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Articles

Molecular Ecological Analysis of Dietary and Antibiotic-Induced Alterations of the Mouse Intestinal Microbiota

Vance J. McCracken^*,1, Joyce M. Simpson, Roderick I. Mackie^*, and H. Rex Gaskins^*,,**2

^* Departments of Animal Sciences and ^** Veterinary Pathobiology and Division of Nutritional Sciences, The University of Illinois at Urbana-Champaign, Urbana, IL 61801

²To whom correspondence should be addressed. E-mail: hgaskins{at}uiuc.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
A cultivation-independent approach, polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), was used to characterize changes in fecal bacterial populations resulting from consumption of a low residue diet or oral administration of a broad-spectrum antibiotic. C57BL/6NHsd mice were weaned to either a standard nonpurified diet (LC-diet) or a low residue diet (LR-diet) and at 17 wk of age were randomly assigned to receive drinking water with or without 25 ppm cefoxitin for 14 d. On d 1, 2, 7 and 14, microbial DNA was extracted from feces, and the V3 region of the 16S rDNA gene was amplified by PCR and analyzed by DGGE. The diversity of fecal microbial populations, assessed using Shannon’s index (H'), which incorporates species richness (number of species, or in this case, PCR-DGGE bands) and evenness (the relative distribution of species), was not affected by cefoxitin. However, use of Sorenson’s pairwise similarity coefficient (C_s), an index that measures the species in common between different habitats, indicated that the species composition of fecal bacterial communities was altered by cefoxitin in mice fed either diet. Dietary effects on fecal microbial communities were more pronounced, with greater H' values (P < 0.05) in mice fed the LR-diet (1.9 ± 0.1) compared with the LC-diet (1.6 ± 0.1). The C_s values were also greater (P < 0.05) in fecal bacterial populations from mice fed the LR-diet (C_s = 69.8 ± 2.0%) compared with mice fed the LC-diet (C_s = 50.1 ± 3.8%), indicating greater homogeneity of fecal bacterial communities in mice fed the LR-diet. These results demonstrate the utility of cultivation-independent PCR-DGGE analysis combined with measurements of ecological diversity for monitoring diet- and antibiotic-induced alterations of the complex intestinal microbial ecosystem.

KEY WORDS: • antibiotic • PCR-DGGE • fiber • intestinal microbiota • microbial ecology

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
The mammalian intestine is inhabited by a complex and diverse microbial community which is in intimate association with the host epithelium and the overlying mucus coat (1 2 3) . Intestinal defenses include nonimmunological mechanisms and components of the innate and acquired immune systems that are concentrated along the intestine to decrease contact between the host and the intestinal microbiota (4 ,5) . The concurrent presence of the largest collection of immune cells in the body (4) and bacterial densities reaching 10¹¹ colony-forming units/g (6) creates the potential for severe intestinal inflammation. However, a low level, "physiologically normal steady state of inflammation" is typically observed in response to intestinal bacteria (7) , consistent with a state of "détente" between the immune system and intestinal microbes, in which the host neither ignores nor overresponds to the indigenous microbiota (8) .

Aberrant host responsiveness to specific organisms such as Helicobacter spp. (9 ,10) or Mycobacteria (11 ,12) and to various facultative and anaerobic indigenous bacteria (13 ,14) is implicated in chronic intestinal disorders such as inflammatory bowel disease. Additionally, general disturbances in bacterial community structure, resulting from antibiotics or changes in diet, induce epithelial damage and intestinal inflammation by disrupting the homeostasis that exists between the host and the indigenous microbiota (15 16 17 18 19 20) .

Analysis of intestinal microbial ecosystems is complicated by the complex nature of local bacterial communities, which may consist of hundreds of different bacterial species (6 ,21) . Until recently, microbial ecologists relied largely on techniques requiring cultivation of organisms on selective media. Although studies utilizing cultivation-based techniques have been useful for analysis of specific groups of bacteria, several limitations are associated with cultivation-based approaches, particularly for surveying the intestinal ecosystem (21) . In addition to being time- and labor-intensive, the use of selective media specific for different types of bacteria imposes an a priori bias on the types of bacteria that can be enumerated. Further, only 20–40% of bacterial species from mammalian gastrointestinal tracts can be cultured and identified using known cultivation techniques. Therefore, up to 80% of intestinal bacterial species may not be represented using cultivation-based techniques (21 22 23) .

