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Antimicrobial Chemotherapy
Research Article
20 January 2021

Effects of Antibiotic Treatment with Piperacillin/Tazobactam versus Ceftriaxone on the Composition of the Murine Gut Microbiota


Effective antimicrobial stewardship requires a better understanding of the impact of different antibiotics on the gut microflora. Studies with humans are confounded by large interindividual variability and difficulty in identifying control cohorts. However, controlled murine models can provide valuable information. In this study, we examined the impact of a penicillin-like antibiotic (piperacillin-tazobactam [TZP]) or a third-generation cephalosporin (ceftriaxone [CRO]) on the murine gut microbiota by analysis of changes in fecal microbiome composition by 16S rRNA amplicon sequencing and standard microbiology. Resistance to colonization by multidrug-resistant Escherichia coli sequence type 131 (ST131) and Klebsiella pneumoniae ST258 was also tested. Changes in microbiome composition and a significant (P < 0.05) decrease in diversity occurred in all treated mice, but dysbiosis was more marked and prolonged after CRO exposure, with a persistent rise in Proteobacteria. Enterobacteriaceae blooms occurred in all antibiotic-treated mice, but for TZP, unlike CRO, these were significant only under direct antibiotic pressure. At the height of dysbiosis after antibiotic termination, the murine gut was highly susceptible to colonization with both multidrug-resistant enterobacterial pathogens. Cohabitation of treated mice with untreated individuals had a notable mitigating effect on dysbiosis of treated guts. The administration of a third-generation cephalosporin caused a more severe imbalance in the murine fecal microflora than that caused by a penicillin/β-lactam inhibitor combination with comparable activity against medically important virulent bacteria. At the height of dysbiosis, both antibiotic treatments equally led to microbial instability associated with loss of resistance to gut colonization by antibiotic-resistant pathogens.


The current global health crisis in antibiotic resistance (AR) requires immediate action (1, 2), and implementation of effective antibiotic stewardship strategies has become a major goal for health agencies worldwide. Optimal policy choices are ideally founded in a deep understanding of both the epidemiology and mechanisms of antimicrobial resistance and of the impact of different antibiotics on patient health. Exposure to antibiotics not only promotes the amplification and transfer of antibiotic resistance genes but also induces gut dysbiosis that facilitates colonization by opportunistic pathogens (36). The nature and duration of this dysbiosis vary depending on the type of antibiotic used, and antibiotics with similar activities against principal human pathogens may have substantially different impacts on the intestinal microbiota (59). Increased presence of enteric Gammaproteobacteria (e.g., Enterobacteriaceae) has been associated with antibiotic treatment (7, 10, 11), but the extent and duration of these blooms with different antibiotic treatments have not yet been fully investigated.
Two of the most widely used antibiotics in hospitals are third-generation cephalosporins (e.g., ceftriaxone [CRO]) and extended-spectrum penicillin–β-lactamase inhibitor combinations (e.g., piperacillin-tazobactam [TZP]). These differ in their activities against opportunistic pathogens, such as Pseudomonas aeruginosa, but have very similar effects on medically important staphylococci and members of the Enterobacteriaceae, such as Escherichia coli and Klebsiella pneumoniae, which play a key role in AR dissemination, with the intestine being a particularly fertile environment for genetic exchange (4, 11). Antibiotic use provides direct selective pressure for AR spread through horizontal gene transfer mechanisms (plasmids), stimulation of the SOS response, and recombination (4, 12, 13). E. coli and K. pneumoniae are among the most important multidrug resistant (MDR) enteric opportunistic pathogens that may amplify during dysbiosis and cause life-threatening infections, particularly in high-risk clinical settings (e.g., critical care units) (14, 15).
The investigation of microbiome dynamics in humans is complicated by large interindividual variability related to diverse diet, lifestyle, disease status, sampling size constraints, and difficulty in identifying “healthy/baseline” microbiomes (control cohorts) (16, 17). Controlled experiments in murine models of gut dysbiosis, though not always directly informative of the specific changes in human gut microbiome composition (7, 18), can provide valuable data on microbiome-wide effects, difficult to obtain in human studies (7, 9, 19, 20).
Here, we examined the fecal microbiota of mice treated with either TZP or CRO, two antibiotics of different classes but with comparable spectra of activity, routinely used in emergency empirical therapy. As the third-generation cephalosporins (though not the penicillins) are notoriously associated with increased AR and pathogen colonization (5, 7, 8, 15, 21, 22), we tested colonization resistance of antibiotic-treated (dysbiotic) guts to MDR extended-spectrum-β-lactamase-producing (ESBL) E. coli sequence type 131 (ST131) and K. pneumoniae ST258. These are two globally disseminated hypervirulent clones, causing outbreaks of severe infection (bacteremia) worldwide, that can reside asymptomatically in the human gut for prolonged periods (23). We found that treatment with ceftriaxone was associated with more severe dysbiosis but that the two antibiotics equally promoted the establishment of antibiotic-resistant pathogens.


Effects on microbiome composition vary with antibiotic treatment.

