Application of denaturant gradient gel electrophoresis for the analysis of the porcine...

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MethodsMicrobiological

Journal of Microbiological Methods 36 (1999) 167–179

Application of denaturant gradient gel electrophoresis for theanalysis of the porcine gastrointestinal microbiota

a a a,b a,bJoyce M. Simpson , Vance J. McCracken , Bryan A. White , H. Rex Gaskins ,a,b ,*Roderick I. Mackie

aDepartment of Animal Sciences, University of Illinois at Urbana-Champaign, 132 Animal Science Lab., 1207 W. Gregory Drive,Urbana, IL 61801, USA

bDivision of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

Accepted 19 January 1999

Abstract

The porcine gastrointestinal tract (GIT) microbiota has been studied to increase production efficiency, improve productquality, and help attempt to reduce disease. During the developmental period from birth through weaning, the intestinalmicrobiota undergoes a rapid ecological succession. There is interest in developing a monitoring technique that allows foranalysis of bacterial population levels and shifts within the pig intestine. The objective of this study was to determine ifdenaturant gradient gel electrophoresis (DGGE) could be effectively applied to measure changes in bacterial populations ofthe pig GIT, as influenced by age, diet or compartment. Bacterial genetic diversity was determined using DGGE analysis ofthe V3 region of 16S rDNA PCR products ( | 200 bp) obtained from primers specific for the domain Bacteria. Protocoldevelopment included optimization of: DNA extraction procedures, PCR amplification, removal of PCR artifacts, andoptimization of gel preparation and image capture. DGGE analysis revealed diverse bacterial populations between pigs ofdifferent ages and among individual gut compartments. Comparison of fecal DNA from different aged pigs revealed severalunique PCR product bands indicating the presence of unique bacterial populations. Comparison of different gutcompartments demonstrated that bacterial populations were most similar (C value . 50%) within a single compartment ands

between adjacent ones. Thus, DGGE can be used to examine bacterial diversity and population shifts in the pig GIT. 1999 Elsevier Science B.V. All rights reserved.

Keywords: Bacterial diversity; DGGE; Gastrointestinal tract; Pig

1. Introduction many questions concerning the composition, struc-ture, and stability of ecosystems, as well as activity

The study of microbial communities has raised and function of individual inhabitants. Traditional,culture-based microbiology techniques are inade-quate for answering these questions due mainly to

*Corresponding author. Corresponding address: University of their physiologically-based limitations such as selec-Illinois at Urbana-Champaign, 132 Animal Science Lab., 1207

tive enrichments, pure culture isolation, and labori-W. Gregory Drive, Urbana, IL 61801, USA. Tel.: 11-217-244-ous methods. Consequently, our knowledge of bac-2526; fax: 11-217-333-8804.

E-mail address: [email protected] (R.I. Mackie) terial diversity in complex ecosystems is limited. The

0167-7012/99/$ – see front matter 1999 Elsevier Science B.V. All rights reserved.PI I : S0167-7012( 99 )00029-9

168 J.M. Simpson et al. / Journal of Microbiological Methods 36 (1999) 167 –179

use of molecular biological techniques based upon growth efficiency, and disease susceptibility. Aftermore direct analysis of 16S rRNA or rDNA is birth, the pig GIT develops rapidly and characteristicimproving characterization of these communities microbial populations such as E. coli, lactobacilli,(Muyzer et al., 1996; Muyzer and Smalla, 1998). streptococci, clostridia, Bifidobacteria and Bac-