The introduction of higher resolution molecular techniques has improved analyses of complex microbial populations (24 25 26 27) . The most important advance has been the use of 16S rRNA or rDNA as a molecular fingerprint to identify and classify organisms, allowing development of cultivation-independent techniques for analyzing community diversity (26 27 28 29) . Polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE)³ is a PCR-based technique in which DNA is isolated from a mixed sample and amplified using conserved 16S rDNA bacteria-domain primers (30 ,31) . Although all PCR products are of approximately equal size, when electrophoresed on a polyacrylamide gel containing an increasing gradient of DNA denaturants, individual amplicons cease to migrate as the double-stranded products denature according to their G + C content (27 ,30 ,32) . This approach thus allows separation of individual sequences based on G + C content, corresponding to the different microbial species within the sample (27 ,30 ,32) . The banding patterns from mixed samples can be compared to evaluate the relative similarity of microbial communities from different habitats or treatments. Further, after electrophoresis, individual bands can be excised from the gel for sequencing and phylogenetic identification, thus providing means to characterize complex microbial populations within the sample independent of bacterial cultivation. PCR-DGGE analysis of microbial communities therefore allows an objective comparison of these communities, which is unbiased by an a priori decision on the type of bacteria to be analyzed.

PCR-DGGE has been widely used for analysis of environmental microbial communities (32 33 34 35) , although fewer studies have utilized PCR-DGGE to analyze gastrointestinal microbial ecosystems (21 ,36 37 38 39) . In this study, C57BL/6NHsd mice were fed either a nonpurified diet (LC-diet) or a low residue diet (LR-diet) and treated with or without the broad-spectrum antibiotic cefoxitin to address the hypothesis that a LR-diet and antibiotic will alter fecal microbial populations and decrease microbial diversity. A further objective of the study was to assess the utility of PCR-DGGE to detect perturbations of the intestinal microbiota.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Animals and treatments.

The C57BL/6NHsd strain was established in the ERML Animal Care facility at UIUC from mice imported from Harlan (Indianapolis, IN). Mice were weaned at 3 wk of age to either Ensure (LR-diet; Abbott Laboratories, Ross Products Division, Columbus, OH) or standard nonpurified diet (LC-diet; Teklad LM-485 7041, Harlan Teklad, Madison, WI). Dietary proximate analyses were as follows: protein (199 g/kg), energy (16.9 MJ/kg), fat (57 g/kg) and fiber (44 g/kg) for the LC-diet, and protein (159 g/kg), energy (18.8 MJ/kg), fat (159 g/kg) and fiber (0%) for the LR-diet. Mice were housed individually in a conventional (nonspecific pathogen–free) facility under a 12-h light:dark cycle and allowed unrestricted access to food and water. Cages had mesh bottoms, which allowed passage of feces to a paper lining underneath the cage for collection. Animal protocols were approved by the Animal Care and Use Committee at the University of Illinois and complied with the NIH Guide for the Care and Use of Laboratory Animals.

Upon initiation of the 14-d study, mice averaged 17 wk of age. They were divided into four experimental groups (n = 5/group; one LR-diet + cefoxitin mouse died before study began) in a 2 x 2 factorial design consisting of LC-diet or LR-diet plus or minus a continuous supply of the broad-spectrum antibiotic cefoxitin (25 ppm in autoclaved drinking water; treatment initiated on d 1).

Fecal DNA isolation and PCR-DGGE analysis.

Fecal samples from C57BL/6NHsd mice were collected on the mornings of d 1, 2, 7 and 14. All fecal samples were snap-frozen in liquid nitrogen and stored at -80°C until DNA isolation. DNA was isolated from fecal samples following a modification of previously described extraction methods (40 ,41) . Specifically, fecal samples were vortexed in 20 mL of sterile PBS for 10 min and then centrifuged for 2 min at 30 x g. The supernatant, which contained the bacteria, was removed and centrifuged for an additional 5 min at 12,000 x g. The supernatant from this step was discarded, and the pellet subjected to lysozyme treatment for 30 min at 37°C, at which time stop solution (0.1 mol/L NaCl, 0.48 mol/L Tris, pH 8.0, 10% SDS) was added for 30 min at 37°C. These samples were subjected to three freeze-thaw cycles (-80°C and room temperature, respectively), proteinase K treatment (30 min at 37°C), and extraction by phenol, phenol/chloroform/isoamyl alcohol (25:24:1) and chloroform, followed by isopropanol precipitation in ammonium acetate (2.5 mol/L final concentration). The mass of feces from some samples did not yield a sufficient amount of DNA for PCR-DGGE analysis; therefore, only three samples from each treatment group were used for PCR-DGGE.

