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Veterinary Sciences and Medicine

Vet Sci Med Volume 2(2): 20191

ReseaRch aRticle

Insights into the Diversity of Gut Microbiota and Associated Antibiotic Resistance Genes in Healthy DogsPillai DK1, Peterson G2,4, and Zurek L1, 3 1Department of Diagnostic Medicine/ Pathobiology, Kansas State University, Manhattan, Kansas, 665062University Research Compliance Office, Kansas State University, Manhattan, Kansas, 665063Department of Pathology and Parasitology, Center for Zoonotic Diseases, Central European Institute of Technology, University of Veterinary and Pharmaceutical Sciences, Czech Republic4University Biosafety Officer, Graduate Faculty Associate, Kansas State University

AbstractAims: The study was conducted to analyze the gut microbial community and antimicrobial resistance genes in healthy dogs.

Methods and Results: Fecal samples from household dogs (n=10) were subjected to bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP) and a spotted DNA microarray to identify antimicrobial resistance genes. Twelve distinct phyla were identified and Firmicutes represented the most dominant phylum, followed by Fusobacterium. Two phyla, Lentisphaerae (<1%) and Fibrobacteres (<1%) are reported for the first time from healthy dogs. A total of twelve different genera were identified across all the fecal samples tested. Notable genera detected in all the fecal samples were Lactobacillus, Ruminococcus, Turicibacter, Clostridium, and Fusobacterium (each represented >2% of the sequences). We observed dog to dog variation in the microbial community composition with respect to relative abundance and diversity in bacterial taxa. The microarray data showed that overall prevalence of antibiotic and metal resistance in the fecal microbiota of healthy dogs was low. Several antibiotic (tetracycline, erythromycin and aminoglycoside) and metal resistance genes (four copper resistance genes) were detected. However, one dog with a history of antibiotic treatment for chronic skin disease had multiple AMR genes.

Conclusions: Our findings indicate that dog to dog variation occurs in the microbial community composition with respect to relative abundance, diversity in bacterial taxa and antibiotic and metal resistance genes harbored by these microbes. There is low prevalence of resistance genes in the healthy dogs. Significance and Impact of study: This is the first study to perform a microarray-based analysis of antibiotic and metal resistance genes in the feces of healthy dogs with insights into the diversity of resistance genes in the gut microbiota of healthy household dogs.

Keywords: bTEFAP, antibiotic and metal resistance genes, fecal microbiota, microarray, healthy dogs

IntroductionThe rise of antimicrobial resistance (AMR) in the human and animal pathogens remains a serious global health concern because of the challenge of treating infections associated with these pathogens [1]. This emergence, persistence, and dissemination of resistant bacteria is the result of complex interactions among antimicrobials, microorganisms, environment, and the host. [2] Evidence suggests that gut commensals represent a reservoir of AMR genes [3]. The intestinal tract, a large reservoir of commensal bacteria, is composed of a diverse population of microbes (> 1014) colonizing specific regions of the intestine within the mammalian body [4, 5]. In fact, the gut is considered to be an ideal environment for the AMR gene transfer among the members of the gut commensals and also to other bacteria that enter the digestive tract through the fecal-oral route [6, 3]. This resistant microbiota when excreted in feces may accelerate the abundance, distribution and exchange of resistance genes to other mammals including humans.

There is an emerging concern regarding the role of companion animals as a reservoir of AMR bacteria. Closer attention needs

to be given to the risk associated with the use of antimicrobials in the pets [7,8 ]. One of the risk factors to humans in acquiring AMR bacteria is through direct (petting, licking, physical injuries and handling of feces) or indirect contact (contamination of food, furnishings such as beds, toilets or being bitten by arthropods of animal origin) with pet animals (Robinson and Pugh. 2002; [7]. There are studies indicating the increased possibility of transfer of pathogenic bacteria, including AMR pathogenic bacteria, from pet animals to humans [9, 10, 11]. Hence, investigating the AMR in gut commensals of healthy animals will be valuable in understanding their contribution to the increased AMR in humans [12, 13]. Therefore, the aim of the present study was to analyze gut microbial community and assess AMR and metal resistance

Correspondence to: Pillai D, 406S University Street, West Lafayette, IN 47906-2065, USA, Tel.: 765-494-6669; Fax: 765-494-9181 E-mail: pillai6[AT]purdue[DOT]edu

Received: Dec 03, 2019; Accepted: Dec 08, 2019; Published: Dec 17, 2019

*This article is reviewed by Enrique Aburto [Canada] and Abdalbari A. Alfaris [Iraq]

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genes prevalence in ten clinically healthy dogs. In this study we performed bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP) to identify the diverse gut microbiota and a spotted DNA microarray to identify AMR and metal resistance genes in the fecal microbiota.

