increasing the plot area so that gene dynamics can be seen more clearly. We do agree with reviewer 2 (“this figure is interesting to look at the variability across the data set and would favor keeping”) that this version of the figure is a nice way to show the entire dataset. As reviewer 1 mentions, through eyeballing it appears impossible to spot waxing and waning in response to antibiotic treatment or to spot differences between treated and untreated patients, and this is exactly the purpose of this figure i.e. to show how noise is dominating the dynamics. Because we think that both are effective ways of presenting the data we attach both versions of Figure 1 for the editor to make the final decision on which one to include should the manuscript be accepted.

For ‘Figure 1 alternative’ the adjusted caption is: “Time series plots demonstrating the diverse range of dynamical patterns of bla_CTX-M resistance gene abundance across patients. For visualisation purpose, a subset of 49 patients was selected uniformly at random from all 132 patients with more than one rectal swab. The x-axis scale is identical across panels, the length of one week is given for scale in the top-left corner. Timelines are ordered by length. The y-axis scale differs between panels, with the space between vertical grey lines representing a 10-fold change in the absolute bla_CTX-M gene abundance (measured in copy numbers). The left-hand side shows patients who received antibiotic treatment (n=113), and the two right-hand side columns are patients without antibiotic treatment (n=19). For clarity, we show only the twelve most frequently used antibiotics in distinct colours and other antibiotics in light grey.”

Reviewer #1:

The motivation for this study and its goals are certainly important and timely. The collateral effects of antibiotic treatment on the distribution of bacteria species and strains in the enteric flora (microbiome) of humans and the incidence of antibiotic resistance genes and resistance encoding plasmids are subjects of considerable importance, epidemiologically as well as clinically. In their Introduction, the authors do a fine job of presenting this and particularly so for the extended-spectrum β-lactamase (ESBL)-producing Enterobacteriacae and the blaCTX family of resistance genes, that are the focus of their study.

This investigation certainly has the virtue of being extensive; fecal samples were taken sequentially from 133 hospitalized patients from three countries (Romania, Serbia and Italy) for a median of five samples from each patient. This virtue does have a downside; these patients were hospitalized for different reasons and treated orally of intravenously with 10 different antibiotics. The results presented suggest that some antibiotics are more likely than others to affect the distribution and abundance of these enteric bacteria and the bla_CRX-M genes, their study is correlative rather than mechanistic. From the results presented, it's not clear how the different modes and purpose of treatment the within-patient dynamics ESBL resistance. From what they say in their Discussion, the authors are very much aware of this limitation of this study.

Indeed, we are aware of the limitations of our observational data regarding causal conclusions, including a potential for selection bias and confounding through patient differences that affect both the treatment decision and resistance dynamics, as well as time-varying confounding. We now add a statement on this in the Discussion.

Although their observation some antibiotics are more likely than others to affect the enteric microbiome and the frequency of bla_CRX-M genes could have epidemiological and clinical implication, they don't really consider these potential implications of their results. It's also not clear how this information can be used.

Even though our Figure 5 shows the impacts of different alternative antibiotic choices on the duration of resistance carriage (a clinically interesting and relevant outcome) we refrain from drawing any clinical conclusions or recommendations from our results. This is because: 1) our study only looks at the effects on a single type of resistance; and 2) it is not clear what the individual patient or the community effects are of the observed changes to the bla_CTX-M dynamics.

Presumably the choice of antibiotics, mode and frequency of administration in these hospitalized patients is based on the nature of the infection and information about the susceptibility of the target bacteria to the drugs employed for treatment. While in an ideal world, consideration should to the collateral effects of treatment; we are far from that ideal world. This is particularly so for hospitalized patients. It would be of some interest to do an analogous study for the treatment of community-acquired infections (about 90% of antibiotic use in humans), where information about the collateral effects of treatment with different antibiotics may have broader and implications that could be implemented.

This point is well taken and the obvious reason for studying hospital patients is the relative ease of following up patients. There are however existing studies on the way that explore similar questions in a community setting.

Reviewer #2:

This is an interesting and important piece of work demonstrating the correlation between certain antimicrobial exposure in patients and associated increase in abundance of bla_CTX-M. The gene increase is then normalized to 16s abundance for overall internal QC for extraction efficiency and bacterial content. The work appears to be very well done and the methods and approach to analysis appear to support the conclusions of the authors. The figures are easy to interpret and additive to the manuscript (not sure if Figure 1 is critical but it is interesting to look at the variability across the data set and would favor keeping). Also the discussion includes a portion focused on relative biomass and bacterial presence in the gut of patients on antimicrobials but it is not clear they would be able to infer this information from the variability in swab collection and extraction efficiency. Would favor revising the discussion to address this issue.