For PCR-DGGE analyses, each DNA sample was amplified using primers specific for conserved sequences flanking the variable V3 region of the 16S rDNA, as described previously (31) . Each reaction mixture contained 125 ng of DNA, 5 µL of 25% acetamide, 25 pmol of forward primer (5'CGCCCGCCGCGCGCGGCGGGCGGGGGGGGCACGGGGGGCCTACGGGAGGCAGCAG3'), 25 pmol of reverse primer (5'ATTACCGCGGCTGCTGG3'), 0.2 mmol/L dNTPs, 5 µL of 10X Ex Taq Buffer (TaKaRa Shuzo, Otsu, Japan) and 5 U TaKaRa Ex Taq DNA polymerase. The forward primer contains a 40-bp region of high G + C content (the "GC clamp") at the 5' end, which prevents complete dissociation of the DNA strands (31) . To reduce spurious PCR products, touchdown PCR was performed (31) . After a single cycle of 94°C melting for 5 min, 64°C annealing for 1 min and 72°C for 3 min, 19 cycles were performed in which the annealing temperature was decreased 1° every other cycle. Nine cycles were then performed using an extension of 55°C, followed by a single cycle of 94°C for 1 min, 55°C for 1 min and 72°C for 10 min.

After visual confirmation of the 200-bp PCR product using agarose gel electrophoresis, mung-bean nuclease (Stratagene, La Jolla, CA) was added to remove single-stranded DNA (37) . For each sample, 3 µL of 10X mung-bean buffer and 0.75 U mung-bean nuclease were added to 15 µL of the PCR product. After 10 min incubation at 37°C, mung-bean nuclease reactions were stopped by addition of 10 µL DGGE gel loading buffer (0.05% bromophenol blue, 0.05% xylene cyanol and 70% glycerol in sterile nanopure H₂0). Reactions were stored at -20°C until PCR-DGGE analysis, which was performed within 5 d of PCR.

DGGE was performed using the Bio-Rad D-Code System (Hercules, CA) as described previously (37) . To separate PCR fragments, 35–60% linear DNA-denaturing gradients (100% denaturant is equivalent to 7 mol/L urea and 40% deionized formamide) were formed in 8% polyacrylamide gels using a Bio-Rad Gradient Former. Gels were polymerized on GelBond PAG gel support films (FMC, Rockland, ME). PCR products were loaded in each lane and electophoresis performed at 150 V for 2 h at 60°C, then for 1 h at 200 V. Additionally, bacterial reference ladders representing known bacterial strains were loaded to allow standardization of band migration and gel curvature among different gels (42) . After electrophoresis, gels were silver-stained (31) and scanned using a GS-710 Calibrated Imagining Densitometer (BioRad). When time- or antibiotic-dependent differences in PCR-DGGE banding profiles were observed, bands were excised, reamplified as described for PCR-DGGE, cloned using a TOPO TA cloning kit (InVitrogen, Carlsbad, CA) and sequenced using an automated sequencing system (Applied Biosystems, Foster City, CA) at the W. M. Keck Center for Comparative and Functional Genomics, University of Illinois Biotechnology Center (Urbana, IL). Sequence data were analyzed using Sequencher 3.0 (Gene Codes, Ann Arbor, MI), and a BLAST search (43) was performed to identify sequences.

Estimates of microbial richness and diversity.

Diversity Database version 2.1 of "The Discovery Series" (BioRad) was first used to analyze PCR-DGGE banding patterns by measuring migration distance and intensity of the bands within each lane of a gel (42) . This information was then used to analyze banding patterns via several measures of community diversity, including band number, Shannon’s index and Ward’s algorithm (44 45 46) . These indices measure ecological diversity using various parameters, including species richness (the number of different species) and evenness (the distribution of individual species in the ecosystem) (46) . These diversity indices were developed originally for macroecological analyses to evaluate evenness and species richness, but have also been validated for cultivation- and molecular-based analyses of microbial diversity (34 ,47 48 49) . In the description of the indices that follows, "species" refers to individual bands on the PCR-DGGE gels. However, because the bands on the PCR-DGGE gels correspond to the percentage of G + C content within the melting domains for the V3 PCR amplicon, bacterial species with similar G + C content in the amplified V3 region may form assemblages and appear as a single band, resulting in fewer bands (50) .