All murine microbiomes preantibiotic treatment (pre-Ab; day 0 to 1) showed comparable compositions at both the phylum (see Fig. S2 in the supplemental material) and order (Fig. 1) levels, and no significant differences in operational taxonomic unit (OTU) abundance were detected using linear discriminant analysis effect size (LEfSe) (P > 0.05). Untreated (none) and sham-treated (saline-treated) mice had comparable microbiome profiles (Fig. S2) with a predominance of Bacteroidetes and other obligate anaerobes (Clostridiales and Deferribacteriales), largely undisturbed during the study period (Fig. 1 and Fig. S2).
FIG 1 Composition of murine fecal microbiomes with and without antibiotic treatment. BALB/c mice (n = 6) were treated with either a penicillin-like antibiotic (TZP) or a third-generation cephalosporin (CRO) for 5 days, and recovery was monitored after cessation of the antibiotic (up to 3 weeks). A control group (none) received no antibiotics. Relative abundance (pooled data for each time frame: pre-Ab, days 0 to 1; antibiotic, days 2 to 5; post-Ab, days 8 and 9; recovery_1, days 11 to 16; recovery_2, days 18 to 21; recovery_3, day 26) was determined by analysis of 16S rRNA amplicon (V4 region) sequencing data using QIIME-1 (49). Full legends are presented in Fig. S6.
The fecal microbiome composition of mice treated with TZP and CRO presented patterns unique to each antibiotic, as illustrated by fluctuations of indicator families such as S24-7 (or Muribaculaceae family, phylum Bacteroidetes) (24) (Table S2), but with a common increase in the proportion of Lactobacillales (Firmicutes) and Proteobacteria during and after antibiotic administration and a concomitant generalized decrease in Bacteroidetes (Fig. 1a, Fig. S2, and Table S2). In all antibiotic-treated mice, the rise in Proteobacteria was associated with increased diversification at the family level (Fig. 1b). Analysis of relative Proteobacteria OTU abundance showed a significant increase (1 to 2 logs) in Enterobacteriaceae with both TZP and CRO during and after treatment (days 3 to 9; P < 0.001) (Fig. 1 and 2 and Table S2). In CRO-treated mice dysbiosis was sustained and the Enterobacteriaceae (Gammaproteobacteria) were still significantly overrepresented during recovery (LEfSe, P < 0.05 [Fig. 2 and Table S2]), along with significant expansion of Betaproteobacteria (e.g., Alcaligenaceae family) (P < 0.001) compared to untreated mice (Fig. 1b).
FIG 2 Differentially represented bacterial families in murine microbiomes after antibiotic treatment with TZP or CRO compared to no treatment. Mice were treated with either piperacillin-tazobactam (TZP) or ceftriaxone (CRO) or received no treatment (none). Microbiome composition of treated and untreated groups during antibiotic and at recovery (3 weeks after antibiotic) were compared by linear discriminant analysis (LDA) coupled with effect size (LEfSe) (54). The histogram shows that the Enterobacteriaceae were overrepresented in microbiomes treated with both TZP and CRO under direct antibiotic pressure, but in recovery these persisted significantly only with CRO treatment as ranked by the LDA score. Graphical outputs were generated by the publicly available LEfSe visualization module in Galaxy (homepage,; main public server,
Cohousing of an antibiotic-treated mouse with untreated individuals promoted rapid recovery from dysbiosis (Fig. S3). The microbiomes of cohoused untreated individuals resembled those of sham-treated and untreated mice, with only minor fluctuations throughout the study period (Fig. S3). The profiles of treated cohoused mice were comparable to those of mice treated with the same antibiotic, but with cohabitation, return to pretreatment conditions was immediate at cessation of antibiotic (Fig. S3). In the CRO cohousing experiment, initial dysbiosis affected one of the treated mice, with administration of the antibiotic exacerbating this disturbance (Fig. S3). However, these changes were also transient, with rapid return to homeostasis and recovery to a healthy microbiome profile by day 9 (post_Ab) (Fig. S3).

Ceftriaxone caused a significant protracted reduction in microbial diversity.

A decrease in diversity in terms of species richness (α-diversity) was observed following antibiotic treatment (Fig. 3a). This effect was significant (P < 0.05) and protracted in both antibiotic-treated groups compared to sham-treated and untreated groups, but CRO caused a more pronounced decrease than TZP (post_Ab, day 9; P < 0.05). Levels at recovery (days 11 to 26) were comparable to those in untreated groups (Fig. 3a). β-Diversity indicators showed clustering of pretreatment samples with sham-treatment (saline) and no-treatment (none) samples (Fig. 3b). Samples from antibiotic treatment and immediately after antibiotic treatment clustered separately from those of untreated mice. The CRO recovery samples grouped on a unique trajectory, and some were clustered with postantibiotic samples, while TZP recovery samples clustered independently or with the untreated group (Fig. 3b).
FIG 3 Diversity within (a) and between (b) murine fecal microbiomes after antibiotic treatment. (a) Diversity in terms of species richness and evenness (α-diversity; within-sample diversity) in fecal samples from mice treated with antibiotics or left untreated (sham, saline only; none, no treatment) was measured using the Shannon index. Shifts toward lower diversity can be observed during and after treatment with both antibiotics. Asterisks indicate significant differences (P < 0.05). (b) Diversity between murine microbiomes (β-diversity) was assessed by principal-component analysis (PCoA; unweighted UniFrac distances) of microbial 16S rRNA gene sequences in fecal samples and showed clustering of preantibiotic samples with no-antibiotic samples. Samples from CRO-treated and TZP-treated mice after 5 days of treatment (_Ab and _post_Ab) clustered independently on similar trajectories (blue and red ovals, respectively). There was little indication of return to preantibiotic profiles for both antibiotic-treated microbiomes (CRO_recovery and TZP_recovery; black oval) compared to untreated microbiomes. Each dot is a composite of the microbial community from pooled samples collected during antibiotic (_Ab, days 3 to 5), immediately after antibiotic (_post_Ab, days 8 and 9), before antibiotic (_pre_Ab, days 0 and 1), and during recovery (1 to 4 weeks after antibiotic).

