Author response: Quantifying antibiotic impact on within-patient dynamics of extended-spectrum beta-lactamase resistance
2019-12-04

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics 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 blaCTX-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 blaCTX-M abundance during treatment (21% and 10% daily increase, respectively), while treatment with meropenem, piperacillin-tazobactam, and oral ciprofloxacin is associated with decreased blaCTX-M (8% daily decrease for all). The model predicts that typical antibiotic exposures can have substantial long-term effects on blaCTX-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 blaCTX-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 blaCTX-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 blaCTX-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 blaCTX-M and of 16S rRNA, and we aimed to fully characterise the within and between host variation of blaCTX-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 blaCTX-M. We then used our model to make counterfactual predictions about how alternative choices of treatment would impact blaCTX-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 Download asset Open asset 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. 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 blaCTX-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 hospitals3 (Serbia, Italy, Romania)Study duration2 years (Jan 2011 - Dec 2012)Inclusion criteriaInpatients of medical and surgical wards, adults, non-pregnant, ESBL producting Enterobacteriaceae carriers (at admission)Number of patients followed up133 (including one with a single swab taken)Intervals between rectal swabstwo to three daysqPCR targetsblaCTX-M (ESBL resistance gene), 16S rRNA (total bacterial load)Number of different antibiotics used35 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. AntibioticNumber of treated patients (total n=133)RouteCeftriaxone64( 98% iv , 2% im )Ciprofloxacin34( 67% iv , 33% or )Metronidazole20( 50% iv , 50% or )Cefuroxime13( 100% iv , 0% or )Vancomycin13( 86% iv , 14% or )Meropenem10( 100% iv , 0% or )Amikacin9( 100% iv , 0% or )Amoxicillin-clavulanic acid9( 57% iv , 43% or )Piperacillin-tazobactam7( 100% iv , 0% or )Imipenem5( 100% iv , 0% or ) Resistance dynamics The time-varying blaCTX-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 blaCTX-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 blaCTX-M and the 16S rRNA gene time series, though autocorrelation was substantially stronger for the blaCTX-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 blaCTX-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 blaCTX-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 blaCTX-M gene variation, we explored how much the blaCTX-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 blaCTX-M (median ratio 16S / blaCTX-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 blaCTX-M. Between-patient abundance of blaCTX-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 σb⁢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 Download asset Open asset Variability of 16S abundance and blaCTX-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 blaCTX-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 Download asset Open asset Association of antibiotic use with change in relative resistance (abundance of blaCTX-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 blaCTX-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 blaCTX-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 blaCTX-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 blaCTX-M abundance (Figure 3b). In contrast, the use of antibiotics with broad spectrum killing activity and to which carriage of blaCTX-M does confer resistance (cefepime, ceftazidime, ceftriaxone, cefotaxime, cefuroxime, amoxicillin, ampicillin) was associated with substantially higher increases in relative blaCTX-M abundance (Figure 3c). Dynamic antibiotic effect model Fitting a dynamic model of blaCTX-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 blaCTX-M abundance (mean daily increase [90% CrI] 21% [1%, 42%] and 10% [4%, 17%], respectively) and relative blaCTX-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 blaCTX-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 (blaCTX-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 blaCTX-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 Download asset Open asset 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 blaCTX-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 blaCTX-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 is in the dynamic model we are to make predictions about the time for the blaCTX-M gene to To we to our model a which the blaCTX-M gene be (see Materials and this the gene may be from the bacterial or may in for in et al., 2019). The predictions of carriage duration show a high of as predictive distributions (Figure Because of the we the median of the mean together with 80% credible We the duration of different to that the estimated antibiotic we find that a single course of cefuroxime or a 14 