# Application of Sequence-Dependent Electrophoresis Fingerprinting in Exploring Biodiversity and Population Dynamics of Human Intestinal Microbiota: What Can Be Revealed?

Authors: Geert Huys; Tom Vanhoutte; Peter Vandamme
Journal: Interdisciplinary Perspectives on Infectious Diseases
 (2008)

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## Abstract

Sequence-dependent electrophoresis (SDE) fingerprinting techniques such as denaturing gradient gel electrophoresis (DGGE) have become commonplace in the field of molecular microbial ecology. The success of the SDE technology lays in the fact that it allows visualization of the predominant members of complex microbial ecosystems independent of their culturability and without prior knowledge on the complexity and diversity of the ecosystem. Mainly using the prokaryotic 16S rRNA gene as PCR amplification target, SDE-based community fingerprinting turned into one of the leading molecular tools to unravel the diversity and population dynamics of human intestinal microbiota. The first part of this review covers the methodological concept of SDE fingerprinting and the technical hurdles for analyzing intestinal samples. Subsequently, the current state-of-the-art of DGGE and related techniques to analyze human intestinal microbiota from healthy individuals and from patients with intestinal disorders is surveyed. In addition, the applicability of SDE analysis to monitor intestinal population changes upon nutritional or therapeutic interventions is critically evaluated.


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## Body

## 1. Introduction

The mammalian intestinal tract comprises a highly
complex population of microorganisms reaching up to 1014 bacteria in
the large intestine  [1].
Starting off as a sterile system at birth, microbial colonization of the
intestine develops in a successive manner in which bacteria predominate along
with lower numbers of archae, yeasts, filamentous fungi, parasites, and viruses
[2, 3]. Following initial domination by facultative anaerobes, the gut
microbiota becomes gradually inhabited by obligate anaerobes which will remain
its major constituents during adult life  [4–7]. Triggered by the
growing number of 16S ribosomal RNA (rRNA)-based approaches,
insights in the evolutionary diversity of the human adult gut flora has
changed drastically in recent years. Based on a delineation level of 98% 16S
rRNA gene sequence similarity, current estimates indicate that the human
gastrointestinal tract encompasses more than 1000 bacterial phylogenetic types,
also referred to as phylotypes or “molecular species”  [8–10]. These taxonomic
inventory studies have revealed that the gut microbiota in adults is
largely dominated by members of only two bacterial phyla, that is, the
Bacteroidetes and the Firmicutes, and one member of the archaea, Methanobrevibacter
smithii. Through a complex network of mutualistic
interactions, the gut microbiota has a profound impact on the host’s health by
acting as a barrier against pathogens, contributing to the degradation of food
components, stimulating the host immune system, and producing a series of
essential vitamins, enzymes, and short-chain fatty acids  [11–14].Until
a decade ago, knowledge on the taxonomic composition and metabolic activity of
the intestinal tract microbiota was mainly based on the use of
culture-dependent techniques. Triggered by the growing awareness that only a
fraction of the gut microbiota is culturable under laboratory conditions,
various culture-independent methods have been evaluated in intestinal microbial
ecology  [15–19]. Depending on the scientific rationale
and technical design of the study, molecular
approaches for assessing diversity and dynamics of intestinal microbiota include population fingerprinting  [this review], clone
libraries  [20–23], dot blot hybridization  [24, 25],
fluorescent in situ hybridization (FISH)  [8, 26–29], real-time PCR  [30–33],
DNA microarrays  [34–36], and metagenomics  [9, 37–39].In contrast to several of the aforementioned techniques that specifically
target one or more autochthonous members of intestinal tract or that require
analysis of large and complex datasets, population fingerprinting is a
universal concept that allows one to characterize and monitor intestinal
microbiota without preexisting knowledge of its structure or composition. The most commonly used
fingerprinting techniques in the field of intestinal microbiology are based on
the sequence-dependent electrophoresis (SDE) principle and include denaturing gradient gel
electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE),
and temporal temperature gradient gel electrophoresis (TTGE). In contrast to conventional
gel electrophoresis based on fragment size, the SDE principle relies on the
sequence-dependent electrophoretic separation of a mixture of equally sized PCR
products in a polyacrylamide gel containing a linear gradient of chemical
denaturants (DGGE) or a linear temperature gradient (TGGE and TTGE). This way,
separation is achieved by the gradually decreasing electrophoretic mobility of
partially melted, double-stranded amplicons in the denaturing gradient. PCR
fragments equal in length but with different sequences have a different melting
behavior and will stop migrating at different positions along the gel,
eventually producing a banding pattern or fingerprint. To a lesser extent, also
single-strand conformation polymorphism (SSCP)  [40] and terminal-restriction
fragment length polymorphism (T-RFLP)  [41] analysis have been applied in
microbial community profiling. Likewise SDE methods, both these methods rely on
PCR amplification of specific target sequences followed by electrophoretic
separation of amplicons. Whereas this separation is based on sequence-specific
melting behavior of amplicons in SDE analysis, the taxonomic resolution of SSCP
and T-RFLP is determined by the secondary structure of ssDNA or by the
distribution of endonuclease restriction sites, respectively. The principle of
SSCP analysis is essentially based on the sequence-dependent differential
intramolecular folding of ssDNA which alters the migration speed of the
molecules  [42]. The ssDNA fragments originating from PCR amplicons are
separated using uniform, low temperature, nondenaturing electrophoresis to
maintain the secondary structure of the single-stranded fragments. T-RFLP
analysis, on the other hand, is based on a size-dependent electrophoretic
separation of digested fluorescently end-labeled PCR products. Upon
electrophoresis using either gel- or capillary-based systems, only the “terminal”
end-labeled restriction fragments are detected. Although less commonly used
than DGGE and related techniques, SSCP  [43] and, especially, T-RFLP  [44–48]
have been applied to study the diversity and dynamics of intestinal microbiota.
This review will specifically focus on the use of SDE techniques, and DGGE in
particular, in the field of intestinal microbiology.Since their introduction in microbial ecology in the early 1990s  [49],
SDE fingerprinting techniques have been employed to analyze microbial
communities in a wide range of environments including aquatic sites  [50–52],
soil  [53], fermented foods  [54, 55], and the human intestinal tract  [this
review]. The value of SDE-based fingerprinting methods in intestinal
microbiology lays in the fact that they allow pattern-based visualization of
the predominant bacterial groups including poorly culturable and currently
uncultured bacteria that are considered to represent up to 50–90% of the
intestinal microbiota.This review will deal with all different aspects of SDE methodology
including its possibilities and limitations in terms of reproducibility,
sensitivity, and data analysis. Through discussion of selected studies that
have contributed to the field, an overview will be presented of SDE-based
research approaches to study human intestinal ecosystems in relation to the
microbial ecology of healthy and disease-affected populations. The scope of
this review excludes SDE applications dealing with the human upper
gastrointestinal tract or with animal intestinal ecosystems.


## 2. Methodology

### 2.1. Principle

The principle of
SDE techniques relies on the electrophoretic separation of PCR amplicons with
equal length in a sequence-specific manner in a polyacrylamide matrix
containing a defined denaturing gradient of urea and formamide (DGGE) or
temperature gradient (TGGE and TTGE). The temperature gradient in TGGE is
created along the length of the gel, whereas in TTGE a temporal temperature
gradient is gradually formed during the electrophoresis run. The
electrophoretic mobility of double-stranded amplicons in a gel matrix with an
increasing denaturing gradient
is retarded at a given chemical denaturant concentration or temperature that
causes (partial) melting of the sequence region with lowest melting temperature
(Tm). The
physical denaturation of the dsDNA fragment is thus largely determined by its nucleotide
sequence and %G+C content and proceeds in discrete portions of the fragment or the so-called
melting domains. These domains interfere with the helical structure of the DNA
molecule and will eventually halt further migration. Amplicons that are
different at the sequence level are likely to display a different melting
behavior and will, therefore, stop migrating at different positions along the
linear gradient of the gel, which upon visualization will result in band
profiles representing the sequence diversity of the amplicon mixture.In practice, SDE-based community profiling comprises four steps: (i)
extraction of total community DNA from the sample; (ii) PCR-controlled
amplification using specific oligonucleotide primers; (iii) sequence-dependent electrophoretic
separation of the amplicons using either DGGE, TGGE or TTGE; and (iv)
fingerprint processing and analysis.


### 2.2. Sampling and Total DNA Extraction

#### 2.2.1. Sample Collection and Processing

The endogenous microbiota
differs along the length of the intestinal tract  [56]. In addition to the longitudinal
diversity gradient, also a cross-sectional differentiation of the microbial
population has been observed in the lumen, the mucosa, and the epithelium
surface  [56, 57]. Mainly due to sampling difficulties, the taxonomic composition of these microhabitats in the
intestinal tract is poorly documented. Because of this spatial distribution,
microbiological data obtained from a subsample of the gut cannot always be
extrapolated to the global composition of the entire intestinal microbiota. Most
often, fecal samples are used to study the intestinal microbiota because they
are the most accessible type of specimen that can be collected from this
environment. In specific clinical cases, also luminal endoscopy samples, mucus,
biopsies, and stoma liquid can be analyzed.In most studies, immediate
processing of samples is not feasible due to the need for transportation and/or
(long-term) storage of the specimen. It has been shown that storage of stool
samples at room temperature and even at 4°C showed a substantial reduction
in bacterial diversity and the degradation of bacterial DNA after 8 hours  [58].
Therefore, it is generally recommended that colon samples should be
(deep-)frozen immediately upon collection and stored at maximally −20°C and
preferably at −70°C, until further processing. However, it should be kept in
mind that repeated freezing and thawing of samples can have negative effects on
bacterial viability and recovery rates  [59–61]. Although poorly documented, the
impact of subsequent sample manipulations on DNA extraction and the yield and
quality of the resulting DNA probably is less dramatic  [62].


#### 2.2.2. Extraction of Community DNA

An efficient,
reproducible, and high-yield method for total DNA extraction is indispensable
in order to obtain a representative view of the actual microbial composition of
an intestinal sample. The most crucial step in any DNA extraction procedure is
cell lysis. A series of methods including commercial kits and inhouse laboratory
protocols have been described and evaluated for the extraction of total
bacterial DNA or RNA from intestinal samples making use of chemical, mechanical
(e.g., beads), and/or enzymatic lysis  [63–67]. Because not all members of the
intestinal microbiota display the same sensitivity to the lysis conditions of a
given procedure, it is extremely difficult, if not impossible, to extract DNA
from all constituting species with the same efficiency. Furthermore, the DNA
isolation procedure should also be able to remove potential PCR inhibitors that may be present in fecal
samples such as phenols, bile salts, degradation products of hemoglobin, and
complex polysaccharides of plant origin. The selection criteria usually
applied to evaluate the efficacy of a DNA extraction method include
electrophoretic verification of DNA integrity, determination of DNA yield and
quality using spectrophotometric analysis and quality control of the obtained
SDE profile  [64–66]. In addition, the lysis efficiency of different DNA extraction
protocols can be compared based on the complexity and band intensity of SDE
community fingerprints. Upon extraction, DNA solutions are generally stored at
−20°C. The influence
of storage conditions and duration of storage on the integrity and quality of
total DNA extracts from intestinal samples has not been studied in great
detail.Depending on the type of (clinical) application, high-quality community
DNA may need to be obtained from a range of different sample types such as
digesta, mucosal, and fecal samples. For this reason, the DNA extraction technique
should be carefully selected and possibly further evaluated or optimized with
particular attention for the type and number of specimens  [66]. In this
respect, it may be less appropriate to use commercial DNA extraction kits given
the limited possibilities to optimize the procedure, for example, by changing
concentrations or composition of extraction reagents. On the other hand,
commercial kits can considerably reduce the hands on time compared to
more complex inhouse protocols. In Table 1, technical details are given for a
number of frequently used total DNA extraction procedures that have been used
in SDE-based profiling of human intestinal microbial communities [6, 21, 24, 32, 33, 43, 63–66, 68–109].Table 1
DNA extraction procedures used
in SDE-based profiling of human intestinal microbial communities.
Description or referenceSample typeCell lysis (reagents or
principle)DNA extractionApplication(s)Selected reference(s)FastDNA kit (Bio101 Carlsbad, Calif,
USA)a
FastDNA SPIN kit (Qbiogene, Carlsbad, Calif,
USA)aFeces; mucosa biopsiesChemical (guanidium salts and
detergents) and mechanical (bead beating using garnet mix)Silica-based binding matrix (and spin filters)aDGGE; SSCP; real-time PCR;
cloning; sequencing[43, 63, 68–76]QIAampDNA Stool Mini
Kit (Qiagen, Valencia, Calif,
USA)Feces; mucosa biopsiesChemical (guanidium salts and
detergents)Silica-gel membrane spin columnsDGGE; TGGE; real-time PCR;
cloning; sequencing[63, 77–86]Modified protocol of  [87]FecesEnzymatic (lysozyme and mutanolysin) and
chemical-enzymatic (SDS and proteinase K)Phenol-chloroform-isoamylalcohol
and chloroformDGGE; sequencing[88]Modified protocol of  [89]FecesEnzymatic (lysozyme and mutanolysin)
and chemical
(guanidiumthiocyanate-EDTA-sarkosyl)Chloroform-isoamylalcoholDGGE; real-time PCR[64, 90][24]Feces; mucosa biopsiesMechanical (bead beating)TTGE[91–94][95]Feces; cecal fluids; mucosa
biopsiesMechanical
(bead beating in acid phenol)Phenol-chloroform
and chloroformDGGE;
TGGE; cloning; sequencing[6, 65, 66, 96–102][32]FecesMechanical-chemical
(bead beating in buffer-saturated phenol and SDS)Phenol-chloroformGroup-specific PCR; real-time PCR[33][103]FecesChemical (guanidiumthiocyanate and sarkosyl) and
mechanical (bead beating)Polyvinyl-polypyrrolidoneTTGE; cloning; sequencing[21, 104–109]baFastDNA
kit (Bio101) and FastDNA SPIN Kit (Qbiogene) only differ in the use of spin
filters during the silica-DNA purification.
bThe authors of [106–108] reported a modified protocol of [103] in which also phenol and
chloroform-isoamylalcohol were applied in the extraction procedure.


### 2.3. Community PCR

Following DNA
extraction and purification, multiple primer sets with different taxonomic
coverage can be applied for community PCR amplification. The use of universal
PCR primers allows any microbial community to be analyzed, although in
ecosystems with a high diversity like the intestinal tract only the
(pre)dominant constituents will in effect generate a visible band in SDE. In
order to focus on a specific subpopulation within the total
community, group-specific PCR primers can be used which allow detection of
bacterial taxa that are less prevalent in the intestinal tract. Traditionally, universal and specific community PCR primers for SDE
applications are designed using the 16S rRNA gene as a target molecule. This preference
stems from the fact that the SSU rRNA gene has a mosaic structure composed of
both invariant, relatively conserved, and highly variable regions (V regions).
In SDE-based population fingerprinting, primers are used that anneal to
conserved sequence parts of the gene in order to cover one up to three
hypervariable regions. In Table2, a selection is
presented of universal and specific primer sets that have been used in
SDE-based profiling of human intestinal microbial communities  [6, 30, 64, 65, 69, 71–74, 76, 77, 79–82, 84, 85, 86, 88, 90, 91, 94, 95, 96, 98, 99, 101, 102, 105–130]. Taking the rumen as model system
of complex microbial community, Yu and Morrison  [131] systematically compared a
set of DGGE profiles obtained with universal
primers targeting different V regions. Based on sequence variability and
temperature heterogeneity of the lowest Tm  domain of the V region and on the
number, resolution, and relative intensity of the bands in the resulting DGGE
profile, the V3 region was most preferred for analyzing intestinal
microbiomes. In addition, the authors recommended to
use the V3–V5 or V6–V8 regions if a longer amplicon is preferred. Next to the SSU rRNA gene, also its rRNA counterpart can be coextracted and used as PCR template in SDE analyses of
intestinal ecosystems when preceded by reverse transcription  [70, 71, 75, 93, 95]. In
this way, SDE profiles are generated that represent the (pre)dominant
metabolically active bacteria based on the assumption that the cells of these
organisms generally have a much higher ribosomal RNA content and rRNA/DNA ratio
compared to resting cells.Table 2
Universal and group-specific PCR primers used in SDE-based
profiling of human intestinal microbial communities.
Target group(s)Primer designationSequence (5′-3′)aTarget regionSelected reference(s)Domain levelBacteriaHDA1bACTCCTACGGGAGGCAGCAGTV2-V3-16S
rDNA[30, 76, 102, 110–114]HDA2bGTATTACCGCGGCTGCTGGCACF357CCTACGGGAGGCAGCAGV3-16S
rDNA[64, 72, 73, 77, 79, 115–117]518RATTACCGCGGCTGCTGG339FcCTCCTACGGGAGGCAGCAGV3-V4-16S
rDNA[94, 106, 107]788RGGACTACCAGGGTATCTAAU968-FAACGCGAAGAACCTTACV6–V8-16S
rDNA[6, 64, 65, 69, 71, 79, 80, 82, 84, 85, 91, 95, 96, 101, 105, 109, 117–123]L1401-RCGGTGTGTACAAGACCCGenus (group) levelBacteroidesFD1AGAGTTTGATCCTGGCTCAG16S
rDNA[124]RbacPreTCACCGTTGCCGGCGTACTCBfr-FCTGAACCAGCCAAGTAGCG16S
rDNA[81]Bfr-RCCGCAAACTTTCACAACTGACTTABifidobacteriumBif164-fGGGTGGTAATGCCGGATG16S
rDNA[69, 79, 82, 85, 99, 101, 105, 108, 118, 125–127]Bif662-rCCACCGTTACACCGGGAAg-Bifid-FCTCCTGGAAACGGGTGG16S
rDNA[64]g-Bifid-RGGTGTTCTTCCCGATATCTACAForTalCGTCGCCTTCTTCTTCGTCTCtransaldolase
gene[74]RevTalCTTCTCCGGCATGGTGTTGACHelicobacter658fTGGGAGAGGTAGGTGGAAT16S
rDNA[128]1067RGCCGTGCAGCACCTGTTTTCAEnterococcusEnt1017FCCTTTGACCACTCTAGAG16S
rDNA[64]Ent1263RCTTAGCCTCGCGACTLactobacillus groupdLac1AGCAGTAGGGAATCTTCCA16S
rDNA[64, 86, 88, 125, 127, 129, 130]Lac2ATTYCACCGCTACACATG27f (also Bact-0011f)AGAGTTTGAT(C/T)(A/C)TGGCTCAG16S rDNA[79, 98, 118]Lab-0677rCACCGCTACACATGGAGLab-0159fGGAAACAG(A/G)TGCTAATACCG16S
rDNA[98, 118]Uni-0515-rATCGTATTACCGCGGCTGCTGGCALab-0159fGGAAACAG(A/G)TGCTAATACCG16S rDNA[124]Lab-0677rCACCGCTACACATGGAGSpecies
group levelBacteroides fragilis subgroupeg-Bact-FATAGCCTTTCGAAAGRAAGAT16S
rDNA[73]g-Bact-RCCAGTATCAACTGCAATTTTABact 596FTCAGTTGTGAAAGTTTGCG16S
rDNA[64]Bact 826RGTRTATCGCMAACAGCGABact 531FATACGGAGGATCCGAGCGTTA16S
rDNA[90]Bact 766RCTGTTTGATACCCACACTClostridium phylogenetic clusters XI and XIVafErec 688FGCGTAGATATTAGGAGGAAC16S
rDNA[90]Erec 841RTGCGTTWGCKRCGGCACCGaA GC-clamp is attached to the 5′ end of either the
forward or reverse primer.
bPrimers HDA1 and HDA2 have the same core sequence
as primers 341 f and 518 r, respectively, but with a few additional
nucleotides at both  5′ and 3′ ends.
cPrimer
339f has the same core sequence as primer 341 f but with two additional
nucleotides at the 5′ end.
dThe Lactobacillus group comprising the genera Lactobacillus, Leuconostoc, Pediococcus, Weissella, and Aerococcus (the latter genus
was originally not described as target of the Lac1/2 primers).
eThe Bacteroides fragilis subgroup comprising B. fragilis, B. acidifaciens, B. caccae, B. eggerthii, B. ovatus, B. stercoris, B. thetaiotaomicron, B. uniformis, and B. vulgatus.
fClostridium phylogenetic cluster XI represents the Clostridium lituseburense group, whereas Clostridium phylogenetic cluster XIVa represents
the Clostridium coccoides-Eubacterium rectale group.An
additional 40-nucleotide GC rich sequence, the so-called GC-clamp, is usually
attached to the5′ end of one or both of the PCR primers and participates in
the PCR reaction. This way, the GC-tail generated at the end of the amplicon
will prevent complete denaturation of the product and is necessary to obtain a
stable melting behavior of the fragments during electrophoresis  [49, 132, 133].
GC-clamps can vary in sequence, length, and location  [100, 134–136], and
their design needs to be based on the target sequence and the primers used.
Mutation analysis data have shown that GC-clamps have the strongest effect on
the melting properties of short fragments (<300 bp) and that this effect may
be drastically reduced for large fragments (>400 bp)  [136]. Also, it has
been demonstrated that a GC-clamp length of 60 bp may be efficient for
detection of fragments with a Tm  value close to 80°C whereas
fragments with Tm>80°C may require longer GC-clamps in
combination with naturally occurring high-melting (thus GC-rich) domains  [136].


