Appropriate Chicken Sample Size for Identifying the Composition of ...

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Appropriate Chicken Sample Size for Identifying the Composition of Broiler Intestinal Microbiota Affected by Dietary Antibiotics, Using the Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis Technique H. Zhou,*† J. Gong,*1 J. T. Brisbin,‡ H. Yu,* B. Sanei,§ P. Sabour,* and S. Sharif‡1 *Food Research Program, Agriculture and Agri-Food Canada, Guelph, Ontario, N1G 5C9, Canada; †Department of Poultry Science, Texas A&M University, College Station 77845; ‡Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, Ontario N1G 2W1, Canada; and §Ontario Ministry of Agriculture, Food and Rural Affairs, Guelph, Ontario N1G 2W1, Canada ABSTRACT The bacterial microbiota in the broiler gastrointestinal tract are crucial for chicken health and growth. Their composition can vary among individual birds. To evaluate the composition of chicken microbiota in response to environmental disruption accurately, 4 different pools made up of 2, 5, 10, and 15 individuals were used to determine how many individuals in each pool were required to assess the degree of variation when using the PCR-denaturing gradient gel electrophoresis (DGGE) profiling technique. The correlation coefficients among 3 replicates within each pool group indicated that the optimal sample size for comparing PCR-DGGE bacterial profiles and downstream applications (such as identifying treatment effects) was 5 birds per pool for cecal

microbiota. Subsequently, digesta from 5 birds was pooled to investigate the effects on the microbiota composition of the 2 most commonly used dietary antibiotics (virginiamycin and bacitracin methylene disalicylate) at 2 different doses by using PCR-DGGE, DNA sequencing, and quantitative PCR techniques. Thirteen DGGE DNA bands were identified, representing bacterial groups that had been affected by the antibiotics. Nine of them were validated. The effect of dietary antibiotics on the microbiota composition appeared to be dose and age dependent. These findings provide a working model for elucidating the mechanisms of antibiotic effects on the chicken intestinal microbiota and for developing alternatives to dietary antibiotics.

Key words: chicken, intestinal microbiota, sample size, dietary antibiotic 2007 Poultry Science 86:2541–2549 doi:10.3382/ps.2007-00267

INTRODUCTION The chicken gastrointestinal (GI) tract is highly adapted to the presence of commensal bacteria and has been shown to have a bacterial population present within 24 h of hatching. The composition of the microbiota continues to change over time under the influence of various factors, such as bird age, diet, and the administration of antibiotics and probiotics (Mackie et al., 1999; Xu et al., 2003; Rastall et al., 2005). Healthy intestinal microbiota are critical for host nutrition, production performance, antigenic stimulation, and development of the gut-associated lymphoid tissues (Amit-Romach et al., 2004). In commercial poultry production, however, the development of intestinal microbiota in chickens may be altered by modern practices, such as facility hygiene, routine medication, artificial egg incubation, hatching, and chick rearing. Consequently, chicks may be

©2007 Poultry Science Association Inc. Received June 26, 2007. Accepted August 23, 2007. 1 Corresponding authors: [email protected]; [email protected]

more susceptible to being colonized by bacterial pathogens. The prophylactic use of dietary antibiotics has been a common practice in commercial poultry production for the last few decades to prevent enteric infection of chicks and to promote growth. This practice potentially affects human health because of drug residues and the emergence of antibiotic-resistant strains of zoonotic microorganisms in food animals. In addition, it is hypothesized that this practice will decrease the therapeutic effectiveness of antibiotics that are used to treat a variety of bacterial infections in humans (World Health Organization, 2002). In 1999, to address increased public concerns regarding the risks involved in using dietary antibiotics as animal growth promoters, the European Union banned their use in food animal production (European Commission, 2001). Consequently novel alternatives to dietary antibiotics that leave behind little or no residue or resistant bacteria are being encouraged and have received much research attention worldwide. A knowledge of the intestinal microbiota, particularly the effect of antibiotics on their modulation, is essential for the development of viable alternatives to dietary antibi-