Band number corresponds to the number of individual bands in a single lane. Band frequency was calculated by measuring the percentage of all samples from all time points containing each individual band. Shannon’s index measures the proportional abundances of species in a community, emphasizing community richness (46) . Shannon’s index is calculated by the following equation:

where p_i is the proportion of individuals in the population belonging to the ith species; for analysis of DGGE patterns, p_i corresponds to the proportional abundance of band i (34 ,44 ,51) . Sorenson’s pairwise similarity coefficient C_s, sometimes referred to as the Dice coefficient, is a similarity index used to compare species composition of different ecosystems (37 ,52 53 54 55) . C_s values were determined as follows:

where a is the number of PCR-DGGE bands in lane 1, b is the number of PCR-DGGE bands in lane 2 and j is the number of common PCR-DGGE bands (46 ,53 ,56) . Thus, two identical profiles create a C_s value of 100%, whereas completely different profiles result in a C_s value of 0%. Each sample was compared with every other sample; therefore, mean percentage similarities (C_s values) can be compared for each diet/treatment group for itself and in relation to all other groups.

Ward’s algorithm was utilized to construct a dendrogram of the bacterial populations for each day. Ward’s algorithm is defined as

where p and q represent two clusters that are joined within a single cluster; k is the index of the cluster formed by joining clusters p and q, i is the index of any remaining clusters other than clusters p, q or k; n_p is the number of samples in the pth cluster, n_q the number of clusters in the qth cluster, n the number of clusters in the kth cluster formed by joining the pth and the qth clusters (n = n_p + n_q), and d_pq is the distance between cluster p and cluster q as discussed by Sneath and Sokal (45) and the Diversity Database Manual (BioRad).

Statistics.

Statistical analysis of diversity and similarity indices was performed using SAS (Version 6.09; SAS Institute, Cary, NC). The General Linear Models procedure was used to compare differences due to antibiotic and diet for all indices. Cefoxitin- and diet-dependent differences were determined by the least significant difference test with an assigned P-value of < 0.05. Measures of central tendency for banding pattern frequency distribution were calculated using the Data Analysis package from Microsoft Excel (Redmond, WA).

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 
Diet- and antibiotic-specific PCR-DGGE bands.

Fecal samples collected from individual C57BL/6NHsd mice on d 1, 2, 7 and 14 of cefoxitin treatment were used for PCR-DGGE of the V3 region from 16S rDNA (Fig. 1 ). The Diversity Database software was used to analyze PCR-DGGE gel banding patterns. This software analyzes PCR-DGGE banding patterns by measuring migration distance and intensity of the bands within each lane of a gel. A chromatograhic representation of selected gel lanes is presented in Figure 2A and B . Several diet- and cefoxitin-dependent differences in PCR-DGGE banding patterns were observed. Additionally, several PCR-DGGE bands occurred more frequently or with greater intensity in certain diet or treatment groups. For example, the frequency and density of band A was much higher in feces from mice treated with cefoxitin than from untreated mice (Fig. 1) . This band is shown in the gel and chromatogram in panels C and D of Figure 2A , appearing just before relative position 0.20 on the x-axis. Band A was cloned and four clones randomly selected and sequenced. Two clones were closely related (96 and 100%) to GenBank accession number AF157056, previously identified as Bacteroides distasonis of the altered murine Schaedler flora (57 ,58) . The other clones were related to the 16S rRNA sequences from an uncultured human fecal bacterium (95%; gbAF132240) and a low G + C Gram-positive member of the altered murine Schaedler flora (93%; gbAF157051). Several diet-specific bands were also observed, although the differences were not as pronounced as for band A. For example, band B was observed more frequently in mice fed the LR-diet than in those fed the LC-diet. Additionally, band C was detected only in mice fed the LC-diet.