Previously, nucleic acid-based methods consisted teroides develop (Stewart, 1997). During this periodmainly of random cloning of environmental DNA from birth to weaning, the intestinal microbiotaextracts, probe hybridizations targeting either RNA undergoes a rapid ecological succession (Tannock etor DNA and direct analysis of complex ecosystem al., 1990). Consequently, there is substantial interestgenomic DNA by methods such as RFLP or ribotyp- in the development of techniques to analyze bacterialing (Muyzer et al., 1996; Muyzer and Smalla, 1998). population levels and shifts within the pig GIT.However, these molecular techniques have their own The objective of this study was to determine iflimitations, such as efficiency of extraction, degra- DGGE could be effectively applied to measuredation of nucleic acid products, and technical com- changes in bacterial populations in the GIT, basedplexities (Raskin et al., 1997). Various cloning upon age, diet or anatomic compartment. The long-strategies which allow sequence analysis of 16S term goal of these studies is the examination ofrDNA fragments derived from environmental sam- community structure and dynamics, as well as di-ples are considered labor and time intensive, while versity and abundance of bacteria within this eco-probes and analysis of nucleic acids are limited to system related to future manipulation of the GITknown microbial groups (taxon specific) when ap- microbiota.plied to complex communities. However, DNAfingerprinting techniques provide patterns or profilesbased upon genetic diversity in microbial com-munities. Denaturing gradient gel electrophoresis 2. Materials and methods(DGGE) is a genetic fingerprinting technique thatexamines microbial diversity based upon electro- 2.1. Sample collectionphoresis of PCR-amplified 16S rDNA fragments,using polyacrylamide gels containing a linear gra- For the initial age-based comparisons (age and dietdient of DNA denaturants (Muyzer et al., 1993). The variables), fecal samples were collected immediatelyPCR product banding pattern is indicative of the after defecation from pigs of different ages. Pignumber of bacterial species or assemblages of groupings consisted of: nursing piglets aged 18 daysspecies that are present and thus allows visualization (n 5 6), weaned pigs aged 6 weeks (weaned at 3of the genetic diversity of microbial populations. weeks) fed a diet of 20% whey, 47% soy meal andThese amplified fragments may be referred to as 28% corn (n 5 5), and a finishing pig aged 6 months‘‘PCR products, fragments, bands or amplicons’’. In fed a diet of 16% soy, 80% corn and 4% tracethis publication, we refer to them as ‘‘PCR prod- minerals (n 5 1). The fecal samples were pooleducts’’ or ‘‘bands’’, in accordance with common within the weaned and nursing groups, and used forusage (Muyzer et al., 1996). DGGE gels can also be subsequent DNA extractions. For analysis of dif-electroblotted and probed with 16S rRNA probes to ferential colonization of intestinal compartments,identify a specific species, genus or domain. Addi- samples were collected immediately after slaughtertionally, a PCR product’s band of interest may be from the digestive tracts of 5-month-old pigs fed aexcised and reamplified for sequencing (Muyzer and diet of 88% corn, 8% soy and 4% trace mineralsSmalla, 1998). (n 5 3). Lumenal and mucosal samples were ob-

Pigs are an important source of animal protein for tained from the stomach, small intestine (proximalhumans, and the swine industry is extensive world- and distal), cecum, colon (proximal and distal), andwide. Additionally, pigs are used frequently as the rectum. Lumenal samples were collected directlymodels to study the function of the human enteric from the GIT using a sterile spatula. For collectionsystem. Through numerous mechanisms, the pig of mucosal samples, tissues were cut longitudinally,gastrointestinal tract (GIT) microbiota impacts washed briefly with sterile anaerobic diluent (Mackie

J.M. Simpson et al. / Journal of Microbiological Methods 36 (1999) 167 –179 169

and Wilkins, 1988), and the mucosal surface scraped 200 bp as determined by electrophoresis in 4%with a dull flat blade. Samples were placed separ- agarose gels and ethidium bromide staining.ately in sterile conical tubes, placed on ice, andimmediately transported to the laboratory. Samples 2.4. Removal of ssDNA from PCR productwere pooled within sampling sites for uniformity,and then DNA was extracted for analysis. ssDNA remaining from the PCR reaction was

degraded using mung bean nuclease (Stratagene , LaJolla, CA). The reaction mixture contained 15 ml2.2. Genomic DNA extractionPCR product, 3 ml 10 3 mung bean buffer, 1.5 mlmung bean nuclease (diluted 1:50 in nuclease dilu-Genomic DNA from fecal samples was obtainedtion buffer), and high purity deionized H O to 30 ml.using modifications of an extraction method de- 2