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ZHOU ET AL. Table 1. Percentage correlation coefficients among 3 replicate denaturing gradient gel electrophoresis profiles within each pool group in chicken cecum and ileum (means ± SE) Pool group Tissue

2

5

Cecum Ileum

83.34 ± 1.53 78.13 ± 2.41a a

10

90.67 ± 2.31 77.16 ± 4.67a

b

15

90.67 ± 2.08 80.15 ± 1.25a

b

94.00 ± 2.65b 86.39 ± 1.06b

Values are different within rows (P < 0.05).

a,b

otics. Recent studies with 16S rRNA gene-based DNA techniques have revealed the significant diversity and complexity of bacterial microbiota in the chicken GI tract (Apajalahti et al., 1998; Gong et al. 2002a,b; Lan et al., 2002; Zhu et al., 2002; Lu et al., 2003; Amit-Romach et al., 2004; Gong et al., 2007). Polymerase chain reaction-based denaturing gradient gel electrophoresis (DGGE) is one of the most commonly used DNA techniques (Muyzer et al., 1993) for investigating chicken microbiota, including the impact of dietary antibiotics, such as avilamycin, bacitracin, enramycin, salinomycin, and tylosin, on the microbiota (Knarreborg et al., 2002; van der Wielen et al., 2002; Collier et al., 2003a; Guan et al., 2003; Hume et al., 2003; Pedroso et al., 2006). Knarreborg et al. (2002) previously demonstrated that dietary antibiotics can alter the composition of the microbiota. For example, they showed that lactobacilli and Clostridium perfringens were the bacterial groups in the chicken ileum most strongly altered by treatment with a dietary fat source and subtherapeutic levels of avilamycin and salinomycin. In addition, Collier et al. (2003b) reported that the use of tylosin reduced the percentage of mucolytic bacteria in general and the abundance of C. perfringens in particular. The microbiota of tylosin-treated birds were more homogeneous and were distinct from the microbiota of control birds. The alteration of bacterial profiles attributable to tylosin treatment also resulted in an increase in the population of Lactobacillus gasseri (Collier et al., 2003b). In a separate report, dietary bacitracin was found to alter the composition of the small intestinal bacterial microbiota of broilers, which was presumably either beneficial or detrimental to chicken growth (Pedroso et al., 2006). However, the bacterial species or groups corresponding to the composition changes were not isolated and determined. Using real-time quantitative PCR assays with group-specific 16S rDNA primers, Wise and Siragusa (2007) recently analyzed the effect of virginiamycin on different bacterial groups of intestinal microbiota of broilers at different ages and found that the effect was most pronounced in the ileal region. The effect of dietary virginiamycin on the chicken intestinal bacterial microbiota was also investigated by Dumonceaux et al. (2006) through sequence analysis of chaperonin 60 (cpn 60) gene libraries, followed by quantitative PCR verification of 15 targeted bacteria. They found that virginiamycin was associated with increased abundance of many of the bacterial targets (including most of the targeted Lactobacillus spp.) in the proximal digestive tract (duodenal loop to proximal ileum), with fewer targets affected in the distal regions (ileocecal junction and cecum).

The bacterial microbiota within the GI tract of chickens consist of a balanced composition of facultative and obligate anaerobic bacteria. Because the microbiota and their development are affected by many factors, there is a considerable amount of variation in the composition of microbiota among individual birds. In fact, we have experienced such variation in our previous studies and have proposed applying statistical tools to the experimental design of animal trials and to data analysis for studies of intestinal microbiota by using molecular techniques (Richard et al., 2005). The objective of the present study was therefore to determine the optimal sample size of individual birds to be pooled to minimize natural individual variation and to identify experimental treatment effects. The established procedure was subsequently used to examine the influences of virginiamycin and bacitracin at both subtherapeutic and prophylactic levels on the composition of the intestinal microflora in broilers.