course of ceftriaxone substantially carriage of by a median estimate of CrI for cefuroxime and CrI for ceftriaxone versus no exposure (Figure of oral ciprofloxacin to a course of amoxicillin-clavulanic acid or ceftriaxone blaCTX-M carriage duration CrI and CrI (Figure A typical 14 course of meropenem or a course of piperacillin-tazobactam reduce blaCTX-M carriage duration relative to no treatment CrI and CrI respectively), and each blaCTX-M carriage more relative to a course of ceftriaxone CrI and CrI respectively) (Figure Finally, a 14 course of meropenem blaCTX-M resistance carriage relative to a 5 course CrI (Figure Figure 5 Download asset Open asset predictions of blaCTX-M carriage duration under different antibiotic The distributions on the left-hand side shows model predictions with but dynamics. The right-hand side shows the predictions with uncertainty as well as process The darker grey shows the 50% credible intervals and the lines show the median density distribution is overlaid with the density of the no treatment and its median prediction vertical line). In the first five we predictions for treatment with amoxicillin-clavulanic acid and ceftriaxone and each in combination treatment with In the three we treatment with ceftriaxone meropenem and piperacillin-tazobactam days). In the two we a 14 course of meropenem with a course days). for amoxicillin-clavulanic Discussion By a dynamic model for both observation noise and within-host dynamics to time series data from 133 patients, we the association between antibiotic exposure and changes in rectal swab abundance of gut bacteria and blaCTX-M resistance genes. The effects were found for exposures to the and third generation cefuroxime and ceftriaxone, both of which were associated with increases in blaCTX-M abundance. that if these are exposure to typical of these antibiotics would be expected to more than the carriage duration of blaCTX-M. Both cefuroxime and ceftriaxone have broad-spectrum killing activity and and but have activity against organisms and et al., Therefore, a effect of these two antibiotics is to for the credible intervals were meropenem, piperacillin-tazobactam, and oral ciprofloxacin – all common for hospital-acquired infections et al., et al., – were associated with in blaCTX-M abundance. All three are broad-spectrum antibiotics with activity against ESBL in the absence of specific that this association may at in be a These antibiotics were also associated with a negative effect on relative resistance divided by 16S rRNA gene This observation can be by a of bacterial that the blaCTX-M abundance to In with our that a typical course of meropenem or of piperacillin-tazobactam would reduce blaCTX-M carriage duration relative to no treatment by about and each course blaCTX-M carriage duration by about relative to a course of ceftriaxone that of ceftriaxone for time treatment between 1 and et al., In contrast, our model predicts that treatment effects on the gut flora can be much longer the of a 14 course of meropenem is predicted to reduce ESBL resistance carriage duration relative to a course by these of ESBL producing bacteria by use of as a measure to reduce the risk of infection in patients, such a measure to be against the risk of for resistance. Finally, we also found that oral ciprofloxacin to amoxicillin-clavulanic acid or ceftriaxone was associated with in predicted median carriage duration of bacteria by This is in with indicating that hospital with cephalosporins is more association with ESBL carriage relative to combination therapy et al., of oral to reduce load of in carriers has been used in with ESBL and et al., where risk for fluoroquinolones resistance was However, the variation in of ciprofloxacin resistance amongst Enterobacteriaceae across et al., is likely to the of this oral ciprofloxacin showed an association with blaCTX-M abundance, ciprofloxacin showed zero Antibiotic selection for resistance due to antibiotics with different of administration has been explored in a model, oral administration has stronger effect on resistance than administration et al., but similar studies for are the relationship between the of antibiotic administration and resistance selection will be important for a better understanding of and of different of administration. are are number of to our over more methods used in et al., First, we use a mechanistic model us to directly estimate ecologically important parameters such as strength of resistance amplification under antibiotic which are of and can further our predictions of resistance carriage duration (Figure are good of the our model the variability in the data to quantify different types of data variability due to the data from the qPCR noise from taking the swab and DNA This our model to fully uncertainty to the Finally, rather than using only data our analysis is to fully the information in the time series data. also has a number of important from the risks of in this analysis did not model antibiotic in the did to model how antibiotics affect the of the gut bacterial would have been to a model of antibiotic studies have been performed in et al., and