One concern is the amount this manuscript overlaps with another previously published manuscript (1). The authors state in the Introduction section that they used a subset of the data but in reviewing the other manuscript it appears that there are 133 patients in this analysis and 122 from the other description. They also used very similar methodology by normalizing the bla_CTX-M abundance with 16s. I think it would be helpful for the authors to further explain how this differs from prior work and is additive to the literature. The conclusions from that manuscript were almost identical as well. Would favor a re-write to address this existing work and contrast the submitted manuscript.

This is the first major point raised by the editor and we addressed this above.

Reviewer #3:

Niehus and colleagues present an original study in which they investigate the impact of different antibiotics on ESBL resistance within-host in Enterobacteriaceae carriers. The data comes from 133 patients from Romania, Serbia and Italy and consists in longitudinal series of rectal swabs (with a median of 5 swabs per patient). Swabs were analysed in order to quantify the abundance of bla_CTX-M and 16SrRNA. For both indicators, the within- and between-host variabilities, and the within-host dynamics are analysed using Bayesian state-space models. Parameters associated to the selection exerted by different classes of antibiotics on the dynamics of bla_CTX-M, 16SrRNA and bla_CTX-M/16SrRNA are estimated.

This is a very exciting and well written article. This is, to my knowledge, the first study attempting to analyse, using hypothesis-driven mechanistic models, the dynamics of within-host resistance genes and the impact of various classes of antibiotics. I believe that the study and results presented here represent a major contribution to the field.

– It would be good for the reader to detail and clarify how each indicator should be interpreted. My understanding is for example that a decrease in bla_CTX-M/16SrRNA would more or less be interpreted as a decrease in resistance rate. However, it is not clear to me how important is the contribution of Enterobacteriaceae species to the global quantity of 16SrRNA.

This is an interesting and important point, and our previous version of the manuscript was not clear enough about why we chose to model effects on bla_CTX-M / 16S rRNA. As the reviewer suggests, we expect the bla_CTX-M / 16S rRNA ratio to be approximately propotional to the fraction of bacterial cells harbouring the bla_CTX-M gene. We then model antibiotic effects on the ratio bla_CTX-M / 16S rRNA (representing effects on the relative fitness of bla_CTX-M carriers) and effects on the total bacterial abundance as approximated by 16S. But our model also gives the resulting dynamics of absolute bla_CTX-M abundance, which is important for predicting its extinction and persistence.

– My main comment is related to the simulation study. It would be good to provide a validation of the model before running simulations, for example by doing out of fit assessment? Is that possible? Would you have enough power?

We fully agree that model validation is important. In our study we therefore performed model comparison in which we compared our model with a model without antibiotic effects – we find that the data supports effects of antibiotics. For this we now performed a leave-one-out cross-validation approximation. We approximate because the number of patients treated with the same antibiotics is small. We have revised the manuscript so that we explain this more clearly now.

– It would be interesting to estimate whether the antibiotic classes have different delays of impact.

This is an interesting point that is on our (long) list of possible extensions to our model. However, while interesting, we consider this to be outside the focus of this study and leave this question to further work on this and similar datasets.

– I am not an expert but my understanding is that the aptitude to induce degradation of the flora may not be fully characterized by the "broad spectrum" characteristic. Other characteristics define whether the antibiotic destroys or not non-pathogenic (and anaerobic?) bacteria. It would be good to discuss more that aspect.

This a great point: the term “broad”-spectrum describes an antibiotic’s killing-spectrum in terms of known clinical pathogens. This may or may not be related to the extent of impact on the entire microbiota biomass. We now clarify this in the Discussion (first paragraph).

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

Essential revisions:

The manuscript remains of high interest because of the conceptual innovation of this work related to the model.

However, reviewers still do not fully understand the dynamical model, which in our view was one of the key points of novelty of this work because it makes the link between mechanistic processes and the noisy observations. Accordingly, further revisions of this section should be completed before the paper can be accepted for publication.

It would be helpful to link the data to the notation so we know which symbols correspond to data. For clarity, even after doing this please note which are the hidden-state variables (in the dynamic model; it's listed in the observation/process noise model).

We have rewritten major parts of the dynamic model description. First, we have explained all symbols in Equation 4 within the paragraph below (previously symbols where explained in two separate parts). Second, we have included a new paragraph that lists all the symbols that represent data, and all the symbols representing estimated variables. We have been explicit about which symbols denote hidden-state variables.

Technically, the notation a, b refers to a closed interval so infinity should not be listed in this form. We would suggest simply the phrase "improper uniform prior".

This is a good point, and we now use the phrase “improper uniform prior” instead.