MATERIALS AND METHODS Chickens and Housing Newly hatched commercial broilers were obtained from Maple Leaf Foods Inc. (New Hamburg, Ontario, Canada). The birds were maintained in floor pens at the Arkell Research Station, University of Guelph. The chicks were provided with free access to water and broiler starter rations. All research procedures complied with the University of Guelph Animal Care Committee Guidelines. Two separate bird experiments, the sample pooling and the treatment with antibiotics, were performed.

Sample Pooling Birds were euthanized at the age of 2 wk, and ileal and cecal digesta were collected from a total of 96 birds. An equal amount of the digesta from each bird was mixed to create pooled samples from 2, 5, 10, and 15 birds. Approximately 0.25 g of pooled digesta was placed in a 2-mL microcentrifuge tube and stored at −20°C until DNA extraction. Three replicates for each pooled sample were collected from separate birds.

Antibiotic Treatment A total of 225 birds were used for this experiment. Chicks of the control group (n = 45) were fed a broiler starter without any antibiotics. The treatment groups were fed

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PCR Amplification The primers used in this study were those described by Walter et al. (2000), which target the V3 region of the 16S rRNA gene of all eubacteria. The forward primer, HDA1GC, contained a GC clamp (5′-CGCCCGGGGCGCGCCC CGGGCGGGGCGGGGGCACGGGGGG-3′) and had the following sequence (5′-ACTCCTACGGGAGGCAGCAG T-3′). The reverse primer, HDA2, consisted of the following sequence (5′-GTATTACCGCGGCTGCTGGCAC-3′). The PCR conditions and mixture were described previously by Gong et al. (2002a,b). Briefly, the PCR mixture (25 ␮L) contained 200 ng of genomic DNA, 0.5 ␮M each primer, 0.5 ␮L of a 10 mM deoxynucleotide triphosphate mixture, 2.5 ␮L of 10× PCR buffer, 1 ␮L of dimethylsulfoxide, and 1.25 units of Taq polymerase (Sigma, St. Louis, MO). The final volume was adjusted to 25 ␮L with sterile deionized water and submitted to the following PCR program: initial denaturation for 4 min at 95°C; 30 cycles of denaturation at 94°C for 30 s, annealing at 58°C for 30 s and elongation at 72°C for 1 min; and a final extension at 72°C for 10 min. The expected size of amplified fragments was approximately 200 bp and was verified on a 1.5% (wt/vol) agarose gel for 30 min at 110 V.

DGGE

Figure 1. Polymerase chain reaction-denaturing gradient gel electrophoresis (DGGE) profiles generated from cecal and ileal template DNA from 2-wk-old birds by using the primer pair HDA1-GC and HDA2. 2 = 2 birds per pool; 5 = 5 birds per pool; 10 = 10 birds per pool; and 15 = 15 birds per pool. There were 3 replicates for each group. A) DGGE profiles in chicken cecal samples; B) DGGE profiles in chicken ileum samples.

the same diet with the following doses of antibiotics: 11 ppm of virginiamycin (V11; n = 45); 22 ppm of virginiamycin (V22; n = 45); 4.4 ppm of bacitracin methylene disalicylate (BMD4.4; n = 45); and 55 ppm of BMD (BMD55; n = 45). Digesta of the duodenum, jejunum, ileum, and cecum was collected from 3-, 7-, and 14-d-old birds. An equal amount of digesta from 5 birds was mixed, and approximately 0.25 g was placed in a 2-mL microcentrifuge tube and stored at −20°C until DNA extraction. Three replicates for each sample were collected from separate birds.

The DGGE was performed by using a Bio-Rad DCode Universal Mutation Detection System (Bio-Rad, Mississauga, Ontario, Canada). The separation of the PCR products was performed with a 16 cm × 16 cm × 1 mm 10% (wt/ vol) polyacrylamide gel (acrylamide-bisacrylamide ratio 37.5:1) containing a 35 to 65% linear denaturant gradient. A 100% denaturing solution consisted of 40% (vol/vol) deionized formamide and 7.0 M urea. Approximately 5 ␮L of the PCR product was loaded and the gels were placed in the D-Code System for electrophoresis in 1× Trisacetate buffer at 60°C for 16 to 18 h at a constant voltage of 100 V. The DNA bands in the gels were visualized by silver staining (van Orsouw et al., 1997). The TIFF files of gel images were analyzed with BioNumerics software (Applied Maths, Sint-Martens-Latem, Belgium). The gel images were normalized for band matching with a 1.0% position tolerance. The Pearson correlation coefficients were calculated between every 2 lanes based on densitometric curves. Student’s t-test (P < 0.05) was used to examine the correlation coefficients among pool groups.