Highly dysbiotic murine guts have reduced resistance to colonization with MDR E. coli and K. pneumoniae.

Prior to bacterial inoculation (day 9), no cefotaxime (CTX)-resistant Gram-negative bacilli were detected on the selective media used for detection of introduced E. coli ST131 and K. pneumoniae ST258 (MacConkey agar with cefotaxime at 8 μg/ml [CTX8]; limit of detection, 2 to 2.5 log10 CFU/g) (Fig. S4), while the presence of endogenous ESBL enterobacteria was expectedly observed on more permissive media (ChromAgar with vancomycin at 20 μg/ml [Van20] and CTX8). Once colonization of dysbiotic guts was established (day 10), E. coli ST131 and K. pneumoniae ST258 persisted in all mice in significant amounts up to at least 5 days (day 14) postinoculation (Fig. S4). At 2 weeks postinoculation (day 23), E. coli ST131 was detected only in one TZP-treated and one CRO-treated mouse (∼106 CFU/g), whereas K. pneumoniae ST258 was still detected in 2/3 TZP-treated (∼106 and 103 CFU/g) and all CRO-treated (∼105 CFU/g) mice. At 4 weeks postinoculation (day 38), levels of both pathogens dramatically decreased (∼103 CFU/g) (Fig. S4). Colony PCR confirmed that all recovered CTX-resistant E. coli bacteria were ST131 and that CTX-resistant K. pneumoniae bacteria were ST258. At 5 weeks (day 44), no pathogens were detected. MDR E. coli and K. pneumoniae colonized untreated (no antibiotic; control) mice only transiently (day 10, 107 to 108 CFU/g), with no bacteria of either species detected by 5 days postinoculation (day 14).

Residual antibiotic activity in feces was negligible.

Prior to colonization, antibiotic activity was detected in all treated mice at 24 h after antibiotic injection at approximately 8 to 32 μg/ml TZP and 0.5 to 1 μg/ml CRO (Fig. S5). Antimicrobial activity was greater at 3 h than at 24 h after treatment with both antibiotics (Fig. S5b and c). There was no indication of antimicrobial activity in our assay in fecal pellets collected 3 days after TZP treatment (day 8 [Fig. S5b]). However, residual antimicrobial activity persisted for 3 days following CRO treatment in 3/6 mice and for 4 days in 2/6 mice (Fig. S5b and c). On the day of colonization with invasive pathogens (day 10, 5 days after antibiotic treatment), there was no detectable activity for either antibiotic (Fig. S5).


