ORIGINAL PAPER

Development of a fluorophore-ribosomal DNA restriction typingmethod for monitoring structural shifts of microbial communities

Tingting Wang • Xiaojun Zhang • Menghui Zhang •

Linghua Wang • Liping Zhao

Received: 11 October 2010 / Revised: 30 December 2010 / Accepted: 7 January 2011 / Published online: 28 January 2011

� Springer-Verlag 2011

Abstract DNA restriction fragment polymorphism tech-

nologies such as amplified ribosomal DNA restriction

analysis (ARDRA) and terminal restriction fragment length

polymorphism (T-RFLP) have been widely used in inves-

tigating microbial community structures. However, these

methods are limited due to either the low resolution or

sensitivity. In this study, a fluorophore-ribosomal DNA

restriction typing (f-DRT) approach is developed for

structural profiling of microbial communities. 16S rRNA

genes are amplified from the community DNA and diges-

ted by a single restriction enzyme Msp I. All restriction

fragments are end-labeled with a fluorescent nucleotide

Cy5-dCTP via a one-step extension reaction and detected

with an automated DNA sequencer. All 50 predicted

restriction fragments between 100 and 600 bp were

detected when twelve single 16S rRNA gene sequences

were analyzed using f-DRT approach; 92% of these frag-

ments were determined with accuracy of ±2 bp. In the

defined model communities containing five components

with different ratios, relative abundance of each component

was correctly revealed by this method. The f-DRT analysis

also showed structural shifts of intestinal microbiota

in carcinogen-treated rats during the formation of precan-

cerous lesions in the colon, as sensitive as multiple

digestion-based T-RFLP analysis. This study provides a

labor and cost-saving new method for monitoring structural

shifts of microbial communities.

Keywords rDNA � Restriction typing � Microbial

community � Structural shifts

Abbreviations

f-DRT Fluorophore-ribosomal DNA restriction typing

rDNA Ribosomal DNA

ARDRA Amplified ribosomal DNA restriction analysis

T-RFLP Terminal restriction fragment length

polymorphism

T-RFs Terminal restriction fragments

DMH 1,2-dimethylhydrazine

ACF Aberrant crypt foci

PCA Principal component analysis

Introduction

With increasing knowledge and interest in the field of

microbial ecology, significant efforts have been made to

investigate bacterial diversity in complex environments,

which is believed to be tightly related to their biological

functions. Consequently, development of techniques for

detecting microbial community structures has become more

and more important (Gray and Head 2001). Due to well-

known shortcomings of culture-dependent techniques, var-

ious kinds of molecular profiling techniques have been

Communicated by Erko Stackebrandt.

T. Wang � X. Zhang (&) � M. Zhang � L. Wang � L. Zhao

Key Laboratory of Ministry of Education for Microbial

Metabolism, School of Life Science & Biotechnology,

Shanghai Jiao Tong University, 800 DongChuan Road,

200240 Shanghai, China

e-mail: xjzhangsjtu@gmail.com

L. Zhao

Ministry of Education Key Laboratory of Systems Biomedicine,

Shanghai Center for Systems Biomedicine,

Shanghai Jiao Tong University, 200240 Shanghai, China

123

Arch Microbiol (2011) 193:341–350

DOI 10.1007/s00203-011-0679-8

developed during the past two decades, which can mainly be

classified as DNA fingerprinting techniques, such as dena-

turing gradient gel electrophoresis (DGGE) (Muyzer et al.

1993), terminal restriction fragment length polymorphism

(T-RFLP) (Liu et al. 1997), amplified fragment length

polymorphism (AFLP) (Vos et al. 1995), amplified ribo-

somal DNA restriction analysis (ARDRA) (Vaneechoutte

et al. 1992), and sequencing-based techniques, including

specific gene clone libraries (Eckburg et al. 2005) and

metagenomic sequencing (Tringe and Rubin 2005). DNA

fingerprinting techniques are widely used in comparing

microbial diversities in different communities and moni-

toring the dynamic composition changes of a microbial

community due to their cost-effectiveness and ease of

operation.

T-RFLP and ARDRA are two fingerprinting techniques

based on restriction fragment polymorphism to explore

microbial diversity. As the universal evolutionary chro-

nometer, 16S rRNA gene (16S rDNA) has become the

most commonly used genetic marker in ARDRA and

T-RFLP studies (Schutte et al. 2008).