Materials and MethodsStudy Animals

The fecal samples were collected from 10 healthy household dogs with no history of antibiotic treatment in the past 12 months. All dogs were clinically healthy during the sample collection. These dogs were of different age, sex and 9 different breeds (Table 1). All animals included in this study were housed indoors at different private homes in urban environment in Manhattan, KS. They were on commercial canine maintenance diet and were provided ad libitum access to water. The study coordinator met with each individual animal owner to review the study details. Animals with no history of past antibiotic exposure for 12 months were included in this study. The owners collected dog fecal samples with sterile gloves during defecation in sterile 50-ml falcon tubes, kept on ice and sent to the lab for storage at -80C.

For pyrosequencing analysis, samples from all ten dogs were included and for analysis of antibiotic and metal resistance genes, eight dogs were included.

454 Pyrosequencing

Total genomic DNA was extracted from the fecal samples (~0.5 g) using FastDNA® SPIN kit (MP Biomedicals, Solon, OH) according to manufacturer’s instructions. The DNA samples were submitted to Medical Biofilm Research Institute (Lubbock, Texas) for tag-encoded FLX-Titanium 16S rDNA amplicon parallel pyrosequencing [14, 15]. The raw sequence data were trimmed and the low quality reads were removed. Chimeras were removed from the sequences using Black Box Chimera Check software and [16] blasted against RDP-II database, 16S database and GeneBank (http://ncbi.nlm.nih.gov). The resulting sequences were analyzed using BLASTn.NEt algorithm. BLASTn best hits for the reads with sequence hits greater than 98% identity and tentative consensus sequence length of 260 bp were evaluated to the genus and species level. The genus clustering and their relative predicted proportions in the samples were determined by a post processing algorithm (% = [#sequences from an organism / total number of sequences from the sample] x 100%).

Data was analyzed and interpreted using Sequencher 4.8 (Gene

Codes) for alignment and sequence editing, MOTHUR [17] for diversity and richness and Blast2GO (www.blast2go.com) for the NCBI GenBank search.

Assessment of the AMR genes by microarray assay

Microarray assay and data analysis were performed as described previously [18]. Briefly, genomic DNA was extracted from 500 mg fecal sample using ZR soil Microbe DNA KitTM (Zymo research, Irvine, CA, USA) according to manufacturer’s instructions. The extracted DNA was purified using Geneclean Turbo kit (MP Biomedicals) and quantified using Nanodrop ND-1000 spectrophotometer (Nanodrop technologies, Wilmington DE). The samples with DNA concentration 1.7-2.0 ng/µl were used for direct labelling with BioPrime Plus Array CGH Genomic labelling system (Invitrogen Co., Carlsbad, CA) according to manufacturer’s instructions. Samples with labelling efficiency of 1 pmol/ul, determined by Nanodrop ND-1000 spectrophotometer, were further used for hybridization. The labelled samples were hybridized overnight at 42ºC on the prehybridized microarray chip followed by washing, spin dry and visualization on a GenePix 4000B slider reader (Molecular Devices, Sunnyvale, CA). The data analysis was performed using TGIR MultiExperiment Viewer (TMEV) program in the laboratory at Department of Diagnostic Medicine/Pathobiology, College of Veterinary Medicine, Kansas State University.

ResultsPyrosequencing

Pyrosequencing resulted in a total of 76,758 sequences from the data set, and 56,320 non-chimeric sequences were included in the final analysis. This resulted in an average of 5,632 reads/sample with a range of 4,622 – 6,102 (Table 2).

