changes in fecal microbiota of healthy dogs administered amoxicillin

R E S E A R C H A R T I C L E

Changes in fecalmicrobiota of healthydogs administeredamoxicillinAnne-Mette R. Grønvold1, Trine M. L`Abee-Lund1, Henning Sørum1, Ellen Skancke2, Anthony C.Yannarell3 & Roderick I. Mackie3

1Department of Food Safety and Infection Biology, Norwegian School of Veterinary Science (NSVS), Oslo, Norway; 2Department of Companion

Animal Clinical Sciences, Norwegian School of Veterinary Science (NSVS), Oslo, Norway; and 3Department of Animal Sciences, University of Illinois

at Urbana-Champaign (UIUC), Urbana, IL, USA

Correspondence: Anne-Mette R. Grønvold,

Department of Food Safety and Infection

Biology, Norwegian School of Veterinary

Science, PO Box 8146 Dep., NO-0033 Oslo,

Norway. Tel.: 147 90 59 61 66; fax: 147 22

96 48 18; e-mail:

[email protected]

Received 20 July 2009; revised 22 October

2009; accepted 22 October 2009.

Final version published online 27 November

2009.

DOI:10.1111/j.1574-6941.2009.00808.x

Editor: Julian Marchesi

Keywords

fecal microbiota; dog; antibiotic resistance;

microbial ecology; PCR-DGGE; real-time PCR.

Abstract

The effect of oral amoxicillin treatment on fecal microbiota of seven healthy adult

dogs was determined with a focus on the prevalence of bacterial antibiotic

resistance and changes in predominant bacterial populations. After 4–7 days of

exposure to amoxicillin, fecal Escherichia coli expressed resistance to multiple

antibiotics when compared with the pre-exposure situation. Two weeks postexpo-

sure, the susceptibility pattern had returned to pre-exposure levels in most dogs. A

shift in bacterial populations was confirmed by molecular fingerprinting of fecal

bacterial populations using denaturing gradient gel electrophoresis (PCR-DGGE)

of the 16S V3 rRNA gene region. Much of the variation in DGGE profiles could be

attributed to dog-specific factors. However, permutation tests indicated that

amoxicillin exposure significantly affected the DGGE profiles after controlling for

the dog effect (P = 0.02), and pre-exposure samples were clearly separated from

postexposure samples. Sequence analysis of DGGE bands and real-time PCR

quantification indicated that amoxicillin exposure caused a shift in the intestinal

ecological balance toward a Gram-negative microbiota including resistant species

in the family Enterobacteriaceae.

Introduction

‘Characterization of the immensely diverse ecosystem of the

gastrointestinal tract is the first step in elucidating its role in

health and disease’ (Eckburg et al., 2005). The large number

of intestinal bacteria, 1010 g�1 dry feces (Davis et al., 1977;

Vanhoutte et al., 2005), plays a vital role in several functions

of the host. Disturbances in this ecosystem can lead to a

variety of pathogenic conditions such as colonization by

potentially pathogenic bacteria (Vollaard & Clasener, 1994)

or antibiotic-associated diarrhea (Hogenauer et al., 1998;

Bartlett, 2002; Young & Schmidt, 2004). Administration of

antimicrobial agents is the most common and significant

cause of upsetting the ecological species balance in the

intestinal microbiota of humans (Nord, 1993; Jernberg

et al., 2007; Dethlefsen et al., 2008).

The gastrointestinal tract is considered a major reservoir

for the emergence and dissemination of antibiotic-resistant

bacteria (Harmoinen et al., 2004; Marshall et al., 2009). Not

only may drug resistance be transferred between resident

inhabitants of the intestinal tract (Salyers, 1993; Shoemaker

et al., 2001), it may also spread to bacteria that pass regularly

through the intestinal tract and from harmless to potentially

pathogenic microorganisms (Levy, 1997; Marshall et al.,

2009). It has been shown that resistance patterns of enteric

bacteria change in response to increased exposure to anti-

biotics (Houndt & Ochman, 2000; Lofmark et al., 2006). In

a study on antibiotic resistance among fecal bacteria of dogs,

it was found that healthy dogs may act as a reservoir of

resistance genes (De Graef et al., 2004). Domestic dogs are

commonly kept as pets living in close contact with humans,

and as such, resistant bacteria might spread between animals

and humans (Guardabassi et al., 2004; Damborg et al.,

2009). Transmission of resistance is likely to be enhanced

because there is an overlap in classes of antimicrobial agents

used in human medicine and in small animal practice

(Guardabassi et al., 2004). The emergence of antibiotic-

resistant bacteria is a phenomenon of concern to the

FEMS Microbiol Ecol 71 (2010) 313–326 c� 2009 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

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mailto:[email protected]

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clinician and the pharmaceutical industry, and a growing

threat to public health in general, because it is a major cause

of failure in the treatment of infectious diseases in humans

as well as animals (Jones et al., 2008; Marshall et al., 2009).

The fecal microbiota of dogs is less investigated than the

human fecal biota (Eckburg et al., 2005; Ley et al., 2008;

Mahowald et al., 2009). However, a study by Simpson et al.

(2002) examined the influence of age, breed and dietary

fiber on bacterial diversity in fecal samples of dogs and

demonstrated that individual dogs have a relatively stable

and unique resident fecal flora. More recently, Suchodolski

et al. (2004, 2005, 2008a) studied the bacterial diversity in

different compartments of the canine intestinal tract, while a

few other studies have compared intestinal microbiota of

healthy dogs with those suffering form intestinal disease

(Bell et al., 2008; Suchodolski et al., 2008b; Xenoulis et al.,

2008). Still, there is a lack of knowledge on the ecology of

canine intestinal microbiota, and also the effect antibiotic

exposure has on resident intestinal microbiota in dogs.