The mixture was then incubated at 378C for 10 min.scribed by Tsai and Olson (1991). Briefly, theAfter incubation, 10 ml of DGGE gel loading buffermethod consisted of enzymatic digestions (lysozyme,(0.05% bromophenol blue, 0.05% xylene cyanol,and proteinase K), sodium dodecyl sulfate (SDS)70% glycerol w/v in H O) was added to stop thetreatment, and freeze-thaw cycles followed by 2

reaction. The mixture was then stored at 2 208Cphenol–chloroform extractions. Modifications in-until DGGE analysis.cluded washing the sample in a larger volume of

phosphate-buffered saline (10 ml instead of 2 ml),shortened initial incubation for the lysozyme step (30 2.5. Denaturing gradient gel electrophoresismin instead of 1 h) and inclusion of a 30 min (DGGE)incubation after the SDS step. Additional changeswere gentle vortexing between steps, and removal of Parallel DGGE was performed essentially as de-1 ml lysate (instead of 3 ml) for DNA extraction. scribed previously (Muyzer and de Waal, 1994;Polyphenolic contaminants were not evident in DNA Muyzer et al., 1996) using a Bio-Rad D-Codesamples after extraction (assessed by agarose gel SystemE (Hercules, CA). PCR fragments wereelectrophoresis), therefore further purification steps separated using 8% polyacrylamide gels with 1.0 3were omitted. TAE buffer (20 mM Tris–acetate, pH 7.4, 10 mM

sodium acetate, 0.5 mM Na EDTA) with 35–60%2

linear gradients of denaturant (100% denaturant2.3. PCR amplification of 16S rDNA V3 regioncorresponds to 7 M urea and 40% deionized form-amide). The polyacrylamide was diluted from a non-The V3 variable region of 16S rDNA was am-de-ionized 40% acrylamide /bis stock solution 37.5:1plified by PCR using primers specific for conserved(Bio-Rad, CA). Gradients were formed using a Bio-regions of the 16S rRNA genes as described byRad Gradient FormerE Model 385 and gels wereMuyzer et al. (1993). The reaction mixture containedpolymerized onto a gel support film (FMC, Rock-125 ng of genomic DNA, 25 pmol of each primer, 4land, ME). PCR samples were applied to gels inml of dNTP mixture, 5 ml of 10 3 Ex Taq buffer, 5aliquots of 3 ml per lane. Electrophoresis wasml of a 25% acetamide solution, and 0.5 ml ofperformed at 608C for 2 h at 150 V and subsequentlyTaKaRa Ex Taq polymerase (TaKaRa Shuzo, Otsu,for 1 h at 200 V. Gels were silver stained (Liu andJapan). The final volume of the reaction mixture wasRitalahti, 1996) and photographed using a Fotodyneadjusted to 50 ml with sterile deionized water. To FOTO/Analyst Investigator System (Heartland,minimize non-specific annealing of the primer, toWI).non-target DNA the DNA polymerase was added to

the reaction mixture after the denaturing step at808C, followed by touchdown PCR (decreased 18C 2.6. Analysis of DGGE gelsevery second cycle) for a total of 29 cycles with afinal extension of 10 min at 728C (Muyzer et al., Gel images were analyzed using the public domain1996). The final product length was approximately NIH Image program (developed at the National


Institutes of Health, http: / / rsb.info.nih.gov/nih- and left lysate remaining for later extractions asimage/ ). Background was removed using the 1D required.vertical option. Lanes were then individually con-verted to linear plots. Intensity of PCR product bands 3.2. Optimization of DGGE conditionswas equivalent to peak heights and peak areas. Peakareas were calculated and converted to percent of In initial studies, utilizing 15–60% denaturanttotal sample area. Migration distances for individual gradients as first described by Muyzer et al. (1993),peaks were measured and relative mobilities of PCR product bands numbered about 12–15 perfragments were calculated for each sample. Diversity sample. With increasing concentrations of denaturant,indices (Shannon’s, Simpson’s, and Hill’s) were maximum resolution of fecal PCR product bandingcalculated using peak numbers and areas (Magurran, patterns was obtained with the gradient range of 351988). DGGE profiles were also compared using to 60% (Fig. 1). The pattern observed in Fig. 1A isSorenson’s index, a pairwise similarity coefficient characteristic of a 15 to 60% gradient, with aC , which was determined by: C 5 [2j /(a 1 b)] 3 discernible number of 18 PCR products. The sames s