Bacterial Richness and Diversity

Diversity within each samples were estimated by four diversity indices, including number of operational taxonomic units (OTUs), ACE, Chao and Shannon index. All sequences were clustered into species and genus level OTUs. OTUs were defined as clustering at 3% (97% similarity) and 5% divergence (95% similarity) using MOTHUR. The number of OTUs, richness (ACE, Chao) and species diversity (H’) at a cut off level of 3% are summarized in Table 2.

The sequences (≥ 260 bp) were clustered into 1,835 OTUs at 3% (Table 2) and 1,392 at 5% divergence (data not shown). Overall, at 3% and 5% divergence, 1.83 ± 0.1x102 and 1.39 ± 0.08 x 102 OTUs

Sample ID Sex/Neuter/Spay Age (yr) Breed DietH-1 FS 5 Golden retriever/Yellow labrador mix Maintanence DietH-2 MN 1 Terrier mix Maintanence DietH-3 FS 17 Border Collie Maintanence DietH-5 FS 12 Terrier mix Maintanence DietH-6 FS 7 Pomeranian/healer mix Maintanence DietH-7 MN 3 Labrador/Shepherd Maintanence DietH-8 MN 4 Yorkshire terrier/bichon Maintanence DietH-9 MI 6 Yorkshire Maintanence DietH-10 MI 3 Rottweiler Shepherd Maintanence Diet

Yr= Year; MI= Male; MN= Male neutered; FS= Female spayed

Table 1: Information on healthy dogs

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Figure 1: Rarefaction analysis of dog fecal samples.Rarefaction curves comparing the number of sequences with the number of phylotypes found in the DNA from the feces of individual healthy dogs.

at species, genus and phylum level reached the plateau, indicating that these libraries were completely sampled (Figure 1).

were detected with Shannon diversity index (H’) 3.09 ± 0.15 and 2.67 ± 0.12, respectively. For all the 10 samples, rarefaction curve

Sample ID of reads OTUs* Species Richness* Species Diversity*Reads before Reads after ACE Chaol

H-1 4622 4622 146 243.57 234.64 1.95H-2 11886 6101 218 284.48 278 3.17H-3 5218 5218 176 210.17 209.71 3.17H-4 11363 6102 195 305.45 307.54 3.5H-5 6634 6101 221 289.57 284.67 3.73H-6 5191 5191 160 239.16 247 3.23H-7 4915 4915 173 216.63 2018.55 3.02H-8 8138 6102 118 182.44 193 2.81H-9 12925 6102 224 304.77 271.54 3.35H-10 5866 5866 204 258.7 300.47 3

*=Observed at 3% cut off on distance units for describing and comparing community; OUT = Operational taxonomic unit; ACE = Abundance –based coverage estimator; Chaol = Richness estimator; H = Non-parametric Shannon diversity index.

Table 2: Total nuber of reads before and after trimming, number of OTUs, species richness and diversity indices from fecal microbiota of healthy dogs

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The non-parametric species richness estimators, ACE (abundance-based coverage estimator) and Chao1 were in the range of 2.53 ± 0.13 x102 and 2.54 ± 0.12 x102 at 3% divergence. Among the ten samples tested, at 3% divergence, dog H4 had the highest species richness (ACE-305.45, Chao1-307.54) (Table 2) and at 5% divergence, dog H9 had the highest species richness (ACE-237.20, Chao1-237.20) (data not shown). Based on Shannon diversity index, dog H5 sample had the most diversity (Table 2), indicating high evenness in the bacterial community.

Distribution of phyla and bacterial community

We detected 178 different bacterial genera belonging to 12 different phyla based on the phylogenetic classification of the sequences (Table S1). Each fecal sample was represented by ≥ 6 different phyla, out of which 5 phyla were present in all fecal samples (Figure 2). The most prevalent phylum was Firmicutes, contributing to an average of 77.1% of the total reads. Smaller contributions were made by Fusobacteria (7.0 %), Proteobacteria (5.1%), Bacteriodetes (3.3%), Spirochaetes (1.8%), Actinobacteria (1.8%), and Spirochaetes (1.9%). contributing to an average 1-5% of total reads. The remaining sequences represented less than 1% of the total reads: Chloroflexi (0.4%), Lentisphaerae (0.06%), Fibrobacteres (0.03%), Verrucomicrobia (0.01%), Tenericutes (0.01%) and Synergistetes (0.01%) (Figure 2). The detailed distribution of the other phyla is presented in Table S1.