### 2.4. Sequence-Dependent Electrophoresis

#### 2.4.1. Electrophoresis Conditions

Essentially, a
DGGE system consists of a heated buffer tank operated under strict control of
temperature and stable buffer circulation. Several systems are currently
available, of which DCode (Bio-Rad Laboratories;http://www.bio-rad.com/), INGENYphorU
(Ingeny; http://www.ingeny.com/), and DGGEK-1001/2001/2401/4001/4801 (CBS Scientific;
http://www.cbsscientific.com/) appear to be most commonly used. The apparatus provided
by Bio-Rad and Ingeny can also be applied for TTGE analysis whereas for TGGE a
temperature gradient block should be integrated in the system. In case a
high number of samples need to be analyzed such as in monitoring studies, the sample capacity of the system is an
important criterion. The maximum capacity per run for the three aforementioned
systems varies from 60 (DCode), 96 (INGENYphorU) to 128 (DGGEK-4801) samples.In general, DGGE makes use of parallel gel electrophoretic systems
that have an increasing vertical gradient of denaturants parallel to the
direction of electrophoresis. In many studies, the optimal denaturing gradient
yielding the highest resolution is first determined by perpendicular gradient
gels. For this purpose, one sample containing one or more PCR fragments is
electrophoretically separated across a denaturing gradient perpendicular to the
direction of the electric field resulting in sigmoid-shaped curves. From these gels, the intermediate range of denaturant concentration,
where different electrophoretic mobilities between PCR products are obtained,
is considered the optimal gradient of denaturants for multilane analysis in
parallel DGGE. The optimal time of electrophoresis can be determined through a “time travel” experiment during which a
mixture of PCR fragments is loaded onto a parallel gel at constant time
intervals. The optimal duration of a DGGE run can be derived from the time
needed to obtain maximal separation of amplicons.A detailed procedure to cast and run DGGE gels has been described by
Muyzer et al.  [134, 137]. Essentially, the desired low and high concentration
of denaturing solution is obtained by mixing zero (0%) and high-concentration
(80–100%) denaturing acrylamide solutions in appropriate ratios. Upon the
addition of ammoniumpersulphate and tetramethylethylenediamine, the mixture is
poured between two vertical glass plates in order to generate a linear
denaturing gradient. The concentration of acrylamide usually ranges from 6–12%
and depends on the size range of the fragments to be separated. In general, the
high-concentration denaturing solution contains 7-8 M urea and 20–40% formamide.
Electrophoresis is mostly carried out in 0.5× or 1× TAE-buffer at a fixed
voltage between 50 V and 250 V and a constant temperature between 55 and 65°C.
Run times generally range from 3–17 hours, although longer run times with lower
voltages tend to produce better quality gels.In the case of TGGE and TTGE, a linearly increasing temperature
gradient parallel to the electrophoresis direction or formed during the length
of electrophoresis, respectively, is applied in combination with a uniform,
high-denaturant polyacrylamide gel to separate PCR fragments. To determine the
temperature range for parallel TGGE or TTGE analysis, a melting profile of the
DNA sequence can be generated using specialized software (e.g., Poland
analysis software;http://www.biophys.uni-duesseldorf.de/local/POLAND/poland.html).
The optimal temperature gradient is theoretically delineated by the lowest and
highest Tm values obtained in the melting profile. The theoretical Tm values can be lowered by adding denaturing components to the gel, for example,
one mole of urea will lower the theoretical Tm with 2°C  [138, 139].
In general, a 6–8 M urea gel is used in combination with a typical temperature
range between 35 and 70°C.Unlike many other fingerprinting methods that make use of
commercially available size standards, SDE techniques suffer from a lack of
consensus regarding standards for normalization. Because denaturing gradients
can slightly vary between different gels, a standard reference composed of
amplicons from pure cultures that spans a maximal range of the applied gradient should be routinely included at several fixed internal
positions on every gel to allow data normalization and gel-to-gel comparison with a high degree
of confidence. Neufeld and Mohn  [140] proposed an approach which
facilitated
and improved normalization of samples from multiple gels by including standards in each lane instead of using
interlane standards. These
intralane standards contain fluorescent tags incorporated in the primers that
excitate at another wavelength than that of the fluorescent molecules attached
to the unknown PCR product. Furthermore, the application of fluorophore-labeled
primers does not require gel staining following electrophoresis, which improves
the overall sensitivity of the population fingerprinting procedure and enables
additional DGGE versatility including simultaneous analysis of DNA- and
RNA-derived mixtures in the same lane.


#### 2.4.2. Gel Staining

Upon
electrophoresis, gels are stained and digitally captured for further analysis.
Three staining agents are commonly used to visualize fragments. Originally, SDE
gels were stained with ethidium bromide (EtBr) given its widespread use as an
intercalating fluorescent dye used to detect nucleic acids. The next generation
of fluorescent nucleic acid dyes such as SYBR Green and similar stains offer an
increased sensitivity compared to EtBr due to a lower overall background signal
allowing detection of
DNA fragments at lower concentrations  [64, 134, 137]. Additional
advantages of these newer dyes are that they are generally considered to be
less toxic or mutagenic than EtBr and can be excited by wavelengths above 400 nm which enables the use of non-UV illumination. One specific member of the
SYBR Green family, SYBR Gold, binds to both dsDNA and ssDNA. This specific
feature may further enhance the detection sensitivity since DNA amplicons in
the SDE gels are partially single stranded. Although less commonly applied, silver staining is generally
considered the most sensitive staining procedure. Following DNA fixation with
ethanol and acid (e.g., nitric acid), Ag+ ions in silver nitrate are
selectively reduced under alkaline conditions by formaldehyde to metallic
silver (Ag) that is visualized as a black precipitate. Potential drawbacks of
this procedure include the fact that silver stained gels impede subsequent
blotting experiments or
band sequence analysis and the aspecific detection of protein components such
as BSA and Taq polymerase present in the PCR mix which may generate
additional background signals  [134, 137].


### 2.5. Data Analysis

Normalized SDE fingerprints can be analyzed visually and/or
numerically. Visual interpretation is attainable when only a limited number of
profiles with low complexity are to be compared. However, once banding patterns
become more complex such as those obtained from intestinal samples or when the
number of profiles increases (e.g., in the course of monitoring studies),
analysis of SDE fingerprints requires implementation of numerical methods
[141]. For this purpose, digitized SDE gels are further processed using
dedicated image analysis software like GelCompar and BioNumerics (Applied Maths; http://www.applied-maths.com/), Quantity One and Molecular
Analyst (Bio-Rad Laboratories), GeneTools (Syngene; http://www.syngene.com/), and
Photo-Capt (Vilber Lourmat; http://www.vilber.com/). These programs permit numerical
analysis of band patterns and usually also include statistical approaches for
data interpretation. Programs that have been used specifically for statistical
analysis of SDE fingerprint data include R (http://www.r-project.org/) and DGGESTAT (developed
at the Netherlands Institute for Ecological Research, NIOO-KNAW, Nieuwersluis,
The Netherlands).


#### 2.5.1. Diversity and Similarity Analysis

Most commonly, numerical analysis of SDE profiles
relies on the use of diversity indices and/or cluster analysis. Diversity measures
for fingerprint
analysis such as the Simpson index and the Shannon-Wiener/Weaver index express the degree of
ecosystem diversity as a function of band profile complexity but fail to
express similarity between profiles based on band positions. Hierarchic
clustering algorithms such as unweighted pair-group method using arithmetic
averages (UPGMA) produce a visual representation of the similarity between SDE
profiles expressed as similarity indices, for example, using the curve-based
Pearson product-moment correlation coefficient, the band-based Dice coefficient,
or Sorenson’s pairwise coefficient. Other authors have used multivariate ordination methods such as nonmetric
multidimensional scaling  [142, 143], principal component analysis  [109, 144],
correspondence analysis  [145], canonical variate analysis  [146], and canonical
correspondence analysis  [147]. These methods are used for integration of
complex datasets such as the bands in an SDE pattern into new mathematical
variables which can be projected into a few-dimension perspective or reduced
space. A more detailed description of these statistical procedures has been
reported elsewhere  [148]. Gafan et al.  [149] evaluated the use of logistic regression for
statistical analysis of complex DGGE profiles. This analysis method takes into
consideration the outcome in addition to differences in overall band profile
complexity and individual band positions. It is beyond doubt that the list of
numerical approaches and statistical tools for analysis of SDE profiles will
further expand in the coming years. Although the choice of method(s) is
depending on the aim of the study and on the complexity of the ecosystem, community
fingerprints generally include more information than are usually revealed with
currently available methods. For this reason, more efforts should be put in the
development of new and extended processing methods for complex SDE data.


#### 2.5.2. Identification Analysis

Next to the first
SDE analysis level based on the use of diversity and similarity coefficients, a
second level can be defined that allows one to identify and monitor specific
members of the intestinal ecosystem. Essentially, identification of individual
bands in SDE fingerprints may be obtained by band position analysis (BPA)
and/or through band sequencing analysis. Essentially, BPA relies on the
comparison of migration distances of band fragments from taxonomically
well-characterized reference strains with those of unknown bands present in the
sample profiles. BPA can either be performed by analyzing samples and reference
strains in adjacent lanes on the same gel (i.e., comigration analysis) or by
comparing unknown band positions with those of reference strains present in a
user-generated SDE database. In intestinal ecosystems, BPA-based identification
may not always yield a conclusive result given the possibility that a single
band may consist of multiple amplicons from different species or that two or
more (phylogenetically related) species are characterized by the same band
position in the sample profile. Ideally, each band position in a sample profile
should represent one species. In practice, however, the multioperon effect
observed for some taxa when using 16S rRNA gene primers may lead to an
overestimation of the number of predominant species in the sample (e.g., see Section4.2). In contrast to SDE profiles obtained with universal primers,
identification of bands in subpopulation profiles by BPA may be more feasible.
Application of SDE using group-specific primers for the genera Bacteroides  [81, 90] and Bifidobacterium  [74, 90, 108, 117] showed that species
identities can be resolved by means of BPA. Temmerman et al.  [117] described a
protocol to identify bifidobacterial communities based on a nested-PCR-DGGE
approach comprising a Bifidobacterium-specific PCR step followed by a
second PCR step in which both the V3 and V6–V8 regions of the 16S rRNA gene
were amplified. A mix of both amplicons was analyzed on a DGGE gel, after which
band positions were compared with a user-generated database of reference
strains.Identification
results from BPA can or even should be verified by band sequencing, and may
help to determine the phylogenetic affiliation of unknown bands. Various
procedures have been described to excise and recover PCR fragments from the
polyacrylamide gel matrix ranging from conventional elution in electrophoresis
buffer to specialized protocols using diffusion buffers and commercial kits
[74]. A critical postextraction step during this process concerns
the reamplification and subsequent SDE analysis of the excised fragment
together with the original environmental sample in order to verify if the
correct band was extracted. Upon confirmation, the recovered PCR fragments can
be directly sequenced without additional cloning. Subsequent identification of
the obtained sequence information can be achieved by comparison with sequences
stored in public databases, for example, EMBL (http://www.ebi.ac.uk/embl/) or GenBank
(http://www.ncbi.nlm.nih.gov/genbank).As
further discussed below, the significance of the obtained species information
is dependent on the length of the fragment and the hypervariable region it
represents in the target gene. This sequence information can also be employed
to develop probes for application in FISH and real-time PCR assays to detect
and quantify the target organisms. Next to sequencing analysis, identities of
individual bands in SDE profiles can also be revealed by Southern hybridization
with taxonomic probes  [150].


## 2.1. Principle

The principle of
SDE techniques relies on the electrophoretic separation of PCR amplicons with
equal length in a sequence-specific manner in a polyacrylamide matrix
containing a defined denaturing gradient of urea and formamide (DGGE) or
temperature gradient (TGGE and TTGE). The temperature gradient in TGGE is
created along the length of the gel, whereas in TTGE a temporal temperature
gradient is gradually formed during the electrophoresis run. The
electrophoretic mobility of double-stranded amplicons in a gel matrix with an
increasing denaturing gradient
is retarded at a given chemical denaturant concentration or temperature that
causes (partial) melting of the sequence region with lowest melting temperature
(Tm). The
physical denaturation of the dsDNA fragment is thus largely determined by its nucleotide
sequence and %G+C content and proceeds in discrete portions of the fragment or the so-called
melting domains. These domains interfere with the helical structure of the DNA
molecule and will eventually halt further migration. Amplicons that are
different at the sequence level are likely to display a different melting
behavior and will, therefore, stop migrating at different positions along the
linear gradient of the gel, which upon visualization will result in band
profiles representing the sequence diversity of the amplicon mixture.In practice, SDE-based community profiling comprises four steps: (i)
extraction of total community DNA from the sample; (ii) PCR-controlled
amplification using specific oligonucleotide primers; (iii) sequence-dependent electrophoretic
separation of the amplicons using either DGGE, TGGE or TTGE; and (iv)
fingerprint processing and analysis.


## 2.2. Sampling and Total DNA Extraction

### 2.2.1. Sample Collection and Processing

The endogenous microbiota
differs along the length of the intestinal tract  [56]. In addition to the longitudinal
diversity gradient, also a cross-sectional differentiation of the microbial
population has been observed in the lumen, the mucosa, and the epithelium
surface  [56, 57]. Mainly due to sampling difficulties, the taxonomic composition of these microhabitats in the
intestinal tract is poorly documented. Because of this spatial distribution,
microbiological data obtained from a subsample of the gut cannot always be
extrapolated to the global composition of the entire intestinal microbiota. Most
often, fecal samples are used to study the intestinal microbiota because they
are the most accessible type of specimen that can be collected from this
environment. In specific clinical cases, also luminal endoscopy samples, mucus,
biopsies, and stoma liquid can be analyzed.In most studies, immediate
processing of samples is not feasible due to the need for transportation and/or
(long-term) storage of the specimen. It has been shown that storage of stool
samples at room temperature and even at 4°C showed a substantial reduction
in bacterial diversity and the degradation of bacterial DNA after 8 hours  [58].
Therefore, it is generally recommended that colon samples should be
(deep-)frozen immediately upon collection and stored at maximally −20°C and
preferably at −70°C, until further processing. However, it should be kept in
mind that repeated freezing and thawing of samples can have negative effects on
bacterial viability and recovery rates  [59–61]. Although poorly documented, the
impact of subsequent sample manipulations on DNA extraction and the yield and
quality of the resulting DNA probably is less dramatic  [62].