The effects of antibiotic treatment vary in terms of both antibiotic resistance transfer and collateral damage to bystander gut microflora depending on the drug used (14, 25, 26). Two broad-spectrum antibiotics, piperacillin/tazobactam (TZP) and ceftriaxone (CRO), with comparable activities against medically important pathogens, are routinely used in hospital care, particularly in the management of the critically ill (5, 14, 22), but they are expected to have considerably different impacts on the gut microbiome. In this study, we aimed to directly compare the differential effects of TZP and CRO in a controlled murine model, by administration of doses equivalent to those used in human therapy and analysis of the composition of the fecal microbiome to determine whether one antibiotic may provide a better clinical choice than the other based on broad microbiome health indicators.
As expected, a relative shift in the proportion of the two phyla dominating the fecal microbiome, Bacteroidetes and Firmicutes (27), was observed with both antibiotics. In our experiment, the fecal flora of all untreated mice was dominated by the Bacteroidetes, and antibiotic treatment promoted an overall decrease in the order Bacteroidales (mostly obligate anaerobes) and a converse increase in Lactobacillales and/or Clostridiales (phylum Firmicutes). Specific compositional shifts were unique to each antibiotic and cannot be explained by the effect of diet (20, 28), which remained identical between mouse groups and throughout the experiment. Similarly, the (expected) absence of significant numbers of colonizing aerobic Pseudomonas spp. in the mammalian gut avoids any confounding due to a differential effect of TZP (strongly active) versus CRO. CRO had an overall more pronounced negative impact on the diversity of the fecal microbial community than TZP, with a significant decrease in both species richness and abundance and poorer recovery to steady state.
Both antibiotics promoted an increase in Enterobacteriaceae, mainly during and just after treatment, i.e., under direct antibiotic selective pressure and at the height of dysbiosis (days 4 to 9), but notably, a significant protracted rise in Proteobacteria (up to 3 weeks after antibiotic treatment, well beyond any detectable antibiotic activity in feces) was observed exclusively in CRO-treated mice and was associated with the expansion of both Gamma- and Betaproteobacteria (orders Enterobacteriales and Burkholderiales). Antibiotic-associated dysbiosis is known to raise levels of available oxygen in the gut lumen, favoring amplification of facultative aerobes (1013, 27, 29, 30). Previously, significant Enterobacteriaceae blooms were also observed following the use of cocktails of multiple antibiotics, oral administration protocols, or subinhibitory dosing of various other antimicrobials (7, 19, 31, 32).
We showed that both TZP and CRO stimulate initial diversification of Proteobacteria, with CRO in particular promoting longer-term expansion, and though Enterobacteriaceae levels were indicative of a dysbiotic state, significant blooms were mainly associated with direct antibiotic selection, suggesting that levels of total Proteobacteria may be a more reliable marker of the differential impact of antibiotics than those of the Enterobacteriaceae alone, and that studies, specifically interested in investigating Enterobacteriaceae fluctuations in humans, may benefit from animal models that more closely reproduce clinical treatment protocols (19, 29). In our study, we used a mouse model in which drug administration closely resembled clinical human protocols with comparable dosing. However, TZP was administered in a single daily dose, instead of multiple doses as customary in clinical practice, and this could potentially mask further dysbiotic signals. Also, other antibiotics (e.g., ampicillin, ciprofloxacin, and β-lactams) have been shown to impact gut microbiome composition with disruption of homeostasis associated with decreased resistance to pathogen colonization (infection) (8, 12, 26) and compromised long-term well-being (immune system function) (33, 34), and these should be trialed in similar murine models to confirm the robustness of the experimental protocol.
In line with our methodological choices, we focused our analysis on compositional changes related to main bacterial phyla and known groups of opportunistic pathogens that may be of particular concern in critical care settings, where TZP and CRO are heavily used. Route and duration of antibiotic administration as well as the strategies used for detection (16S rRNA amplicon, etc.) and sampling (single or multiple time points, etc.) can influence observed microbial shifts and confound interpretation. Feces are commonly used as a proxy for gut microbiome analysis due to noninvasiveness and ease of repeated sampling. However, this is not without bias, as the fecal microbial populations are reflective of a physiology different from that of the gut per se. Also, fecal microbiomes do not represent the whole gastrointestinal tract, being more correlated with luminal microflora (diet) than the mucosal one (immune system; host correlation) (35). The microbial composition of feces seems to also better reflect that of the gut at higher levels (phylum and order), while analysis at microorganism level needs to be interpreted cautiously (hence our choice not to comment on changes deeper than family level). These constraints are also associated with the choice of analysis based on 16S rRNA gene amplicon sequencing, which can inform only on compositional changes in the microbial communities tested and is correlated with the specific amplification protocol selected (here V4 as in reference 36). More invasive studies including biopsies of different gut sections and true/deep metagenomic approaches are necessary to investigate more complex changes correlating with immune status and metabolism. Though sampling in mice introduces less variability than in human studies because diet is uniform and feces are collected “fresh” with better control of environmental conditions, in the absence of validating human studies, results from this work should still be interpreted with caution.
In this study, we also tested susceptibility to invasion by opportunistic MDR pathogens at the height of antibiotic-induced dysbiosis, showing that an imbalanced gut microflora is prone to prolonged colonization independently from the specific antibiotic used. There was no evidence of residual antibiotic activity past the end of antibiotic treatment to bias these observations. MDR E. coli ST131 and K. pneumoniae ST258, which are apt colonizers of the healthy human gut, have been identified as major agents of both community-acquired and nosocomial infections (23). Establishment of these key pathogens in murine guts has been linked to antibiotic-induced decreases in anaerobic flora (1921, 2729, 37), and ESBL K. pneumoniae colonization has been associated with piperacillin/tazobactam, ceftriaxone, and ceftazidime when administered in large doses (38). We know that the invading E. coli and K. pneumoniae strains used in our study do not stably colonize healthy murine guts without antibiotic pressure (controls in this study and as described in reference 39), indicating that their persistence was a direct consequence of dysbiosis. Loss of colonization resistance is pathogen and disturbance dependent (40, 41), with different bacterial strains having variable propensities for persistent colonization in a context-dependent manner based on the community structure after depletion of specific beneficial microbes by specific antibiotics. The inhibitory activity of TZP against ESBL-producing organisms (e.g., E. coli ST131) may be sufficient to prevent initial establishment of colonization, but, as shown here, in guts with disrupted homeostasis (e.g., in intensive care unit [ICU] patients) where competition from indigenous flora is reduced, the use of this antibiotic may still lead to persistent high-density colonization by exogenous clones and potential contribution to antibiotic resistance transmission. Our work shows that CRO use promotes longer-term dysbiosis with expansion of Proteobacteria, as well as reduced microbial diversity and slower recovery of murine microbiomes to baseline conditions than with TZP. These results highlight the need to investigate the effects of these antibiotics in human longitudinal studies in order to inform clinical practice on the optimal use of these antimicrobial agents.