At the genus level, the phylum Firmicutes (range, %) included Turicibacter (77.2-0.3), Lactobacillus (65.5-0.01), Megamonas (38.13-0.07), Ruminococcus (28.7-1.05) and Clostridium (21.6-1.06). These genera were present in all the samples, with the exception of Megamonas, which was absent in one of 10 dogs (H-10). In the phylum Fusobacteria, Fusobacterium (22.4- 0.1%) was the predominant genus. In Proteobacteria, the most prevalent genera were Escherichia (22.3-0.01%) and Moraxella (5.7-0.02%), and both were present in 8 of 10 dogs. In the phylum Bacteriodetes, majority were identified

as Bacteroides genus (in 9 fecal samples). Figure 2 shows the bacterial diversity at the genus level in individual dogs with cut off ≥ 2% of all the sequences per sample. The predominant bacterial genera identified in each fecal sample were as follows: Turicibacter (H-1; H-8); Megamonas (H-2); Lactobacillus (H-3; H-6; H-7;-10); Escherichia (H-4); Clostridium (H-5); Fusobacterium (H-9).

Of the 178 genera present in our library, only twelve bacterial genera (mean sequence %) were found in all 10 samples: Leuconostoc (0.16), Weissella (0.23), Lactobacillus (26.75), Streptococcus (1.15), Lactococcus (0.13), Ruminococcus (5.84), Turicibacter (13.11), Clostridium (9.54), Roseburia (1.85), Dorea (1.28), Eubacterium (1.30), and Fusobacterium (6.89). Among these genera, more than 2% of the sequences belonged to Lactobacillus, Ruminococcus, Turicibacter, Clostridium, and Fusobacterium. A wide range of species diversity was observed among these genera, with the Clostridium genus, comprising the highest bacterial diversity of 53 different species (Figure 3). However, in the genus Turicibacter, only one bacterial species, Turicibacter sanguinis was the dominating species in all the samples (Figure 3).

Antibiotic and metal resistance genes

We used a 70-mer oligonucleotide probe consisting of conserved regions (300-400 bp) of the AMR genes for screening the fecal samples [18]. The microarray contained a total of 489 oligonucleotides, targeting 227 antibiotic and 99 metal resistance genes. The microarray result showed positive hybridization to probes corresponding to 10 different antimicrobial classes: aminoglycosides, beta-lactams, chloramphenicol, fluroquinilones, glycopeptides, lincosamide, macrolides, streptogramins, sulpha drugs, and tetracyclines. Of 227 probes designed for different AMR genes, our result showed that 33 different resistance genes were detected in the fecal samples of healthy dogs. The result from the microarray array analysis is shown in Table 3.

Figure 2: Fecal bacterial analysis by tag-encoded FLX-Titanium 16S rDNA amplicon parallel pyrosequencing.2A. The distribution of phyla in fecal samples of 10 healthy dogs. 2B. The distribution of genera in fecal samples of 10 helathy dogs.

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Most of the fecal samples (6 of 8) positive for AMR genes belonged to two or three different classes of antibiotics, except for the samples, H-4 and H-10, where AMR genes from ≥ 5 antibiotic classes were observed (Table 3A). Samples from H-1 and H-2 dogs harbored only tetracycline resistance genes. Resistance to aminoglycosides was observed in 6 of 8 samples (except H-1 and H-2). Macrolide resistance genes were detected in two samples, H-4 and H-7, with dog H-4 harboring most of the resistance genes (ereB, erm(TM)2, ermX, mef(A/E)). The parC gene encoding for fluroquinolone resistance was detected only in dog (H-10). Samples from H-4 and H-10 were the only two samples that possessed beta-lactam and glycopeptide resistance determinants. Genes conferring resistance to metals such as arsenic, cadmium, copper, cobalt/nickel, transferable copper, copper/zinc/cadmium and copper/silver (Table 3B) were also detected in the fecal samples. In the samples positive for metal resistance genes, most of them had only one or two resistance genes, except for the sample H-4, that carried 11 genes conferring resistance to cadmium, copper, copper/zinc/

cadmium, cobalt and transferrable copper. Four of the fecal samples tested (H-3, H-5, H-7, and H-8) were negative for all the metal resistance genes. Diverse resistance genes were detected for copper, and all of them were harbored in dog H-4. The genes, czcA2 copABCD and cusA, conferring resistance to copper/zinc/cadmium and copper, respectively, were the most prevalent (25%) metal resistance genes identified in the samples