### 2.2.2. Extraction of Community DNA

An efficient,
reproducible, and high-yield method for total DNA extraction is indispensable
in order to obtain a representative view of the actual microbial composition of
an intestinal sample. The most crucial step in any DNA extraction procedure is
cell lysis. A series of methods including commercial kits and inhouse laboratory
protocols have been described and evaluated for the extraction of total
bacterial DNA or RNA from intestinal samples making use of chemical, mechanical
(e.g., beads), and/or enzymatic lysis  [63–67]. Because not all members of the
intestinal microbiota display the same sensitivity to the lysis conditions of a
given procedure, it is extremely difficult, if not impossible, to extract DNA
from all constituting species with the same efficiency. Furthermore, the DNA
isolation procedure should also be able to remove potential PCR inhibitors that may be present in fecal
samples such as phenols, bile salts, degradation products of hemoglobin, and
complex polysaccharides of plant origin. The selection criteria usually
applied to evaluate the efficacy of a DNA extraction method include
electrophoretic verification of DNA integrity, determination of DNA yield and
quality using spectrophotometric analysis and quality control of the obtained
SDE profile  [64–66]. In addition, the lysis efficiency of different DNA extraction
protocols can be compared based on the complexity and band intensity of SDE
community fingerprints. Upon extraction, DNA solutions are generally stored at
−20°C. The influence
of storage conditions and duration of storage on the integrity and quality of
total DNA extracts from intestinal samples has not been studied in great
detail.Depending on the type of (clinical) application, high-quality community
DNA may need to be obtained from a range of different sample types such as
digesta, mucosal, and fecal samples. For this reason, the DNA extraction technique
should be carefully selected and possibly further evaluated or optimized with
particular attention for the type and number of specimens  [66]. In this
respect, it may be less appropriate to use commercial DNA extraction kits given
the limited possibilities to optimize the procedure, for example, by changing
concentrations or composition of extraction reagents. On the other hand,
commercial kits can considerably reduce the hands on time compared to
more complex inhouse protocols. In Table 1, technical details are given for a
number of frequently used total DNA extraction procedures that have been used
in SDE-based profiling of human intestinal microbial communities [6, 21, 24, 32, 33, 43, 63–66, 68–109].Table 1
DNA extraction procedures used
in SDE-based profiling of human intestinal microbial communities.
Description or referenceSample typeCell lysis (reagents or
principle)DNA extractionApplication(s)Selected reference(s)FastDNA kit (Bio101 Carlsbad, Calif,
USA)a
FastDNA SPIN kit (Qbiogene, Carlsbad, Calif,
USA)aFeces; mucosa biopsiesChemical (guanidium salts and
detergents) and mechanical (bead beating using garnet mix)Silica-based binding matrix (and spin filters)aDGGE; SSCP; real-time PCR;
cloning; sequencing[43, 63, 68–76]QIAampDNA Stool Mini
Kit (Qiagen, Valencia, Calif,
USA)Feces; mucosa biopsiesChemical (guanidium salts and
detergents)Silica-gel membrane spin columnsDGGE; TGGE; real-time PCR;
cloning; sequencing[63, 77–86]Modified protocol of  [87]FecesEnzymatic (lysozyme and mutanolysin) and
chemical-enzymatic (SDS and proteinase K)Phenol-chloroform-isoamylalcohol
and chloroformDGGE; sequencing[88]Modified protocol of  [89]FecesEnzymatic (lysozyme and mutanolysin)
and chemical
(guanidiumthiocyanate-EDTA-sarkosyl)Chloroform-isoamylalcoholDGGE; real-time PCR[64, 90][24]Feces; mucosa biopsiesMechanical (bead beating)TTGE[91–94][95]Feces; cecal fluids; mucosa
biopsiesMechanical
(bead beating in acid phenol)Phenol-chloroform
and chloroformDGGE;
TGGE; cloning; sequencing[6, 65, 66, 96–102][32]FecesMechanical-chemical
(bead beating in buffer-saturated phenol and SDS)Phenol-chloroformGroup-specific PCR; real-time PCR[33][103]FecesChemical (guanidiumthiocyanate and sarkosyl) and
mechanical (bead beating)Polyvinyl-polypyrrolidoneTTGE; cloning; sequencing[21, 104–109]baFastDNA
kit (Bio101) and FastDNA SPIN Kit (Qbiogene) only differ in the use of spin
filters during the silica-DNA purification.
bThe authors of [106–108] reported a modified protocol of [103] in which also phenol and
chloroform-isoamylalcohol were applied in the extraction procedure.


## 2.2.1. Sample Collection and Processing

The endogenous microbiota
differs along the length of the intestinal tract  [56]. In addition to the longitudinal
diversity gradient, also a cross-sectional differentiation of the microbial
population has been observed in the lumen, the mucosa, and the epithelium
surface  [56, 57]. Mainly due to sampling difficulties, the taxonomic composition of these microhabitats in the
intestinal tract is poorly documented. Because of this spatial distribution,
microbiological data obtained from a subsample of the gut cannot always be
extrapolated to the global composition of the entire intestinal microbiota. Most
often, fecal samples are used to study the intestinal microbiota because they
are the most accessible type of specimen that can be collected from this
environment. In specific clinical cases, also luminal endoscopy samples, mucus,
biopsies, and stoma liquid can be analyzed.In most studies, immediate
processing of samples is not feasible due to the need for transportation and/or
(long-term) storage of the specimen. It has been shown that storage of stool
samples at room temperature and even at 4°C showed a substantial reduction
in bacterial diversity and the degradation of bacterial DNA after 8 hours  [58].
Therefore, it is generally recommended that colon samples should be
(deep-)frozen immediately upon collection and stored at maximally −20°C and
preferably at −70°C, until further processing. However, it should be kept in
mind that repeated freezing and thawing of samples can have negative effects on
bacterial viability and recovery rates  [59–61]. Although poorly documented, the
impact of subsequent sample manipulations on DNA extraction and the yield and
quality of the resulting DNA probably is less dramatic  [62].


## 2.2.2. Extraction of Community DNA

An efficient,
reproducible, and high-yield method for total DNA extraction is indispensable
in order to obtain a representative view of the actual microbial composition of
an intestinal sample. The most crucial step in any DNA extraction procedure is
cell lysis. A series of methods including commercial kits and inhouse laboratory
protocols have been described and evaluated for the extraction of total
bacterial DNA or RNA from intestinal samples making use of chemical, mechanical
(e.g., beads), and/or enzymatic lysis  [63–67]. Because not all members of the
intestinal microbiota display the same sensitivity to the lysis conditions of a
given procedure, it is extremely difficult, if not impossible, to extract DNA
from all constituting species with the same efficiency. Furthermore, the DNA
isolation procedure should also be able to remove potential PCR inhibitors that may be present in fecal
samples such as phenols, bile salts, degradation products of hemoglobin, and
complex polysaccharides of plant origin. The selection criteria usually
applied to evaluate the efficacy of a DNA extraction method include
electrophoretic verification of DNA integrity, determination of DNA yield and
quality using spectrophotometric analysis and quality control of the obtained
SDE profile  [64–66]. In addition, the lysis efficiency of different DNA extraction
protocols can be compared based on the complexity and band intensity of SDE
community fingerprints. Upon extraction, DNA solutions are generally stored at
−20°C. The influence
of storage conditions and duration of storage on the integrity and quality of
total DNA extracts from intestinal samples has not been studied in great
detail.Depending on the type of (clinical) application, high-quality community
DNA may need to be obtained from a range of different sample types such as
digesta, mucosal, and fecal samples. For this reason, the DNA extraction technique
should be carefully selected and possibly further evaluated or optimized with
particular attention for the type and number of specimens  [66]. In this
respect, it may be less appropriate to use commercial DNA extraction kits given
the limited possibilities to optimize the procedure, for example, by changing
concentrations or composition of extraction reagents. On the other hand,
commercial kits can considerably reduce the hands on time compared to
more complex inhouse protocols. In Table 1, technical details are given for a
number of frequently used total DNA extraction procedures that have been used
in SDE-based profiling of human intestinal microbial communities [6, 21, 24, 32, 33, 43, 63–66, 68–109].Table 1
DNA extraction procedures used
in SDE-based profiling of human intestinal microbial communities.
Description or referenceSample typeCell lysis (reagents or
principle)DNA extractionApplication(s)Selected reference(s)FastDNA kit (Bio101 Carlsbad, Calif,
USA)a
FastDNA SPIN kit (Qbiogene, Carlsbad, Calif,
USA)aFeces; mucosa biopsiesChemical (guanidium salts and
detergents) and mechanical (bead beating using garnet mix)Silica-based binding matrix (and spin filters)aDGGE; SSCP; real-time PCR;
cloning; sequencing[43, 63, 68–76]QIAampDNA Stool Mini
Kit (Qiagen, Valencia, Calif,
USA)Feces; mucosa biopsiesChemical (guanidium salts and
detergents)Silica-gel membrane spin columnsDGGE; TGGE; real-time PCR;
cloning; sequencing[63, 77–86]Modified protocol of  [87]FecesEnzymatic (lysozyme and mutanolysin) and
chemical-enzymatic (SDS and proteinase K)Phenol-chloroform-isoamylalcohol
and chloroformDGGE; sequencing[88]Modified protocol of  [89]FecesEnzymatic (lysozyme and mutanolysin)
and chemical
(guanidiumthiocyanate-EDTA-sarkosyl)Chloroform-isoamylalcoholDGGE; real-time PCR[64, 90][24]Feces; mucosa biopsiesMechanical (bead beating)TTGE[91–94][95]Feces; cecal fluids; mucosa
biopsiesMechanical
(bead beating in acid phenol)Phenol-chloroform
and chloroformDGGE;
TGGE; cloning; sequencing[6, 65, 66, 96–102][32]FecesMechanical-chemical
(bead beating in buffer-saturated phenol and SDS)Phenol-chloroformGroup-specific PCR; real-time PCR[33][103]FecesChemical (guanidiumthiocyanate and sarkosyl) and
mechanical (bead beating)Polyvinyl-polypyrrolidoneTTGE; cloning; sequencing[21, 104–109]baFastDNA
kit (Bio101) and FastDNA SPIN Kit (Qbiogene) only differ in the use of spin
filters during the silica-DNA purification.
bThe authors of [106–108] reported a modified protocol of [103] in which also phenol and
chloroform-isoamylalcohol were applied in the extraction procedure.


## 2.3. Community PCR

Following DNA
extraction and purification, multiple primer sets with different taxonomic
coverage can be applied for community PCR amplification. The use of universal
PCR primers allows any microbial community to be analyzed, although in
ecosystems with a high diversity like the intestinal tract only the
(pre)dominant constituents will in effect generate a visible band in SDE. In
order to focus on a specific subpopulation within the total
community, group-specific PCR primers can be used which allow detection of
bacterial taxa that are less prevalent in the intestinal tract. Traditionally, universal and specific community PCR primers for SDE
applications are designed using the 16S rRNA gene as a target molecule. This preference
stems from the fact that the SSU rRNA gene has a mosaic structure composed of
both invariant, relatively conserved, and highly variable regions (V regions).
In SDE-based population fingerprinting, primers are used that anneal to
conserved sequence parts of the gene in order to cover one up to three
hypervariable regions. In Table2, a selection is
presented of universal and specific primer sets that have been used in
SDE-based profiling of human intestinal microbial communities  [6, 30, 64, 65, 69, 71–74, 76, 77, 79–82, 84, 85, 86, 88, 90, 91, 94, 95, 96, 98, 99, 101, 102, 105–130]. Taking the rumen as model system
of complex microbial community, Yu and Morrison  [131] systematically compared a
set of DGGE profiles obtained with universal
primers targeting different V regions. Based on sequence variability and
temperature heterogeneity of the lowest Tm  domain of the V region and on the
number, resolution, and relative intensity of the bands in the resulting DGGE
profile, the V3 region was most preferred for analyzing intestinal
microbiomes. In addition, the authors recommended to
use the V3–V5 or V6–V8 regions if a longer amplicon is preferred. Next to the SSU rRNA gene, also its rRNA counterpart can be coextracted and used as PCR template in SDE analyses of
intestinal ecosystems when preceded by reverse transcription  [70, 71, 75, 93, 95]. In
this way, SDE profiles are generated that represent the (pre)dominant
metabolically active bacteria based on the assumption that the cells of these
organisms generally have a much higher ribosomal RNA content and rRNA/DNA ratio
compared to resting cells.Table 2
Universal and group-specific PCR primers used in SDE-based
profiling of human intestinal microbial communities.
Target group(s)Primer designationSequence (5′-3′)aTarget regionSelected reference(s)Domain levelBacteriaHDA1bACTCCTACGGGAGGCAGCAGTV2-V3-16S
rDNA[30, 76, 102, 110–114]HDA2bGTATTACCGCGGCTGCTGGCACF357CCTACGGGAGGCAGCAGV3-16S
rDNA[64, 72, 73, 77, 79, 115–117]518RATTACCGCGGCTGCTGG339FcCTCCTACGGGAGGCAGCAGV3-V4-16S
rDNA[94, 106, 107]788RGGACTACCAGGGTATCTAAU968-FAACGCGAAGAACCTTACV6–V8-16S
rDNA[6, 64, 65, 69, 71, 79, 80, 82, 84, 85, 91, 95, 96, 101, 105, 109, 117–123]L1401-RCGGTGTGTACAAGACCCGenus (group) levelBacteroidesFD1AGAGTTTGATCCTGGCTCAG16S
rDNA[124]RbacPreTCACCGTTGCCGGCGTACTCBfr-FCTGAACCAGCCAAGTAGCG16S
rDNA[81]Bfr-RCCGCAAACTTTCACAACTGACTTABifidobacteriumBif164-fGGGTGGTAATGCCGGATG16S
rDNA[69, 79, 82, 85, 99, 101, 105, 108, 118, 125–127]Bif662-rCCACCGTTACACCGGGAAg-Bifid-FCTCCTGGAAACGGGTGG16S
rDNA[64]g-Bifid-RGGTGTTCTTCCCGATATCTACAForTalCGTCGCCTTCTTCTTCGTCTCtransaldolase
gene[74]RevTalCTTCTCCGGCATGGTGTTGACHelicobacter658fTGGGAGAGGTAGGTGGAAT16S
rDNA[128]1067RGCCGTGCAGCACCTGTTTTCAEnterococcusEnt1017FCCTTTGACCACTCTAGAG16S
rDNA[64]Ent1263RCTTAGCCTCGCGACTLactobacillus groupdLac1AGCAGTAGGGAATCTTCCA16S
rDNA[64, 86, 88, 125, 127, 129, 130]Lac2ATTYCACCGCTACACATG27f (also Bact-0011f)AGAGTTTGAT(C/T)(A/C)TGGCTCAG16S rDNA[79, 98, 118]Lab-0677rCACCGCTACACATGGAGLab-0159fGGAAACAG(A/G)TGCTAATACCG16S
rDNA[98, 118]Uni-0515-rATCGTATTACCGCGGCTGCTGGCALab-0159fGGAAACAG(A/G)TGCTAATACCG16S rDNA[124]Lab-0677rCACCGCTACACATGGAGSpecies
group levelBacteroides fragilis subgroupeg-Bact-FATAGCCTTTCGAAAGRAAGAT16S
rDNA[73]g-Bact-RCCAGTATCAACTGCAATTTTABact 596FTCAGTTGTGAAAGTTTGCG16S
rDNA[64]Bact 826RGTRTATCGCMAACAGCGABact 531FATACGGAGGATCCGAGCGTTA16S
rDNA[90]Bact 766RCTGTTTGATACCCACACTClostridium phylogenetic clusters XI and XIVafErec 688FGCGTAGATATTAGGAGGAAC16S
rDNA[90]Erec 841RTGCGTTWGCKRCGGCACCGaA GC-clamp is attached to the 5′ end of either the
forward or reverse primer.
bPrimers HDA1 and HDA2 have the same core sequence
as primers 341 f and 518 r, respectively, but with a few additional
nucleotides at both  5′ and 3′ ends.
cPrimer
339f has the same core sequence as primer 341 f but with two additional
nucleotides at the 5′ end.
dThe Lactobacillus group comprising the genera Lactobacillus, Leuconostoc, Pediococcus, Weissella, and Aerococcus (the latter genus
was originally not described as target of the Lac1/2 primers).
eThe Bacteroides fragilis subgroup comprising B. fragilis, B. acidifaciens, B. caccae, B. eggerthii, B. ovatus, B. stercoris, B. thetaiotaomicron, B. uniformis, and B. vulgatus.
fClostridium phylogenetic cluster XI represents the Clostridium lituseburense group, whereas Clostridium phylogenetic cluster XIVa represents
the Clostridium coccoides-Eubacterium rectale group.An
additional 40-nucleotide GC rich sequence, the so-called GC-clamp, is usually
attached to the5′ end of one or both of the PCR primers and participates in
the PCR reaction. This way, the GC-tail generated at the end of the amplicon
will prevent complete denaturation of the product and is necessary to obtain a
stable melting behavior of the fragments during electrophoresis  [49, 132, 133].
GC-clamps can vary in sequence, length, and location  [100, 134–136], and
their design needs to be based on the target sequence and the primers used.
Mutation analysis data have shown that GC-clamps have the strongest effect on
the melting properties of short fragments (<300 bp) and that this effect may
be drastically reduced for large fragments (>400 bp)  [136]. Also, it has
been demonstrated that a GC-clamp length of 60 bp may be efficient for
detection of fragments with a Tm  value close to 80°C whereas
fragments with Tm>80°C may require longer GC-clamps in
combination with naturally occurring high-melting (thus GC-rich) domains  [136].