All animal experiments were approved by the appointed Animal Ethics Committee (ARA 4205.06.13; Western Sydney Local Health District, NSW government) and were conducted in the Biosafety Animal Facility at the Westmead Institute for Medical Research (Sydney, Australia), complying with the national standards and guidelines for animal experimentation (42).

Experimental setup.

The experimental workflow for this study is summarized in Fig. 4. Feces were considered a proxy of intestinal microflora as is standard (19, 43, 44), and in all experiments, bacterial load was calculated as CFU per gram of stool (limit of detection, 102 CFU/g). In all instances, fecal pellets were weighed and homogenized in 0.9% saline by shaking (∼15 min) with 6-mm glass beads (Benchmark Scientific, Sayreville, NJ).
FIG 4 Schematic of experimental setup for antibiotic administration and fecal pellet sampling in BALB/c mice. Female BALB/c mice (17 to 20 g; 6 weeks old) were housed in groups of three per cage. The mice intestinal flora was unaltered prior to the initiation of the study, and no form of anesthesia was used. Mice (n = 6 per cohort) were administered antibiotics—Tazocin (TZP) or ceftriaxone (CRO)—or sham treated (saline) by subcutaneous injection, once a day, for 5 consecutive days. Gut dysbiosis and recovery were monitored through analysis of the fecal microflora for 5 weeks (orange line). A colonization resistance experiment was performed after treatment at the height of dysbiosis and in recovery (black line) ending at day 44. E_1, endpoint of “antibiotic effects” component; E_2, endpoint of “colonization resistance” component. “pathogen” refers to E. coli ST131 or K. pneumoniae ST258. Untreated (none) control cohorts (n = 3) for both experiments were also included in the study.

Antibiotic treatment and microbiology.

Six-week-old female BALB/c mice (n = 6 per group) were injected subcutaneously with either saline (group C2, “saline”), coformulated piperacillin/tazobactam (Tazocin at 6 mg/750 ng; group A1, “TZP”), or ceftriaxone (2 mg/day; group A2, “CRO”) once a day for 5 consecutive days (day 1 = 24 h from first antibiotic injection) in daily weight-adjusted doses equivalent to those recommended for adult human patients (38, 40). Recovery after antibiotic treatment was monitored for 4 weeks (days 8 to 26). Fecal pellets were collected every day during antibiotic therapy (days 1 to 5) and on selected days in recovery for microbiome analysis (Fig. 4). We also exploited the coprophagic lifestyle of rodents to determine the effect of fecal transplantation on the recovery of dysbiotic mice by including a cohort of mice in which one mouse treated with either antibiotic was cohoused with two untreated mice (group C3, “co-TZP,” and group C4, “co-CRO”). An experimental control group (group C1, “none,” no treatment) was also included.
In order to exclude an effect from residual antimicrobial activity, we measured antibiotic levels in homogenized feces by modification of the CDS disc diffusion method, placing 9-mm paper disks (Schleicher & Schuell, Dassel, Germany) soaked in fecal resuspensions on lawns of sensitive bacteria (E. coli ATCC 25922) on Mueller-Hinton agar (45). The limit of detection was maximized by concentrating fecal resuspensions by vacuum centrifugation. The zone of clearing was compared to known concentrations of TZP and CRO similarly applied to the paper discs. Throughout the study, the total murine microbial flora was monitored by standard microbiology (growth on selective media): ChromAgar supplemented with vancomycin (20 μg/ml [Van20]) for detection of total enterobacteria (no enterococci) or with Van20 plus cefotaxime (8 μg/ml [CTX8]) for ESBL enterobacteria (Fig. S1). Commensal E. coli and ESBL E. coli ST131 were identified on MacConkey agar (Becton, Dickinson, Sparks, MD) without and with CTX8, respectively (Fig. S1). MacConkey agar base (Becton, Dickinson) supplemented with inositol (10 mM) and carbenicillin (100 μg/ml) was used for detection of K. pneumoniae ST258 (46). Representative colonies with different morphologies were typed using the Bruker MALDI Biotyper system (Bruker, MA).

Colonization resistance.

Two MDR pathogens, common opportunistic colonizers of the gut, were selected for colonization experiments. ESBL E. coli ST131 (JIE3430 with blaCTX-M-15; MIC CRO >16 mg/liter and MIC TZP ≤ 4/4 mg/liter) (47) and carbapenemase-resistant K. pneumoniae ST258 (JIE2709; MIC CRO >16 mg/liter and MIC TZP > 64/4 mg/liter] (48) were routinely cultured in lysogeny broth (LB) at 37°C with shaking. Six-week-old female BALB/c mice (n = 3 per group) treated with either CRO or TZP (as described above) were colonized orally, as previously described (39), with either E. coli or K. pneumoniae (109 CFU/ml in 20% sucrose solution) on day 9 (4 days after cessation of antibiotics and highest point of dysbiosis as determined by our microbiome analysis results). Two control groups (n = 3 each), which had not received any antibiotic treatment, were also colonized with either E. coli or K. pneumoniae. Water intake was monitored to ensure that comparable amounts were consumed in each experimental group. Bacterial load was assessed for 5 weeks after the first antibiotic administration by growth on selective MacConkey agar supplemented with CTX8 only for E. coli and on MacConkey agar base with CTX8 plus 10 mM inositol plus 100 μg/ml of carbenicillin for K. pneumoniae (25) (Fig. S1). To exclude the presence of other ESBL E. coli and K. pneumoniae organisms in the murine gut, dilutions of feces for each treatment group were plated on these selective media supplemented with CTX8 prior to colonization (and throughout the experiment for controls). To further confirm the identity of the E. coli and K. pneumoniae grown from feces, we tested random colonies (n = 8 each) by strain-specific PCR (Table S1). Fecal pellets from day 1 (preantibiotic) and day 30 (endpoint, recovery week 4) were also plated on selective agar and total fecal Gram-negative aerobic flora (CFU per gram) were quantified by standard microbiology techniques as described above.