100, where a is the number of DGGE bands in lane sample represented in Fig. 1B is characteristic of a1, b is the number of DGGE bands in lane 2, and j is 35 to 60% gradient and 29 PCR products arethe number of common DGGE bands (Magurran, discernible, indicating a 38% increase in resolution.1988; Murray et al., 1996; Gillan et al., 1998). If two Further variations using higher gradient ranges didprofiles were identical, C had a value of 100% and not reveal a greater number of bands or otherwises

if the profiles were completely different, C had a enhance resolution.s

value of 0%. Other optimization steps were included during gelThe dendrogram for gastrointestinal compartment preparation and photoimaging. Better band pattern

comparisons was created using PHYLIP package ver- resolution and clarity was achieved using purchasedsion 3.57c (Felsenstein, 1993). Alignments used to pre-mixed acrylamide solutions, rather than havinggenerate Fig. 6 were done using DOLLOP which self-prepared polyacrylamide reagents. Variabilityestimates parsimonious phylogenies by the Dollo or among individual gels and gel batches decreasedpolymorphism parsimony criteria for discrete-charac- significantly upon switching to purchased acrylamideter data with two states (0 and 1). The dendrogram solutions. Gradients were more reproducible, and thewas drawn using DRAWTREE which plots unrooted gels produced sharper and more defined band sepa-phylogenies. rations. Variation between replicates was investigated

and banding patterns were found to be stable andhighly reproducible (Fig. 2). Thus when a singlefecal sample was sub-sampled five times, DNA

3. Results extracted, PCR amplified individually and analyzedby DGGE, the banding patterns were highly

3.1. Optimization of DNA extraction homogeneous (Fig. 2A), with a pairwise similaritycoefficient (C ) of 91%. Next, DNA from a singles

Optimization of the Tsai and Olson (1991) ex- fecal sample was PCR amplified and five sub-sam-traction protocol for gut and fecal samples included ples of the reaction mix analyzed by DGGE. Again,modifications of washing steps and incubation times. the banding patterns were highly repeatable with a Cs

Larger volumes of wash buffer were used to increase of 99% (Fig. 2B). The absence of one band in one ofremoval of suspended particulate matter, particularly the sample lanes accounted for the 1% difference.undigested feed material. Modification of enzymatic Optimal image capturing was obtained by limitingincubation times increased the lysis efficiency, as the gel fixation time to 2 h, thereby decreasing thedetermined by visual examination of the quantity and amount of background discoloration on silver-stainedquality of extracted DNA in agarose gels. Addition- gels. Another consideration during image captureally, the removal of a smaller aliquot of lysate was to make sure that the camera and gel supportallowed for quicker, more cost effective extractions table were perfectly level and aligned with each


Fig. 1. Comparison of DGGE profiles generated after PCR amplification of V3-16S rDNA fragments from the same pig fecal DNA sample,before and after denaturant gradient optimization. (A) Separation using a denaturant gradient of 15 to 60%. Corresponding image analysisindicates 18 peaks /PCR product bands. (B) Separation using a denaturant gradient of 35 to 60%. Corresponding image analysis indicates 29peaks /PCR product bands.

other. Misalignments caused image capturing prob- ssDNA from the sample preparations (Fig. 3). Thelems, in particular banding patterns would appear elimination of these artifacts allowed visualization ofblurry or fuzzy or the captured image did not previously obscured bands and thereby increasedaccurately represent the resolving power of the gel. accuracy in the analysis of differences between

banding patterns. Treatment with mung bean nu-3.3. Effect of mung bean nuclease clease may also slightly lessen the intensity of bands

within the pattern, however these bands are notInitial sample analysis indicated the presence of removed and if all samples are treated, the loss of

ssDNA within banding patterns, which complicated intensity can be normalized.numerical analysis. The dominant presence ofssDNA bands obscured banding patterns and created 3.4. Effect of age and diet on banding patternsimage processing problems due to staining intensity derived from fecal bacterial communitiesof ssDNA in comparison with the PCR productbands. Subsequent treatment of PCR products with DGGE profiles of composite fecal samples frommung bean nuclease after amplification removed nursing pigs (n56), weaned pigs (n55) and a