Cloning and Sequencing DNA Extraction Deoxyribonucleic acid was extracted from digesta samples by using the QIAamp DNA Stool Mini Kit (Qiagen Inc., Valencia, CA), according to the manufacturer’s recommendations. The amount of DNA extracted was determined by measuring absorbance with a spectrophotometer at 260 nm. The DNA was stored at −20°C until use.

Bands of interest were excised aseptically from the DGGE gels into 1× PCR buffer (Sigma), rinsed twice, and then incubated overnight at 4°C in 1× PCR buffer and 0.1% Triton X-100. One microliter of the above eluant was amplified with the HDA primers containing the GC clamp in a standard PCR reaction, as described above. After the second run of gel electrophoresis, PCR amplicons comigrating with the original bands in a DGGE gel were recov-

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ered and cloned into pDrive (Qiagen) according to the manufacturer’s protocols. Plasmid clones were identified based on blue-white screening, and the insert was amplified with the HDA primers. After confirmation that the DGGE bands comigrated with the originals, the cloned DNA fragments were sequenced with an ABI Prism 377 Automated DNA Sequencer (Applied Biosystems, Foster City, CA). The partial sequences were compared directly with nonredundant nucleotides in the GenBank database by using BLAST.

Real-Time PCR To verify the effects of antibiotics on chicken intestinal microbiota observed in the present study, one of the DGGE DNA bands identified as Lactobacillus salivarius by DGGE comigration and DNA sequence analysis was selected for quantitative analysis to determine the abundance of the bacterial species by real-time PCR assays. All DNA samples of ileal digesta collected on different days were analyzed for the abundance of the bacterium, although the antibiotic effect was detected by DGGE profiling only in the samples of d 14. Real-time quantification was performed in a LightCycler 2.0 instrument (Roche Diagnostics, Laval, Quebec, Canada) by using the SYBR Green dye. Polymerase chain reaction mixtures (20 ␮L) contained 2 ␮L of the LightCycler FastStart DNA Master SYBR Green I (Roche Diagnostics), 2 ␮L of a 1:100 dilution of the DNA, 4 ␮M MgCl2, and 0.5 mM L. salivarius primers (5′CGAAACTTTCTTACACCGAATGC-3′ and 5′-GTCCATTGTGGAAGATTCCC-3′). These primers have been validated for specificity (Song et al., 2000). The cycling conditions included an initial heat-denaturing step at 95°C for 10 min, 40 cycles at 95°C for 10 s, 62°C for 5 s, and product elongation and signal acquisition (single mode) at 72°C for 10 s. Following amplification, the melting curves were determined in a 3-segment cycle of 95°C for 0 s, 65°C for 15 s, and 95°C for 0 s at the continuous acquisition mode. The temperature transition rates were set at 20°C/s except for segment 3 of the melting curve analysis, for which it was set to 0.1°C/s. Quantitative analysis was performed by using LC software (Roche Diagnostics). Relative quantification of L. salivarius abundance was determined by comparison with the standard curve, which was generated by amplifying serial dilutions of a known amount of L. salivarius genomic DNA. The genomic DNA was quantified by measuring the absorbance at 260 nm. The fold change was calculated by dividing the relative abundance of each treatment by the control for each day. The real-time PCR data were used for analysis of treatment effects, and the significance was examined by Student’s t-test (P < 0.05). The fold change results are presented as means and SD.