View larger version (116K): [in this window] [in a new window]   Figure 1. Polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) analysis of mouse fecal bacterial populations affected by antibiotic and a low residue diet. Microbial DNA was isolated from fecal samples from C57BL/6NHsd mice (n = 3) from each treatment group on d 1, 2, 7 and 14 (antibiotic administration, 25 ppm cefoxitin in drinking water, was begun on d 1) and PCR-DGGE performed as described in Materials and Methods. LC, nonpurified diet; LR, low residue diet; the minus and plus signs correspond to the absence or presence of cefoxitin, respectively. M, marker corresponding to bacterial standard ladder. Letters indicate bands differentially expressed in specific diet or treatment.

View larger version (28K): [in this window] [in a new window]   Figure 2. Representative linear plots of polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) bands from fecal samples of mice fed nonpurified (LC) or low residue (LR) diets and treated or not with antibiotic. (A) As a visual representation of the method used to determine the effects of cefoxitin and diet on PCR-DGGE profiles, one lane from each of the four treatment groups was plotted (Panel A, LC-diet; Panel B, LR-diet; Panel C, LC-diet + cefoxitin; Panel D, LR-diet + cefoxitin). Plots are read from the left, with the left-most area corresponding to the origin of the lane; numbers along the x-axis represent relative band migration distance in the gel. The peaks represent individual band positions within the gel; the area underneath the peak corresponds to the intensity of the peak within the gel. Note that the scale of the y-axis, representing the optical density of each band, is different for the different panels. This is a limitation of the imaging software, but does not affect the quantitative analysis or interpretation of data. (B) The relative stability of fecal PCR-DGGE profiles is demonstrated in these plots from fecal samples taken on d 1, 2, 7 and 14. Plots are based on samples from a single, nonantibiotic-treated mouse fed either the LC-diet or the LR-diet for all four time points.

The percentage of samples containing specific PCR-DGGE bands was calculated to characterize the distribution frequency of PCR-DGGE bands among the different samples. Measures of central tendency (mean ± SEM, 32 ± 3%; median, 31%; and mode, 29%) indicated that of the 32 distinct bands observed across treatments, the majority of PCR-DGGE bands were expressed in a low percentage of the samples. Only 5 bands were present in 50% of the samples across treatments, and there were no bands present in >80% of the samples (Fig. 3A ).

View larger version (32K): [in this window] [in a new window]   Figure 3. Frequency distribution and number of denaturing gradient gel electrophoresis (DGGE) bands from fecal samples of mice fed nonpurified (LC) or low residue (LR) diets and treated or not with antibiotic. (A) The frequency distribution of DGGE gel bands is expressed as the number of common bands observed in each decile of samples from all four time points. Thus, 5 bands were expressed in 0–10% of the DGGE gel lanes, whereas only 1 band was expressed in 70–80% of all samples. (B) Band number corresponds to the average number of DGGE bands + SEM (n = 3) from the samples for the corresponding treatment group. Within-day values not sharing a common superscript letter are different (P < 0.05).

Alterations in fecal bacterial populations.

Although H' was not affected by cefoxitin, comparisons of PCR-DGGE banding patterns using C_s revealed several diet- and antibiotic-dependent differences in the bands comprising each fecal microbial population (Fig. 4 ). For this comparison, the banding pattern for each sample was compared with the other members in the same treatment group and to each other group, thus allowing intragroup and intergroup comparisons of fecal bacterial populations. Intragroup C_s values were not affected by antibiotic (Fig. 4) . However, diet affected the similarity of bacterial populations; C_s values were greater (P < 0.05) in fecal bacterial populations from mice fed the LR-diet alone (69.8 ± 2.9%) compared with mice fed the LC-diet alone (50.1 ± 3.8%). The similarity value for the intergroup comparison of the LC- and LR-diets was decreased to 40.3 ± 1.7%.

View larger version (39K): [in this window] [in a new window]   Figure 4. Percentage of similarities for polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) banding patterns from fecal samples of mice fed nonpurified (LC) or low residue (LR) diets and treated or not with antibiotic. Sorenson’s similarity index was used to compare average percent similarities of PCR-DGGE banding patterns (based on the average number of bands in common) within each treatment group and for intragroup comparisons. Calculations are based on the formula: Cs = [2j/(a + b)]x100, where a is the number of PCR-DGGE bands in lane 1, b is the number of PCR-DGGE bands in lane 2 and j is the number of common PCR-DGGE bands. Values represent means + SEM (n = 3) from each experimental treatment from d 1, 2, 7 and 14 of the antibiotic treatment period. Values not sharing a common superscript letter are different (P < 0.05).