Fig. 2. DGGE gel demonstrating reproducibility of bandingpatterns. (A) A single fecal sample from a weaned piglet wassub-sampled five times. DNA was extracted from each sub-sample, the V3 region of 16S rDNA PCR amplified and separatedin adjacent lanes 1 through 5 of a DGGE gel. (B) DNA from asingle fecal sample from an adult pig was extracted and the V3region of 16S rDNA PCR amplified. Five sub-samples of the PCRreaction mix were separated in adjacent lanes 6 through 10 of aDGGE gel. Banding patterns in Gels A and B were comparedusing Sorenson’s index.

finishing pig were compared to determine if therewere any apparent gross differences in pattern forma-tion (Fig. 4). Analysis of these profiles revealeddefinite differences in the banding patterns andintensities between the samples. Comparison ofrelative mobilities and percent composition of bandsare shown in Table 1. Calculated relative mobilities(Table 1) revealed four unique bands in the fecalsample from the nursing piglets (a–d), the fecalsample from the weaned pigs exhibited two unique Fig. 3. DGGE gel demonstrating removal of ssDNA from banding

patterns after mung bean nuclease treatment of PCR productbands (e, f), while the sample from the adult pig fedobtained from piglet fecal DNA sample. (A) PCR product beforethe finishing diet also had two unique bands (g, h).treatment with nuclease. (B) Same sample after treatment with

There were 10 common remaining PCR product nuclease (3 units) for 10 min at 378C.bands in all three samples, five bands common to theweaning and adult pigs, one common to nursing andweaned, and one common to nursing and adult pigs.The most similarities in banding patterns were shifts in bacterial populations present in the GIT thatbetween the adult and weaned piglet samples. These occurred as the pigs aged and diets changed (Fig.differences in banding patterns were indicative of 4A,B).


Fig. 4. DGGE profiles generated from pig fecal samples after DNA extraction and PCR amplification of the V3 region of the 16S rDNA. (A)Gel image of composite samples from: adult pig (n51), weaned pigs (n55), and preweaned pigs (n56). Fecal samples were collected andhomogenized within groupings before extraction. Bands identified (a–h) are unique in each sample. (B) Image analysis of the DGGEpatterns in (A). Peak areas (shown) are equivalent to the density of the PCR product bands. Filled peaks correspond to unique PCR productbands.

3.5. Effect of sampling site within gut ous differences between the various sampling sitescompartments on bacterial population profiles along the length of the gut. The stomach lumen,

stomach mucosa, proximal small intestine lumen,DGGE profiles were generated from lumenal and and proximal small intestine mucosa (lanes 1–4)

mucosal GIT samples of 5-month-old pigs with an contained a PCR product (b) not found in otherexample from one pig presented in Fig. 5. Within compartments. Additionally, these two mucosal sam-each compartment, lumenal and mucosal banding ples (lanes 214) contained a unique PCR product (a)patterns were highly similar to each other, with the not found in other samples. Starting with the cecalexception of the cecum. However, there were obvi- mucosa, a PCR product (c) appeared that was not


Table 1 compartmental patterns tend to group according toNumerical comparison of relative mobilities and percent of total their anatomical position in the gut (Fig. 6). Thesample areas for DGGE banding patterns obtained using the V3

stomach and small intestine samples formed a claderegion of 16S rDNA extracted from fecal samples of pigs of(Group I) of closely related patterns, with thedifferent ages presented in Fig. 3exception being the small intestine distal mucosa

Relative Percent of total sample area(SIDM) which formed an isolated branch betweendistanceGroups II and III. Within Group I, the stomachNursing Weaned Adult

a b mucosa (SM) and small intestinal proximal mucosa0.27 16.20 NR NR(SIPM) showed a stronger relationship to each other0.37 13.81 NR NR