RESULTS Similarity Analysis of Bacterial Profiles of Intestinal Microbiota from the 2-, 5-, 10-, and 15-Bird Pool Groups The PCR-DGGE profiles of cecal and ileal bacterial microbiota from 2-, 5-, 10-, and 15-bird pools collected at 2

wk of age are shown in Figure 1. The similarity of the profiles among the 3 replicates within each pool group was represented by correlation coefficients. Pearson correlation coefficients were obtained between every 2 pools within each group. The average percentage correlation coefficient and SE within each group are presented in Table 1. For cecal samples, the percent correlation coefficients were 83.34 ± 1.53, 90.67 ± 2.31, 90.67 ± 2.08, and 94.00 ± 2.65 in the 2-, 5-, 10-, and 15-bird pool groups, respectively. The difference between the 2-bird pool group and other 3 pool groups was significant (P < 0.05). However, there was no significant difference between any other 2 pool groups. In the analysis of ileal samples, average percentage correlation coefficients and SE within each group were 78.13 ± 2.41, 77.16 ± 4.67, 80.15 ± 1.25, and 86.39 ± 1.06 in the 2-, 5-, 10-, and 15-bird pool groups, respectively. There were significantly different percentage correlation coefficients between the 15-bird pool group and the other 3 pool groups (P < 0.05), whereas there was no significant difference between any other 2 pool groups.

Identification of Bacterial Species Affected by BMD and Virginiamycin in the Diet Three replicate pool samples (5 birds per pool) of ileal or cecal digesta from each treatment group and the control group (antibiotics free; 5 groups per tissue) at d 3, 7, and 14 were used for the analysis of PCR-DGGE bacterial profiles. When a DGGE DNA band of 3 replicates of a pool sample consistently appeared in one of the treatment groups and disappeared from the control group, or vice versa, the band was considered as a potential bacterial species affected by either BMD or virginiamycin. A total of 13 bands were identified at d 3, 7, and 14, with 5 bands coming from the ceca and 8 bands coming from the ileum. The DGGE DNA bands identified from the ileal samples with different locations in gels between the treatment groups and the control group are indicated in Figure 2. Figure 2A displays the PCR-DGGE profiles of microbiota samples from 3-d-old chicks. Band a was consistently presented in the V11, BMD4.4, BMD55 groups with high intensity but with low intensity in the V22 group, and was not presented in the control group. For ileal samples at d 7 (Figure 2B), band a appeared in all groups, although with much lower intensity in the V22 group. Band b was detected in the BMD55 group and in part of the V11 and V22 groups, whereas band c was detected only in the V11 and BMD55 groups and in part of the BMD4.4 group. Figure 2C demonstrates the results from the ileal samples at d 14. Band a occurred only in the V11, V22, and BMD4.4 groups. Band b was presented in the V11, V22, and BMD55 groups, band c in the BMD4.4 and control groups, and band d consistently in all groups except for the BMD55 and control groups, with weak and partial occurrence. Among the 5 DGGE DNA bands identified in the cecal samples (figures not shown), 2 were from 3-d-old chicks. One of them was detected only in the BMD55 group, and the other was detected only in the V11 group. Two DNA bands were also identified in the cecal samples collected

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Putative Bacterial Species Identified by Cloning and Sequencing The 13 DNA bands described above were further characterized for the identification of their putative species. After confirmation of the DNA bands by further PCR-DGGE analysis, in which their PCR amplicons comigrated with the originals, the DNA bands were cloned and then subjected to sequence analysis. At least 3 clones from each individual band were randomly selected for sequence analysis. Nine of the 13 bands (4 out of 5 in the cecum, 5 out of 8 in the ileum) were determined for their putative bacterial species. A summary of characterization of these bands, including their putative species, accession number used to detect homology, and similarity, is presented in Table 2. Most clones were closely related to uncultured bacteria without specific species information. The closest relatives of cultivable bacteria to which some clones corresponded included Klebsiella granulomatis at d 3 from the ceca, Enterococcus sp. AK61 at d 3 from the ileum, and L. salivarius at d 14 from the ileum.