The effects of diet or cefoxitin on microbial composition were more clearly distinguished by the cluster analysis based on Ward’s algorithm. Distinct clusters by diet were observed on d 1, before addition of antibiotic (Fig. 5 ). Addition of cefoxitin differentially altered microbial populations for each diet. On d 2, samples grouped together also according to diet. By d 7 and 14, however, the microbial populations from mice receiving the two antibiotic-treated diets (LR-diet + cefoxitin and LC-diet + cefoxitin) more closely resembled each other than they did populations from the nonantibiotic-treated mice fed the same diets, forming a cluster separate from the dietary controls.

View larger version (26K): [in this window] [in a new window]   Figure 5. Dendrogram representing dietary and antibiotic-associated correlations of polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) banding patterns in fecal samples from mice fed nonpurified (LC) or low residue (LR) diets and treated or not with antibiotic. The dendrogram was constructed using Ward’s algorithm and the Diversity Database software. Open circles indicate PCR-DGGE pattern obtained from fecal samples from C57BL/6NHsd mice fed the LC-diet. Closed circles indicate LR-diet–fed mice. Closed squares indicate mice also receiving cefoxitin (25 ppm in drinking water). Numbers indicate lane position on the day corresponding to each gel (read from right to left in Fig. 1 ). Distances are measured in arbitrary units.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

Although for therapeutic use cefoxitin is injected rather than administered orally, it alters fecal microbial populations when injected intravenously or intramuscularly (60) . Similarly, oral administration of antibiotics often causes a shift in intestinal microbial populations, with a decrease in anaerobic populations and a concomitant increase in aerobic populations (15 ,16) . The changes in microbial populations occurring with cefoxitin treatment may also result from the low dose of cefoxitin administered. Subtherapeutic doses of antibiotics have been shown to select for antibiotic resistance and transfer of resistance for cefoxitin and other antibiotics. Therefore, the changes in community composition may reflect the replacement of antibiotic-susceptible strains by resistant organisms (61 62 63 64 65) . In the present study, the absence of cefoxitin-specific differences in band number and Shannon’s index demonstrates that microbial diversity, characterized by the number and intensity of the different DGGE bands, was unaffected by cefoxitin. On the other hand, analysis of C_s values demonstrated that the bacterial species comprising each microbial community were significantly altered by cefoxitin. Specifically, although the intragroup similarities of the antibiotic-treated mice remained comparable to the diet controls, an 20% decrease in intestinal bacterial similarities occurred in the intergroup comparisons between mice fed control diets and mice treated with cefoxitin. The maintenance of diversity combined with the decrease in similarity values between the control and cefoxitin-treated groups indicates the presence of different bands in the cefoxitin-treated samples rather than an overall change in community complexity.

Previous studies have reported that diets containing fiber support increased populations of intestinal bacteria, although total community structure was not evaluated (18 ,66 67 68 69 70 71 72) . In the present study, diversity as measured by Shannon’s index was increased in mice fed the LR-diet relative to those fed the LC-diet. The increased number of bands in mice fed the LR-diet is in agreement with a previous report of increased total counts of cecal bacteria in mice fed this diet, indicating increased numbers of species as well as increased overall diversity (73) . Additionally, comparison of C_s values demonstrated greater similarity of the fecal microbiota in LR-diet–fed mice than in those fed the LC-diet, indicating less animal-to-animal variation in LR-diet–fed mice. The reasons for increased diversity (Shannon’s index) and similarity of fecal microbial populations from LR-diet–fed mice are unclear. It is possible that the decreased number of bands from mice fed the LC-diet resulted from a selective loss of fiber-associated bacteria during extraction of DNA. Additionally, because of the decreased number of bands in the LC-diet–fed mice, the C_s values in these mice are more sensitive to the presence or absence of individual bands, thus explaining the lower C_s values in mice fed the LC-diet. Another potential explanation for the greater number of bands in mice fed the LR-diet is that new niches may have been created by the absence of exogenous fermentable substrate, allowing increased numbers of bacterial species capable of utilizing endogenous carbohydrate sources such as host mucin (18 ,74 75 76 77 78) .