16S rRNA amplicon sequencing.

For 16S rRNA gene amplicon sequencing, total microbial DNA was extracted from homogenized fecal pellets using the QIAmp fast stool minikit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. Sequencing was performed at the Ramaciotti Centre for Genomics (University of New South Wales [UNSW], Sydney, NSW, Australia) on an Illumina MiSeq platform (2 × 250-bp paired-end reads) using the 515F and 806R primers for amplification of the V4 region of the 16S rRNA encoding gene (36). Sequence clustering into operational taxonomic units (OTUs) was performed using the QIIME 1.9.1 pipeline (49) for closed reference OTU picking against the Greengenes database (97% sequence similarity) (50) using the UCLUST algorithm (51). More than 8 million total reads were obtained for a total of 257 samples. The median OTU count per sample was 71,719 ± 35,028. This study focused on dominant changes in fecal microbial composition with analysis of most abundant taxa (OTUs). Classified, unfiltered OTU counts were used to calculate the relative abundance of bacterial groups (phylum, order, and family). Main compositional shifts in the microbiome were visualized using bar charts generated in QIIME-1. Diversity within samples as species richness (α-diversity) was measured by comparison of Shannon diversity indices calculated in R (phyloseq package) (52). Principal-coordinate analysis (PCoA) was used to assess community similarity among samples (β-diversity) using UniFrac distance metrics based on the generated phylogeny tree. Matrixes of β-diversity were then visualized in a two-dimensional plot using Emperor in QIIME-1.

Statistical analysis.

(i) Colonization resistance. The mean bacterial load (CFU per gram) for each group of mice (n = 3) was log10 transformed and analyzed using PRISM, version 7 (GraphPad, San Diego, CA).
(ii) 16S rRNA amplicon data. Statistical analyses were performed in GenStat (18th ed., VSN International, Hempstead, UK) on log10-transformed values. Significant differences in OTU abundance (order/family level) were calculated using one-way analysis of variance (ANOVA) at each time point with Fisher’s protected least significance difference test for multiple comparisons; significant fold changes in taxa abundance (family level) between time points in each test group were calculated in R (Wald test; DESeq2 package) (53). Significantly overrepresented orders/families in different treatments were identified using LEfSe (54) in Galaxy (homepage,; main public server,
Data availability. All 16S r-RNA amplicon sequencing data and metadata are available through the Sequence Read Archive (SRA; NCBI) under accession no. PRJNA602745.


This work was supported by the Australian National Health and Medical Research Council (NHMRC) (GRP1046889) to J.I.
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
We acknowledge the Ramaciotti Centre for Genomics (RCG) at the University of New South Wales Sydney as the service provider for our microbiome sequencing data. We thank Nouri L. Ben Zakour and Brian Gloss for their bioinformatics support.
We have no conflict of interest to declare.