ARDRA displays a profile of restriction fragments via

a gel electrophoresis system and has been used for

description of microbial community structures (Tiedje

et al. 1999). However, the low resolution and reproduc-

ibility of gel electrophoresis limit its application in

investigating complex microbial communities. The

T-RFLP technique uses fluorescence-labeled primer(s) to

amplify a particular gene fragment (such as 16S rRNA

gene). In combination with the use of an automated DNA

sequencer, the size and abundance of all terminal restric-

tion fragments (T-RFs) are determined by detecting

the migration and intensity of fluorescent digital signals

(Liu et al. 1997). Taking advantages of the capillary

electrophoresis technology, the T-RFLP profiling is high

throughput, highly reproducible, and more amenable for

multivariate statistical analysis; thus it has been widely

used for assessing the composition of a microbial com-

munity such as soil (Chim Chan et al. 2008), ground water

(Euringer and Lueders 2008), and animal gut (Li et al.

2007). However, bacteria within one phylogenetic lineage

may share identical length of the T-RF, and changes

among such bacteria in a community may be underesti-

mated (Marsh et al. 2000).

In this study, we developed a new fluorophore-ribo-

somal DNA restriction typing (f-DRT) method by labeling

all restriction fragments with a fluorescence dye and

detecting their lengths and abundances with an automated

DNA sequencer. By combining the advantages of ARDRA

and T-RFLP, we are able to monitor the shifts of microbial

communities with reasonably high throughput, resolution,

and sensitivity.

Experimental

Reference sequences

Twelve 16S rRNA gene sequences were selected as ref-

erence sequences representing common human intestinal

bacteria. All of them came from clone libraries of human

intestinal microbiota from a traditional Chinese family (Li

et al. 2008). The twelve reference sequences were defined

as A to L, three of them were from the phylum Proteo-

bacteria, four from the phylum Firmicutes, and five from

the phylum Bacteroidetes (Table 1).

Animals and sample collection

A carcinogen-induced rat model was constructed by injection

of DMH (1,2-dimethylhydrazine) to study the dynamic

structural shifts of intestinal microbiota in response to pre-

cancerous lesions in our previous study (Wei et al. 2010).

Shortly, twelve male Wistar rats were divided into two groups,

each containing six animals. After 1 week of acclimatization,

one group received subcutaneous injection of DMH twice

with 1-week interval and the other group was considered as

control and received injections of placebo. Fecal samples of

each rat from both groups were collected at two time points:

1 week (T1) and 7 weeks (T2) after the second injection.

Totally, 24 fecal samples were collected. After the last fecal

sample collection, rats were killed for histopathologic exam-

ination, and formation of 37.7 ± 2.6 ACF (aberrant crypt

foci) in the six animals from model group was observed but

none in control group; this indicated the putative preneoplastic

lesions in rat colon.

DNA extraction

Clones containing reference sequences preserved at -80�C

were resuscitated and cultivated in LB broth. Plasmid DNA

was extracted with E.Z.N.A.TM Plasmid Mini Kit I

according to manufacturer’s protocol (Omega Bio-tek, Inc.,

USA). Fecal DNA was extracted using bead-beater lysis

(Biospec Products, Bartlesville, OK, USA) combined with

phenol–chloroform purification as described by Li et al.

(2009). Integrity of DNA was checked on 0.8% agarose

gel, and the concentration was determined using DyNA

Quant 200 (Amersham Pharmacia Biotech, USA).

Construction of defined model communities

A series of defined model communities MC1 to MC5 were

constructed by using the plasmids each containing a single

reference sequence. DNA from plasmids A, B, E, F, and K

was mixed with certain proportions as showed in Table 2.

342 Arch Microbiol (2011) 193:341–350

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Table 1 Analysis of 12 reference sequences by f-DRT and in silico prediction

Reference

sequences

Acc. no. Closest relatives

identified using RDP

database (similarity)

Fragment numbers

within 100–600 bp

(total fragment

number)a

Total length of the

16S rRNA gene

Predicted fragment length

(detected fragment length)b

A EF403826 Faecalibacteriumprausnitzii (T);

AJ413954 (99.5%)

4 (7) 1,479 38, 90d, 103(101.52), 126(124.88), 184c(182.19),

241(241.72), 697

B EF403827 Faecalibacteriumprausnitzii (T);

AJ413954 (98.1%)