Equation 4 and text: it appears that you haven't "added them on a log scale" because the sum that you have computed goes into the mean of the normal distributions in Equation 4.

What does x_t=1 = x_t=1a₀c₁c₂ have to do with any of the terms in (4) ? The text in remains inscrutable compared to the equations and notation. There is mention of the model "looping" through time points – how does this work? Please expand the text and be explicit about the likelihood at each stage, what the looping steps are, whether the terms are added to the mean or are multiplicative, and so on. Please define x_i,j,g=ratio, x_i,j,g=16S and all notation.

We agree that this part was not well explained. In response, we have substantially changed this part of our model description. The new description is explicit about which parameters are on a log-scale, and what the real scale reflects (gene copy numbers). To clarify the way antibiotic effects work, we give a numerical example, in which we used the same symbols as our Equation 4, instead of introducing new ones. We have also clarified how the sums of the definition of f(abx) work, and we explain each likelihood term in our model, and also give the full posterior distribution (see below). Finally, we have defined all notation, including x_i,j,g=ratio, x_i,j,g=16S.

When you say that you "fitted the model with STAN" please give the posterior decomposition, and the likelihood. State which parameters are estimated. The notation of the model should be connected to Figure 4 and 5 where the model's inferences are shown.

It was not entirely clear to us what was meant by ‘posterior decomposition’. Based on what Neiswanger et al. (https://arxiv.org/pdf/1510.04163.pdf) refer to as posterior decomposition, we now (in addition to Equation 4) give the full posterior distribution as the product of different terms (Equation 5). But in addition we also explain our three different likelihoods in terms of decomposing the total amount of variation in the data into variation attributable to different processes. We finally also added the marginal posterior distributions (together with prior distributions) of the antibiotic effect parameters (c_z,b) as an additional Supplementary Figure. To address the second point, we now explicitly list all parameters of Equation 4 that are estimated. Finally, to link methods to figures, we added an explanation of how the posterior draws of c_z,b were transformed before plotting them in Figure 4. We added a reference to Figure 4 at that point, and we added two references to Figure 5 into the description of the posterior predictions.

Despite the response to review where you agree that it will be difficult to assess "abundance", you then include the following statement. It seems that the authors agree with the limitations around abundance in the response to review but then do not fully incorporate into the discussion. Please consider revising. Example: “Surprisingly, despite the relatively broad antibacterial spectrum of cefuroxime and ceftriaxone, there was no evidence that exposure to these antibiotics reduced 16S rRNA abundance (although we note that a "broad" spectrum is not defined in relation to the microbiota but to bacterial species of clinical importance).”

Based on this comment, we have now decided to remove any interpretation of antibiotic effects on 16S gene abundance. We think that this also helps the discussion be more focused on the CTX-M resistance gene.

https://doi.org/10.7554/eLife.49206.sa2

Article and author information

Author details

Rene Niehus

University of Oxford, Oxford, United Kingdom

Present address
Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, United States

Contribution
Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology

For correspondence
rniehus@hsph.harvard.edu

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-6751-4124
Esther van Kleef

National Institute for Public Health and theEnvironment, Bilthoven, Netherlands

Contribution
Conceptualization, Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Project administration

Competing interests
No competing interests declared
Yin Mo

University of Oxford, Oxford, United Kingdom

Contribution
Conceptualization, Validation, Methodology

Competing interests
No competing interests declared
Agata Turlej-Rogacka

University of Antwerp, Antwerp, Belgium

Contribution
Resources, Validation, Investigation, Methodology

Competing interests
No competing interests declared
Christine Lammens

University of Antwerp, Antwerp, Belgium

Contribution
Resources, Data curation, Validation, Methodology

Competing interests
No competing interests declared
Yehuda Carmeli

Tel-Aviv University, Tel-Aviv, Israel

Contribution
Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration

Competing interests
No competing interests declared
Herman Goossens

University of Antwerp, Antwerp, Belgium

Contribution
Conceptualization, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration

Competing interests
No competing interests declared
Evelina Tacconelli
1. University of Tuebingen, Tuebingen, Germany
2. Infectious Diseases, University of Verona, Verona, Italy
Contribution
Conceptualization, Supervision, Validation, Investigation, Methodology, Project administration

Competing interests
No competing interests declared
Biljana Carevic

Clinical Centre of Serbia, Belgrade, Serbia

Contribution
Resources, Investigation, Methodology

Competing interests
No competing interests declared
Liliana Preotescu

Matei Balş National Institute for Infectious Diseases, Bucharest, Romania

Contribution
Resources, Investigation

Competing interests
No competing interests declared
Surbhi Malhotra-Kumar

University of Antwerp, Antwerp, Belgium

Contribution
Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration

Competing interests
No competing interests declared
Ben S Cooper

University of Oxford, Oxford, United Kingdom

Contribution
Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration

Competing interests
Reviewing Editor, eLife

"This ORCID iD identifies the author of this article:" 0000-0002-9445-7217

Funding

Medical Research Council

Rene Niehus
Ben S Cooper

Wellcome Trust (Major Overseas Programme, 106698/Z/14/Z)