Discussion Most of the information available with respect to the canine gut microbial community is based on cultivation approach and thus microbial diversity is greatly underestimated [19, 20]. DNA-based techniques have been recently used, and there are a number of studies that have analyzed microbiota in canine feces [21, 15, 22, 23]. Most of these studies are focused mainly on determining the effects of diet [24], dietary formulation [25], dietary fiber [15], prebiotics (Mazcorro et al., 2017) and antibiotics on the fecal microbial community composition

Figure 3: Bacterial species diversity in the fecal samples of healthy dogs. A. Lactobacillus.species; B. Ruminococcus species; C. Fusobacterium species; D. Turicibacter species, and E. Clostridium species.

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[26]. In this study, we provide additional information on the diversity of resistance genes carried by these microbial communities in the gut flora of healthy household dogs.

The prevalence of AMR in commensal bacteria in various ecosystems suggests that commensal bacteria play an important role in the dissemination of AMR genes [27, 28] Huddleston etal., 2014. Studies have shown that genes conferring resistance to metals can play an important role in the dissemination of AMR genes by co-resistance and cross-resistance [29, 30, 31]. AMR in the commensal bacteria associated with pet animals with respect to the AMR pool and its role in the horizontal gene transfer is not well known. The transfer of variety of resistant bacteria and resistance genes from pets to humans has been reported [32, 33] Harrison et al., 2014). Hence, this study investigated diverse gut microbial community and presence of diverse antibiotic and metal resistance genes in the healthy canine gut microbiota.

The objective of this study was to characterize the antibiotic and metal resistance genes and bacterial microbiome composition, and diversity in the fecal sample of healthy household dogs that were not exposed to any antibiotics at least for a year prior to sample collection.

Our result shows dog-to-dog variation in the composition of the fecal microbiota. Such variations in the GIT microbial

population are driven by various factors such as diet, environment and host genetics [34, 23] Garcia-Mazcorro et al., 2017). Studies have shown that healthy dogs harbor more diverse bacterial population in their GIT compared to dogs under antibiotic selective pressure [26]. The most prevalent phylum encountered in the present study was Firmicutes, similar to those reported in the healthy hound dogs, cattle, macaque and human gut (Eckburg et al., 2005; [35, 36]. The results from the present study were similar to the data reported by [37, 15, 22, 38, 23] where they found Fusobacteria, Firmicutes, and Bacteroidetes as the dominant phyla in the gut communities of healthy dogs. In our study two phyla Lentisphaerae (<1%) and Fibrobacteres (<1%) were also identified that have not been previously reported in healthy dogs. Fusobacteria (5.3%) represented the second most abundant phylum in the canine gut microbiome with all OTUs identified as Fusobacterium genus belonging to 4 different species (Figure 3C). We detected higher abundance of phylum Fusobacteria than previously reported in healthy dogs (5.3% versus 0.3%) [22].

The genera belonging to the phylum Firmicutes and Actinobacteria are Gram positive bacteria, while in the remaining phyla, Gram negative bacteria formed the major group. Gram positive bacteria were dominant in the dog fecal sample and this is in agreement with the previously published

Table 3: Detection of resistance genes in the fecal microbiota of healthy dogs

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data [39]. The main bacterial genera that have been shown to be present in healthy dogs, such as Lactobacillus, Clostridium, Streptococci and Bacteroides [40, 38, 22], were also identified in our samples. The fact that the rarefaction curves reached a stable value suggests complete estimation of species richness as well as most bacterial diversity were included in our sampling size. A difference in the relative abundance (ACE, Chao1) and diversity (Shannon index) of bacterial taxa were observed among the samples. This is likely the result of changes in gut microbiota due to difference in age, breed and diet of these animals. Based on Chao1, the average species richness estimated at 3% divergence was 254.51, which is similar to that reported in other studies [41] and is much lower than that reported in cattle (637) [35]. Most of our samples did not show much difference in their diversity index (3% divergence). As reported by others [41], our data also showed the average estimate as 3.09, a value lower than that observed in cattle (4.85) [35] at 3% sequence dissimilarity. Although the diversity index is widely accepted for determining the biodiversity, two communities with same index value cannot be considered to have same bacterial diversity. Samples with same diversity index may differ with respect to high or low evenness or richness, respectively [42].