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IACG. 2019. No time to wait: securing the future from drug-resistant infections. Report to the secretary-general of the United Nations.
CDC. 2019. Antibiotic resistance threats in the United States, 2019. CDC, US Department of Health and Human Services, Atlanta, GA.
Cantón R, Morosini MI. 2011. Emergence and spread of antibiotic resistance following exposure to antibiotics. FEMS Microbiol Rev 35:977–991.
Venturini C, Ginn AN, Wilson BE, Tsafnat G, Paulsen I, Partridge SR, Iredell JR. 2018. Ecological effects of cefepime use during antibiotic cycling on the Gram-negative enteric flora of ICU patients. Intensive Care Med Exp 6:19.
Ginn AN, Wiklendt AM, Gidding HF, George N, O’Driscoll JS, Partridge SR, O’Toole BI, Perri RA, Faoagali J, Gallagher JE, Lipman J, Iredell JR. 2012. The ecology of antibiotic use in the ICU: homogeneous prescribing of cefepime but not Tazocin selects for antibiotic resistant infection. PLoS One 7:e38719.
Sekirov I, Tam NM, Jogova M, Robertson ML, Li Y, Lupp C, Finlay BB. 2008. Antibiotic-induced perturbations of the intestinal microbiota alter host susceptibility to enteric infection. Infect Immun 76:4726–4736.
Antonopoulos DA, Huse SM, Morrison HG, Schmidt TM, Sogin ML, Young VB. 2009. Reproducible community dynamics of the gastrointestinal microbiota following antibiotic perturbation. Infect Immun 77:2367–2375.
Jernberg C, Lofmark S, Edlund C, Jansson JK. 2010. Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology (Reading) 156:3216–3223.
Kang SS, Bloom SM, Norian LA, Geske MJ, Flavell RA, Stappenbeck TS, Allen PM. 2008. An antibiotic-responsive mouse model of fulminant ulcerative colitis. PLoS Med 5:e41.
Shin NR, Whon TW, Bae JW. 2015. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol 33:496–503.
Litvak Y, Byndloss MX, Tsolis RM, Bäumler AJ. 2017. Dysbiotic Proteobacteria expansion: a microbial signature of epithelial dysfunction. Curr Opin Microbiol 39:1–6.
Modi SR, Collins JJ, Relman DA. 2014. Antibiotics and the gut microbiota. J Clin Invest 124:4212–4218.
Stecher B, Maier L, Hardt W. 2013. ‘Blooming’ in the gut: how dysbiosis might contribute to pathogen evolution. Nat Rev Microbiol 11:277–284.
MacVane SH. 2017. Antimicrobial resistance in the intensive care unit: a focus on Gram-negative bacterial infections. J Intensive Care Med 32:25–37.
Price LB, Hungate BA, Koch BJ, Davis GS, Liu CM. 2017. Colonizing opportunistic pathogens (COPs): the beasts in all of us. PLoS Pathog 13:e1006369.
Dethlefsen L, Eckburg PB, Bik EM, Relman DA. 2006. Assembly of the human intestinal microbiota. Trends Ecol Evol 21:517–523.
Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, Gill SR, Nelson KE, Relman DA. 2005. Diversity of the human intestinal microbial flora. Science 308:1635–1638.
Krych L, Hansen CHF, Hansen AK, van den Berg FWJ, Nielsen DS. 2013. Quantitatively different, yet qualitatively alike: a meta-analysis of the mouse core gut microbiome with a view towards the human gut microbiome. PLoS One 8:e62578.
Mullineaux-Sanders C, Suez J, Elinav E, Frankel G. 2018. Sieving through gut models of colonization resistance. Nat Microbiol 3:132–140.
Ng KM, Aranda-Díaz A, Tropini C, Frankel MR, Van Treuren W, O’Laughlin CT, Merrill BD, Yu FB, Pruss KM, Oliveira RA, Higginbottom SK, Neff NF, Fischbach MA, Xavier KB, Sonnenburg JL, Huang KC. 2019. Recovery of the gut microbiota after antibiotics depends on host diet, community context, and environmental reservoirs. Cell Host Microbe 26:650–665.e4.
Perez F, Pultz MJ, Endimiani A, Bonomo RA, Donskey CJ. 2011. Effect of antibiotic treatment on establishment and elimination of intestinal colonization by KPC-producing Klebsiella pneumoniae in mice. Antimicrob Agents Chemother 55:2585–2589.
Ruiz J, Gordon M, Villarreal E, Frasquet J, Sánchez MA, Martín M, Castellanos A, Ramirez P. 2019. Influence of antibiotic pressure on multi-drug resistant Klebsiella pneumoniae colonisation in critically ill patients. Antimicrob Resist Infect Control 8:38.
Woodford N, Turton JF, Livermore DM. 2011. Multiresistant Gram-negative bacteria: the role of high-risk clones in the dissemination of antibiotic resistance. FEMS Microbiol Rev 35:736–755.
Lagkouvardos I, Lesker TR, Hitch TCA, Gálvez EJC, Smit N, Neuhaus K, Wang J, Baines JF, Abt B, Stecher B, Overmann J, Strowig T, Clavel T. 2019. Sequence and cultivation study of Muribaculaceae reveals novel species, host preference, and functional potential of this yet undescribed family. Microbiome 7:28.
Bartosch S, Fite A, Macfarlane GT, McMurdo ME. 2004. Characterization of bacterial communities in feces from healthy elderly volunteers and hospitalized elderly patients by using real-time PCR and effects of antibiotic treatment on the fecal microbiota. Appl Environ Microbiol 70:3575–3581.
Ianiro G, Tilg H, Gasbarrini A. 2016. Antibiotics as deep modulators of gut microbiota: between good and evil. Gut 65:1906–1915.
Kim S, Covington A, Pamer EG. 2017. The intestinal microbiota: antibiotics, colonization resistance, and enteric pathogens. Immunol Rev 279:90–105.
Carmody RN, Gerber GK, Luevano JM, Jr, Gatti DM, Somes L, Svenson KL, Turnbaugh PJ. 2015. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17:72–84.
Pamer EG. 2016. Resurrecting the intestinal microbiota to combat antibiotic-resistant pathogens. Science 352:535–538.
Zaura E, Brandt BW, Teixeira de Mattos MJ, Buijs MJ, Caspers MPM, Rashid M, Weintraub A, Nord CE, Savell A, Hu Y, Coates AR, Hubank M, Spratt DA, Wilson M, Keijser BJF, Crielaard W. 2015. Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. mBio 6:e01693-15.