0.40 4.64 1.13 2.13 (Group Ia). Groups II and III both formed from the0.42 NR 1.02 3.84 colonic samples. The distal colon and rectal samples0.43 NR 0.62 NR clustered in Group II with the colonic distal mucosa0.45 NR 16.62 13.44

(CoDM) showing closer relation to the rectal sample0.47 2.51 3.15 NR(IIa) than to the colonic distal lumen (CoDL) sample.0.51 2.11 3.36 2.97

0.52 NR 4.44 3.72 However, the colonic proximal samples (III) showed0.54 2.51 NR NR a close relationship to each other, although complete-0.55 1.55 2.08 3.59 ly separate from the distal colonic samples. The0.58 1.46 8.10 8.18

cecal samples (CM and CL) did not cluster with any0.62 3.86 2.21 1.34of the other compartmental groups, and were distant-0.64 5.79 NR 3.56

0.65 NR NR 3.80 ly related to each other.0.66 2.77 2.91 4.20 The DGGE profiles were also compared to each0.69 NR NR 4.44 other by calculating the Sorensen index pairwise0.71 0.61 NR NR

similarity coefficient C (Table 2). The C valuess s0.75 13.18 9.86 9.32ranged from 6 to 79% with a mean of 28%. The0.77 10.97 9.27 13.45

0.81 1.67 2.26 2.45 highest C values (.50%) were obtained froms0.82 NR 3.10 NR compartments located immediately adjacent to each0.84 NR 14.37 7.33 other and from mucosal and lumenal samples within0.86 16.36 15.50 12.22

a compartment. For example, the stomach and smalla Boldface numbers represent unique PCR fragments not pres- intestine samples had the following C values: SMs

ent in the other two fecal samples. and SL (72%), SIPL and SL (59%), SIPL and SMb NR, fragment not represented in lane.(57%), SIPM and SM (69%) as well as SIPM andSIPL (61%), all of which are .57% similar to each

observed previously in the tract, but continues distal- other. Additionally, the CoDL and CoDM (62%) andly through to the rectal material (lanes 7–13). A the CoDM and rectum (79%) were more closelyPCR product (d) appears prior to the cecum (lanes related to each other than with other GIT compart-1–6) which was not found in more distal regions of ments.the tract. The cecal lumen (lane 7) sample exhibitedfewer bands than other samples and only containedone (c) of the aforementioned unique bands. Al- 4. Discussionthough conventional ecological pattern diversityindices were calculated for the intestinal compart- Recent studies have demonstrated that DGGE is aments, there were no statistically significant shifts in tool which can be used to examine complex micro-diversity from one compartment to the next, nor bial communities (Ferris and Ward, 1997; Ferris etfrom mucosal to lumenal content (range of values for al., 1997; Heuer et al., 1997; Kowalchuk et al., 1997;Shannon 2.19–2.53, Simpson 0.09–0.17 and Even- Muyzer and Smalla, 1998). However, these studiesness 0.65–0.84 indices). have previously been limited to observation of

The DGGE banding patterns were analyzed by differences or changes within a very defined en-

PHYLIP . The resulting dendrogram indicates that the vironmental community, such as hot spring microbial


Fig. 5. DGGE profiles generated from V3-16S rDNA fragments, obtained after PCR amplification of genomic DNA extracted from lumenaland mucosal samples taken along the intestinal tract of a pig. Unique bands are designated (a–d). Lanes are: (1) stomach lumen, (2) stomachmucosa, (3) small intestine proximal lumen, (4) small intestine proximal mucosa, (5) small intestine distal lumen, (6) small intestine distalmucosa, (7) cecum lumen, (8) cecum mucosa, (9) colon proximal lumen, (10) colon proximal mucosa, (11) colon distal lumen, (12) colondistal mucosa, and (13) rectum.