Substantial individual-to-individual variation was observed among mice within each treatment group, with intragroup C_s values ranging from 50 to 70%. Other molecular-based studies of gastrointestinal microbial ecology in pigs and humans have also demonstrated that although the intestinal bacterial community within a single individual is relatively stable over time, the bacterial populations from different individuals vary significantly (42 ,79 80 81) . Such differences are somewhat more surprising in genetically identical mice that were housed and fed identically and indicate the complexity of microbe-microbe interactions in establishment and maintenance of the gut bacterial community.

Although PCR-DGGE provides a convenient method to evaluate entire microbial ecosystems and also allows analysis of a large number of samples, this technique is most useful for detecting shifts in predominant microbial populations. For example, microbial populations comprising <1–9% of the total intestinal microbial ecosystem were not detected using temperature gradient gel electrophoresis (TGGE), an approach similar to PCR-DGGE (81) . Additionally, the apparent community diversity may be decreased using PCR-DGGE because different bacterial species possessing similar G + C content in the V3 region of the 16S rDNA gene may be represented in the same PCR-DGGE band (27 ,82) . Conversely, unrelated species may have similar or identical rDNA gene sequences (27 ,81 ,82) . For these reasons, the PCR-DGGE band number generally is lower than the number of bacterial species detectable by cultivation-based methods and direct cloning strategies (6 ,26 ,27 ,37 ,54 ,81) . These limitations may account in part for the decreased band number in the present study and may also have influenced the apparent diversity and similarity values.

On the other hand, although cultivation-based studies have estimated that the intestinal microbiota may contain up to 400 species of bacteria (6) , many of these species are rarely detected (83) . Although newer techniques such as direct cloning and terminal restriction fragment length polymorphism indicate the presence of 80 microbial species in the mammalian intestine (23 ,54 ,81) , these techniques do not indicate proportional abundance of microbial species. Therefore, many of the bacterial species represented both in cultivation-based techniques and in newer molecular techniques may be minor constituents of the intestinal microbiota, in agreement with estimates that up to 99% of the intestinal bacterial population is composed of only 30–40 species (84) . Similarly, the mouse fecal bacterial community from the present study, determined by the combined number of bands present in all samples from all days, consisted of 32 PCR-DGGE bands. The maximum number of bands within a single sample was 24. These results are only slightly different than those reported in a study using TGGE, in which the highest number of bands for any individual human fecal sample was 38 (81) . Similarly, we recently observed a total of 35 PCR-DGGE bands in a set of 9 different pigs (42) . These findings indicate a similar degree of resolution of intestinal microbial communities across animal species with PCR-DGGE or TGGE and demonstrate the usefulness of PCR-DGGE for assessing alterations in intestinal microbial communities.

The aim of this study was to evaluate bacterial population changes using a cultivation-independent technique. Therefore, extensive cloning of diet- or treatment-specific bands to identify all of the members of the population was not attempted. Because of its prominence in the antibiotic-treated samples, however, band "A" was cloned and sequenced. Of the four clones sequenced and compared with the database using BLAST, three species were represented, indicating a consortium of species within the band. The best match was from two sequences that were closely related (100 and 96% similarity) to gbAF157056, a member of the altered murine Schaedler flora formerly classified as Bacteroides distasonis and now classified as a member of an unnamed genus in the Cytophaga-Flavobacterium-Bacteroides phylum (58) . The presence of several cefoxitin resistance genes in intestinal Bacteroides isolates may explain the prominence of this species in cefoxitin-treated mice (85) .

This study demonstrates the utility of PCR-DGGE analysis for monitoring diet- and antibiotic-induced alterations of the complex intestinal microbial ecosystem and correlating these changes with host responses. This cultivation-independent technique is less time- and labor-intensive than traditional microbiological approaches and could be similarly applied to evaluate other dietary-, drug- or disease-associated alterations of intestinal microbial populations. In addition to screening shifts in microbial populations, differentially expressed bands can be cloned and sequenced to allow an objective identification of organisms whose appearance or loss is associated with diet or disease, with the ultimate goal of defining causal effects.

	FOOTNOTES

 
¹ Current address: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110.

³ Abbreviations used: C_S, Sorenson’s similarity coefficient; H', Shannon’s diversity index; LC-diet, nonpurified diet; LR-diet, low residue diet; PCR-DGGE, polymerase chain reaction-denaturing gradient gel electrophoresis; TGGE, temperature gradient gel electrophoresis.

Manuscript received October 12, 2000. Initial review completed December 9, 2000. Revision accepted March 6, 2001.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

 

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