Winter SE, Baumler AJ. 2014. Why related bacterial species bloom simultaneously in the gut: principles underlying the ‘Like will to like’ concept. Cell Microbiol 16:179–184.
Loof T, Johnson TA, Allen HK, Bayles DO, Alt DP, Stedtfeld RD, Sul WJ, Stedtfeld TM, Chai B, Cole JR, Hashsham SA, Tiedje JM, Stanton TB. 2012. In-feed antibiotic effects on the swine intestinal microbiome. Proc Natl Acad Sci U S A 109:1691–1696.
Guo Y, Yang X, Qi Y, Wen S, Liu Y, Tang S, Huang R, Tang L. 2017. Long-term use of ceftriaxone sodium induced changes in gut microbiota and immune system. Sci Rep 7:43035.
Cheng RY, Li M, Li SS, He M, Hong Yu X, Shi L, He F. 2017. Vancomycin and ceftriaxone can damage intestinal microbiota and affect the development of the intestinal tract and immune system to different degrees in neonatal mice. Pathog Dis 75:ftx104.
Tange Q, Jin G, Wang G, Liu T, Liu X, Wang B, Cao H. 2020. Current sampling methods for gut microbiota: a call for more precise devices. Front Cell Infect Microbiol 10:151.
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A 108:4516–4522.
Sorbara MT, Pamer EG. 2019. Interbacterial mechanisms of colonization resistance and the strategies pathogens use to overcome them. Mucosal Immunol 12:1–9.
Hoyen CK, Pultz NJ, Paterson DL, Aron DC, Donskey CJ. 2003. Effect of parenteral antibiotic administration on establishment of intestinal colonization in mice by Klebsiella pneumoniae strains producing extended-spectrum β-lactamases. Antimicrob Agents Chemother 47:3610–3612.
Fajardo-Lubián A, Ben Zakour NL, Agyekum A, Qin Q, Iredell JR. 2019. Host adaptation and convergent evolution increases antibiotic resistance without loss of virulence in a major human pathogen. PLoS Pathog 15:e1007218.
Rice LB, Hutton-Thomas R, Lakticova V, Helfand MS, Donskey CJ. 2004. β-Lactam antibiotics and gastrointestinal colonization with vancomycin-resistant enterococci. J Infect Dis 189:1113–1118.
Vollaard EJ, Clasener HAL. 1994. Colonization resistance. Antimicrob Agents Chemother 38:409–414.
National Health and Medical Research Council, Australian Government. 2013. Australian code for the care and use of animals for scientific purposes. National Health and Medical Research Council, Australian Government, Canberra, Australia.
Vincent T, Clark J, Dore J. 2015. Fecal microbiota analysis: an overview of sample collection methods and sequencing strategies. Future Microbiol 10:1485–1504.
Behr C, Sperber S, Jiang X, Strauss V, Kamp H, Walk T, Herold M, Beekmann K, Rietjens IMCM, van Ravenzwaay B. 2018. Microbiome-related metabolite changes in gut tissue, cecum content and feces of rats treated with antibiotics. Toxicol Appl Pharmacol 355:198–210.
Bell SM, Pham JN, Newton PJ, Nguyen TT. 2013. Antibiotic susceptibility testing by the CDS method. A Manual for Medical and Veterinary Laboratories 2013, 7th ed. The Antibiotic Reference Laboratory Department of Microbiology, The Prince of Wales Hospital, South Eastern Area Laboratory Services, Randwick, Australia.
Tomás JM, Ciurana B, Jofre JT. 1986. New, simple medium for selective, differential recovery of Klebsiella spp. Appl Environ Microbiol 51:1361–1363.
Venturini C, Ben Zakour NL, Bowring B, Morales S, Cole R, Kovach Z, Branston S, Kettle E, Thomson N, Iredell JR. 29 June 2020. Fine capsule variation affects bacteriophage susceptibility in Klebsiella pneumoniae ST258. FASEB J.
Partridge S, Ginn A, Wiklendt A, Ellem J, Wong JSJ, Ingram P, Guy S, Garner S, Iredell SR. 2015. Emergence of blaKPC carbapenemase genes in Australia. Int J Antimicrob Agents 45:130–136.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Gonzalez Peña A, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336.
Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. 2010. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26:266–267.
Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.
McMurdie PJ, Holmes S. 2013. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217.
Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550.
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. 2011. Metagenomic biomarker discovery and explanation. Genome Biol 12:R60.

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Published In

cover image Antimicrobial Agents and Chemotherapy
Antimicrobial Agents and Chemotherapy
Volume 65Number 220 January 2021
eLocator: 10.1128/aac.01504-20


Received: 18 July 2020
Returned for modification: 31 August 2020
Accepted: 4 November 2020
Published online: 20 January 2021


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  1. antibiotic therapy
  2. ceftriaxone
  3. colonization resistance
  4. gut microbiome



Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Sydney, Australia
School of Medicine, Sydney Medical School, University of Sydney, Sydney, Australia
Bethany Bowring
Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Sydney, Australia
Alicia Fajardo-Lubian
Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Sydney, Australia
School of Medicine, Sydney Medical School, University of Sydney, Sydney, Australia
Carol Devine
Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Sydney, Australia
Present address: Carol Devine, Centenary Institute, Sydney, Australia.
Jonathan Iredell
Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Sydney, Australia
School of Medicine, Sydney Medical School, University of Sydney, Sydney, Australia
Westmead Hospital, Western Sydney Local Health District, Sydney, Australia


Address correspondence to Carola Venturini, [email protected], or Jonathan Iredell, [email protected].

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