Analysis of gastrointestinal communities has traditionally

relied on bacterial culture methods and microscopy (Clap-

per & Meade, 1963). However, to reflect bacterial diversity in

fecal samples, molecular approaches yield more reliable

results because most intestinal bacteria are not culturable

(Suau et al., 1999; Eckburg et al., 2005). Still, no single

method can accurately describe the total microbial commu-

nity in a complex ecosystem such as the intestinal tract. In

this study, we use an integrated approach consisting of

culture-based analysis of Escherichia coli for antibiotic

sensitivity, denaturing gradient gel electrophoresis (DGGE)

analysis of 16S rRNA gene amplicons, band sequencing and

analysis with qPCR to monitor and quantify pronounced

changes in the fecal microbiota of seven healthy dogs upon

amoxicillin exposure. Amoxicillin is a moderate-spectrum

b-lactam antibiotic active against a wide range of Gram-

positive and a limited range of Gram-negative organisms.

Absorption of amoxicillin in the gastrointestinal tract is very

good, and elimination occurs rapidly and is mainly renal

(Rang et al., 1996). Amoxicillin is one of the most com-

monly used antibiotics in small animal practice in Norway,

frequently prescribed for respiratory and urinary tract

diseases in dogs (Guardabassi et al., 2004).

The aims of this study were to determine whether (1) the

prevalence of antibiotic resistance among fecal E. coli

increased during amoxicillin exposure, (2) there was a

change in the predominant bacterial populations of dogs

during antibiotic administration and (3) antimicrobial ex-

posure was required to maintain the shifts in bacterial

populations and/or antibiotic resistance pattern. An in-

creased understanding of the normal diversity and genetic

variability of canine intestinal microbiota can inform clin-

icians when it comes to the total impact of antibiotic

treatment.

Materials and methods

Animals and treatments

Seven dogs with no history of antibiotic treatment in the

past 6 months were included in the study. All dogs were

considered healthy on veterinary clinical examination be-

fore, during and after the study. The dogs were of different

age and sex, three different breeds and kept at three different

home locations (A–C) on three different regular canine

maintenance diets (Table 1). Home locations A–C represent

three different private homes where the dogs were kept

mostly indoors and had a moderate exercise level. The dogs’

diets were Purina pro plans adult digestion lamb &

rice (home location A), Hill’sTM Canine adult with beef

(home location B) and Hill’sTM Canine adult with chicken

(home location C) (http://www.purina-proplan.no, http://

www.hillspet.no). All dogs had an ideal body condition

score (Laflamme, 1997). Each dog was given

10 mg amoxicillin kg�1 body weight (Clamoxyl vet.; Pfizer,

Oslo, Norway) orally twice daily for 7 days. The antibiotic

was administered by veterinarians ensuring a proper intake.

No food was given in relation to medication. The doses

administered were chosen based on therapeutic recommen-

dations for treatment of urinary tract infection. The research

Table 1. Demographic data from seven dogs included in the study

Dog no.� Breed Sex Age (years) Body weight (kg) Relationship Home location Diet

1 Gordon setter F 0.5 14 Daughter of dog 5 A a

2 Gordon setter F 2 20 – A a

4 Gordon setter M 4.5 24 – A a

5 Gordon setter M 6.5 21 Father of dog 1 A a

6 Mixed breed F 6 12 – B b

7 Whippet M 0.5 14 Sibling to dog 8 C c

8 Whippet M 0.5 14 Sibling to dog 7 C c

�Dog no. 3 was excluded from the study due to an unrelated problem.

F, female; M, male; A–C, three different home locations; a–c, three different regular canine diets.

FEMS Microbiol Ecol 71 (2010) 313–326c� 2009 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

314 A.M.R. Grønvold et al.

http://www.hillspet.no











protocol was approved by the Norwegian Animal Research

Authority (NARA).

Fecal sample collection

A total of 13 fecal samples were collected from each dog over

a period of 30 days. Four samples were collected before

amoxicillin exposure (over a period of 10 days) to monitor

normal fluctuations in bacterial populations. Samples were

taken every day during exposure (7 days), in addition to 1

and 2 weeks postexposure. All samples were collected

directly into plastic bags during natural defecation. Fecal

scores were determined according to the Waltham Fecal

Scoring System (Moxham, 2001). From four of the dogs

(dogs 1, 2, 4 and 5, all from the same home location of

practical reasons), each fecal sample was undiluted streaked

onto a bromothymol-blue (BTB) lactose agar plate incu-

bated aerobically at 37 1C for 24 h for specific identification

of bacteria in the family Enterobacteriaceae. The

fecal samples were then stored at � 20 1C until further

processing.

Antibiotic resistance

To monitor the antibiotic resistance pattern in fecal micro-

biota before, during and after amoxicillin exposure, E. coli

was chosen as the indicator organism (De Graef et al., 2004).

Escherichia coli is the most completely characterized bacter-

ial model organism and a natural inhabitant of the gastro-

intestinal tract. Lactose-fermenting strains were identified

based on blue–yellow screening after overnight growth on

BTB plates. The quantitatively most-dominating yellow

colony from every second or third fecal sample of the four

dogs (dogs 1, 2, 4 and 5) were biochemically confirmed to be

E. coli by the oxidase test and by indole, methyl red,

Voges–Proskauer and citrate (IMViC) tests, and analyzed

for antibiotic susceptibility to nine different antibiotics

(Table 2) using the disc-diffusion method (Neo Sensitabs,

Rosco, Taastrup, Denmark). Inhibition zones were mea-

sured and interpreted according to MIC breakpoints of

the Norwegian AFA group (Arbeidsgruppen for anti-

biotikaspørsmal, 2006), described in ‘User’s guide Neo-

sensitabss’ (http://www.rosco.dk). Intermediate zones were

recorded as susceptible.

Total DNA extraction

QIAamps DNA Stool Mini Kit (Qiagen, Hilden, Germany)

was used to extract bacterial genomic DNA from 200 mg of

the frozen fecal samples following the manufacturer’s in-

structions, including both Gram-positive and Gram-nega-

tive bacteria. This kit has previously been evaluated for DNA

extraction from fecal samples of pigs and found appropriate

for studies of intestinal microbiota (Li et al., 2003).