## 2.4. Sequence-Dependent Electrophoresis

### 2.4.1. Electrophoresis Conditions

Essentially, a
DGGE system consists of a heated buffer tank operated under strict control of
temperature and stable buffer circulation. Several systems are currently
available, of which DCode (Bio-Rad Laboratories;http://www.bio-rad.com/), INGENYphorU
(Ingeny; http://www.ingeny.com/), and DGGEK-1001/2001/2401/4001/4801 (CBS Scientific;
http://www.cbsscientific.com/) appear to be most commonly used. The apparatus provided
by Bio-Rad and Ingeny can also be applied for TTGE analysis whereas for TGGE a
temperature gradient block should be integrated in the system. In case a
high number of samples need to be analyzed such as in monitoring studies, the sample capacity of the system is an
important criterion. The maximum capacity per run for the three aforementioned
systems varies from 60 (DCode), 96 (INGENYphorU) to 128 (DGGEK-4801) samples.In general, DGGE makes use of parallel gel electrophoretic systems
that have an increasing vertical gradient of denaturants parallel to the
direction of electrophoresis. In many studies, the optimal denaturing gradient
yielding the highest resolution is first determined by perpendicular gradient
gels. For this purpose, one sample containing one or more PCR fragments is
electrophoretically separated across a denaturing gradient perpendicular to the
direction of the electric field resulting in sigmoid-shaped curves. From these gels, the intermediate range of denaturant concentration,
where different electrophoretic mobilities between PCR products are obtained,
is considered the optimal gradient of denaturants for multilane analysis in
parallel DGGE. The optimal time of electrophoresis can be determined through a “time travel” experiment during which a
mixture of PCR fragments is loaded onto a parallel gel at constant time
intervals. The optimal duration of a DGGE run can be derived from the time
needed to obtain maximal separation of amplicons.A detailed procedure to cast and run DGGE gels has been described by
Muyzer et al.  [134, 137]. Essentially, the desired low and high concentration
of denaturing solution is obtained by mixing zero (0%) and high-concentration
(80–100%) denaturing acrylamide solutions in appropriate ratios. Upon the
addition of ammoniumpersulphate and tetramethylethylenediamine, the mixture is
poured between two vertical glass plates in order to generate a linear
denaturing gradient. The concentration of acrylamide usually ranges from 6–12%
and depends on the size range of the fragments to be separated. In general, the
high-concentration denaturing solution contains 7-8 M urea and 20–40% formamide.
Electrophoresis is mostly carried out in 0.5× or 1× TAE-buffer at a fixed
voltage between 50 V and 250 V and a constant temperature between 55 and 65°C.
Run times generally range from 3–17 hours, although longer run times with lower
voltages tend to produce better quality gels.In the case of TGGE and TTGE, a linearly increasing temperature
gradient parallel to the electrophoresis direction or formed during the length
of electrophoresis, respectively, is applied in combination with a uniform,
high-denaturant polyacrylamide gel to separate PCR fragments. To determine the
temperature range for parallel TGGE or TTGE analysis, a melting profile of the
DNA sequence can be generated using specialized software (e.g., Poland
analysis software;http://www.biophys.uni-duesseldorf.de/local/POLAND/poland.html).
The optimal temperature gradient is theoretically delineated by the lowest and
highest Tm values obtained in the melting profile. The theoretical Tm values can be lowered by adding denaturing components to the gel, for example,
one mole of urea will lower the theoretical Tm with 2°C  [138, 139].
In general, a 6–8 M urea gel is used in combination with a typical temperature
range between 35 and 70°C.Unlike many other fingerprinting methods that make use of
commercially available size standards, SDE techniques suffer from a lack of
consensus regarding standards for normalization. Because denaturing gradients
can slightly vary between different gels, a standard reference composed of
amplicons from pure cultures that spans a maximal range of the applied gradient should be routinely included at several fixed internal
positions on every gel to allow data normalization and gel-to-gel comparison with a high degree
of confidence. Neufeld and Mohn  [140] proposed an approach which
facilitated
and improved normalization of samples from multiple gels by including standards in each lane instead of using
interlane standards. These
intralane standards contain fluorescent tags incorporated in the primers that
excitate at another wavelength than that of the fluorescent molecules attached
to the unknown PCR product. Furthermore, the application of fluorophore-labeled
primers does not require gel staining following electrophoresis, which improves
the overall sensitivity of the population fingerprinting procedure and enables
additional DGGE versatility including simultaneous analysis of DNA- and
RNA-derived mixtures in the same lane.


### 2.4.2. Gel Staining

Upon
electrophoresis, gels are stained and digitally captured for further analysis.
Three staining agents are commonly used to visualize fragments. Originally, SDE
gels were stained with ethidium bromide (EtBr) given its widespread use as an
intercalating fluorescent dye used to detect nucleic acids. The next generation
of fluorescent nucleic acid dyes such as SYBR Green and similar stains offer an
increased sensitivity compared to EtBr due to a lower overall background signal
allowing detection of
DNA fragments at lower concentrations  [64, 134, 137]. Additional
advantages of these newer dyes are that they are generally considered to be
less toxic or mutagenic than EtBr and can be excited by wavelengths above 400 nm which enables the use of non-UV illumination. One specific member of the
SYBR Green family, SYBR Gold, binds to both dsDNA and ssDNA. This specific
feature may further enhance the detection sensitivity since DNA amplicons in
the SDE gels are partially single stranded. Although less commonly applied, silver staining is generally
considered the most sensitive staining procedure. Following DNA fixation with
ethanol and acid (e.g., nitric acid), Ag+ ions in silver nitrate are
selectively reduced under alkaline conditions by formaldehyde to metallic
silver (Ag) that is visualized as a black precipitate. Potential drawbacks of
this procedure include the fact that silver stained gels impede subsequent
blotting experiments or
band sequence analysis and the aspecific detection of protein components such
as BSA and Taq polymerase present in the PCR mix which may generate
additional background signals  [134, 137].


## 2.4.1. Electrophoresis Conditions

Essentially, a
DGGE system consists of a heated buffer tank operated under strict control of
temperature and stable buffer circulation. Several systems are currently
available, of which DCode (Bio-Rad Laboratories;http://www.bio-rad.com/), INGENYphorU
(Ingeny; http://www.ingeny.com/), and DGGEK-1001/2001/2401/4001/4801 (CBS Scientific;
http://www.cbsscientific.com/) appear to be most commonly used. The apparatus provided
by Bio-Rad and Ingeny can also be applied for TTGE analysis whereas for TGGE a
temperature gradient block should be integrated in the system. In case a
high number of samples need to be analyzed such as in monitoring studies, the sample capacity of the system is an
important criterion. The maximum capacity per run for the three aforementioned
systems varies from 60 (DCode), 96 (INGENYphorU) to 128 (DGGEK-4801) samples.In general, DGGE makes use of parallel gel electrophoretic systems
that have an increasing vertical gradient of denaturants parallel to the
direction of electrophoresis. In many studies, the optimal denaturing gradient
yielding the highest resolution is first determined by perpendicular gradient
gels. For this purpose, one sample containing one or more PCR fragments is
electrophoretically separated across a denaturing gradient perpendicular to the
direction of the electric field resulting in sigmoid-shaped curves. From these gels, the intermediate range of denaturant concentration,
where different electrophoretic mobilities between PCR products are obtained,
is considered the optimal gradient of denaturants for multilane analysis in
parallel DGGE. The optimal time of electrophoresis can be determined through a “time travel” experiment during which a
mixture of PCR fragments is loaded onto a parallel gel at constant time
intervals. The optimal duration of a DGGE run can be derived from the time
needed to obtain maximal separation of amplicons.A detailed procedure to cast and run DGGE gels has been described by
Muyzer et al.  [134, 137]. Essentially, the desired low and high concentration
of denaturing solution is obtained by mixing zero (0%) and high-concentration
(80–100%) denaturing acrylamide solutions in appropriate ratios. Upon the
addition of ammoniumpersulphate and tetramethylethylenediamine, the mixture is
poured between two vertical glass plates in order to generate a linear
denaturing gradient. The concentration of acrylamide usually ranges from 6–12%
and depends on the size range of the fragments to be separated. In general, the
high-concentration denaturing solution contains 7-8 M urea and 20–40% formamide.
Electrophoresis is mostly carried out in 0.5× or 1× TAE-buffer at a fixed
voltage between 50 V and 250 V and a constant temperature between 55 and 65°C.
Run times generally range from 3–17 hours, although longer run times with lower
voltages tend to produce better quality gels.In the case of TGGE and TTGE, a linearly increasing temperature
gradient parallel to the electrophoresis direction or formed during the length
of electrophoresis, respectively, is applied in combination with a uniform,
high-denaturant polyacrylamide gel to separate PCR fragments. To determine the
temperature range for parallel TGGE or TTGE analysis, a melting profile of the
DNA sequence can be generated using specialized software (e.g., Poland
analysis software;http://www.biophys.uni-duesseldorf.de/local/POLAND/poland.html).
The optimal temperature gradient is theoretically delineated by the lowest and
highest Tm values obtained in the melting profile. The theoretical Tm values can be lowered by adding denaturing components to the gel, for example,
one mole of urea will lower the theoretical Tm with 2°C  [138, 139].
In general, a 6–8 M urea gel is used in combination with a typical temperature
range between 35 and 70°C.Unlike many other fingerprinting methods that make use of
commercially available size standards, SDE techniques suffer from a lack of
consensus regarding standards for normalization. Because denaturing gradients
can slightly vary between different gels, a standard reference composed of
amplicons from pure cultures that spans a maximal range of the applied gradient should be routinely included at several fixed internal
positions on every gel to allow data normalization and gel-to-gel comparison with a high degree
of confidence. Neufeld and Mohn  [140] proposed an approach which
facilitated
and improved normalization of samples from multiple gels by including standards in each lane instead of using
interlane standards. These
intralane standards contain fluorescent tags incorporated in the primers that
excitate at another wavelength than that of the fluorescent molecules attached
to the unknown PCR product. Furthermore, the application of fluorophore-labeled
primers does not require gel staining following electrophoresis, which improves
the overall sensitivity of the population fingerprinting procedure and enables
additional DGGE versatility including simultaneous analysis of DNA- and
RNA-derived mixtures in the same lane.


## 2.4.2. Gel Staining

Upon
electrophoresis, gels are stained and digitally captured for further analysis.
Three staining agents are commonly used to visualize fragments. Originally, SDE
gels were stained with ethidium bromide (EtBr) given its widespread use as an
intercalating fluorescent dye used to detect nucleic acids. The next generation
of fluorescent nucleic acid dyes such as SYBR Green and similar stains offer an
increased sensitivity compared to EtBr due to a lower overall background signal
allowing detection of
DNA fragments at lower concentrations  [64, 134, 137]. Additional
advantages of these newer dyes are that they are generally considered to be
less toxic or mutagenic than EtBr and can be excited by wavelengths above 400 nm which enables the use of non-UV illumination. One specific member of the
SYBR Green family, SYBR Gold, binds to both dsDNA and ssDNA. This specific
feature may further enhance the detection sensitivity since DNA amplicons in
the SDE gels are partially single stranded. Although less commonly applied, silver staining is generally
considered the most sensitive staining procedure. Following DNA fixation with
ethanol and acid (e.g., nitric acid), Ag+ ions in silver nitrate are
selectively reduced under alkaline conditions by formaldehyde to metallic
silver (Ag) that is visualized as a black precipitate. Potential drawbacks of
this procedure include the fact that silver stained gels impede subsequent
blotting experiments or
band sequence analysis and the aspecific detection of protein components such
as BSA and Taq polymerase present in the PCR mix which may generate
additional background signals  [134, 137].


## 2.5. Data Analysis

Normalized SDE fingerprints can be analyzed visually and/or
numerically. Visual interpretation is attainable when only a limited number of
profiles with low complexity are to be compared. However, once banding patterns
become more complex such as those obtained from intestinal samples or when the
number of profiles increases (e.g., in the course of monitoring studies),
analysis of SDE fingerprints requires implementation of numerical methods
[141]. For this purpose, digitized SDE gels are further processed using
dedicated image analysis software like GelCompar and BioNumerics (Applied Maths; http://www.applied-maths.com/), Quantity One and Molecular
Analyst (Bio-Rad Laboratories), GeneTools (Syngene; http://www.syngene.com/), and
Photo-Capt (Vilber Lourmat; http://www.vilber.com/). These programs permit numerical
analysis of band patterns and usually also include statistical approaches for
data interpretation. Programs that have been used specifically for statistical
analysis of SDE fingerprint data include R (http://www.r-project.org/) and DGGESTAT (developed
at the Netherlands Institute for Ecological Research, NIOO-KNAW, Nieuwersluis,
The Netherlands).


### 2.5.1. Diversity and Similarity Analysis

Most commonly, numerical analysis of SDE profiles
relies on the use of diversity indices and/or cluster analysis. Diversity measures
for fingerprint
analysis such as the Simpson index and the Shannon-Wiener/Weaver index express the degree of
ecosystem diversity as a function of band profile complexity but fail to
express similarity between profiles based on band positions. Hierarchic
clustering algorithms such as unweighted pair-group method using arithmetic
averages (UPGMA) produce a visual representation of the similarity between SDE
profiles expressed as similarity indices, for example, using the curve-based
Pearson product-moment correlation coefficient, the band-based Dice coefficient,
or Sorenson’s pairwise coefficient. Other authors have used multivariate ordination methods such as nonmetric
multidimensional scaling  [142, 143], principal component analysis  [109, 144],
correspondence analysis  [145], canonical variate analysis  [146], and canonical
correspondence analysis  [147]. These methods are used for integration of
complex datasets such as the bands in an SDE pattern into new mathematical
variables which can be projected into a few-dimension perspective or reduced
space. A more detailed description of these statistical procedures has been
reported elsewhere  [148]. Gafan et al.  [149] evaluated the use of logistic regression for
statistical analysis of complex DGGE profiles. This analysis method takes into
consideration the outcome in addition to differences in overall band profile
complexity and individual band positions. It is beyond doubt that the list of
numerical approaches and statistical tools for analysis of SDE profiles will
further expand in the coming years. Although the choice of method(s) is
depending on the aim of the study and on the complexity of the ecosystem, community
fingerprints generally include more information than are usually revealed with
currently available methods. For this reason, more efforts should be put in the
development of new and extended processing methods for complex SDE data.


### 2.5.2. Identification Analysis

Next to the first
SDE analysis level based on the use of diversity and similarity coefficients, a
second level can be defined that allows one to identify and monitor specific
members of the intestinal ecosystem. Essentially, identification of individual
bands in SDE fingerprints may be obtained by band position analysis (BPA)
and/or through band sequencing analysis. Essentially, BPA relies on the
comparison of migration distances of band fragments from taxonomically
well-characterized reference strains with those of unknown bands present in the
sample profiles. BPA can either be performed by analyzing samples and reference
strains in adjacent lanes on the same gel (i.e., comigration analysis) or by
comparing unknown band positions with those of reference strains present in a
user-generated SDE database. In intestinal ecosystems, BPA-based identification
may not always yield a conclusive result given the possibility that a single
band may consist of multiple amplicons from different species or that two or
more (phylogenetically related) species are characterized by the same band
position in the sample profile. Ideally, each band position in a sample profile
should represent one species. In practice, however, the multioperon effect
observed for some taxa when using 16S rRNA gene primers may lead to an
overestimation of the number of predominant species in the sample (e.g., see Section4.2). In contrast to SDE profiles obtained with universal primers,
identification of bands in subpopulation profiles by BPA may be more feasible.
Application of SDE using group-specific primers for the genera Bacteroides  [81, 90] and Bifidobacterium  [74, 90, 108, 117] showed that species
identities can be resolved by means of BPA. Temmerman et al.  [117] described a
protocol to identify bifidobacterial communities based on a nested-PCR-DGGE
approach comprising a Bifidobacterium-specific PCR step followed by a
second PCR step in which both the V3 and V6–V8 regions of the 16S rRNA gene
were amplified. A mix of both amplicons was analyzed on a DGGE gel, after which
band positions were compared with a user-generated database of reference
strains.Identification
results from BPA can or even should be verified by band sequencing, and may
help to determine the phylogenetic affiliation of unknown bands. Various
procedures have been described to excise and recover PCR fragments from the
polyacrylamide gel matrix ranging from conventional elution in electrophoresis
buffer to specialized protocols using diffusion buffers and commercial kits
[74]. A critical postextraction step during this process concerns
the reamplification and subsequent SDE analysis of the excised fragment
together with the original environmental sample in order to verify if the
correct band was extracted. Upon confirmation, the recovered PCR fragments can
be directly sequenced without additional cloning. Subsequent identification of
the obtained sequence information can be achieved by comparison with sequences
stored in public databases, for example, EMBL (http://www.ebi.ac.uk/embl/) or GenBank
(http://www.ncbi.nlm.nih.gov/genbank).As
further discussed below, the significance of the obtained species information
is dependent on the length of the fragment and the hypervariable region it
represents in the target gene. This sequence information can also be employed
to develop probes for application in FISH and real-time PCR assays to detect
and quantify the target organisms. Next to sequencing analysis, identities of
individual bands in SDE profiles can also be revealed by Southern hybridization
with taxonomic probes  [150].


## 2.5.1. Diversity and Similarity Analysis

Most commonly, numerical analysis of SDE profiles
relies on the use of diversity indices and/or cluster analysis. Diversity measures
for fingerprint
analysis such as the Simpson index and the Shannon-Wiener/Weaver index express the degree of
ecosystem diversity as a function of band profile complexity but fail to
express similarity between profiles based on band positions. Hierarchic
clustering algorithms such as unweighted pair-group method using arithmetic
averages (UPGMA) produce a visual representation of the similarity between SDE
profiles expressed as similarity indices, for example, using the curve-based
Pearson product-moment correlation coefficient, the band-based Dice coefficient,
or Sorenson’s pairwise coefficient. Other authors have used multivariate ordination methods such as nonmetric
multidimensional scaling  [142, 143], principal component analysis  [109, 144],
correspondence analysis  [145], canonical variate analysis  [146], and canonical
correspondence analysis  [147]. These methods are used for integration of
complex datasets such as the bands in an SDE pattern into new mathematical
variables which can be projected into a few-dimension perspective or reduced
space. A more detailed description of these statistical procedures has been
reported elsewhere  [148]. Gafan et al.  [149] evaluated the use of logistic regression for
statistical analysis of complex DGGE profiles. This analysis method takes into
consideration the outcome in addition to differences in overall band profile
complexity and individual band positions. It is beyond doubt that the list of
numerical approaches and statistical tools for analysis of SDE profiles will
further expand in the coming years. Although the choice of method(s) is
depending on the aim of the study and on the complexity of the ecosystem, community
fingerprints generally include more information than are usually revealed with
currently available methods. For this reason, more efforts should be put in the
development of new and extended processing methods for complex SDE data.


## 2.5.2. Identification Analysis

Next to the first
SDE analysis level based on the use of diversity and similarity coefficients, a
second level can be defined that allows one to identify and monitor specific
members of the intestinal ecosystem. Essentially, identification of individual
bands in SDE fingerprints may be obtained by band position analysis (BPA)
and/or through band sequencing analysis. Essentially, BPA relies on the
comparison of migration distances of band fragments from taxonomically
well-characterized reference strains with those of unknown bands present in the
sample profiles. BPA can either be performed by analyzing samples and reference
strains in adjacent lanes on the same gel (i.e., comigration analysis) or by
comparing unknown band positions with those of reference strains present in a
user-generated SDE database. In intestinal ecosystems, BPA-based identification
may not always yield a conclusive result given the possibility that a single
band may consist of multiple amplicons from different species or that two or
more (phylogenetically related) species are characterized by the same band
position in the sample profile. Ideally, each band position in a sample profile
should represent one species. In practice, however, the multioperon effect
observed for some taxa when using 16S rRNA gene primers may lead to an
overestimation of the number of predominant species in the sample (e.g., see Section4.2). In contrast to SDE profiles obtained with universal primers,
identification of bands in subpopulation profiles by BPA may be more feasible.
Application of SDE using group-specific primers for the genera Bacteroides  [81, 90] and Bifidobacterium  [74, 90, 108, 117] showed that species
identities can be resolved by means of BPA. Temmerman et al.  [117] described a
protocol to identify bifidobacterial communities based on a nested-PCR-DGGE
approach comprising a Bifidobacterium-specific PCR step followed by a
second PCR step in which both the V3 and V6–V8 regions of the 16S rRNA gene
were amplified. A mix of both amplicons was analyzed on a DGGE gel, after which
band positions were compared with a user-generated database of reference
strains.Identification
results from BPA can or even should be verified by band sequencing, and may
help to determine the phylogenetic affiliation of unknown bands. Various
procedures have been described to excise and recover PCR fragments from the
polyacrylamide gel matrix ranging from conventional elution in electrophoresis
buffer to specialized protocols using diffusion buffers and commercial kits
[74]. A critical postextraction step during this process concerns
the reamplification and subsequent SDE analysis of the excised fragment
together with the original environmental sample in order to verify if the
correct band was extracted. Upon confirmation, the recovered PCR fragments can
be directly sequenced without additional cloning. Subsequent identification of
the obtained sequence information can be achieved by comparison with sequences
stored in public databases, for example, EMBL (http://www.ebi.ac.uk/embl/) or GenBank
(http://www.ncbi.nlm.nih.gov/genbank).As
further discussed below, the significance of the obtained species information
is dependent on the length of the fragment and the hypervariable region it
represents in the target gene. This sequence information can also be employed
to develop probes for application in FISH and real-time PCR assays to detect
and quantify the target organisms. Next to sequencing analysis, identities of
individual bands in SDE profiles can also be revealed by Southern hybridization
with taxonomic probes  [150].