3 (6) 1,478 38, 90d, 126(125.13), 241(241.9), 286c(284.06),

697

C EF403833 Prevotella veroralis (T);

L16473 (92.7%)

5 (11) 1,504 12, 32, 38, 42, 81d, 91, 101c(99.15), 167(166.77),

233(231.51), 307(306.63), 400(399.70)

D EF403836 Bacteroides coprocola(T); AB200224

(95.8%)

4 (10) 1,497 33d, 38, 47, 49, 91, 99c, 121(119.08),

231(230.27), 337(338.74), 451(452.60)

E EF403840 Clostridium sp. BI-114

(T); AJ518869

(92.9%)

3 (7) 1,483 36, 53, 91, 129d(128.97), 189(189.34),

288c(287.00), 697

F EF403957 Streptococcusthermophilus (T);

X68418 (99.1%)

6 (7) 1,516 12, 126(125.03), 127d(126.60), 164(164.15),

212(213.59), 318(317.84), 557c(560.80)

G EF404131 Alistipes onderdonkii(T);

AY974071 (91.8%)

7 (10) 1,500 8, 57, 93c, 116(113.46), 118d(117.49),

150(148.29), 167(167.62), 176(176.38),

279(280.14), 336(336.87)

H EF404244 Parabacteroidesgoldsteinii (T);

AY974070 (84.4%)

4 (14) 1,507 11, 12, 33d, 38, 49, 54, 57, 59, 91, 97c,167(167.27), 177(177.64), 269(268.06),

393(392.11)

I EF404279 Alistipes shahii (T);

AY974072 (93.2%)

3 (7) 1,510 57, 87, 97c, 118d(117.58), 167(167.42),

192(191.50), 792

J EF404297 Desulfovibrio piger (T);

AF192152 (98.8%)

4 (9) 1,521 10, 54, 76d, 82, 87, 132(132.79), 156(155.42),

420(417.89), 504c(504.34)

K EF404313 Sutterella stercoricanis(T); AJ566849

(93.4%)

2 (6) 1,503 38, 84c, 90d, 111(108.97), 413(414.05), 767

L EF404522 Desulfovibrio oxamicus(T); DQ122124

(91.4%)

5 (10) 1,522 18, 54, 61, 76d, 82, 140(138.14), 157(156.49),

198(197.49), 291c(291.51), 445(443.35)

a Fragment numbers obtained by in silico predictionb The fragments \100 or [600 bp are shown in italic. Lengths of these fragments are from predictionc Fragments on the 50 terminal of the 16s rRNA gened Fragments on the 30 terminal of the 16s rRNA gene

Table 2 Proportion of each component in five defined model communities and its relative abundance detected by f-DRT

Reference

sequences

Proportion in each model community (relative abundance by detection)

MC1 MC2 MC3 MC4 MC5

A – 15% (18.2 ± 1.6%) 5% (4.6 ± 0.9%) 25% (26.5 ± 1.8%) 10% (10.6 ± 0.9%)

B 60% (64.2 ± 0.8%) – 45% (54.3 ± 3.3%) 25% (34.3 ± 1.2%) 20% (25.0 ± 3.1%)

E 15% (16.5 ± 1.6%) 15% (16.4 ± 0.8%) – 25% (20.7 ± 0.9%) 30% (26.1 ± 1.0%)

F – 25% (28.1 ± 2.7%) 35% (32.5 ± 2.2%) – 40% (38.3 ± 2.5%)

K 25% (19.3 ± 0.8%) 45% (37.3 ± 3.9%) 15% (8.6 ± 0.3%) 25% (18.5 ± 1.0%) –

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PCR amplification of full-length 16S rRNA gene