The Abundance of L. salivarius in the Ileum of Chickens

Figure 2. Polymerase chain reaction-denaturing gradient gel electrophoresis profiles generated from chicken ileal content template DNA with 4 different antibiotic treatments and nonmedicated control at d 3, 7, and 14. V11 = 11 ppm of virginiamycin; V22 = 22 ppm of virginiamycin; BMD4.4 = 4.4 ppm of bacitracin methylene disalicylate; BMD55 = 55 ppm of bacitracin methylene disalicylate; control = nonmedicated control. There were 3 replicates for each group. A) Day 3. B) Day 7. C) Day 14.

Lactobacillus salivarius bacteria in the ileal samples of chickens from each treatment group, compared with the control group, at different ages were quantified by realtime PCR. At d 3 and 7, there was a significant difference between the chickens in the V22 group and the other 3 groups (P < 0.05; Figure 3A and 3B). The V22 group completely or almost inhibited L. salivarius at both ages. The V11 and BMD4.4 groups enriched L. salivarius, whereas the BMD5.5 group reduced it at d 3; for d 7, only the V11 group enriched L. salivarius, whereas both BMD groups had a similar abundance of L. salivarius. There was a significant difference at d 14 between the chickens in the V11, V22, and BMD55 treatment groups and the BMD4.4 group (Figure 3C). Both doses of virginiamycin (11 and 22 ppm) and BMD at 55 ppm significantly reduced the level of L. salivarius in the ileum of chickens at d 14. This result supports the observation with the PCR-DGGE analysis, in which the DNA band corresponding to L. salivarius was detected only in the ileal samples of the BMD4.4 and control groups (Figure 2C).

DISCUSSION

on d 7. One band consistently occurred only in the control group, whereas the other band was present consistently in the control and BMD55 groups and occasionally in the BMD4.4 group. Cecal samples collected at d 14 exhibited only one obvious DNA band, which was apparently presented in the BMD55 and control groups. Very few DGGE DNA bands were observed in the samples from the duodenum and jejunum of chickens at different ages, and no antibiotic effect was obvious (data not shown).

The microbiota in the GI tract of animals change constantly, particularly during the development of the host. These changes occur in response to host and environmental factors. Individual animals also demonstrate considerable variation in their microbiota. In our previous studies of intestinal microbiota with PCR-DGGE analysis, we have observed such individual variations in both pigs (Richard et al., 2005) and broiler chickens (Gong et al., 2005). As such, it is difficult to determine whether changes in the composition of microbiota are a result of experimental effects, such as antibiotic or probiotic treatment, or are

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Figure 3. Fold changes in Lactobacillus salivarius of treatment groups compared with the control group in chicken ileum at d 3, 7, and 14 by quantitative real-time PCR. Bars represent mean ± SD. V11 = 11 ppm of virginiamycin; V22 = 22 ppm of virginiamycin; BMD4.4 = 4.4 ppm of bacitracin methylene disalicylate; BMD55 = 55 ppm of bacitracin methylene disalicylate. a,bMeans among the treatment groups with no common superscripts were different (P < 0.05). A) Day 3; no L. salivarius was detected by real-time PCR in the V22 group. B) Day 7. C) Day 14.