## 3. Analysis of Human Intestinal Microbiota

The human intestinal tract harbors a highly dense and complex microbial
community which plays a pivotal role in maintaining the health status of the
gut. Despite the fact that SDE-based methods only allow a superficial view on
the microbial diversity and population dynamics of what is considered the
predominant part of complex ecosystems, their use in the field of
intestinal microbiology has increased exponentially over the past 10 years. The
following section aims at reviewing the main contributions of SDE population
fingerprinting to our current knowledge on the composition and ecological
balance of the human intestinal microbiota linked to health, disease, and
dietary intervention.


### 3.1. Normal Intestinal Microbiota

Next to a
relative minority of organisms belonging to other microbial domains, the human
intestinal microbiota mainly consists of bacteria. Although the major site of
microbial fermentation is the large intestine (colon), bacterial populations
are encountered along the total length of the digestive tract. Starting from the upper
bowel, bacterial concentrations gradually increase up to 1011-1012/g
in the colon. Parallel to the increase in bacterial density, also the bacterial
diversity expands from the small intestine to the colon  [151, 152]. From the community
point of view, it is important to realize that the intestinal ecosystem evolves
from an initially sterile system that becomes successively colonized by various
microorganisms.


#### 3.1.1. From Newborn to Adult

Several studies
have used SDE-based techniques to monitor the development of the newborn gut
microbiota in humans  [6, 96, 101, 102, 106, 107]. At birth, the initially
sterile gut becomes inhabited by a variety of bacterial taxa. Succession continues during
weaning until a more complex and stable microbiota is established. Two
studies by Favier et al.  [6, 96] have shown that the intestinal bacterial
community of newborns is extremely unstable as evidenced by the fact that many
dominant bands in DGGE profiles of fecal samples from healthy full-term babies reduced in intensity,
gradually disappeared after a few days and were substituted by other bands. In
the first weeks of life, DGGE profiles obtained with universal 16S rRNA gene V6–V8 primers consisted of only a few
bands but progressively increased in complexity over time. In
combination with clone libraries constructed from 16S rRNA gene sequences, identification of
bacterial species corresponding to specific bands in DGGE profiles was possible
by BPA. This approach indicated that E. coli and Clostridium spp. were the main groups among the initial colonizers, which were rapidly
replaced by a more complex microbiota consisting of Bifidobacterium, Clostridium, Enterococcus, Ruminococcus, Enterobacter, Streptococcus, Bacteroides, and Actinomyces. The diversity revealed by DGGE
analysis was fairly consistent with previous insights in infant succession
patterns based on traditional culture studies  [5, 7]. In addition, the
successive colonization of the infant gut by bifidobacteria was monitored
during the first five months after birth using Bifidobacterium-specific
primers  [96].
Whereas some subjects showed very stable DGGE profiles, others revealed temporal
variation in their bifidobacterial population. At each point in time, one to
four Bifidobacterium-related DGGE bands were observed which always included Bifidobacterium infantis. In another study, the dynamics of the developing bacterial
community in the neonatal intestinal tract of nine Japanese infants was
monitored during the first two months of life  [102]. Although the development
of individual species was different among the subjects, DGGE profiles of the
predominant fecal microbiota together with 16S rRNA gene clone library
sequencing revealed a global stepwise evolution from an aerobic to an anaerobic
microbial ecosystem. The aerobic organisms that were initially present such as Pseudomonas were immediately replaced by facultative anaerobes including Enterococcus, Streptococcus, and Enterobacteriaceae during the first month.
Finally, strictly anaerobic bifidobacteria and clostridia appeared. The
establishment and succession of bacterial communities in hospitalized preterm
infants tend to follow a different pattern compared to full-term infants  [101].
Fecal samples from 29
preterm infants hospitalized in a neonatal intensive care unit and 15 full-term infants
were analyzed using DGGE to characterize and compare bacterial succession of
the dominant bacterial species in the large intestine. In the first four weeks
of life, DGGE patterns increased in complexity over time for all preterm
infants. During this observation period, the intraindividual band
pattern similarity increased over time as indicated by an increase in Sorenson’s pairwise
similarity coefficient (Cs) from 0 to 80%. In addition, also
the interindividual Cs values increased (18.1 to 57.4%) all of which
indicated the acquisition of a highly similar bacterial community in these
infants. In contrast, breastfed full-term infants showed a considerably lower
interindividual Cs value (11.2%). The strikingly high similarity
between bacterial communities from different preterm infants was considered to
be associated with hospitalization because the major bacterial groups identified by DGGE BPA belonged
to taxa that are routinely isolated in baby care units such as E. coli, Enterococcus spp., and Klebsiella pneumoniae. This finding thus indicates
that the initial colonization of the newborn’s intestinal tract is highly
dependent on the immediate environment of the individual. In another
study assessing the global diversity of the fecal microbiota of preterm infants (n = 16), a
remarkably low-species diversity and high-interindividual variability were
reported  [106]. The low-bacterial diversity was revealed by random sequencing
of 16S rRNA gene clones and TTGE analysis. The main fecal groups encountered here included members
of the Enterobacteriaceae family and of the genera Enterococcus, Streptococcus, and Staphylococcus. Seven out of 16 preterm infants were colonized by
anaerobes, of which four infants were shown to harbor bifidobacteria.Several studies have
documented that bifidobacteria predominate in the fecal flora of breastfed
babies, whereas in
formulafed infants, other bacterial groups such as coliforms, enterococci, andBacteroides represent the main constituents  [7, 153]. In contrast, the possible effect of dietary supplementation
in the intestinal development of nursing infants is less well understood. In a longitudinal
study, TTGE was used to monitor the predominant and bifidobacterial microbiota
of 11 Algerian infants during breastfeeding, breastfeeding with artificial milk
supplementation (weaning) and artificial milk alone (postweaning, i.e.,
cessation of breastfeeding)  [107]. In the TTGE profiles, the major bands were
assigned by subsequent cloning and sequencing to E. coli, Ruminococcus spp., and several Bifidobacterium species including B. longum, B.
infantis, and B. breve. Both for the bacterial and bifidobacterial
TTGE profiles, distance analysis indicated the expected maturation of the faecal
microbiota between 5 and 20 weeks of age, but did not reveal any correlation
with the dietary supplementation. Despite a high-interindividual variability,
it was observed that the composition of the faecal microbiota appeared more
homogenous after weaning which may suggest a correlation with the cessation of
breastfeeding. In another study, 65 10-month old infants were included in a
randomized dietary intervention study that compared the effect of cow’s
milk (CM) with infant formula (IF) with or without fish oil (FO) supplement on
the diversity of the fecal microbiota  [80]. Based on clustering
analysis of V3- and V6–V8-16S rDNA DGGE profiling using the Pearson correlation
coefficient, it was reported that supplementation of CM or IF
appeared to have an influence on the composition of the intestinal microbiota
whereas FO intake only showed an effect in the CM group. The authors speculated
that these differences may be influenced by the intake of iron and n-3
polyunsaturated fatty acids, respectively, but further indepth analysis of the
DGGE profiles in combination with other molecular tools is required to
substantiate this hypothesis.Besides the
influence of environmental and dietary factors, also the host genotype may have
a significant effect on the species composition of the intestinal microbiota. Stewart
et al.  [84] used TTGE analysis of the predominant bacterial biota to investigate the influence
of host genotype on the fecal microbiota in genetically related and unrelated children. In that
study, TTGE profiles
of identical twin pairs (n = 13), fraternal twin pairs (n = 7), and
unrelated control pairs (n = 12) were compared both visually and numerically. Although
the community fingerprints of each individual were unique, increased levels of
similarity were found between TTGE profiles of genetically related individuals,
with the highest
similarity values obtained for genetically identical twins (median
Cs of
82%) which was significantly different from fraternal twin pairs (median Cs of 68%) and from the unrelated control group (median Cs of 45%). The
results of this TTGE study thus suggested that host genetics can have an impact
on the composition of the predominant fecal bacterial community in children.
Likewise, DGGE analysis of the dominant intestinal microbiota amongst adults
displaying varying degrees of genetic relatedness showed that the host genotype
had a significant effect on the species composition of the intestinal community
[122].Upon succession, it is thought that a relatively stable
intestinal community is established in the adult intestine that appears to be
specific for each individual. Zoetendal et al.  [95] were the first to report on
the stability and uniqueness of the predominant human adult fecal microbiota
that can be visualized with SDE-based approaches. TGGE analysis of fecal
samples from two healthy individuals showed stable profiles over a period of at
least six months which in addition were unique for each individual. These
findings were consolidated in a later study  [64] in which the host specificity
and temporal stability of the DGGE patterns was demonstrated for four subjects
over a 16-week period by visual inspection and clustering analysis. In the
latter study, also the temporal stability of selected subpopulations was
monitored using group-specific primers. DGGE profiles obtained with primers
designed to visualize the Lactobacillus-Leuconostoc-Pediococcus-Weissella-group tended to show strong
temporal variations. Among other autochthonous groups such as the Bacteroidesfragilis subgroup, however, DGGE profiling using group-specific primers
did not reveal such variations. Importantly, the specificity of these
group-specific primers was only validated using a set of taxonomic reference
strains. A more elaborated strategy was followed in the validation of DNA- and
RNA-based DGGE protocols specifically designed to assess the diversity and
stability of the Clostridium coccoides-Eubacterium
rectale (clostridial phylogenetic cluster XIVa) group in fecal samples
[70]. In that study, the specificity of the Ccoc-f and Ccoc-r primers was
assessed by constructing a clone library in which all 205 DGGE fragments proved
to belong to the Clostridium cluster XIVa. The authors concluded that
the members of this cluster, representing one of the most dominant bacterial
groups in the normal intestinal microbiota, followed the same pattern of
relative stability as the total predominant population in 12 healthy Finnish
adults during six months to two years. Although using protocols
differing in sample type, SDE method and primer target, the current view
on the uniqueness and temporal stability of the predominant intestinal flora in
adult individuals has also been confirmed in other human volunteer studies
using SDE-based analyses of fecal samples  [77, 81, 83, 98, 99, 118, 154] and
mucosa samples originating from different parts of the large intestine  [79, 98, 123].Although the vast majority of SDE-based studies in intestinal
microbiology rely on direct DNA extraction from human samples in order to
obtain a culture-independent inventory of the microbial diversity, there has
also been interest in using DGGE and related fingerprinting techniques to
specifically explore the composition of culturable intestinal subpopulations.
For instance, DGGE analyses of resuspended bacterial biomass
obtained from agar plates of different media selective and nonselective for
lactic acid bacteria (LAB) have been used to evaluate the choice of medium and
incubation conditions on LAB recovery and to gain insight in the diversity of culturable
fecal LAB in healthy adults  [129].


#### 3.1.2. Spatial Distribution

The different physicochemical conditions such as pH and concentration of
fermentation products prevailing in the ascending, transverse, and descending
parts of the colon  [155] suggest that also the bacterial composition in each of
these three compartments is unique. However, this assumption is not
substantiated by SDE-based studies  [30, 79, 91, 115, 123]. In most of these
studies, DGGE and TTGE fingerprint profiles reflecting the predominant
bacterial communities in biopsy samples from different sites of the colon were
host specific but highly similar between sites. These findings may indicate
that the spatial distribution of at least the predominant mucosa-associated
bacterial community is relatively uniform along the length of the colon and its
physicochemical gradient. Nielsen et al. [79] reported that DGGE profiles of the
bifidobacterial community were relatively simple and consisted of one or two
bands for most of the sites sampled along the length of the colon. However, the
mucosa-associated subcommunity encompassing the genera Lactobacillus, Leuconostoc, Weissella,
Pediococcus, and Aerococcus produced relatively
complex DGGE profiles that varied between hosts and between sampled sites in
the colon. In contrast, Zoetendal et al.  [123] obtained DGGE profiles with low
diversity and little or no variation along the colon when using the same set of
group-specific PCR primers. Presumably, the contradictory findings of the two
aforementioned studies are due to differences in sampling procedure, DNA
extraction method, and/or composition of the subject group.Given the fact that each individual displays a unique
fecal SDE fingerprint  [64, 95], investigation into spatial distribution should preferably
be based on analysis of a series of site-specific biopsy samples from the same
individual. To some extent, this may explain why interindividual comparison of
DGGE profiles of single biopsy samples from different sites did not provide any
evidence for the existence of site-specific colonization patterns in the human
colon  [30].A number of studies have also investigated to what
extent the composition of the fecal microbiota reflects the composition of the
mucosa-associated colonic microbiota  [91, 123]. In these studies, the DGGE/TTGE
profiles of amplicons of the variable V6–V8 region of the 16S rRNA gene
reflecting the predominant bacterial community of biopsy samples differed
significantly from those of fecal samples within the same individual, suggesting
that different bacterial populations are dominating the human mucosa and feces.
The population diversity revealed by SDE-based community fingerprinting
of fecal samples may thus not
necessarily reflect the ecosystem composition in other parts of the intestinal
tract including the colonic mucosa. This leads to the conclusion that the most
accurate information on the diversity and stability of local intestinal
communities can thus only be obtained by taking samples through endoscopy or
during colonic surgery.


### 3.2. Intestinal Disorders

The pathogenesis of many chronic intestinal disorders
and even a number of nonintestinal diseases is believed to be directly or
indirectly linked to some
members of the indigenous microbiota. Several studies have implemented
an SDE-based approach to analyze and monitor the composition and temporal
stability of the intestinal microbiota of patients suffering from gut
disorders. As an initial approach, SDE techniques permit a rapid and global assessment of microbial diversity
without previous knowledge of the composition and are well suited to analyze
intestinal microbiota in relation to different experimental conditions
and parameters such as healthy versus
disease status, active versus quiescent disease phase, different segments
of the intestinal tract and response to nutritional or therapeutic
interventions. Moreover, the combined use of SDE techniques and quantitative
assays such as real-time PCR and FISH that allow to determine the relative
concentration of specific indicator organisms offers great potential in this type of studies. The
following sections of this review are based on a selected number of studies
that have implemented SDE-based methods to assess the potential role of the
intestinal microbiota in the (etio)pathogenesis of chronic intestinal
disorders.