Bacterial universal primers 27f (50-AGA GTT TGA TCC

TGG CTC AG) and 1492r (50-GGTT ACC TTG TTAC

GAC TT) were used to amplify full-length 16S rRNA gene

(Weisburg et al. 1991), in which 27f was only one nucle-

otide different from P0 used in the clone library study. The

Beckman Coulter fluorescent dye D4-labeled 27f (Invitro-

gen, Shanghai, China) was used in PCRs for T-RFLP

analysis. All PCRs were performed with a PCR Express

system (Thermo Hybaid, Middlesex, UK). Twenty-five-

microliter reactions contained 2.5 ll of 10 9 PCR buffer,

2 mM MgCl2, 200 lM of each deoxynucleoside triphos-

phate, 6.25 pmol of each primer, and 0.75 U of rTaq DNA

polymerase (Takara, Dalian, China). One nanogram of

plasmid DNA or 10 ng of fecal DNA was used as template

respectively. PCR was performed under the following

conditions: 1 cycle of 95�C for 5 min; 20 cycles of 95�C

for 30 s, 56�C for 30 s, 72�C for 90 s; and a final

extension of 8 min at 72�C (Eckburg et al. 2005). In order

to avoid artifact formation, a ‘‘re-conditioning PCR’’ was

performed in doubled volume of PCR mixture with the

same program, cycle number reduced to 5, and 5 ll of

amplicon was used as template for each reaction

(Thompson et al. 2002). Re-conditioned PCR products

were resolved on 1.2% agarose gel. All 16S rRNA gene

amplicons were purified with E.Z.N.A.TM Cycle-Pure Kit

according to manufacturer’s protocol (Omega Bio-tek,

Inc., USA). Before purification, product from each PCR

for T-RFLP analysis was digested by 1 U of mung bean

nuclease at 37�C for 30 min to remove pseudo peaks

(Promega, Madison, WI, USA).

Fluorophore-ribosomal DNA restriction typing

Two hundred nanograms of purified 16S rRNA gene

amplicon from each sample was mixed with 2 ll of 109

buffer and 10 U of Msp I (C^CGG) (MBI Fermentas,

Lithuania) for restriction digestion, and ddH2O was added

to get a total volume of 20 ll. Incubation was carried out at

37�C for 3 h and followed by inactivation at 80�C for

20 min. After digestion, terminal restriction fragments with

one 50-GC cohesive end and other restriction fragments

with both 50-GC cohesive ends were generated. All

restriction digests were purified with E.Z.N.A.TM Cycle-

Pure Kit (Omega Bio-tek, Inc., USA). One hundred nano-

grams of each purified digest was mixed with 1 U of

Klenow Fragment (MBI Fermentas, Lithuania), 2 ll of

109 buffer, and 0.0125 nmol Cy5-dCTP (GE healthcare

Ltd., Buckinghamshire, UK) for labeling reaction, and

ddH2O was added to generate a mixture of 20 ll. Incuba-

tion was carried out at 37�C for 30 min and followed

by inactivation at 70�C for 10 min. In the reaction, one

Cy5-dCTP molecule matched itself with the first nucleotide

on the cohesive end.

A volume of 0.2–0.5 ll of 10-time-diluted Cy5-labeled

fragments was mixed with 20 ll of Sample Loading

Solution and 0.1 ll of GenomeLabTM DNA Size Standard-

600 (Beckman Coulter, Fullerton, USA) and separated by

capillary electrophoresis on a CEQTM 8000 genetic anal-

ysis system (Beckman Coulter, Fullerton, USA). The

electrophoresis program was as follows: denaturation at

90�C for 120 s; injection under a voltage of 2.0 kV for

30 s; separation at 50�C for 65 min under a voltage of

4.8 kV. After electrophoresis, size standards were cali-

brated by a default Quartic Model with dye emendation

parameters (PA ver.1). Fragment sizes were estimated by

peak migration positions, and relative abundance of each

fragment was calculated by the intensity of fluorescent

signals represented by peak height. Three parallel experi-

ments including rRNA gene amplification and restriction

typing were performed on each sample to check the

reproducibility of the method.

ARDRA

Amplified ribosomal DNA restriction analysis was per-

formed among the twelve reference sequences and the 24

fecal DNA samples. For each sample, 100 ng of purified

16S rRNA gene amplicon was digested by Msp I as

described earlier. Restriction fragments were separated on

3.0% agarose gel, stained by 0.5 lg of ethidium bromide

per ml gel, and visualized by UV excitation (UVItec,

Cambridge, UK). The optical density of bands was quan-

tified using software Image J (National Institutes of

Health). Digitalized ARDRA patterns were analyzed by

principal component analysis (PCA) to compare the

structural shifts of the rat intestinal microbiota during the

carcinogen treatment, using programs in Matlab� 2007a

environment (The MathWorks, Inc., Natick, MA, USA).