caused by natural individual variations. Thus, there is a need to determine whether the individual variations can be minimized by pooling animal samples as well as how many individual animals are required for each pooled sample to differentiate treatment effects from natural individual variations. Three replicates within each pool group based on the PCR-DGGE profiles were used. The correlation coefficients among them were calculated to evaluate the similarities among the 3 replicates. There was a significant difference in the average correlation coefficient in the 2-bird pool group and the other 3 pool groups in cecal samples, and no significant difference was observed among the 5-, 10-, and 15-bird pool groups. The correlation coefficient in the 2-bird pool group was the lowest among the 4 pool groups. This suggests that pooling digesta from 5 birds provides the optimal sample size to examine changes in the composition of microbiota, as influenced by experimental treatments, by using PCR-DGGE profiling techniques. Although the average correlation coefficients were statistically different only between the 15-bird pool group and the other 3 pool groups in the ileum samples, the estimate of correlation coefficients with the 5-bird pool in DGGE profiles would be reduced about 10% compared with the 15-bird pool (Table 1). One major focus of this study was to compare the response of intestinal microbiota in different regions of the chicken gut with the antibiotic treatments. Therefore, 5 birds as a pool would provide a relatively accurate estimate of the effect of an antibiotic on intestinal microbiota. The use of antibiotics at the subtherapeutic level in the diet has been a common practice in the poultry industry (Stutz and Lawton, 1984; Barrow 1998). In some cases, prophylactic doses of antibiotics are necessary for the prevention and control of disease caused by a variety of pathogens (Phillips et al., 2004). Both subtherapeutic and prophylactic doses of virginiamycin and bacitracin were tested in the present study to determine the effects of antibiotics on bacterial communities in the chicken GI tract. Chicken intestinal microbiota start to colonize between 2 and 4 d posthatch (Mead and Adams, 1975), and are usually established in the small intestine by approximately 14 d after hatch, but might take as long as 14 to 30 d to fully develop in the cecum (Amit-Romach et al., 2004). We analyzed the microbiota on d 3, 7, and 14 of age because these are critical times for the development of the intestinal microbiota in broilers. There appeared to be different degrees of antibiotic effects on the composition of bacterial microbiota between the subtherapeutic and the prophylactic doses, and the effect could be either positive or negative to the host, depending on the bacterial species. The microbiota of virginiamycin- and zinc bacitracin-treated birds at different ages therefore represented informative virginiamycin- and zinc bacitracin-adapted microbiota profiles in chickens. The PCR-DGGE analysis of d 3 microbiota samples suggested that Enterococcus spp. were enriched in the ileum of broilers treated with antibiotics compared with the control group in the current study (Table 2 and Figure 2A, band a). Our observation with the PCR-DGGE analysis was con-

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SAMPLE SIZE FOR IDENTIFYING INTESTINAL MICROBIOTA Table 2. Characterization of bacteria affected by antibiotic treatment Tissue

Day

Band

Cecum Cecum Cecum Cecum Ileum Ileum Ileum Ileum Ileum

3 3 7 14 3 7 7 14 14

a b a a a b c c d

Bacteria identified

Accession number

Similarity (%)

Lachnospiraceae, uncultured bacterium Klebsiella granulomatis Uncultured bacterium Uncultured bacterium Enterococcus sp. AK61 Uncultured gamma proteobacterium Uncultured bacterium Lactobacillus salivarius Uncultured Enterobacteriaceae bacterium

AY278618 AF009171 DQ057388 AF429377 AY098492 DQ187853 AY668492 DQ235661 DQ234207

98 99 97 98 99 100 99 100 99

sistent with several previous studies by other groups. Zinc bacitracin, virginiamycin, and both were shown to enrich Enterococcus in the fecal samples (Kaukas et al., 1988) and in the proximal part of the digestive tract of broilers (Engberg et al., 2000). Enterococcus cecorum was found to be more abundant in the midjejunum and proximal ileum of 47-d-old virginiamycin-fed broilers than in those fed no virginiamycin (Dumonceaux et al., 2006). Recently, more members of the genus Enterococcus were also reported in the ileum of BMD- or virginiamycin-fed broilers, or both, at d 14 and 21 compared with the drug-free group (Wise and Siragusa, 2007). Regardless of these findings on the enrichment of Enterococcus spp. by dietary antibiotics, the exact mechanisms underlying the antibiotic effects are still unclear. Both BMD and virginiamycin are streptogramin. One could speculate that some members of the Enterococcus spp. are streptogramin resistant (Welton et al., 1998; Jensen et al., 2002), which might explain why BMD and virginiamycin increase the number of Enterococcus spp. in the broiler ileum. Lactobacillus salivarius has been shown to inhibit the intestinal colonization of Salmonella enterica serovar Enteritidis in chickens (Pascual et al., 1999). This indicates that L. salivarius could be used as a probiotic bacterium for Salmonella control in chickens. In the present study, the PCR-DGGE analysis revealed the effect of different types and doses of antibiotics on the ileal microtobia (Table 2 and Figure 2C, band c). Bacitracin methylene disalicylate at the growth-promoter level appeared to increase the density of L. salivarius slightly, whereas the other 3 treatments significantly inhibited L. salivarius. This suggests that L. salivarius was resistant to BMD at the subtherapeutic level, but was susceptible to BMD at the prophylactic dose and to both doses of virginiamycin. The PCR-DGGE results were further confirmed by real-time quantitative PCR assays (Figure 3C). The assays also revealed a low level of L. salivarius in the ileum of d-3 and d-7 samples, but a significant increase at d 14 in comparison with the level of L. salivarius in the control birds (data not shown). Virginiamycin at the prophylactic dose (22 ppm) always suppressed the growth of L. salivarius. In addition, the subtherapeutic dose of BMD was inhibitive to L. salivarius in the young birds (d 3 and 7), but had no inhibitory effect when the birds aged (d 14). These data suggest that there was a dynamic time-course interaction among BMD, virginiamycin, and L. salivarius, although the mechanisms are