PCR-DGGE analysis

The variable V3 region of the 16S rRNA gene was amplified

by PCR with primers to conserved regions of the 16S rRNA

gene. The nucleotide sequences of the primers were as

follows: primer 1; 50-ATTAACCGCGGCTGCTGG-30 and

primer 2; 50-CGCCCGCCGCGCGCGGCGGGCGGGGCG

GGGGCACGGGGGGCCTACGGGAGGCAGCAG-30 (Muy-

zer et al., 1993). The PCR reaction mixture contained 1 mL

DNA template (c. 100 ng of DNA), 25 pmol of each primer,

2 mL of dNTP mixture, 2.5 mL of 10�Ex Taq buffer and

0.1 mL of TaKaRa Ex Taq polymerase (TaKaRa, Shuzo, Otsu,

Japan). The final volume of the reaction mixture was

adjusted to 25 mL with sterile deionized water. To reduce

spurious PCR products, touchdown PCR was performed

(Muyzer et al., 1998; Simpson et al., 2002). The annealing

temperature was lowered by 0.5 1C every cycle until it

reached 61 1C, at which temperature nine additional cycles

were carried out for a total of 30 cycles. The final product

length was approximately 220 bp. Mung bean nuclease

(Roche Applied Science, Indianapolis) was used to remove

single-stranded DNA from the PCR products. The reaction

mixture contained 10 mL PCR product, 1.5mL 10� mung

bean buffer, 1 mL mung bean nuclease (diluted 1 : 1000 in

nuclease dilution buffer) and sterile deionized water to

15 mL. After 10 min incubation at 37 1C, the reaction was

stopped by addition of 15 mL DGGE loading buffer (0.05%

bromophenol blue, 0.05% xylene cyanol and 70% glycerol in

sterile deionized water).

DGGE was performed essentially as described by Muyzer

et al. (1998) and Simpson et al. (1999), using a Bio-Rad D-

Code System (Bio-Rad Inc., Hercules, CA). In short, PCR

fragments were separated using 8% polyacrylamide gels with

0.5�TAE buffer (20 mM Tris-acetate, pH 7.4, 10 mM

sodium acetate, 0.5 mM Na2EDTA) with linear 35–60%

gradients of denaturant (100% denaturant corresponds to

Table 2. Antibiotic resistance among fecal Escherichia coli from four

dogs housed at location A before, during and after amoxicillin exposure

Exposure day:

0–34–7 14 days after

D1, 2, 4, 5 D1 D2 D4 D5 D1, 4, 5 D2

Sulfa/trimethoprim

(240/5.2 mg)

S S R S R S S

Tetracycline (80 mg) S S R R R S S

Cephalexin (30 mg) S S S S S S S

Amoxicillin (30 mg) S S R R R S R

Amoxicillin/clavulanic

acid (30/15 mg)

S S S S R S S

Enrofloxacin (10mg) S S S S S S S

Gentamicin (40 mg) S S S S S S S

Streptomycin (100mg) S S R R R S R

Trimethoprim (5.2 mg) S S R S R S S

D, dog; R, resistant; S, susceptible.


315Changes in fecal microbiota of dogs administered amoxicillin



http://www.rosco.dk

http://www.rosco.dk

7 M urea and 40% deionized formamide). Electrophoresis

was performed at 60 1C, for 10 min at 50 V, followed by 4 h

at 150 V. Bacterial reference ladders, as described by Simp-

son et al. (2000), were used to allow standardization of band

migration and gel curvature for between-gel comparisons.

After electrophoresis, the gels were silver-stained (Muyzer

et al., 1998), and scanned using a GS-710 Calibrated

Imaging Densitometer (Bio-Rad Inc.).

Sequence analysis of DGGE bands

For sequence analysis of DGGE bands, the gel from dog 5

was chosen because it had the most clear and varied banding

pattern that would capture the highest number of sequence

variants from a single animal (Supporting Information, Fig.

S1). Selected band fragments were excised with sterile razors

and placed in 20 mL sterile deionized water for 6 h. The 20mL

water containing DNA was used in a touchdown PCR

reaction with primers without GC-clamp, 341F and 534R.

The annealing temperature was lowered 0.5 1C every cycle

until it reached 60 1C, at which temperature 19 additional

cycles were carried out for a total of 40 cycles. The PCR

products were ligated into the pGEM-T Easy vector (Pro-

mega, Madison, WI) and transformed into competent cells

(JM109, UIUC, IL). Plasmid-clones were identified based on

blue–white screening after overnight growth on Luria–

Bertani (LB) plates with ampicillin (100mg mL�1), isopropyl

b-D-1-thiogalactopyranoside (0.5 mM) and 5-bromo-

4-chloro-3-indolyl-b-D-galactopyranoside (80mg mL�1). Ran-

domly picked white colonies were subject to a PCR reaction

with M13F and M13R primers. Clones that proved positive in

this PCR reaction were grown overnight in LB medium

amended with ampicillin (100mg mL�1). Plasmid DNA was

subsequently isolated using QIAprep Spin Miniprep Kit

(Qiagen, Valencia, CA) and sequenced at the W.M. Keck

Center for Comparative and Functional Genomics, Biotech-

nology Center, University of Illinois (Table S1). Sequences

were analyzed with the BLAST (BASIC LOCAL ALIGNMENT SEARCH

TOOL) family of programs to search and align nucleotide

sequences with similar sequences in GenBank (National

Center for Biotechnology Information, Bethesda MD; http://

www.ncbi.nlm.nih.gov/BLAST/).

qPCR analysis

Quantification of E. coli and bacteria from the genera

Bacteroides, Campylobacter, Clostridium, Enterococcus and

Lactobacillus was performed with the MX3000Ps QPCR

System (Stratagene, LaJolla, CA) using the QuantiTectTM

SYBRs Green PCR Kit (Qiagen, Hilden, Germany) and

group or species-specific PCR primers (Table 3), essentially

as described by Rinttila et al. (2004). The specific bacterial

genera were chosen for qPCR because they represent known

resident inhabitants of the canine intestinal microbiota,

some are opportunistic pathogens (e.g. Campylobacter and

Enterococcus) or receive attention due to potential transmis-

sion of resistance to human strains (e.g. E. coli and Entero-

coccus). The 25 mL reaction mixture contained 12mL of 2�Mastermix, 5 mL of template DNA, 1 mL of each primer

(0.4 mM) and sterile deionized water. Amplification involved

one cycle at 95 1C for 15 min for initial denaturation,

followed by 35 cycles of denaturation at 95 1C for 15 s,

primer annealing at the optimal temperatures (Table 3) for

20 s, extension at 72 1C for 30 s and an additional incubation

step at 78 1C for 8 s to collect the fluorescent data.