Ben S Cooper

European Union 7th Framework Programme (R-GNOSIS Integrated project, grant agreement number 241796)

Rene Niehus
Esther van Kleef
Agata Turlej-Rogacka
Christine Lammens
Yehuda Carmeli
Herman Goossens
Evelina Tacconelli
Biljana Carevic
Surbhi Malhotra-Kumar
Ben S Cooper

Department for International Development (MR/K006924/1)

Rene Niehus
Ben S Cooper

National Medical Research Council

Yin Mo

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

Acknowledgements

We thank Jonas Schluter, Marc Lipsitch, Thomas Crellen, and Pierre Jacob for valuable feedback along the way. RN as well as the study were supported by funding from the European Community’s R-GNOSIS Integrated project (FP7/2007-2013) under grant agreement number 241796. RN and BSC were also supported by the Medical Research Council and Department for International Development (grant number MR/K006924/1). BSC works within the Wellcome Trust Major Overseas Programme in SE Asia (grant number 106698/Z/14/Z). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Clinical trial registration NCT01208519.

Human subjects: Written informed consent and consent to publish was obtained from all patients before study enrolment. All collected data was entered de-identified into the central study database which sat in Tel Aviv in accordance with the local rules of personal data privacy protection. The study protocol was reviewed and approved by the Catholic University Ethics Commission in Rome, Italy (protocol P/291/CE/2010 approved on 6.4.2010) and the Clinical Center of Serbia Ethic Committee (protocol 451/34 approved on 18.03.2010). At the site in Romania patient screening for multidrug-resistant bacteria was considered, due to the local epidemiology, a quality improvement intervention and did not require institutional ethical approval.

Senior Editor

Wendy S Garrett, Harvard T.H. Chan School of Public Health, United States

Reviewing Editor

Caroline Colijn, Simon Fraser University, Canada

Reviewers

Amy Mathers
Lulla Opatowski, Institut Pasteur, France

Version history

Received: June 12, 2019
Accepted: March 22, 2020
Version of Record published: May 7, 2020 (version 1)

Copyright

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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Rene Niehus
Esther van Kleef
Yin Mo
Agata Turlej-Rogacka
Christine Lammens
Yehuda Carmeli
Herman Goossens
Evelina Tacconelli
Biljana Carevic
Liliana Preotescu
Surbhi Malhotra-Kumar
Ben S Cooper

(2020)

Quantifying antibiotic impact on within-patient dynamics of extended-spectrum beta-lactamase resistance

eLife 9:e49206.

https://doi.org/10.7554/eLife.49206

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Time series plots demonstrating the diverse range of dynamical patterns of blaCTX-M resistance gene abundance across the 132 patients with two or more samples.

Summary of the study.

Overview of antibiotic treatments showing the ten most used antibiotics in this patient cohort.

Variability of 16S abundance and blaCTX-M abundance within and between patient time series using a Bayesian hierarchical model.

Association of antibiotic use with change in relative resistance (abundance of blaCTX-M divided by abundance of 16S rRNA).

Estimated effects of different antibiotics on within-host dynamics from a multivariable model.

Simulated predictions of blaCTX-M carriage duration under different antibiotic treatments.

Author details

Rene Niehus

Present address

Contribution

For correspondence

Competing interests

Esther van Kleef

Contribution

Competing interests

Yin Mo

Contribution

Competing interests

Agata Turlej-Rogacka

Contribution

Competing interests

Christine Lammens

Contribution

Competing interests

Yehuda Carmeli

Contribution

Competing interests

Herman Goossens

Contribution

Competing interests

Evelina Tacconelli

Contribution

Competing interests

Biljana Carevic

Contribution

Competing interests

Liliana Preotescu

Contribution

Competing interests

Surbhi Malhotra-Kumar

Contribution

Competing interests

Ben S Cooper

Contribution

Competing interests

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

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Further reading

Time series plots demonstrating the diverse range of dynamical patterns of bla_CTX-M resistance gene abundance across the 132 patients with two or more samples.

Variability of 16S abundance and bla_CTX-M abundance within and between patient time series using a Bayesian hierarchical model.

Association of antibiotic use with change in relative resistance (abundance of bla_CTX-M divided by abundance of 16S rRNA).

Simulated predictions of bla_CTX-M carriage duration under different antibiotic treatments.