T-RFLP analysis

Terminal restriction fragment length polymorphism anal-

ysis was performed with twenty-four rat fecal DNA sam-

ples. Three restriction enzymes: Msp I (C^CGG), Hha I

(GCG^C), and Hae III (GG^CC) (MBI Fermentas, Lithu-

ania) were separately used to digest 200 ng of purified 16S

rRNA gene amplicon from every sample. The 20-ll reac-

tion mixture contained 2 ll of 109 buffer and 10 U of each

restriction enzyme. Incubation was carried out at 37�C for

3 h followed by inactivation at an appropriate temperature

according to the manufacturer’s protocols. Restriction

fragments from each digestion were individually separated

with CEQTM 8000 system the same way as mentioned

earlier.

344 Arch Microbiol (2011) 193:341–350

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Statistic analysis

Due to the detecting limitation of the DNA sequencer,

fragments in the range of 100–600 bp were selected in the

following analysis. For the single plasmid DNA samples

and model communities, fragment number and size from

each sequence were in silico predicted by searching for the

4-bp cutter sites along the sequence. The 1-bp difference in

length of primer P0 and 27f was taken into account when

predicting fragment sizes. Furthermore, 1 bp was added to

the predicted length of each fragment because of the

fluorescence-labeled nucleotide added to every cohesive

end. Peak height of each fragment was normalized to sum

of all peaks in one sample. For each community, the rel-

ative abundance of a reference sequence was represented

by the average abundance of all fragments from this

sequence. In detail, average abundance of all fragments

generated from each reference sequence was first calcu-

lated by Ai = Si/n (for example AE for average relative

abundance of all fragments generated from E). Si repre-

sented sum of peak height percentages of all fragments

from this sequence, and n represented number of frag-

ments. For each terminal restriction fragments with one 50-GC cohesive end, n = 0.5; for each of the other restriction

fragments with both 50-GC cohesive ends, n = 1. Ai was

then normalized to sum of A in this community to represent

the relative abundance of this sequence. For example, MC1

was constructed with B, E, and K; therefore, E% in

MC1 = AE/(AB ? AE ? AK). Fragments from different

reference sequences but sharing the same predicted length

were discarded from the calculation. Linear regression was

conducted between detected and predicted abundance of a

sequence in each model community.

For the fecal DNA samples, data of the detected frag-

ment size and abundance were exported from the CEQTM

8000 software. In detail, peaks with height of less than

1,000 rfu (relative fluorescence units) were excluded from

analysis to reduce noise. Peaks differed by ±1 bp in size

were considered as identical and binned together. Peak

height of each fragment was normalized to sum of all peaks

in each sample to standardize the data in order to compare

between samples. The generated data took a form of

fragment by abundance matrix for each sample (commu-

nity), and the matrix was analyzed by PCA as described

previously.

Results

Labeling and detection of restriction fragments

Restriction typing of each of the twelve reference

sequences was performed to verify the new fragment

labeling method and to test whether the labeled fragments

can be effectively detected by the sequencer. Two to seven

fragments were detected from each reference sequence,

which was consistent with both the prediction and the

ARDRA profile (Fig. 1).

Fragment sizes determined by this method were also

consistent with the prediction. Of all the 50 fragments

between 100 and 600 bp, 92% showed discrepancy within

±2 bp, and 62% within ±1 bp; only one fragment showed

difference of more than 3 bp, which was the largest frag-

ment predicted to be 557 bp (Table 1). The accuracy of

size determination was the same as previous T-RFLP

studies (Osborn et al. 2000; Kaplan and Kitts 2003).

Determination of structures of defined model

communities

8, 14, 12, 10, and 13 peaks were predicted to exist in the

profiles of MC1 to MC5, respectively. Numbers of frag-

ments measured by f-DRT were consistent with the

prediction.

Proportion of a reference sequence in the model com-

munities varied from 5 to 60%, and these values were

consistent with the relative abundance determined by the

f-DRT method (Table 2; Fig. 2a). There was a reasonable

linear relationship between the proportion of a sequence

and its detected relative abundance in all model commu-

nities (R2 = 0.9817, 0.9784, 0.9493, 0.9294, 0.9979,

respectively for A, B, E, F, K in all communities, Fig. 2b),

indicating the changes of each member in model commu-

nities were reliably reflected.