For construction of standard curves, 10-fold serial dilu-

tions (100–0.001 ng) of target species genomic DNA pre-

parations, extracted from pure culture target bacteria, were

used for PCR. For total DNA standard curves, a 3209-bp

plasmid (pGEM-T Easy vector) including the 16S rRNA

gene insert was used (Koike et al., 2007). The standard

curves of individual real-time PCR assays were used for

quantification of the target bacterial DNA from fecal DNA

preparations. The bacterial strains used as positive

Table 3. Group-specific 16S-targeted primers and optimized conditions for quantitative real-time PCR (qPCR)

PCR assay (amplicon size) Oligonucleotide sequence (50–30) Annealing temperature ( 1C) Mg21 (mM) Reference

Bacteroides spp. (140 bp) F: 50-GGTGTCGGCTTAAGTGCCAT-30

R: 50-CGGA(C/T)GTAAGGGCCGTGC-3068 2.5 Rinttila et al. (2004)

Campylobacter spp. (246 bp) F: 50-GGATGACACTTTTCGGAG-3 0

R: 50-AATTCCATCTGCCTCTCC-3061 2.5 Rinttila et al. (2004)

Clostridium group (120 bp) F: 50-ATGCAAGTCGAGCGA(G/T)G-3 0

R: 50-TATGCGGTATTAATCT(C/T)CCTTT-3055 2.5 Rinttila et al. (2004)

Enterococcus spp. (144 bp) F: 50-CCCTTATTGTTAGTTGCCATCATT-30

R: 50-ACTCGTTGTACTTCCCATTGT-3061 2.5 Rinttila et al. (2004)

Escherichia coli (340 bp) F: 50-GTTAATACCTTTGCTCATTGA-30

R: 50-ACCAGGGTATCTAATCCTGTT-3061 2.5 Malinen et al. (2005)

Lactobacillus group (341 bp) F: 50-AGCAGTAGGGAATCTTCCA-30

R: 50-CACCGCTACACATGGAG-3058 2.5 Walter et al. (2001)

Heilig et al. (2002)

16S rRNAgene (194 bp) F: 50-CCTACGGGAGGCAGCAG-30

R: 50-TTACCGCGGCTGCTGGCAC-3060 2.5 Koike et al. (2007)



http://www.ncbi.nlm.nih.gov/BLAST/

http://www.ncbi.nlm.nih.gov/BLAST/

quantification controls in this study included Bacteroides

fragilis (NVH 517), Campylobacter jejuni (ATCC 33460),

Clostridium perfringens (DSM 756), Enterococcus faecalis

(DSM 20478), E. coli DH5a (CCUG 32825) and Lactobacil-

lus acidophilus (DSM 20079). Genomic DNA was extracted

from all control strains except E. coli using QIAamp DNA

Minikit (Qiagen, Hilden, Germany) following the manufac-

turers recommendations for Gram-positive bacteria. Geno-

mic DNA from E. coli was extracted using Easy-DNA

(Invitrogen, Carlsbad, CA). The primers (Table 3) were

designed to detect a wide range of phylogenetically related

bacterial species, most likely with varying ribosomal DNA

copy numbers and genome sizes; therefore, estimated aver-

age genome sizes for each target bacterial group were used

while differences in the rrn copy numbers were not included

in the estimation of population size (Rinttila et al., 2004).

Three parallel qPCR reactions were analyzed. Mean values of

triplicate qPCR analysis of the same DNA extracts are

presented as the average estimate of target species bacterial

genomes present in 1 g of feces wet weight (Table S2) and as

a percentage of total bacteria in each sample (Fig. 1).

Statistical analysis

In community ecology, correspondence analysis (CA) is

used to explore variation in species composition along

hypothetical ecological gradients (Jongman et al., 1995).

The hypothesis in this study was that amoxicillin affects the

structuring of the intestinal microbial communities. For the

DGGE profiles, canonical correspondence analysis (CCA)

was used to analyze the patterns associated with the experi-

mental amoxicillin exposure (ter Braak, 1986; Jongman

et al., 1995). All CA, CCA and partial CCA (pCCA) were

performed using (binary) presence–absence of DGGE bands

from the Diversity Database 2.1 bands report, part of the

Discovery series (Bio-Rad). Constraining and conditioning

variables for CCA and pCCA were defined as factors based

on the experimental design: dog (seven levels) and treatment

(two levels; pre- and post-amoxicillin). All statistical tests on

ordination results were performed by permutation of sam-

ples; all samples were permuted for CCA, and residualized

sample scores were permuted for pCCA. All ordinations and

permutation tests were performed in the R statistical envir-

onment (R Development Core Team, 2005). In addition,

diversity indices (Shannon–Weiner index) were calculated

based on the DGGE profiles.

Results

Analysis of antibiotic resistance patterns

All dogs included remained healthy based on veterinary

examination throughout the study period and were clini-

cally unaffected by amoxicillin exposure. Fecal scores were

all within the ideal range during the study (data not shown).