In the present study, Lactobacillus, Clostridium, Turicibacter, Megamonas, Fusobacterium, and Ruminococcus appeared to be prevalent in the gut microbiota of healthy dogs, although the abundance of these species were not always same in each sample [37]. Based on the number of OTUs, richness, Shannon diversity index, differences observed were comparable to the study reported by [21] and [15] studies. These differences in the richness and diversity likely reflects differences in the age, breed [41] and diet of the individual dogs [15] Garcia- Mazcorro et al., 2017) and differences in the sampling site of intestine [37, 43].

We performed the microarray analysis from the same fecal samples to study the overall prevalence and the diversity of resistance genes in their gut microbiota. Of the eight samples tested, the overall prevalence of AR genes harbored by the healthy dogs were low except for one dog (H-4) that was positive for most of the AR genes. According to the information obtained from the owner, this dog had a history of skin problems and had multiple exposures to antibiotic therapy (gentamicin, neomycin, polymyxin B sulfate, and cephalexin two years prior to the sample collection. This may be a possible explanation for the presence of most of the resistance genes we found in the fecal microbiota of dog H-4. This also indicates the possibility of long lasting effects of antibiotic treatment on the gut microbiota of dogs as observed in human intestinal microbiota [44, 45, 46].

We observed a high prevalence of tetracycline resistance in our samples. The tetracycline resistance tet O gene (87.5%), encoding for ribosomal protection protein, and aadE gene (50%), which encodes for putative aminoglycoside 6-adenyltransferase, were the most prevalent resistance genes detected in all samples. However, this is not surprising

as tetracycline, being a broad-spectrum antibiotic; bacteria from different ecosystems are exposed to this antibiotic, contributing to higher rates of resistance (Roberts, 1996; [47]. Two samples were positive for glycopeptide resistance genes, vanD and vanH. The vanD gene is located on the chromosome and is not self-transferrable (Depardieu et al., 2003). Multiple resistance genes in combinations are required for vancomycin resistance, and vanH is one gene that encodes for an enzyme that is essential for resistance [48] Murray, 1998). However, further verification of the positive resistance genes needs to be done with a PCR assay to confirm the microarray results.

One of the limitations of our study is the lack of detailed data about the diet composition and family environment of the animals included in this study. However, in spite of this limitation, we consider our data valuable because our study shows that dogs not exposed to antibiotics at least for a year shows diversity in their gut microbiome profile. This supports the data in the existing literature [21] Suchodolski et al., 2009).

In conclusion, the present study shows that variation occurs in the microbial community composition with respect to relative abundance, diversity in bacterial taxa and antibiotic and metal resistance genes harbored by these microbes in healthy dogs [49-52]. This variation might be due to several inter and intra variable factors such as age, diet and previous antibiotic exposure of the animal. However further studies are needed to fully understand the link between these variable and the population structure of the gut microflora. To our knowledge, this is the first report to study the diverse antimicrobial and metal resistance genes in combination with the microbial community flora in the feces of healthy dogs. This study serves as baseline information on the diversity of AR and metal resistance genes in the fecal microbiota of healthy dogs.

AcknowledgementsWe thank the pet owners who participated in this study. The authors are thankful to Mal Hoover for her assistance with preparing the figures. We thank Dr. Scot Dowd from MR DNA laboratory for pyrosequencing. The author would like to thank Dr. T.G. Nagaraja for constructive criticism of the manuscript.

Conflict of interestThe authors declare no conflict of interest.

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Citation: Pillai DK, Peterson G, Zurek L (2019) Insights into the Diversity of Gut Microbiota and Associated Antibiotic Resistance Genes in Healthy Dogs. Vet Sci Med 1: 001-009.

Copyright: © 2019 Pillai DK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.