Reproducibility of the method was evaluated by com-

paring results from three parallel experiments. No differ-

ence with fragment numbers was found between replicates

of the same model community. Run-to-run variations of

fragment size were within ±1 bp. A high reproducibility of

quantification analysis was shown by standard deviation

of triplicates. For example, in MC2, MC3, MC4, and MC5,

the detected relative abundances of A were 18.23 ± 1.56%,

4.63 ± 0.90%, 26.47 ± 1.75%, and 10.59 ± 0.92%, while

its defined proportions in the four communities were 15, 5,

25, and 10%, respectively.

Monitoring structural shifts of gut microbiota

in carcinogen-treated rats

The present method was further used to monitor the

structural shifts of gut microbiota of carcinogen-treated

rats. Compared with ARDRA, f-DRT method produced

profiles in much higher resolution. For example, only seven

main peaks in the range of 100–600 bp were shown in the

digitalized ARDRA profile of one sample at T2, while

fifty-three peaks were detected in the same sample by

Arch Microbiol (2011) 193:341–350 345

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f-DRT method. The highest peak near 400 bp in the

ARDRA profile was shown to be constructed with eleven

peaks in f-DRT profile (Fig. 3). Moreover, highly identical

profiles were revealed by triplicate measurement of the

same sample.

Mean-centered data of triplicates from each sample were

used in PCA. Samples at time points T1 and T2 were

separated from each other along PC1, which accounted for

51.4% of the total variations, indicating that gut microbiota

changed significantly during animal development. At T1,

no difference was seen between control and model groups,

whereas the twelve rats of the two groups at T2 were

separated distinctly into two spaces, suggesting that the

structure of gut microbiota changed significantly along

with the host’s health status (Fig. 4a).

Due to gel size restriction and difficulty of data nor-

malization among different gels, only samples from the

same time point were profiled with ARDRA and analyzed

by PCA. The same result was generated, as no difference

was observed between control and model animals at T1 but

obvious segregation at T2 (Fig. 5).

Terminal restriction fragment length polymorphism

analysis with the restriction enzymes Msp I, Hha I, or Hae

III was also performed on these twenty-four rat fecal

samples. In the PCA scores plot generated from each single

digestion data, samples from different time points tended to

separate along PC1. The two groups at T1 were mixed

together and at T2 were still hardly separated from each

other (Fig. 4b, data of Hha I and Hae III digestion not

shown). When the data set of Msp I digestion was com-

bined with those of Hha I and Hae III digestions, both

difference between the two time points and that between

the two groups at T2 became more significant (Fig. 4c).

Discussion

Primers and the restriction enzyme in the study were

carefully chosen in order to generate more diverse frag-

ment patterns. The 27f-1492r primer pair used here has

Fig. 1 Analyses of single sequences by ARDRA and f-DRT meth-

ods. ARDRA electrophoresis profiles (a) and digital images of

reference sequences A (b) and B (c); f-DRT profiles of reference

sequences A (d) and B (e). The area between segments M and

N represents the range of 100–600 bp in (b) and (c). Mr the 100-bp

DNA ladder (Fermentas)

Fig. 2 Analyses of defined model communities by f-DRT. a The

relative abundance of each sequence in all model communities. Opencircle, open square, open triangle, open diamond, multisymbolrepresent A, B, E, F, K, respectively. b Linear fitting curve based

on the proportion of A (X axis) and its detected abundance (Y axis) in

each model community. The data points are average values of

triplicates; the error bars represent SD (standard deviation)

346 Arch Microbiol (2011) 193:341–350

123

been used widely for universal amplification of bacterial

16S rRNA gene in the studies on human and animal

intestinal microbiota (Eckburg et al. 2005; Kuehl et al.

2005). Out of 13 restriction enzymes tested, Msp I showed

best capacity in distinguishing different sequences in the in

silico prediction based on 7,225 bacterial 16S rRNA gene

sequences from a Chinese four-generation family clone

library study (Li et al. 2008) (Prediction data not shown). A

previous study also showed that Msp I was among the

enzymes with the highest resolving capacities by computer

simulation on 4,603 bacterial 16S rRNA gene sequences

from RDP release 8.1 (Engebretson and Moyer 2003). It

has been shown by some T-RFLP studies that different

primer–enzyme combinations can be used to get a broader

range of diversity and achieve a better discrimination of

phylotypes (Nagashima et al. 2003; Alvarado and Manjon

2009). The f-DRT method developed here also has the

potential to be optimized for targeting different types of

microbial diversity by amplification with other primers,

digestion with other enzymes, and labeling by corre-

sponding complimentary fluorescent nucleotides.