In the 10 days pre-exposure period and during the first 3

days of amoxicillin exposure, E. coli from all dogs tested

(dogs 1, 2, 4 and 5) had a stable susceptibility pattern

reflecting the normal intrinsic susceptibility to the nine

antibiotics included (Table 2). All E. coli tested from dog 1

appeared unaffected by amoxicillin exposure and expressed

the same susceptibility pattern before, during and after

treatment. On the contrary, after 4–7 days of amoxicillin

exposure, E. coli in the three other dogs (dogs 2, 4 and 5)

expressed resistance to several antibiotics (tetracycline,

Dog 1

–14–12–10–8–6–4–20246

Log

|<–Amoxicillin–>|2 8 11 12

Dog 2

–12–10–8–6–4–20246

Log


Dog 5

–14–12–10–8–6–4–2024

Log


Fig. 1. Real-time PCR quantification (qPCR) of target species bacterial

genomes from fecal samples of three dogs. The results are shown in

diagram as the percentage (log scale) of each bacterial group compared

with total DNA in each sample. X-axis shows the sample number: sample

2, before amoxicillin exposure; samples 8 and 11, days 4 and 7 of

exposure; sample 12, 1 week postexposure.



amoxicillin and streptomycin) compared with E. coli tested

in the first samples. In addition, E. coli from two of these

dogs (dogs 2 and 5) were also resistant to sulfonamides,

trimethoprim and gentamicin. Two weeks postexposure, the

susceptibility pattern had returned to pre-exposure levels in

two of the three dogs (dogs 4 and 5). However, E. coli tested

from dog 2 still showed resistance to amoxicillin and

streptomycin.

PCR-DGGE analysis of fecal bacterial populations

Banding patterns for the V3-16S rRNA gene PCR amplicons

are presented in Fig. S1. The gel images display six fecal

samples from each dog; three pre-exposure samples (1, 2, 4),

two during exposure (9, 11) and one postexposure sample

(12 or 13). For dog 5, which was randomly chosen, all 13

fecal samples were included in the DGGE analysis. For dog 8,

no samples were collected postexposure due to an unrelated

cause. The database set created for these samples contained a

total of 96 unique bands, which were found in different

combinations among the DGGE profiles. Although some

differences were noted in position, intensity and number of a

limited number of bands present, the DGGE profiles demon-

strated a relatively stable banding pattern before amoxicillin

exposure. Analysis of DGGE profiles tended to group

samples together based on dog (Figs 2 and 3). Each animal

had its own unique profile, indicating that within-animal

variation was less than between-animal variation.

CCA constrained by the factor dog confirmed that

animal-to-animal variation was significant (Po 0.005 by

permutation) and strong (sum of canonical

eigenvalues = 1.203, out of a total inertia of 3.612). Different

animals exhibited different amounts of DGGE variation

over the course of the study, with the variation being the

most extreme in dogs 4 and 5, whereas dogs 7 and 8 showed

little variation (Fig. 3). The DGGE profiles appeared to

cluster into ‘supergroups’ that correspond to the home

location to which the dog belongs (Figs 2 and 3). Dogs 1–5

(home location A) are located on the top of the dendrogram

(Fig. 2) and on the right side of the plot (Fig. 3), dogs 7–8

(home location C) cluster together in the dendrogram and

on the left side of the plot, whereas dog 6 (home location B)

is located between these supergroups.

Data from the DGGE band analysis were used to calculate

diversity indices (Shannon–Weiner index). No significant

effect of amoxicillin exposure was found for diversity (Fig. S2).

PCR-DGGE analysis of changes in fecal bacterialpopulations associated with amoxicillinexposure

The DGGE profiles of each dog before amoxicillin exposure

seemed sufficiently stable to allow observation of changes

associated with antibiotic administration. For dogs 1–5,

there was a tendency for the DGGE profiles from pre- and

post-amoxicillin samples to plot in separate regions (Figs 2

and 3). This effect was particularly pronounced for dog 5,

which showed the highest variation in DGGE profiles.

Because all 13 samples from dog 5 were subject to PCR-

DGGE analysis, it was possible to follow the time-course of

change associated with the amoxicillin exposure. DGGE

profiles did not change much from the prechallenge condi-

tion to the first 2 days of exposure, but by the third day of

amoxicillin exposure, the DGGE profiles for dog 5 were

dramatically different (Figs 2 and 3). By the fifth exposure

day, the DGGE profiles indicated that the microbiota of dog

5 had settled into an alternate state that was different from

both the pre-exposure communities and those of exposure

days 3 and 4. They remained in this altered state for the rest

of the amoxicillin challenge, and for at least 14 days after

antibiotic withdrawal (Figs 2 and 3). For dogs 6–8, the total

variation in DGGE profiles was low and it is difficult to say

whether pre- and post-amoxicillin communities were dis-

tinct with some degree of certainty.

Because much of the variation in DGGE profiles could be

attributed to dog-specific factors, and because the micro-

biota of dogs may have responded differently to amoxicillin

exposure, pCCA was used in order to remove the dog-

specific effect and see whether amoxicillin exposure had any

consistent effects on the microbiota. Permutation tests

indicated that amoxicillin exposure significantly affected

the DGGE profiles after controlling for the previously seen

dog effect (P = 0.02). The resulting pCCA axis explained a

very small portion of the total variation in DGGE profiles

(eigenvalue = 0.0911), but it clearly separated pre-amoxicil-

lin samples from post-amoxicillin samples.

Sequencing of DGGE bands

In order to determine to which bacterial group or taxon

particular bands could be ascribed, specific DGGE band

fragments that appeared, became intensified or disappeared

during the period of amoxicillin exposure of dog 5 were

further characterized by sequencing. In all, 13 different

bands from the study period were excised from the gel, from

each of which three independent clones were generated and

subjected to DNA sequence analysis (Fig. S1). The sequences

obtained were compared with the NCBI database (Table S1).

Bands that disappeared during exposure belonged to the

Gram-negative Bacteroides group (bands 4-1, 4-2 and 4-3)

and Anaerobiospirillium succiniciproducens (bands 4-2 and

4-6). One band (4-5) that appeared less intense during

exposure showed 92% similarity to Bacteroides coprophilus.

All excised bands that became intensified or appeared

during exposure belonged to the Gram-negative Enterobac-

teriaceae family. One excised band that seemed unaffected by

amoxicillin exposure showed 99% similarity to



Fusobacterium russii (bands 4-4 and 8-3), a Gram-negative

anaerobe found in the oral cavity and intestinal tract of dogs

and cats (Balish et al., 1977; Talan et al., 1999; Goldstein

et al., 2002; Mentula et al., 2005; Suchodolski et al., 2008a).

qPCR

A qPCR-based method was used to specifically measure

concentration of selected bacterial groups. Results are pre-

sented as estimated average number of target species bacter-

ial genomes in 1 g of feces wet weight (Table S2) and as a

percentage of total 16S rRNA genes in each sample (Fig. 1).