mats, biofilms, tidal sands, and individual species key components of a ‘‘harsh lysis’’ protocol, whichinteractions. These studies produced ‘‘simpler’’ have been shown to give broader, more completebanding patterns that only had a restricted number of DNA recovery (Muyzer and Smalla, 1998). Smallcommunity members. Other complex banding pat- modifications which optimize extractions for giventerns have been obtained by other investigators using samples are often vital to success. The application ofrelated techniques such as temperature gradient gel this extraction protocol slightly modified for gut andelectrophoresis (TGGE) to examine complex en- pig fecal samples worked well, and produced clean,vironmental communities (Felske et al., 1998; intact genomic DNA with no polyphenolic /phenolicZoetendal et al., 1998). Zoetendal et al. (1998) was contamination, analogous to humic substances in soilone of the first to apply the gradient gel technique to samples. Due to the inhibitory nature of thesethe human GIT. Molecular analysis of the GIT compounds to further analysis, an extraction pro-bacterial community, which is not only highly com- cedure that yields good quality DNA and little or noplex, but also difficult to characterize due to its contamination is essential (Tsai and Olson, 1992).abundant and varied populations, is still a novel, yet One factor that must be taken into considerationexpanding area for this application. for any DGGE protocol is the choice of polymerase

The extraction method used in these experiments used for amplification. Since DGGE is used normallywas designed originally for DNA extraction from to determine the numbers or differences of genera orsoils (Tsai and Olson, 1991). However, it includes species of bacteria present in a sample, it is im-


Fig. 6. Dendrogram representing relationships between PCR amplified V3-16S rDNA fragment banding patterns from the lumen andmucosa of the intestinal tract of a pig. The dendrogram was constucted using PHYLIP analysis software and based upon the migrationaldistances of the rDNA PCR product within a DGGE gel (Fig. 4). S, stomach; SI, small intestine; C, cecum; Co, colon; P, proximal region;D, distal region; M, mucosal; L, lumenal; RECT, rectum.

Table 2C matrix for pig GIT compartment sampless

Lane Pairwise similarity coefficient (C ) percents

IDStomach Small intestine Cecum Colon

SL SM SIPL SIPM SIDL SIDM CL CM CoPL CoPM CoDL CoDM

SM 72SIPL 59 57SIPM 53 69 61SIDL 28 29 40 31SIDM 23 6 12 6 30CL 23 6 12 6 30 50CM 19 20 6 14 13 28 41CoPL 22 17 17 18 23 24 59 19CoPM 23 18 18 19 30 25 44 34 65CoDL 18 25 30 20 25 19 6 21 12 13CoDM 24 32 31 14 39 20 13 22 13 13 62RECT 30 32 31 14 45 13 13 7 19 27 41 79

portant to obtain high fidelity during PCR amplifica- efficient 39–59 exonuclease activity which allows fortion. In our experiments, TaKaRa Ex Taq performed high quality and fidelity during PCR (Barnes, 1994).in accordance with company specifications and has The PCR amplification kit also included an opti-


mized buffer solution which promoted greater prod- ering this abundant diversity found in the GIT,uct yield. DGGE provides an underestimate of total bacterial

Optimization of the denaturant gradient for sample diversity (Raskin et al., 1997). However, many ofsets is also of critical importance (Muyzer and the GIT populations are minor constituents and thereSmalla, 1998). Clarity and resolution of banding are detection limits for populations comprising lesspatterns is dependent not only on the proper gradient than 1% of the total template DNA and PCRand electrophoresis conditions, but also upon the amplification with semi-conserved primer setsquality of the gel preparation prior to experimental (Muyzer and Smalla, 1998). In terms of bacterialapplications (Fig. 1). Although slight, variation populations in the GIT, it is unlikely that genera,introduced by the investigator during gel preparation species, or assemblages of bacteria that number

8 11may cause a decrease in ability to obtain high ,10 /g (in a total population of 10 cells /g) can beresolution of the patterns. Purchasing pre-mixed visualized using the DGGE technique. Based on thereagents and assuring proper adjustment between the complexity of the banding pattern, it is possible thatlight source and camera during photographing con- some of these bands may not be individual species,siderably reduced variation in results. However, if but rather groups which have the same relative G1Cproper care is taken, results are stable and highly content within the 16S V3 region and have co-reproducible and variation in banding patterns is migrated .reduced to a minimum (Fig. 2). Another considera- Experiments with two-dimensional gel electropho-tion is that development and imaging of gels can be resis (consisting of DGGE as described previously,expected to vary between labs depending upon the then application of a second electrophoresis basedstaining and photographic equipment available which upon a 40 to 708C temperature gradient) did notmay cause lab-to-lab variations in analysis of similar produce any further separation or resolution of thesamples. Further limitations which affect DGGE and banding patterns (data not presented). PreviousTGGE analysis of complex microbial communities DGGE studies have shown that communities with ahave been reviewed by Muyzer and Smalla (1998). few dominant species will produce simpler patterns