The segregation of rats with precarcinogenic lesions

away from healthy controls was observed with f-DRT,

which was consistent with the result obtained from the

analysis of PCR-DGGE profiling and 454 pyrosequencing

of V3 region of 16S rRNA gene (Wei et al. 2010).

Fig. 3 Analyses of fecal

microbiota by ARDRA and

f-DRT methods. ARDRA

electrophoresis profiles (a,

d) and digital images (b, e) of

the fecal microbial structure of

one rat at two time points;

f-DRT profiles of the same

samples (c, f). The area between

segments M and N represents

the range of 100–600 bp in

b and e. Mr the 100-bp DNA

ladder (Fermentas)

Arch Microbiol (2011) 193:341–350 347

123

However, T-RFLP analysis based on single enzyme

digestion failed to show this segregation. This may be

attributed to the sharing of the same T-RF length by dif-

ferent species, especially species with close phylogeny

(Marsh et al. 2000). Several strategies have been developed

to avoid underestimation of microbial diversity by

T-RFLP. Sometimes two or more primer pairs targeting the

same gene are labeled by different dyes and used in parallel

PCR assays, and digests from different amplification

reactions are combined before electrophoresis (Zhou et al.

2007). Another common way to increase resolution of

T-RFLP is to use more than one restriction enzymes (Wang

et al. 2004; Zhang et al. 2008). In the present study, the

differentiation of microbial community among the two

groups was clearly detected only when the combined data

sets from Msp I, Hha I, and Hae III digestion were used in

PCA. However, the multiple digestions and electrophoreses

are labor-consuming and time-costing and also need addi-

tional standardization methods for data analysis (Osborne

et al. 2006). We have demonstrated that the f-DRT method

at least has the equivalent resolution and sensitivity with

multiple digestion-based T-RFLP and costs much less.

A notable advantage of T-RFLP is that it provides the

possibility to predict phylotypes of the bacteria in a com-

munity by comparing their T-RF lengths to existing phy-

logenetic assignment database, such as the T-RFLP

analysis program (TAP) (Marsh et al. 2000); T-RFLP

Fig. 4 PCA scores plots of fecal microbiota based on f-DRT and

T-RFLP methods. a f-DRT based PCA scores plot of the fecal

microbial structure of rats from both groups at two time points;

b PCA scores plot based on Msp I generated T-RFLP; c PCA scores

plot based on Msp I, Hha I, and Hae III generated T-RFLP. Each

symbol contains unique information on fragment size and relative

abundance for one individual. Open triangle control animals at T1,

filled triangle model animals at T1, open circle control animals at T2,

filled triangle model animals at T2

Fig. 5 PCA scores plots of fecal microbiota based on ARDRA.

ARDRA-based PCA scores plots of the fecal microbiota structure of

rats from both groups at T1 (a) and T2 (b). open triangle control

animals at T1, filled triangle model animals at T1, open circle control

animals at T2, filled circle model animals at T2

348 Arch Microbiol (2011) 193:341–350

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phylogenetic assignment tool (PAT) (Kent et al. 2003); and

the human colonic microbiota database for T-RFLP (PAD-

HCM) (Matsumoto et al. 2005). In the present approach, a

single species can contribute four to six restriction frag-

ments to the community pattern, which makes the profile

complex and brings difficulty in relating a fragment with

possible phylogenetic information. However, by comparing

ARDRA patterns of isolates or rDNA clones with those

from community DNA, a previous study showed that

identification of the dominant members of the community

was also possible (Tiedje et al. 1999). Similar approach

could also be developed for f-DRT method in future study.

In conclusion, we have developed a new restriction

typing method based on high-throughput capillary elec-

trophoresis and florescence detection of all restriction

fragments from a single enzyme digestion. This fluoro-

phore-ribosomal DNA restriction typing (f-DRT) method

has added a new tool in the toolbox of microbial ecologists

for monitoring structural changes in complex microbial

communities with reasonably high throughput, sensitivity,

and resolution.

Acknowledgments This work was financially supported by the

National Natural Science Foundation of China (key project 30730005

and project 20677041), 863 High-tech R&D Program (2008AA02Z315,

2007AA021301), Projects in the National Science & Technology Pillar

Program (2006BAI11B08-02), and the Shanghai Leading Academic

Discipline Project (B203).

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