All primers yielded one distinct band of the correct size

when visualized on agarose gel (data not shown). The Gram-

negative E. coli subgroup increased in numbers in the

presence of amoxicillin in all three dogs. In two of three

dogs, the Enterococcus group also increased during amox-

icillin exposure. The Lactobacillus-like group remained

relatively stable in all three dogs during exposure, while the

Bacteroides-like, Campylobacter and C. perfringens-like

groups decreased during exposure in two of three dogs.

During days 5–7 of amoxicillin exposure, the Campylobacter

Fig. 2. Dendrogram generated from DGGE

profiles of fecal samples from seven dogs,

representing similarities in banding pattern

among animals, and variation within individual

animals before and after amoxicillin exposure.

Individual animals are indicated by color and

numbers (D1, D2, etc.). Last number indicates

sample (1–4 before, 5–11 during and 12–13 after

amoxicillin exposure). Household/breed/diet are

indicated by colored symbols; red, location A;

brown, location B; blue, location C.



group increased in two of three dogs. One week after

antibiotic withdrawal, the Enterococcus group decreased

below pre-exposure levels in all three dogs, and in two of

three dogs, the E. coli and Campylobacter groups also

decreased. The Bacteroides-like and C. perfringens-like

groups increased in two of three dogs following antibiotic

withdrawal. One week after antibiotic withdrawal, the

Lactobacillus-like group increased markedly and the Bacter-

oides-like group did not increase at all in the youngest dog

(dog 1) as opposed to the situation in the older dogs (dogs 2

and 5).

Discussion

In this study, we focused on intestinal microbiota of healthy

dogs before, during and after amoxicillin exposure. Mon-

itoring the antibiotic susceptibility pattern of E. coli from

fecal samples revealed that exposure to amoxicillin causes

increased expression of antibiotic resistance against several

unrelated drugs. This confirmed our first hypothesis and is

consistent with previous studies that have shown that

resistance patterns of enteric bacteria change in response to

antibiotic exposure (Houndt & Ochman, 2000; Lofmark

et al., 2006). It is further shown by Anderson et al. (2006)

that gastrointestinal E. coli populations have high genetic

diversity, temporal variability of subtypes within individuals

and differences in diversity among their various hosts. In

our study, the observed shift in E. coli resistance pattern

during amoxicillin exposure may reflect the succession of

one or more dominant ecotypes over others. On the other

hand, recent studies demonstrate that resistance genes can

be silenced (Enne et al., 2006), and that stress conditions

such as antibiotic exposure can induce the SOS response and

thus activate silenced genes (Guerin et al., 2009). However,

the mechanism behind the shift in resistance pattern that we

observed is difficult to assess and was outside the scope of

this study. Other factors that may contribute to temporal

variability of E. coli ecotypes in individuals include diet,

exposure to novel strains and the health of the animal.

However, during this study, the diet was not altered, the

dogs were not in contact with other unfamiliar dogs or new

environments, and they were all in good health, and it is

likely that the amoxicillin exposure was responsible for the

changes observed.

Two weeks postexposure, the resistance patterns were

returned to pre-exposure level in three of four dogs,

demonstrating that withdrawal of amoxicillin decreased the

prevalence of antibiotic-resistant fecal E. coli in this study.

The results were generated by measuring antimicrobial

resistance in one E. coli strain randomly isolated from each

sample. Similar random isolation strategies are used by

others and by national surveillance systems in Europe and

provide a good overview (Damborg et al., 2008). However,

reliance on one bacterial species and phenotypic expression

of resistance might underestimate the gene-pool because the

remaining intestinal bacteria most likely also act as a

reservoir for resistance genes and resistance genes can be

silenced (Randall et al., 2004; Enne et al., 2008). Jernberg

et al. (2007) used real-time PCR to determine the relative

changes in levels of specific resistance genes in human

intestinal microbiota after clindamycin administration.

They found a drastic and persistent increase in the specific

resistance genes following antibiotic exposure.

Dog 5 demonstrated the highest variation in DGGE

profiles (Figs 2 and 3) as well as in the E. coli resistance

pattern (Table 2). One year earlier, this particular dog was

subject to extensive orthopedic surgery, with long-time

hospitalization and extensive antibiotic therapy (data not

shown). This may explain why the shifts were more severe in

this dog compared with the others. Damborg et al. (2008)

found a significantly higher prevalence of resistant E. coli

from dogs exposed to recent antimicrobial treatment than

among dogs that were not exposed. In addition to increased

drug resistance among fecal E. coli, lateral transfer of

resistance genes may have occurred in the intestinal micro-

biota during antibiotic exposure. If lateral transfer of

resistance genes has occurred during the amoxicillin expo-

sure, more drug-resistant E. coli and other species may exist

in low numbers in the microbiota after, than before,

amoxicillin exposure. If so, a higher number of resistant

bacteria can potentially be selected during the next period of

antibiotic exposure. The consequences of such selection may

Fig. 3. CA for DGGE profiles from seven dogs exposed to amoxicillin.

Points, which are color coded based on their occurrence in the amoxicillin

exposure regimen, correspond to sample scores on the first two CA axes.

Dashed lines are convex hulls enclosing all samples collected from each

dog. For dog 5, which was sampled more frequently, the numbers next

to post-amoxicillin points show the number of days since the beginning

of the exposure regimen. Exposure day seven is also indicated for dog 4.



be treatment failure and transmission of multiresistant

strains (Salyers & Amabile-Cuevas, 1997; Guardabassi et al.,

2004). This issue needs to be studied further, and other

methods such as qPCR could be applied to monitor specific

resistance genes.