unknown. Several studies have been conducted on the effects of antibiotics on L. salivarius in broilers. Knarreborg et al. (2002) reported that L. salivarius was abundant in broiler ileal contents only at d 35, but not in d-7, d-14, and d-21 samples. Another study (Engberg et al., 2000) showed that the combination of zinc bacitracin and salinomycin also inhibited L. salivarius in the ileal digesta of 5-wk-old broilers. Dumonceaux et al. (2006) used quantitative PCR to examine the effect of virginiamycin (20 ppm) on the composition of the bacterial community in 47-d-old broilers. A significant decrease in L. salivarius was reported in the proximal ileum, especially in the ileocecal junction. This is consistent with our findings. Dietary antibiotics have been reported to have a beneficial effect on animal growth, feed conversion efficiency, and inhibition of pathogen growth (Stutz and Lawton, 1984; Gaskins et al., 2002). Although detailed mechanisms are still unknown, bacterial species reduced by dietary antibiotics may be considered as potential harmful species for animal performance, health, or both, whereas organisms stimulated by dietary antibiotics could serve as potential probiotics. Nearly all bacterial groups affected by the antibiotic treatments in the current study appeared to have been enriched in both the ileum and cecum. According to a BLAST analysis of the DNA fragments recovered from the DGGE gels, approximately 50% of them are still unknown, which correspond to uncultured bacteria. The beneficial effects of some bacteria could be used to develop viable alternatives to dietary antibiotics for poultry after verification of these bacteria. Polymerase chain reaction-DGGE profiling techniques have been widely used to detect changes in the bacterial composition of intestinal microbiota (Knarreborg et al., 2002; Hume et al., 2003). In this study, a total of 13 DNA bands were initially identified through the PCR-DGGE analysis. Because of the limitations of this technique, including the detection limit and the limitation of gel resolution, verification through further separation in DGGE gels, sequence analysis of clones, and quantitative PCR assays is normally required to identify the bacterial species truly affected by experimental treatments. In fact, nearly 80% of the total DNA bands initially identified were confirmed by further PCR-DGG and sequence analysis in the current study. In summary, in the current study we determined the optimal size of chicken samples for identification of the

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bacterial groups influenced by dietary antibiotics by means of PCR-DGGE profiling and quantitative PCR analyses. The procedure was shown to be effective, thus providing a useful tool to evaluate the changes in bacterial colonization in the GI tract in response to experimental treatments, such as dietary changes and supplementation with dietary antibiotics.

ACKNOWLEDGMENTS This research was supported by the Canadian Poultry Research Council, Poultry Industry Council, Agriculture and Agri-Food Canada (through its MII Program), and Natural Science and Engineering Research Council of Canada through research grants to JG and SS. H. Zhou was a Natural Science and Engineering Research Council of Canada Visiting Fellow to Canadian Federal Government Laboratories.

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