#### 3.2.1. Inflammatory Bowel Disease

Although the
exact etiology of inflammatory
bowel disease (IBD) is not known to date, it is generally assumed to
result from an inappropriate response of the mucosal immune system to the
normal enteric microbiota in a genetically susceptible individual  [156]. It has
been hypothesized that specific genetic polymorphisms, such as those in
intracellular NOD2 sensors with abnormal function, results in a failure to
efficiently regulate expression of Paneth cell-derived antimicrobial peptides  [157, 158]. The partial loss of this
protective function may allow commensals to damage epithelial cells hereby
inducing an inflammatory response. Crohn’s disease (CD) and ulcerative colitis (UC) are the two
major IBD phenotypes and are characterized by chronic inflammation of the
intestinal tract lining which causes severe watery and bloody diarrhoea and
abdominal pain  [156]. Whereas CD can virtually affect any segment of the intestinal tract, UC
is usually confined to the colon and rectum.The majority of SDE-based studies on IBD have primarily attempted to find
differences between CD/UC and healthy fecal or mucosal populations. As such,
V3–V5-16S rDNA DGGE profiling and subsequent band sequencing analysis of fresh
mucosal biopsy samples revealed a significantly higher prevalence ofClostridium spp., Ruminococcus torques, and E. coli in samples from CD
patients (n = 19) compared to healthy specimens (n = 15)  [159]. In turn, the
butyrate-producing Faecalibacterium prausnitzii was more
frequently encountered in the latter group. Overall, DGGE fingerprints of
mucosal CD populations displayed a higher patient-to-patient variability
compared to healthy subjects. The authors postulated that this difference may
reflect the difficulty of patients genetically predisposed to CD to maintain
and regulate a stable intestinal microbiota. A study of Bibiloni et al.  [160]
showed that the phylogenetic composition of biopsy-associated bacteria differed
between newly diagnosed untreated CD (n = 20) and UC patients (n = 15) and healthy
subjects (n = 14). Biopsies collected from inflamed and noninflamed sites of the terminal ileum and
various colonic regions were analyzed by DGGE, 16S rRNA gene clone
libraries, and qualitative
and quantitative PCR for detection of selected bacterial groups. DGGE profiles of universal
V3-16S rRNA gene amplicons were very similar within each subject (mean 85.0±2.4%),
irrespective of the intestinal region. However, enumeration by quantitative PCR
revealed approximately double numbers of biopsy-associated bacteria for UC patients than CD patients
and healthy subjects. In addition, the clone library composition indicated that the composition of
biopsy populations in UC and CD patients (P < .05), and those from healthy subjects
(P = .05) were statistically different. This comparison highlighted a significantly higher prevalence
of unclassified members of the phylum Bacteroidetes in CD patients, which may indicate
that UC and CD are bacteriologically distinct diseases.Depending on the
individual effectiveness, IBD patients undergoing immunomodulatory
therapy continuously balance between active disease and remission status. However, it is unclear if and in
what way the intestinal microbiota of these patients undergoes compositional
changes during these subsequent transitions. In this context, Seksik et al. [24]
monitored the fecal microbiota of patients with active colonic CD (n = 8),
patients in remission (n = 9), and healthy volunteers (n = 16). TTGE profiles of universal 16S rRNA gene V6–V8 amplicons were very stable over time in the healthy controls but varied markedly for a number of patients
(n = 4) who were monitored during both active and quiescent phase of CD. Fecal
TTGE profiles of these four patients revealed only a slight decrease in the
number of bands during the active phase (mean
loss of 1.7±2.7 bands), which indicated that the predominant fecal microbiota
retained a high degree of diversity in both phases. Based on TTGE band profile composition, no specific
bacterial groups could be assigned to active or quiescent CD state. In
contrast, quantitative dot blot hybridization of stool samples showed that the fecal microbiota in patients with CD (both active and inactive)
differed considerably from those of healthy subjects. Both the Bacteroides group (including the genera Bacteroides, Prevotella, and Porphyromonas)
and the bifidobacteria tended to be less represented in CD patients whereas
significantly more enterobacteria could be detected. In addition, approximately
30% of the endogenous microbiota of CD patients did not belong to the dominant
phylogenetic groups commonly found in healthy controls.Although currently
available data from SDE profiling and other molecular tools implicate a role of
intestinal bacteria in CD pathogenesis, a detrimental effect of localized
qualitative dysbiosis in CD-associated ulceration has so far not be
demonstrated by community fingerprinting. TTGE analysis of biopsy samples of
ulcerated and adjacent nonulcerated mucosa of 15 patients with active CD did
not reveal qualitative differences in the dominant bacterial population
profiles (V6–V8 region of the 16S rRNA gene) within a given patient although a high biodiversity was retained in both cases  [92]. Mean similarity values between TTGE
profiles of ulcerated and nonulcerated mucosa expressed with the Pearson correlation coefficient did not differ significantly across the
different intestinal segments (ileum, right colon, left colon, and rectum)
analyzed and ranged from 95.2±4.2% to 97.9±1.7%.
Solely based on TTGE analysis using universal primers, it thus appears that
local ulceration is not associated with pronounced variation in local bacterial
diversity. This
conclusion was further substantiated in a later study by the same group on the
basis of V3-V4-16S rDNA TTGE profiling and FISH analysis  [94]. Also in other
studies applying SDE-based population fingerprinting, no particular
mucosa-associated microbial pattern could be linked to the (etio)pathogenesis
of IBD  [91, 123]. Possibly, local dysbiosis among less predominant species may play a
role in the pathogenesis of ulceration. Because
these minor differences in diversity will largely remain undetected in SDE
fingerprinting using universal PCR primers or are difficult to reveal by
routine methods for band pattern analysis, future studies should employ
group-specific primers to focus on the composition of specific subpopulations
and/or should use more indepth mathematical approaches for differential profile
analysis.Whereas most studies concentrated on the
inventorization and monitoring of bacterial groups potentially associated with
CD, very few studies aimed to address the same question within the
metabolically active compartment of the gut microbiota. Sokol et al.  [93] analyzed the biodiversity of active bacteria in the dominant fecal
microbiota of UC patients (n = 9) in comparison with that of healthy subjects
(n = 9) by applying DNA- and RNA-based TTGE analysis of V6–V8 ribosomal
amplicons. The
number of bands in DNA-derived TGGE profiles were significantly higher than in RNA-derived profiles for UC
patients (15.3±3.2 and 9.1±2.8 bands, resp.) but not for controls (18.3±5.0 and 14.7±5.1 bands, resp.) which indicated a reduction in the
biodiversity of the active portion of the fecal microbiota in UC patients
relative to healthy controls. Irrespective of the initial
template (RNA or DNA), Pearson-UPGMA clustering analysis of TGGE profiles
tended to group the samples on the basis of their clinical affiliation (UC
versus controls) suggesting that each group has its specific bacterial
signature. Interindividual comparison of the “active” microbiota (RNA-derived
profiles) revealed a band that was significantly associated with UC patients
(89% versus 22% for controls). Sequence analysis attributed this band to E.
coli or related enterobacteria. Clearly, the possible pathophysiological
role of this overrepresentation in the active microbiota of UC patients should
be further assessed during remission and within the mucosa-associated
microbiota.


#### 3.2.2. Other Intestinal Disorders

Irritable bowel syndrome (IBS) is an intestinal
disorder that is characterized by bowel dysfunction and pain  [161, 162]. IBS is
a very heterogeneous condition and includes three symptom categories: (i) diarrhoea-dominant,
(ii) constipation-dominant, and (iii) alternating type  [163, 164]. Although the
pathophysiology of IBS is not fully understood, it is highly probable that
alterations in the diversity and stability of intestinal microbiota play a role
in the development and/or maintenance of this disorder  [165]. In a Finnish
study  [121], culture-based techniques and DGGE analysis were employed to
compare the composition and temporal stability of the fecal microbiota of 21
IBS patients and 17 healthy controls. Culturing revealed slightly higher
coliform numbers as well as an increased aerobe/anaerobe ratio in the IBS
group. DGGE analysis of 16S rRNA gene V6–V8 amplicons revealed considerable
biodiversity and subject specificity of the predominant microbiota in both
study groups, but did not identify IBS-specific bacterial groups. Visual
comparison of DGGE fingerprints revealed a higher frequency of temporal
instability in the predominant bacterial population of IBS subjects (43%)
compared to controls (29%). However, profile similarity analysis using the Pearson correlation
coefficient revealed comparable interindividual similarity percentages
for both groups with a mean similarity of 87.5±11.2% for the IBS group and
85.7±12.7% for the control group. Still, the instability in some of the IBS subjects could
partly be explained by disturbances of the intestinal microbiota due to
antibiotic therapy during the study. Moreover, the authors suggested
that these findings could be associated with a subset of IBS subjects sharing
specific symptoms and thus not necessarily reflect the general microbial status
of all IBS patients. In this regard, future studies should include subject
groups with well-defined symptom-based IBS parameters to evaluate the association of intestinal instability with
specific IBS symptoms or with specific bacterial groups and species. In a subsequent
study of the same group  [71], the predominant and clostridial fecal microbiota of IBS patients and
healthy controls were compared to reveal possible differences in the composition, abundance,
and stability of selected groups by applying DNA- and RNA-based DGGE analyses and transcript analysis with the aid of affinity capture, a
multiplexed and quantitative hybridization-based technique. Clostridia, that is, C. histolyticum, C. coccoides-Eubacterium rectale, C. lituseburense, and C. leptum, were shown to represent the dominant fecal microbiota in 26
of the 32 subjects under study, contributing altogether 29–87%. The proportion of the C. coccoides-E. rectale group was found to be significantly
lower in the constipation-type IBS subjects compared to the controls. Although DNA- and
RNA-derived predominant community profiles showed considerable biodiversity and
subject-specificity, RNA-based DGGE profiles contained significantly fewer
amplicons (16±5 compared to 22±5 amplicons). In addition, only RNA-based DGGE profiles
of the IBS subjects indicated higher instability of the bacterial population
compared to the control subjects. Although intraindividual temporal instability of the
predominant microbiota was observed in both IBS and control subjects (with both
DNA- and RNA-based DGGE), only RNA-derived DGGE profiles of IBS subjects showed
a broader range in similarity values (39–95%) compared to control subjects (68–94%). When considering symptomatic
IBS subgroups, the
largest intraindividual variability in DGGE similarity values was observed in
the diarrhoea-type subgroup. These observations suggest that clostridial microbiota, in
addition to the instability of the active predominant fecal bacterial
population (RNA-derived profiles), may be involved in IBS. For future
research, the use of group-specific
primers in SDE analysis focussing on apparently affected groups (e.g.,
coliforms and clostridia) could be a valuable and effective approach to
identify potential IBS indicator organisms.The
use of SDE-based methodologies to determine the diversity and stability of
microbiota in inflammatory diseases has meanwhile expanded from IBD and IBS to
other intestinal diseases in which dysbiosis of the human microbiome is thought
to play a role such as neonatal necrotizing enterocolitis  [116] and coeliac
disease  [130] or diseases beyond the intestinal system such as (atopic) allergies  [82, 85, 166] and ankylosing spondylitis  [113].


### 3.3. Intervention Studies

Apart from
components naturally occurring in a normal diet, also functional foods
(including pre- and probiotics) and antimicrobial agents are able to induce
beneficial or detrimental changes in intestinal ecosystems. Starting from the
first weeks upon birth, the human diet is able to modulate the composition and
balance of the intestinal microbiota  [7, 153]. The
SDE approach is routinely applied in administration studies to monitor the
effects on the intestinal microbiota upon consumption of various active
components.


#### 3.3.1. Functional Foods

The fact that diet is a major factor controlling the
human intestinal balance has triggered the development of a new generation of foods specifically designed
to strengthen the gut microbiota via modulation. Functional foods include foods
and food products with a clearly identifiable health benefit in addition to
their basic nutritional value  [167]. In functional foods, the addition
or incorporation of pro- and/or prebiotic components as active ingredients
plays a key role in functional applications aiming at modulation of intestinal
microbiota. According to the FAO/WHO definition  [168], probiotics are live
microorganisms which, when administered in adequate amounts, confer a health
benefit for the host. An extended version of this definition is still under
debate, including the question whether the live status is truly required for
probiotic action  [169, 170]. Beneficial effects induced by probiotic activities are mediated
either through modulation of the indigenous microbiota or through the
immunomodulatory potential of the probiotic strains used. Bacterial
cultures incorporated in probiotic products for human consumption commonly—but
not exclusively—originate from the intestinal system of healthy (human)
subjects and most frequently
belong to the bifidobacteria and to LAB such as Lactobacillus spp. A
prebiotic, on the other hand, is a nondigestible selectively fermented compound
that induces specific changes both in the composition and/or the activity of
the gastrointestinal microbiota thereby conferring benefits upon host
well-being and health  [171]. Essentially, the functionality of a prebiotic
compound is determined
by its potential to stimulate beneficial bacteria indigenous to the gut
ecosystem. Complex oligosaccharides are most commonly used as prebiotics
including lactulose, galactooligosaccharides (GOS) and fructooligosaccharides
(FOS; e.g., oligofructose and inulin). A wide range of beneficial effects have
been attributed to probiotics, prebiotics, or a combination thereof (i.e.,
synbiotics), including modulation of the gut immune system, resistance to microbial
infections, antimutagenic/anticarcinogenic
effects, reduction of blood ammonia and cholesterol levels, prevention
and/or alleviation of diarrhoea and constipation, prevention and reducing
symptoms of intestinal chronic disorders, relief of lactose intolerance and
increased mineral absorption as reviewed in  [172–178].SDE-based methods have
played a key role in human dietary intervention studies aiming at demonstrating
the efficacy of functional food components and to substantiate potential health
claim. A selection of relevant studies that have contributed to this field is listed
in Table3  [68, 69, 72, 73, 75, 76, 88, 90, 98, 100, 105, 110, 111, 120, 127, 179, 180]. Solely based on findings from SDE analysis, it appears that
prebiotic administration can potentially affect the predominant bacterial
population of healthy human subjects, whereas most probiotic interventions only
seem to induce marked effects in patient groups. This could indicate that some probiotic components may have
more of a therapeutic effect in subjects with a disturbed intestinal
balance but less effective as general health promoting agents. On the other hand, it should be
kept in mind that SDE-based approaches focus on diversity and dynamics of
predominant intestinal microbiota, and are as such unsuitable to monitor
probiotic interventions that are based on the immunomodulatory potential of the
administered organism(s). Bibiloni et
al.  [110] used DGGE to evaluate the safety and efficacy of the mixed
probiotic preparation VSL#3® (http://www.vsl3.com/) consisting of three Bifidobacterium strains and five LAB strains (i.e., four Lactobacillus strains and one Streptococcus
thermophilus strain) in patients with active mild to moderate UC. DGGE analysis of V3-16S rRNA
gene amplicons generated from biopsies collected from seven patients
before and after 6-week VSL#3 administration revealed considerable variation of
the predominant microbiota in four out of five patients in remission (mean dice similarity coefficient (Ds) of 69.9±12.7%). In
contrast, the DGGE profiles of the two patients with continued active
disease remained relatively stable after VSL#3 consumption (mean Ds  of 92.3±4.1%).
Importantly, it should be noted that the study did not report on the temporal
stability of biopsy profiles in the absence of probiotic treatment. In another
study, the effect of a 4-week administration of the candidate prebiotic di-D-fructofuranose-1,
2′ : 2, 3′-dianhydride (DFA III) on human fecal microbiota was studied by DGGE
analysis using universal V3-16S rRNA primers and Bacteroides fragilis subgroup-specific primers  [73]. Visual and numerical analysis of the DGGE profiles generated with both
primer sets revealed no pronounced changes related to DFA III administration in
healthy subjects. In a followup long-term human feeding trial (2 to 12 months)
with DFA III, however, DGGE profiles of the predominant bacterial population
revealed a marked increase in the intensity of bands related to Bacteroides spp.  [72]. In a study on the effect
of 3-week consumption of a GOS-containing probiotic yogurt on the diversity and
temporal stability of fecal microbiota in elderly  [69], DGGE revealed that the
predominant bacterial population and the Clostridium coccoides-Eubacterium
rectale group remained relatively stable during the study period. In
contrast, the Lactobacillus group showed temporal variation which
confirms previous observations under basal conditions  [64].Table 3
Selection of dietary intervention studies using SDE-based community fingerprinting.
ComponentaAdministered componentReference(s)bpLevan-type
exopolysaccharides, levan, inulin and FOS[111]pGOS and FOS[75]pDifructose
anhydride III (DFA III)[72, 73]PLactobacillus rhamnosus DR20[76, 88]PLactobacillus paracasei F19[98]PVSL#3® (probiotic
mixture of eight strains)[110]PBifidobacterium longum (Bifina®)
and yogurt with Bifidobacterium animalis DN-173 010[179]yLactobacillus
delbrueckii subsp. bulgaricus and Streptococcus thermophilus[68]p, PInulin orB. longum (Bifina®)[180]pPInulin-containing probiotic
yogurts[127]pPGOS-containing probiotic yogurt[69]p, P, pPGOS and/orBifidobacterium lactis Bb-12[100]p, P, pPLactulose
and/orSaccharomyces boulardii[90]oBlack tea[120]o, op, oPIsoflavones and FOS orB. animalis DN-173 010[105]ap: prebiotic; P: probiotic; pP: synbiotic; y, yogurt; o:
other.
bAll studies used DGGE as SDE method except in  [105], where
TTGE was used.In the course of probiotic
intervention studies, DGGE and related fingerprinting techniques have been used
to verify if the administered strain(s) is (are) detectable in intestinal
samples  [76, 88, 98, 100, 105, 110, 125, 179]. Based on a combination of
culture-based methods and 16S rDNA DGGE, Wall et al.  [86] even reported the
recovery of probiotic strains Lactobacillus
paracasei NFBC 338
and B. animalis subsp. lactis Bb12
in ileostomy effluents of two infants without a history of probiotic intake. In this context, it should
be noted that SDE fingerprinting is not the most optimal tool for detection of administered
strains because of the relatively poor detection limit (especially when using
universal primers) and the lack of resolution to discriminate the introduced
strain(s) from other strains of the same or highly related autochthonous member
species of the intestinal microbiota. More suitable approaches are those applying strain-specific primers
(e.g., conventional or real-time PCR) or probes (e.g., fluorescent FISH probes)
which will not only provide a higher sensitivity but may also allow relative
quantification of the probiotic target  [181–184]. On the other hand, it should
be kept in mind that all aforementioned DNA-based approaches do not allow to
discriminate between living and dead cells and thus do not provide information
on probiotic survival throughout the gastrointestinal tract.In recent years, SDE-based community fingerprinting has been integrated
in larger polyphasic studies in combination with conventional culture methods
and/or with other molecular culture-independent methods to detect and monitor
changes in human intestinal ecosystems upon administration of probiotic,
prebiotic, or other (in)organic compounds with claimed functionalities. As
such, DGGE and FISH approaches were combined with selective culture methods to
evaluate the impact of a 3-week diet supplementation with prebiotic GOS or FOS on the composition and
activities of the fecal microbiota of 15 healthy human volunteers  [75]. V3-16S rRNA gene
DGGE profiles remained relatively stable during the study, whereas clear
alterations in response to dietary supplementation were observed in rRNA-DGGE
profiles as evidenced by the detection of additional fragments or increased
staining intensity of band fragments attributed to Bifidobacterium adolescentis and/or Collinsella aerofaciens. In contrast, DGGE analysis using
genus-specific primers derived from the transaldolase gene generated relatively stable profiles
for fecal bifidobacteria. Although the taxonomic composition of the bifidobacterial population
was not substantially different and both DGGE and FISH revealed that the Bifidobacterium and Collinsella populations
remained relatively unchanged, rRNA-DGGE provided evidence of increased
metabolic activity in
response to prebiotic consumption. A combination of DGGE and FISH was
also used to investigate the effect of black tea drinking on the fecal
microbiota of healthy volunteers with hypercholesterolemy  [120]. DGGE of 16S rRNA gene V6–V8
amplicons showed that each subject harboured a specific predominant bacterial
population that exhibits little change over time and that was not significantly
changed by drinking black tea. Even though black tea did not affect the
specific bacterial groups analyzed by FISH (i.e., Bifidobacterium, Bacteroides and Prevotella, Clostridium phylogenetic clustersIV and XIVa, Atopobium group, Faecalibacterium-like
species and E. coli), it did decrease the total amount of bacteria
detected by the universal bacterial probe. In a study that combined the use of
TGGE and FISH analysis, it was demonstrated that isoflavone supplementation with and without pro- or
prebiotics induced significant dynamic changes on the composition of the dominant
intestinal microbiota of 39 postmenopausal women  [105]. Results of FISH analysis
indicated that several of the dominant fecal groups were stimulated by isoflavones alone,
whereas TGGE
profiling of 16S rRNA gene V6–V8 amplicons revealed marked changes in the
predominant intestinal microbiota. Intraindividual comparison of TGGE
fingerprints showed a mean Pearson similarity value of 73% before and after one
month of isoflavone supplementation. In combination with a pro- or prebiotic
compound, isoflavones triggered comparable population changes as evidenced by
mean fingerprint similarity values of 71 ± 18% and 68 ± 16% obtained for the
probiotic (Bifidobacterium
animalis DN-173
010) and the
prebiotic (FOS) test groups, respectively.
In addition, FISH results showed a bifidobacterial increase following prebiotic supplementation, often
referred to as the bifidogenic effect. Amongst others  [76, 125, 180], the aforementioned studies
have demonstrated the potential of using SDE fingerprinting and FISH analyses
in a complementary approach to characterize basic interactions between
intestinal microbiota and functional food compounds and to quantify
subpopulations responding to the introduced component(s).Next to FISH, also real-time PCR has been used in combination with DGGE
to verify and substantiate compositional changes in a semiquantitative manner.
The latter two methods were used in an integrated approach to monitor and
quantify pronounced changes in fecal microbiota of healthy subjects upon
long-term administration of a prebiotic (lactulose), a probiotic (Saccharomyces boulardii), and their synbiotic combination  [90]. Although the DGGE profiles obtained
with the universal V3-16S rRNA gene primers as well as those generated using group-specific
primers targeting the Bacteroides fragilis subgroup, the genus Bifidobacterium and the Clostridium lituseburense and Clostridium coccoides-Eubacterium rectale groups remained fairly stable, one pronounced change was
observed in the universal fingerprint profiles after lactulose ingestion. The DGGE band appearing or intensifying in 27
of the 30 subjects could be assigned to Bifidobacterium adolescentis by band position analysis and band
sequencing. In subsequent real-time PCR analysis, this finding was
correlated to a statistically significant stimulation of total bifidobacteria
and of B. adolescentis. In contrast, the probiotic yeast S. boulardii did not
display any detectable universal changes in the DGGE profiles nor influenced
bifidobacterial levels. In a double-blind crossover study on the qualitative
and quantitative effects of fresh and heat-treated yogurt on the bacterial
intestinal microbiota from healthy subjects  [68], DGGE profiling revealed overall stability of
the predominant bacterial population and the LAB population at baseline, after
fresh yogurt intake and after heat-treated yogurt intake. However, real-time
PCR with group-specific primers indicated a significantly higher density of LAB
and Clostridium perfringens and a significant decrease in the density of Bacteroides after consumption of both types of yogurt.