Removal of ssDNA artifacts from PCR samples and the less abundant species may not be adequatelyprior to DGGE analysis is critical for optimal represented in the community pattern (Muyzer et al.,visualization of banding patterns. These ssDNA 1993; Heuer et al., 1997). For a detailed discussioncontaminants cause large predominantly smeared of known limitations of the DGGE technique (sepa-bands to appear within the patterns that mask ration of relatively small fragments ,500 bp, co-underlying PCR product bands and cause interfer- migration of DNA fragments or limitations in theence with image analysis (Fig. 3). Silver stain is total number of resolvable fragments) the reviewtaken up by the ssDNA, although not as efficiently as article by Muyzer and Smalla (1998) should bedsDNA and usually appears reddish in color before consulted.darkening. Removal of the ssDNA by application of From the DGGE profiles that were generated frommung bean nuclease is a quick and effective method the various gut compartments, it was possible toto eliminate this problem (Muyzer and Smalla, discern differences in banding patterns and several1998). Mung bean nuclease is used mainly after unique PCR products were obtained from separateenzymatic digestions for removal of overhangs to regions (Fig. 5). As could be expected, the mucosalproduce blunt ends, but adapts well to this particular and lumenal patterns closely matched each otherapplication. within compartments (Table 2) and an overall shift

It is possible to discern differences between PCR in pattern was observed from one section to the next.product banding patterns for pigs of different ages While there are a number of common bands which(Fig. 4, Table 1). However, the number of bands in are found throughout the patterns, there are distinctthe DGGE patterns did not correspond to the ex- PCR product bands that are found solely by region orpected number of different bacteria present in the section, and this accounts for the wide range of Cs

GIT, from which some 400 different species have values obtained, as well as the different cladebeen isolated (Moore and Holdeman, 1974). Consid- formations in the dendrogram (Fig. 6). These differ-


Ferris, M.J., Nold, S.C., Revsbech, N.P., Ward, D.M., 1997.ences also correlate with the changes in bacterialPopulation structure and physiological changes within a hotpopulations indigenous to their particular GIT loca-spring microbial mat community following disturbance. Appl.tions (Stewart, 1997).Environ. Microb. 63, 1367–1374.

Gillan, D., Speksnijder, A., Zwart, G., de Ridder, C., 1998.Genetic diversity in the biofilm covering Montacuta ferrugin-osa (Mollusca, Bivalvia) as evaluated by denaturing gradient5. Conclusionsgel electrophoresis analysis and cloning of PCR-amplified genefragments coding for 16S rRNA. Appl. Environ. Microb. 64,DGGE can be applied effectively to the pig GIT3464–3472.

system to monitor changes in bacterial populations Heuer, H., Krsek, M., Baker, P., Smalla, K., Wellington, E., 1997.throughout the gut. Not only can differences be seen Analysis of Actinomycete communities by specific amplifica-between animals on different diets, but also among tion of genes encoding 16S rRNA and gel-electrophoretic

separation in denaturing gradients. Appl. Environ. Microb. 63,different gut compartments and specific locations3233–3241.within these gut compartments. The ability to detect

Kowalchuk, G., Stephen, J., Boer, W., Prosser, J., Embley, T.,these differences will allow future investigations toWoldendorp, J., 1997. Analysis of ammonia-oxidizing bacteria

more closely monitor the effects of treatments or of the b subdivision of the class Proteobacteria in coastal sandregimens on the gut microbiota. Currently, phylo- dunes by denaturing gradient gel electrophoresis and sequenc-genetic identification of these banding assemblages is ing of PCR-amplified 16S ribosomal DNA fragments. Appl.

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