Traditional selective plating offers the advantage of rapid

sample processing and direct linking of results to a given

bacterial genus or species. However, as the majority of the

gastrointestinal tract bacterial species cannot be cultivated

(Langendijk et al., 1995; Suau et al., 1999; Zoetendal et al.,

2008), bacterial counts were not conducted in this study.

Instead, culture-independent PCR-DGGE analysis was used

to include the nonculturable part of the microbiota, and

selected groups of the biota were quantified with qPCR.

These approaches have been widely applied to study bacter-

ial communities in the gastrointestinal tract (Simpson et al.,

2002; Collier et al., 2003; Rinttila et al., 2004; Zoetendal

et al., 2004). Analysis of DGGE profiles in our study

demonstrated a tendency for dogs to cluster together

according to which home location the dog belongs. Because

all dogs within the same home location were of the same

breed and fed the same diet, it is unclear at this time whether

these subgroupings are due to diet, breed, household or

more likely some combination of the three. Previous pub-

lications have mainly utilized the beagle dog as a research

model (Clapper & Meade, 1963; Balish et al., 1977; Mentula

et al., 2005), although there are some exceptions (Greetham

et al., 2002; Simpson et al., 2002; De Graef et al., 2004). We

aimed to study ‘ordinary dogs’ in their home environments.

Because the microbiota is very unique for each individual

and because there were enough samples before exposure to

allow evaluation of normal variation, each dog could act as

its own control. The animals in our study exhibited different

amounts of variation in the DGGE profile during the study.

The variation was most extreme in the oldest dogs (dogs 4

and 5), while dogs o 1 year of age (dogs 1, 7 and 8) showed

little variation. These age-based pattern separations support

previous research on the influence of age on fecal profiles

(Benno & Mitsuoka, 1989; Benno et al., 1992; Mitsuoka,

1992). Simpson et al. (2002) also observed a distinct separa-

tion of young from old dogs in DGGE profiles. However, in

our study, the most obvious finding in the DGGE profile

analysis was the tight clustering according to individual. The

relative stability and individuality of the patterns indicate

that each individual harbors a unique and characteristic

fecal microbiota, which is consistent with other studies on

various species including pigs (Simpson et al., 2000), dogs

(Simpson et al., 2002), humans (Zoetendal et al., 1998) and

horses (unpublished data).

Donskey et al. (2003) used DGGE to investigate the

human fecal microbiota and found disruptions of the

intestinal biota associated with antibiotic therapy. However,

we are not aware of any studies that have examined the effect

of antibiotic exposure on fecal microbiota of healthy dogs.

The conclusion obtained from the DGGE profiles in our

study was that within individual dogs, pre-exposure samples

significantly differed from samples taken during and after

exposure. In addition, the diversity indices indicate that five

of seven dogs show higher diversity before than after

amoxicillin administration. After controlling for the pre-

viously observed dog effect, permutation tests indicated that

amoxicillin exposure significantly affected the DGGE pro-

files. Our results from DGGE band sequencing show that

bands disappearing during amoxicillin exposure represent

the Gram-negative Bacteroides group and A. succiniciprodu-

cens, whereas bands appearing represent bacteria in the

family Enterobacteriaceae. These findings were supported

by our qPCR results demonstrating a decrease in the

Bacteroides-like group and increase in the E. coli subgroup

during antibiotic exposure. The qPCR results also showed a

tendency of the Enterococcus group to increase during

antibiotic exposure. This confirmed our second hypothesis

that antibiotic exposure changes the predominant fecal

bacterial populations of dogs. In addition, the fact that the

Enterococcus group tended to increase during antibiotic

exposure is interesting with regard to the fact that enter-

ococci are opportunistic pathogens in humans and animals,

and an emerging cause of nosocomial infections (Damborg

et al., 2009).

The shift in bacterial populations was maintained to some

degree over time (4 14 days after antibiotic withdrawal), as

shown by analysis of DGGE profiles. However, as shown by

qPCR, 1 week after antibiotic withdrawal, there was a

tendency of returning to pre-exposure levels in the biota.

Those bacterial groups that decreased during exposure

(Bacteroides-like and C. perfringens-like groups) increased

after antibiotic withdrawal, while those groups that in-

creased during exposure (E. coli subgroup and Campylobac-

ter and Enterococcus groups) now decreased. Also, in six of

seven dogs, the diversity index was returning to pre-expo-

sure levels by the end of the study. These results indicate that

antibiotic exposure is required to maintain the shifts in

predominant bacterial populations, and thus would confirm

our third hypothesis. This supports other studies that also

found an approximate return to pretreatment conditions in

the overall community structure of human intestinal micro-

biota after cessation of antibiotic treatment (Donskey et al.,

2003; De La Cochetiere et al., 2005; Dethlefsen et al., 2008).

However, others have demonstrated that the effects of

antibiotic exposure on specific microbial populations in the

human intestinal tract can persist for years (Lofmark et al.,

2006; Jernberg et al., 2007).

The activity of amoxicillin is based on inhibition of

peptidoglycan synthesis in the bacterial cell wall, and

resistance in enteric bacteria is due to b-lactamase activity

(Rang et al., 1996). Bacteroides is a b-lactamase-producing



bacterial genus. The observed decrease in this presumptively

resistant bacterial group during exposure elucidates how

antibiotic exposure affects the total microbial community

composition. Nord (1993) refers in a review that several

antimicrobial agents cause changes in the human intestinal

microbiota, and the severity depends on the agent’s spec-

trum and concentration in luminal contents. Administra-

tion of ampicillin is reported to result in strong suppression

of both aerobic and anaerobic intestinal microbiota, while

acid-resistant derivates of ampicillin, like amoxicillin, will

cause minor ecological alterations (Nord, 1993). In this

study, we demonstrate significant changes in the microbiota

of each dog during the 7-day administration of amoxicillin,

although all the dogs were clinically unaffected.

Fecal samples provide only partial insight into the re-

sponse to different interventions because the microbiota

present in feces does not necessarily reflect the specific

features of the microbiota in the upper gastrointestinal tract.