#### 3.3.2. Antimicrobial Agents

Apart from their generally well-documented
therapeutic effects on the site of infection, antimicrobials can also exert a
detrimental effect on the microbial balance of the gut ecosystem. So far, studies analyzing
the effect of antibiotic therapy on the selection and transmission of
antibiotic resistance among pathogens and commensals within the human
intestinal microbiota have mainly relied on culture-dependent approaches  [185–187]
which are highly restricted by the selectivity of the media used. In this
respect, SDE-based techniques provide a more suitable approach to monitor the
effects of antimicrobial agents on the total community structure of intestinal
microbiota.In several human studies mostly focussing on infant
populations, DGGE analysis has revealed drastic alterations among the
indigenous bacterial diversity upon therapy with various antimicrobials  [77, 96, 101, 102]. Antimicrobial-induced disruptions of fingerprinting profiles
were generally accompanied by a reduction in band numbers suggesting an overall
decrease in predominant intestinal ecosystem diversity. Favier et al.  [96]
monitored bacterial succession of the intestine during the first four months of
life of five babies, including one infant who received continuous antibiotic therapy
consisting of Augmentin (a mixture of clavulanic acid and amoxicillin) for 13 days
followed by Bactrimel (a mixture of trimethoprim and sulfamethoxazol) to combat
urinary reflux. During
the first month after birth, the universal 16S rRNA gene V6–V8 DGGE profiles of
the
antibiotic-treated baby were highly unstable. Main bacterial groups were
identified as E. coli and Enterococcus spp., despite the fact that the
administered antibiotics were expected to suppress enteric bacteria. After one month, DGGE
patterns indicated the presence of a simple but remarkably stable community until the end of the study. The most
significant differences between the profiles from the antibiotic-treated baby and the other four
healthy babies related to the absence of Bifidobacterium bands in spite of a partly
breast-milk diet. Likewise, a study in which nine Japanese infants were
monitored during the first two months after birth demonstrated that antibiotic
treatment at the beginning of life exhibits a strong influence on the
establishment of a normal microbial ecosystem in the intestine  [102]. Two
infants who received Cefalex (a cephalosporin antibiotic) therapy in the first four
days of life showed a remarkably deviating developmental pattern from the
trends observed in the other nontreated subjects. DGGE analysis of V3-16S rRNA
gene amplicons generated profiles with an overall low complexity that lacked
bands corresponding to bifidobacteria and other strict anaerobes. In fact, band
sequence analysis and random sequencing of 16S rRNA gene clone libraries
indicated that Enterobacteriaceae were the most dominant group
throughout the entire study period. In contrast to the findings of the two
aforementioned studies, the SDE fingerprinting data reported by Schwiertz et
al.  [101] indicated that the bacterial composition in infants was not
necessarily influenced by antibiotic treatment. In the latter study, the
establishment and succession of the neonatal microbiota in the first month of
life of 29 preterm hospitalized infants was monitored, and included seven
antibiotic-treated infants receiving cefotaxime and piperazine during the first three days followed
by vancomycin and amikacin therapy until inflammation was reduced which ranged
up to 21 days. Overall, DGGE analysis with universal V6–V8-16S rDNA primers showed
relatively stable profiles during and after antibiotic treatment, although the
complexity of the banding pattern generally appeared to be lower compared to
nontreated infants.Not suprisingly, it has been shown that the human adult intestinal
microbiota is affected to a different degree during antimicrobial therapy
depending on the type and/or activity spectrum of the therapeutic component
[77]. In the latter study, DGGE analysis of fecal samples from one patient was
performed for 12 months during which different antimicrobials were administered. Visual and
numerical analysis of the V3-16S rRNA gene fingerprints representing the
predominant microbiota
remained stable over eight months in the absence of antimicrobials (Ds of 88–91%) and were
only minimally affected following one week ingestion of ciprofloxacin (Ds of 73%). In contrast, clindamycin markedly reduced the microbial complexity (Ds of 11–18%). However, once
clindamycin therapy ceased, recovery of some intestinal groups was evident within days
as indicated by the increasing similarity indices when compared to the pattern
prior to antibiotic treatment (Ds of 36–44%). In three other
patients, cefazolin
(i.e., a cephalosporin with relatively low activity against intestinal
anaerobes) caused only
minimal alteration of V3-16S rRNA gene patterns (Ds of 81–83%) whereas
amoxicillin/clavulanate triggered marked changes in profile compositions (Ds of 19–42%). Overall, the
relative degree of alterations in the universal DGGE patterns tended to
correspond to the relative activity spectrum of the antimicrobials against intestinal anaerobes.In order to reduce the possible side-effects of antimicrobial therapy,
probiotics are commonly administered in combination with antimicrobials during
and after the period of intake  [188]. In such combinatorial approaches, the
absence of potentially transferable antibiotic resistance genes in the
administered strain has been recognized as one of the major safety critera for
human probiotics  [189]. In this context, the survival and stability of
probiotic strains during antimicrobial therapy are particularly relevant but
have not been studied into large detail. Upon combined doxycycline (a
tetracycline) and probiotic therapy, Saarela et al.  [190] found that the
complexity of V6–V8-16S rDNA DGGE profiles of fecal microbiota was lower (mean
number of bands, 14–25) compared to those of the (control) group only taking
probiotics (mean number of bands, 25–42). Probiotic strains Lactobacillus
acidophilus LaCH-5 and Bifidobacterium animalis subsp.
lactis Bb-12 from the administered commercial
preparation Trevis were recovered from fecal samples,
and phenotypically and genotypically characterized for their tetracycline (Tc)
resistance. The Tc-susceptible strain LaCH-5 remained so during therapy,
whereas recovered isolates of the Tc-resistant strain Bb-12 containing the tet(W)
resistance gene were not found to have acquired additional Tc resistance genes.
Although these observations evidence the stability of the probiotic strains as
such, however, the authors did not investigate the possible effect of
introducing a tet(W)-carrying strain during doxycycline therapy on the
dissemination of this gene throughout the intestinal microbiota.


## 3.1. Normal Intestinal Microbiota

Next to a
relative minority of organisms belonging to other microbial domains, the human
intestinal microbiota mainly consists of bacteria. Although the major site of
microbial fermentation is the large intestine (colon), bacterial populations
are encountered along the total length of the digestive tract. Starting from the upper
bowel, bacterial concentrations gradually increase up to 1011-1012/g
in the colon. Parallel to the increase in bacterial density, also the bacterial
diversity expands from the small intestine to the colon  [151, 152]. From the community
point of view, it is important to realize that the intestinal ecosystem evolves
from an initially sterile system that becomes successively colonized by various
microorganisms.


### 3.1.1. From Newborn to Adult

Several studies
have used SDE-based techniques to monitor the development of the newborn gut
microbiota in humans  [6, 96, 101, 102, 106, 107]. At birth, the initially
sterile gut becomes inhabited by a variety of bacterial taxa. Succession continues during
weaning until a more complex and stable microbiota is established. Two
studies by Favier et al.  [6, 96] have shown that the intestinal bacterial
community of newborns is extremely unstable as evidenced by the fact that many
dominant bands in DGGE profiles of fecal samples from healthy full-term babies reduced in intensity,
gradually disappeared after a few days and were substituted by other bands. In
the first weeks of life, DGGE profiles obtained with universal 16S rRNA gene V6–V8 primers consisted of only a few
bands but progressively increased in complexity over time. In
combination with clone libraries constructed from 16S rRNA gene sequences, identification of
bacterial species corresponding to specific bands in DGGE profiles was possible
by BPA. This approach indicated that E. coli and Clostridium spp. were the main groups among the initial colonizers, which were rapidly
replaced by a more complex microbiota consisting of Bifidobacterium, Clostridium, Enterococcus, Ruminococcus, Enterobacter, Streptococcus, Bacteroides, and Actinomyces. The diversity revealed by DGGE
analysis was fairly consistent with previous insights in infant succession
patterns based on traditional culture studies  [5, 7]. In addition, the
successive colonization of the infant gut by bifidobacteria was monitored
during the first five months after birth using Bifidobacterium-specific
primers  [96].
Whereas some subjects showed very stable DGGE profiles, others revealed temporal
variation in their bifidobacterial population. At each point in time, one to
four Bifidobacterium-related DGGE bands were observed which always included Bifidobacterium infantis. In another study, the dynamics of the developing bacterial
community in the neonatal intestinal tract of nine Japanese infants was
monitored during the first two months of life  [102]. Although the development
of individual species was different among the subjects, DGGE profiles of the
predominant fecal microbiota together with 16S rRNA gene clone library
sequencing revealed a global stepwise evolution from an aerobic to an anaerobic
microbial ecosystem. The aerobic organisms that were initially present such as Pseudomonas were immediately replaced by facultative anaerobes including Enterococcus, Streptococcus, and Enterobacteriaceae during the first month.
Finally, strictly anaerobic bifidobacteria and clostridia appeared. The
establishment and succession of bacterial communities in hospitalized preterm
infants tend to follow a different pattern compared to full-term infants  [101].
Fecal samples from 29
preterm infants hospitalized in a neonatal intensive care unit and 15 full-term infants
were analyzed using DGGE to characterize and compare bacterial succession of
the dominant bacterial species in the large intestine. In the first four weeks
of life, DGGE patterns increased in complexity over time for all preterm
infants. During this observation period, the intraindividual band
pattern similarity increased over time as indicated by an increase in Sorenson’s pairwise
similarity coefficient (Cs) from 0 to 80%. In addition, also
the interindividual Cs values increased (18.1 to 57.4%) all of which
indicated the acquisition of a highly similar bacterial community in these
infants. In contrast, breastfed full-term infants showed a considerably lower
interindividual Cs value (11.2%). The strikingly high similarity
between bacterial communities from different preterm infants was considered to
be associated with hospitalization because the major bacterial groups identified by DGGE BPA belonged
to taxa that are routinely isolated in baby care units such as E. coli, Enterococcus spp., and Klebsiella pneumoniae. This finding thus indicates
that the initial colonization of the newborn’s intestinal tract is highly
dependent on the immediate environment of the individual. In another
study assessing the global diversity of the fecal microbiota of preterm infants (n = 16), a
remarkably low-species diversity and high-interindividual variability were
reported  [106]. The low-bacterial diversity was revealed by random sequencing
of 16S rRNA gene clones and TTGE analysis. The main fecal groups encountered here included members
of the Enterobacteriaceae family and of the genera Enterococcus, Streptococcus, and Staphylococcus. Seven out of 16 preterm infants were colonized by
anaerobes, of which four infants were shown to harbor bifidobacteria.Several studies have
documented that bifidobacteria predominate in the fecal flora of breastfed
babies, whereas in
formulafed infants, other bacterial groups such as coliforms, enterococci, andBacteroides represent the main constituents  [7, 153]. In contrast, the possible effect of dietary supplementation
in the intestinal development of nursing infants is less well understood. In a longitudinal
study, TTGE was used to monitor the predominant and bifidobacterial microbiota
of 11 Algerian infants during breastfeeding, breastfeeding with artificial milk
supplementation (weaning) and artificial milk alone (postweaning, i.e.,
cessation of breastfeeding)  [107]. In the TTGE profiles, the major bands were
assigned by subsequent cloning and sequencing to E. coli, Ruminococcus spp., and several Bifidobacterium species including B. longum, B.
infantis, and B. breve. Both for the bacterial and bifidobacterial
TTGE profiles, distance analysis indicated the expected maturation of the faecal
microbiota between 5 and 20 weeks of age, but did not reveal any correlation
with the dietary supplementation. Despite a high-interindividual variability,
it was observed that the composition of the faecal microbiota appeared more
homogenous after weaning which may suggest a correlation with the cessation of
breastfeeding. In another study, 65 10-month old infants were included in a
randomized dietary intervention study that compared the effect of cow’s
milk (CM) with infant formula (IF) with or without fish oil (FO) supplement on
the diversity of the fecal microbiota  [80]. Based on clustering
analysis of V3- and V6–V8-16S rDNA DGGE profiling using the Pearson correlation
coefficient, it was reported that supplementation of CM or IF
appeared to have an influence on the composition of the intestinal microbiota
whereas FO intake only showed an effect in the CM group. The authors speculated
that these differences may be influenced by the intake of iron and n-3
polyunsaturated fatty acids, respectively, but further indepth analysis of the
DGGE profiles in combination with other molecular tools is required to
substantiate this hypothesis.Besides the
influence of environmental and dietary factors, also the host genotype may have
a significant effect on the species composition of the intestinal microbiota. Stewart
et al.  [84] used TTGE analysis of the predominant bacterial biota to investigate the influence
of host genotype on the fecal microbiota in genetically related and unrelated children. In that
study, TTGE profiles
of identical twin pairs (n = 13), fraternal twin pairs (n = 7), and
unrelated control pairs (n = 12) were compared both visually and numerically. Although
the community fingerprints of each individual were unique, increased levels of
similarity were found between TTGE profiles of genetically related individuals,
with the highest
similarity values obtained for genetically identical twins (median
Cs of
82%) which was significantly different from fraternal twin pairs (median Cs of 68%) and from the unrelated control group (median Cs of 45%). The
results of this TTGE study thus suggested that host genetics can have an impact
on the composition of the predominant fecal bacterial community in children.
Likewise, DGGE analysis of the dominant intestinal microbiota amongst adults
displaying varying degrees of genetic relatedness showed that the host genotype
had a significant effect on the species composition of the intestinal community
[122].Upon succession, it is thought that a relatively stable
intestinal community is established in the adult intestine that appears to be
specific for each individual. Zoetendal et al.  [95] were the first to report on
the stability and uniqueness of the predominant human adult fecal microbiota
that can be visualized with SDE-based approaches. TGGE analysis of fecal
samples from two healthy individuals showed stable profiles over a period of at
least six months which in addition were unique for each individual. These
findings were consolidated in a later study  [64] in which the host specificity
and temporal stability of the DGGE patterns was demonstrated for four subjects
over a 16-week period by visual inspection and clustering analysis. In the
latter study, also the temporal stability of selected subpopulations was
monitored using group-specific primers. DGGE profiles obtained with primers
designed to visualize the Lactobacillus-Leuconostoc-Pediococcus-Weissella-group tended to show strong
temporal variations. Among other autochthonous groups such as the Bacteroidesfragilis subgroup, however, DGGE profiling using group-specific primers
did not reveal such variations. Importantly, the specificity of these
group-specific primers was only validated using a set of taxonomic reference
strains. A more elaborated strategy was followed in the validation of DNA- and
RNA-based DGGE protocols specifically designed to assess the diversity and
stability of the Clostridium coccoides-Eubacterium
rectale (clostridial phylogenetic cluster XIVa) group in fecal samples
[70]. In that study, the specificity of the Ccoc-f and Ccoc-r primers was
assessed by constructing a clone library in which all 205 DGGE fragments proved
to belong to the Clostridium cluster XIVa. The authors concluded that
the members of this cluster, representing one of the most dominant bacterial
groups in the normal intestinal microbiota, followed the same pattern of
relative stability as the total predominant population in 12 healthy Finnish
adults during six months to two years. Although using protocols
differing in sample type, SDE method and primer target, the current view
on the uniqueness and temporal stability of the predominant intestinal flora in
adult individuals has also been confirmed in other human volunteer studies
using SDE-based analyses of fecal samples  [77, 81, 83, 98, 99, 118, 154] and
mucosa samples originating from different parts of the large intestine  [79, 98, 123].Although the vast majority of SDE-based studies in intestinal
microbiology rely on direct DNA extraction from human samples in order to
obtain a culture-independent inventory of the microbial diversity, there has
also been interest in using DGGE and related fingerprinting techniques to
specifically explore the composition of culturable intestinal subpopulations.
For instance, DGGE analyses of resuspended bacterial biomass
obtained from agar plates of different media selective and nonselective for
lactic acid bacteria (LAB) have been used to evaluate the choice of medium and
incubation conditions on LAB recovery and to gain insight in the diversity of culturable
fecal LAB in healthy adults  [129].