Previous investigations suggest that there are differences in

the intestinal microbiota between stool and mucosal com-

munities and between anatomical sites (Simpson et al.,

1999; Marteau et al., 2001; Zoetendal et al., 2002; Eckburg

et al., 2005; Suchodolski et al., 2008a). Mentula et al. (2005)

compared cultured small intestinal and fecal microbiotas of

Beagle dogs. They concluded that jejunal microbiota was

distinctive in species distribution, proportions of main

bacterial groups and variability, representing both qualita-

tive and quantitative differences in comparison with the

corresponding fecal samples. However, fecal samples are

widely used because they are easily collected and representa-

tive of interindividual differences in gut microbial ecology

(Eckburg et al., 2005).

The metabolic activities of the bacteria in the gut are

important in determining the healthy functioning of the gut,

and a shift in bacterial populations may influence this. Our

study demonstrates that the number of A. succiniciproducens

found in dog 5 decreases with amoxicillin exposure. This

probably affects the intestinal volatile fatty acid concentra-

tion as A. succiniciproducens is an efficient succinic acid

producer. Succinate is rapidly decarboxylated to proprionate

by intestinal anaerobes. In future studies, it would be

interesting to include investigation of indirect indices on

metabolic function such as pH and volatile fatty acid

concentration. These have been used to determine gut

health in a number of species including humans, rats and

horses (Benno & Mitsuoka, 1992; Campbell et al., 1997; Berg

et al., 2005).

PCR-DGGE analysis is recognized as a semi-quantitative

method (Nubel et al., 1999). However, PCR amplification

may not accurately estimate the relative abundance of DNA

sequences, because of PCR bias and selection for certain

sequences (von Wintzingerode et al., 1997). Therefore, a

more intense band on the DGGE gel may not indicate higher

abundance in a sample. For this reason, band intensity was

not taken into account when the molecular fingerprints were

analyzed. When sequencing DGGE band fragments, three

clones were made from each excised band, and more than

one phylotypes were detected from several bands (Table S1).

This indicates that sequences from different microbial

species comigrate and that diversity estimates based solely

on counting the number of bands in DGGE lanes are

probably conservative. Molecules with different primary

sequence might have similar denaturation kinetics due to

having similar G1C content. Based on this limitation, other

potentially important antibiotic effects may not have been

detected.

The DGGE approach will not give the true total diversity

of any given sample. However, we intended to characterize

changes in the composition of communities, not necessarily

the diversity of the communities. It is conceivable that

administration of amoxicillin allowed minor, antibiotic-

resistant populations of bacteria, which were below our

detection level for DGGE, to increase at the expense of their

antibiotic-susceptible competitors. This does not register as

a significant change in diversity (Fig. S2), but as a shift in the

composition of communities. This highlights a potential

problem with deriving estimates of diversity with techniques

such as DGGE, which only detects the dominant commu-

nity members, and thus underestimates a-diversity. It can,

however, detect changes in the composition of the dominant

community members, and thus we have used it to estimate

the species turnover (b-diversity) in response to antibiotic

administration. For studies on a-diversity of intestinal

microbiota, other molecular approaches such as clone

library sequencing and pyrosequencing, which detect more

taxa and provide a more accurate estimate of the relative

abundance of a large number of moderate- and low-

abundance taxa, could be applied to circumvent these

limitations (Sogin et al., 2006; Dethlefsen et al., 2008).

In conclusion, this study demonstrates the high value

dogs may have as an animal model for humans, as also

shown by others (Benno et al., 1992; Harmoinen et al., 2004;

Mentula et al., 2005). In addition, it demonstrates the value

of using molecular microbial ecology to bridge gaps between

clinical microbiologists, pathobiologists and microbiologists

(Salyers, 1989). Our results have the following implications;

first, it demonstrates increased prevalence of antibiotic

resistance among fecal E. coli during antibiotic exposure.

Second, it shows that oral amoxicillin administration for 7

days significantly changed the fecal microbiota of healthy

dogs. Finally, it indicates that antibiotic exposure is required

to maintain the shifts in predominant bacterial populations

and their resistance pattern. However, more data on the

species-level is needed to determine whether or not there are

any long-term impacts of antibiotic exposure. Antimicrobial

agents are important in the treatment and prophylaxis of



infections, but it may cause pronounced disturbances in the

resident intestinal microbiota. Antibiotics must therefore be

used responsibly and restrictively to minimize resistance and

thus retain the efficacy of currently available antimicrobial

agents, and to maintain a healthy and diverse gut microbiota.

Acknowledgements

The authors acknowledge Dr Svetlana Kocherginskaya

(UIUC) and Dr Takumi Shinkai (UIUC) for assistance with

sequencing, and Dr Isaac Cann (UIUC) for insightful

discussions and support. We would also like to thank Ann

Øye (NSVS) and Aud Kari Fauske (NSVS) for assistance

with qPCR, and Akershus University Hospital and the

National Veterinary Institute in Norway for providing

bacterial strains.

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Supporting Information

Additional Supporting Information may be found in the

online version of this article:

Fig. S1. (a) DGGE profiles generated from PCR-amplified

V3-16S rRNA gene obtained from genomic DNA extracted

from fecal samples of six dogs pre- (designations 1-4),

during- (designations 9-11) and post- (designations 12–13)

amoxicillin exposure. M denotes marker lanes. (b)DGGE

profiles for Dog 5 showing identity of bands selected for

cloning and sequencing.



Fig. S2. Diversity analysis of DGGE banding patterns based

on the Shannon–Weiner index.

Table S1. DNA sequence analysis of DGGE band fragments

before (band 4-1 to 4-6) and during (band 8-1 to 8-7)

amoxicillin exposure of dog 5.

Table S2. Average estimated number of target species

bacterial genomes present in 1 g of feces (wet weight) as

found by real-time PCR analysis of fecal samples from three

dogs before (sample 2), during (samples 8 and 11) and

1 week after (sample 12) amoxicillin.

Please note: Wiley-Blackwell is not responsible for the

content or functionality of any supporting materials sup-

plied by the authors. Any queries (other than missing

material) should be directed to the corresponding author

for the article.



changes in fecal microbiota of healthy dogs administered amoxicillin

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