### 3.1.2. Spatial Distribution

The different physicochemical conditions such as pH and concentration of
fermentation products prevailing in the ascending, transverse, and descending
parts of the colon  [155] suggest that also the bacterial composition in each of
these three compartments is unique. However, this assumption is not
substantiated by SDE-based studies  [30, 79, 91, 115, 123]. In most of these
studies, DGGE and TTGE fingerprint profiles reflecting the predominant
bacterial communities in biopsy samples from different sites of the colon were
host specific but highly similar between sites. These findings may indicate
that the spatial distribution of at least the predominant mucosa-associated
bacterial community is relatively uniform along the length of the colon and its
physicochemical gradient. Nielsen et al. [79] reported that DGGE profiles of the
bifidobacterial community were relatively simple and consisted of one or two
bands for most of the sites sampled along the length of the colon. However, the
mucosa-associated subcommunity encompassing the genera Lactobacillus, Leuconostoc, Weissella,
Pediococcus, and Aerococcus produced relatively
complex DGGE profiles that varied between hosts and between sampled sites in
the colon. In contrast, Zoetendal et al.  [123] obtained DGGE profiles with low
diversity and little or no variation along the colon when using the same set of
group-specific PCR primers. Presumably, the contradictory findings of the two
aforementioned studies are due to differences in sampling procedure, DNA
extraction method, and/or composition of the subject group.Given the fact that each individual displays a unique
fecal SDE fingerprint  [64, 95], investigation into spatial distribution should preferably
be based on analysis of a series of site-specific biopsy samples from the same
individual. To some extent, this may explain why interindividual comparison of
DGGE profiles of single biopsy samples from different sites did not provide any
evidence for the existence of site-specific colonization patterns in the human
colon  [30].A number of studies have also investigated to what
extent the composition of the fecal microbiota reflects the composition of the
mucosa-associated colonic microbiota  [91, 123]. In these studies, the DGGE/TTGE
profiles of amplicons of the variable V6–V8 region of the 16S rRNA gene
reflecting the predominant bacterial community of biopsy samples differed
significantly from those of fecal samples within the same individual, suggesting
that different bacterial populations are dominating the human mucosa and feces.
The population diversity revealed by SDE-based community fingerprinting
of fecal samples may thus not
necessarily reflect the ecosystem composition in other parts of the intestinal
tract including the colonic mucosa. This leads to the conclusion that the most
accurate information on the diversity and stability of local intestinal
communities can thus only be obtained by taking samples through endoscopy or
during colonic surgery.


## 3.1.1. From Newborn to Adult

Several studies
have used SDE-based techniques to monitor the development of the newborn gut
microbiota in humans  [6, 96, 101, 102, 106, 107]. At birth, the initially
sterile gut becomes inhabited by a variety of bacterial taxa. Succession continues during
weaning until a more complex and stable microbiota is established. Two
studies by Favier et al.  [6, 96] have shown that the intestinal bacterial
community of newborns is extremely unstable as evidenced by the fact that many
dominant bands in DGGE profiles of fecal samples from healthy full-term babies reduced in intensity,
gradually disappeared after a few days and were substituted by other bands. In
the first weeks of life, DGGE profiles obtained with universal 16S rRNA gene V6–V8 primers consisted of only a few
bands but progressively increased in complexity over time. In
combination with clone libraries constructed from 16S rRNA gene sequences, identification of
bacterial species corresponding to specific bands in DGGE profiles was possible
by BPA. This approach indicated that E. coli and Clostridium spp. were the main groups among the initial colonizers, which were rapidly
replaced by a more complex microbiota consisting of Bifidobacterium, Clostridium, Enterococcus, Ruminococcus, Enterobacter, Streptococcus, Bacteroides, and Actinomyces. The diversity revealed by DGGE
analysis was fairly consistent with previous insights in infant succession
patterns based on traditional culture studies  [5, 7]. In addition, the
successive colonization of the infant gut by bifidobacteria was monitored
during the first five months after birth using Bifidobacterium-specific
primers  [96].
Whereas some subjects showed very stable DGGE profiles, others revealed temporal
variation in their bifidobacterial population. At each point in time, one to
four Bifidobacterium-related DGGE bands were observed which always included Bifidobacterium infantis. In another study, the dynamics of the developing bacterial
community in the neonatal intestinal tract of nine Japanese infants was
monitored during the first two months of life  [102]. Although the development
of individual species was different among the subjects, DGGE profiles of the
predominant fecal microbiota together with 16S rRNA gene clone library
sequencing revealed a global stepwise evolution from an aerobic to an anaerobic
microbial ecosystem. The aerobic organisms that were initially present such as Pseudomonas were immediately replaced by facultative anaerobes including Enterococcus, Streptococcus, and Enterobacteriaceae during the first month.
Finally, strictly anaerobic bifidobacteria and clostridia appeared. The
establishment and succession of bacterial communities in hospitalized preterm
infants tend to follow a different pattern compared to full-term infants  [101].
Fecal samples from 29
preterm infants hospitalized in a neonatal intensive care unit and 15 full-term infants
were analyzed using DGGE to characterize and compare bacterial succession of
the dominant bacterial species in the large intestine. In the first four weeks
of life, DGGE patterns increased in complexity over time for all preterm
infants. During this observation period, the intraindividual band
pattern similarity increased over time as indicated by an increase in Sorenson’s pairwise
similarity coefficient (Cs) from 0 to 80%. In addition, also
the interindividual Cs values increased (18.1 to 57.4%) all of which
indicated the acquisition of a highly similar bacterial community in these
infants. In contrast, breastfed full-term infants showed a considerably lower
interindividual Cs value (11.2%). The strikingly high similarity
between bacterial communities from different preterm infants was considered to
be associated with hospitalization because the major bacterial groups identified by DGGE BPA belonged
to taxa that are routinely isolated in baby care units such as E. coli, Enterococcus spp., and Klebsiella pneumoniae. This finding thus indicates
that the initial colonization of the newborn’s intestinal tract is highly
dependent on the immediate environment of the individual. In another
study assessing the global diversity of the fecal microbiota of preterm infants (n = 16), a
remarkably low-species diversity and high-interindividual variability were
reported  [106]. The low-bacterial diversity was revealed by random sequencing
of 16S rRNA gene clones and TTGE analysis. The main fecal groups encountered here included members
of the Enterobacteriaceae family and of the genera Enterococcus, Streptococcus, and Staphylococcus. Seven out of 16 preterm infants were colonized by
anaerobes, of which four infants were shown to harbor bifidobacteria.Several studies have
documented that bifidobacteria predominate in the fecal flora of breastfed
babies, whereas in
formulafed infants, other bacterial groups such as coliforms, enterococci, andBacteroides represent the main constituents  [7, 153]. In contrast, the possible effect of dietary supplementation
in the intestinal development of nursing infants is less well understood. In a longitudinal
study, TTGE was used to monitor the predominant and bifidobacterial microbiota
of 11 Algerian infants during breastfeeding, breastfeeding with artificial milk
supplementation (weaning) and artificial milk alone (postweaning, i.e.,
cessation of breastfeeding)  [107]. In the TTGE profiles, the major bands were
assigned by subsequent cloning and sequencing to E. coli, Ruminococcus spp., and several Bifidobacterium species including B. longum, B.
infantis, and B. breve. Both for the bacterial and bifidobacterial
TTGE profiles, distance analysis indicated the expected maturation of the faecal
microbiota between 5 and 20 weeks of age, but did not reveal any correlation
with the dietary supplementation. Despite a high-interindividual variability,
it was observed that the composition of the faecal microbiota appeared more
homogenous after weaning which may suggest a correlation with the cessation of
breastfeeding. In another study, 65 10-month old infants were included in a
randomized dietary intervention study that compared the effect of cow’s
milk (CM) with infant formula (IF) with or without fish oil (FO) supplement on
the diversity of the fecal microbiota  [80]. Based on clustering
analysis of V3- and V6–V8-16S rDNA DGGE profiling using the Pearson correlation
coefficient, it was reported that supplementation of CM or IF
appeared to have an influence on the composition of the intestinal microbiota
whereas FO intake only showed an effect in the CM group. The authors speculated
that these differences may be influenced by the intake of iron and n-3
polyunsaturated fatty acids, respectively, but further indepth analysis of the
DGGE profiles in combination with other molecular tools is required to
substantiate this hypothesis.Besides the
influence of environmental and dietary factors, also the host genotype may have
a significant effect on the species composition of the intestinal microbiota. Stewart
et al.  [84] used TTGE analysis of the predominant bacterial biota to investigate the influence
of host genotype on the fecal microbiota in genetically related and unrelated children. In that
study, TTGE profiles
of identical twin pairs (n = 13), fraternal twin pairs (n = 7), and
unrelated control pairs (n = 12) were compared both visually and numerically. Although
the community fingerprints of each individual were unique, increased levels of
similarity were found between TTGE profiles of genetically related individuals,
with the highest
similarity values obtained for genetically identical twins (median
Cs of
82%) which was significantly different from fraternal twin pairs (median Cs of 68%) and from the unrelated control group (median Cs of 45%). The
results of this TTGE study thus suggested that host genetics can have an impact
on the composition of the predominant fecal bacterial community in children.
Likewise, DGGE analysis of the dominant intestinal microbiota amongst adults
displaying varying degrees of genetic relatedness showed that the host genotype
had a significant effect on the species composition of the intestinal community
[122].Upon succession, it is thought that a relatively stable
intestinal community is established in the adult intestine that appears to be
specific for each individual. Zoetendal et al.  [95] were the first to report on
the stability and uniqueness of the predominant human adult fecal microbiota
that can be visualized with SDE-based approaches. TGGE analysis of fecal
samples from two healthy individuals showed stable profiles over a period of at
least six months which in addition were unique for each individual. These
findings were consolidated in a later study  [64] in which the host specificity
and temporal stability of the DGGE patterns was demonstrated for four subjects
over a 16-week period by visual inspection and clustering analysis. In the
latter study, also the temporal stability of selected subpopulations was
monitored using group-specific primers. DGGE profiles obtained with primers
designed to visualize the Lactobacillus-Leuconostoc-Pediococcus-Weissella-group tended to show strong
temporal variations. Among other autochthonous groups such as the Bacteroidesfragilis subgroup, however, DGGE profiling using group-specific primers
did not reveal such variations. Importantly, the specificity of these
group-specific primers was only validated using a set of taxonomic reference
strains. A more elaborated strategy was followed in the validation of DNA- and
RNA-based DGGE protocols specifically designed to assess the diversity and
stability of the Clostridium coccoides-Eubacterium
rectale (clostridial phylogenetic cluster XIVa) group in fecal samples
[70]. In that study, the specificity of the Ccoc-f and Ccoc-r primers was
assessed by constructing a clone library in which all 205 DGGE fragments proved
to belong to the Clostridium cluster XIVa. The authors concluded that
the members of this cluster, representing one of the most dominant bacterial
groups in the normal intestinal microbiota, followed the same pattern of
relative stability as the total predominant population in 12 healthy Finnish
adults during six months to two years. Although using protocols
differing in sample type, SDE method and primer target, the current view
on the uniqueness and temporal stability of the predominant intestinal flora in
adult individuals has also been confirmed in other human volunteer studies
using SDE-based analyses of fecal samples  [77, 81, 83, 98, 99, 118, 154] and
mucosa samples originating from different parts of the large intestine  [79, 98, 123].Although the vast majority of SDE-based studies in intestinal
microbiology rely on direct DNA extraction from human samples in order to
obtain a culture-independent inventory of the microbial diversity, there has
also been interest in using DGGE and related fingerprinting techniques to
specifically explore the composition of culturable intestinal subpopulations.
For instance, DGGE analyses of resuspended bacterial biomass
obtained from agar plates of different media selective and nonselective for
lactic acid bacteria (LAB) have been used to evaluate the choice of medium and
incubation conditions on LAB recovery and to gain insight in the diversity of culturable
fecal LAB in healthy adults  [129].


## 3.1.2. Spatial Distribution

The different physicochemical conditions such as pH and concentration of
fermentation products prevailing in the ascending, transverse, and descending
parts of the colon  [155] suggest that also the bacterial composition in each of
these three compartments is unique. However, this assumption is not
substantiated by SDE-based studies  [30, 79, 91, 115, 123]. In most of these
studies, DGGE and TTGE fingerprint profiles reflecting the predominant
bacterial communities in biopsy samples from different sites of the colon were
host specific but highly similar between sites. These findings may indicate
that the spatial distribution of at least the predominant mucosa-associated
bacterial community is relatively uniform along the length of the colon and its
physicochemical gradient. Nielsen et al. [79] reported that DGGE profiles of the
bifidobacterial community were relatively simple and consisted of one or two
bands for most of the sites sampled along the length of the colon. However, the
mucosa-associated subcommunity encompassing the genera Lactobacillus, Leuconostoc, Weissella,
Pediococcus, and Aerococcus produced relatively
complex DGGE profiles that varied between hosts and between sampled sites in
the colon. In contrast, Zoetendal et al.  [123] obtained DGGE profiles with low
diversity and little or no variation along the colon when using the same set of
group-specific PCR primers. Presumably, the contradictory findings of the two
aforementioned studies are due to differences in sampling procedure, DNA
extraction method, and/or composition of the subject group.Given the fact that each individual displays a unique
fecal SDE fingerprint  [64, 95], investigation into spatial distribution should preferably
be based on analysis of a series of site-specific biopsy samples from the same
individual. To some extent, this may explain why interindividual comparison of
DGGE profiles of single biopsy samples from different sites did not provide any
evidence for the existence of site-specific colonization patterns in the human
colon  [30].A number of studies have also investigated to what
extent the composition of the fecal microbiota reflects the composition of the
mucosa-associated colonic microbiota  [91, 123]. In these studies, the DGGE/TTGE
profiles of amplicons of the variable V6–V8 region of the 16S rRNA gene
reflecting the predominant bacterial community of biopsy samples differed
significantly from those of fecal samples within the same individual, suggesting
that different bacterial populations are dominating the human mucosa and feces.
The population diversity revealed by SDE-based community fingerprinting
of fecal samples may thus not
necessarily reflect the ecosystem composition in other parts of the intestinal
tract including the colonic mucosa. This leads to the conclusion that the most
accurate information on the diversity and stability of local intestinal
communities can thus only be obtained by taking samples through endoscopy or
during colonic surgery.


## 3.2. Intestinal Disorders

The pathogenesis of many chronic intestinal disorders
and even a number of nonintestinal diseases is believed to be directly or
indirectly linked to some
members of the indigenous microbiota. Several studies have implemented
an SDE-based approach to analyze and monitor the composition and temporal
stability of the intestinal microbiota of patients suffering from gut
disorders. As an initial approach, SDE techniques permit a rapid and global assessment of microbial diversity
without previous knowledge of the composition and are well suited to analyze
intestinal microbiota in relation to different experimental conditions
and parameters such as healthy versus
disease status, active versus quiescent disease phase, different segments
of the intestinal tract and response to nutritional or therapeutic
interventions. Moreover, the combined use of SDE techniques and quantitative
assays such as real-time PCR and FISH that allow to determine the relative
concentration of specific indicator organisms offers great potential in this type of studies. The
following sections of this review are based on a selected number of studies
that have implemented SDE-based methods to assess the potential role of the
intestinal microbiota in the (etio)pathogenesis of chronic intestinal
disorders.


### 3.2.1. Inflammatory Bowel Disease

Although the
exact etiology of inflammatory
bowel disease (IBD) is not known to date, it is generally assumed to
result from an inappropriate response of the mucosal immune system to the
normal enteric microbiota in a genetically susceptible individual  [156]. It has
been hypothesized that specific genetic polymorphisms, such as those in
intracellular NOD2 sensors with abnormal function, results in a failure to
efficiently regulate expression of Paneth cell-derived antimicrobial peptides  [157, 158]. The partial loss of this
protective function may allow commensals to damage epithelial cells hereby
inducing an inflammatory response. Crohn’s disease (CD) and ulcerative colitis (UC) are the two
major IBD phenotypes and are characterized by chronic inflammation of the
intestinal tract lining which causes severe watery and bloody diarrhoea and
abdominal pain  [156]. Whereas CD can virtually affect any segment of the intestinal tract, UC
is usually confined to the colon and rectum.The majority of SDE-based studies on IBD have primarily attempted to find
differences between CD/UC and healthy fecal or mucosal populations. As such,
V3–V5-16S rDNA DGGE profiling and subsequent band sequencing analysis of fresh
mucosal biopsy samples revealed a significantly higher prevalence ofClostridium spp., Ruminococcus torques, and E. coli in samples from CD
patients (n = 19) compared to healthy specimens (n = 15)  [159]. In turn, the
butyrate-producing Faecalibacterium prausnitzii was more
frequently encountered in the latter group. Overall, DGGE fingerprints of
mucosal CD populations displayed a higher patient-to-patient variability
compared to healthy subjects. The authors postulated that this difference may
reflect the difficulty of patients genetically predisposed to CD to maintain
and regulate a stable intestinal microbiota. A study of Bibiloni et al.  [160]
showed that the phylogenetic composition of biopsy-associated bacteria differed
between newly diagnosed untreated CD (n = 20) and UC patients (n = 15) and healthy
subjects (n = 14). Biopsies collected from inflamed and noninflamed sites of the terminal ileum and
various colonic regions were analyzed by DGGE, 16S rRNA gene clone
libraries, and qualitative
and quantitative PCR for detection of selected bacterial groups. DGGE profiles of universal
V3-16S rRNA gene amplicons were very similar within each subject (mean 85.0±2.4%),
irrespective of the intestinal region. However, enumeration by quantitative PCR
revealed approximately double numbers of biopsy-associated bacteria for UC patients than CD patients
and healthy subjects. In addition, the clone library composition indicated that the composition of
biopsy populations in UC and CD patients (P < .05), and those from healthy subjects
(P = .05) were statistically different. This comparison highlighted a significantly higher prevalence
of unclassified members of the phylum Bacteroidetes in CD patients, which may indicate
that UC and CD are bacteriologically distinct diseases.Depending on the
individual effectiveness, IBD patients undergoing immunomodulatory
therapy continuously balance between active disease and remission status. However, it is unclear if and in
what way the intestinal microbiota of these patients undergoes compositional
changes during these subsequent transitions. In this context, Seksik et al. [24]
monitored the fecal microbiota of patients with active colonic CD (n = 8),
patients in remission (n = 9), and healthy volunteers (n = 16). TTGE profiles of universal 16S rRNA gene V6–V8 amplicons were very stable over time in the healthy controls but varied markedly for a number of patients
(n = 4) who were monitored during both active and quiescent phase of CD. Fecal
TTGE profiles of these four patients revealed only a slight decrease in the
number of bands during the active phase (mean
loss of 1.7±2.7 bands), which indicated that the predominant fecal microbiota
retained a high degree of diversity in both phases. Based on TTGE band profile composition, no specific
bacterial groups could be assigned to active or quiescent CD state. In
contrast, quantitative dot blot hybridization of stool samples showed that the fecal microbiota in patients with CD (both active and inactive)
differed considerably from those of healthy subjects. Both the Bacteroides group (including the genera Bacteroides, Prevotella, and Porphyromonas)
and the bifidobacteria tended to be less represented in CD patients whereas
significantly more enterobacteria could be detected. In addition, approximately
30% of the endogenous microbiota of CD patients did not belong to the dominant
phylogenetic groups commonly found in healthy controls.Although currently
available data from SDE profiling and other molecular tools implicate a role of
intestinal bacteria in CD pathogenesis, a detrimental effect of localized
qualitative dysbiosis in CD-associated ulceration has so far not be
demonstrated by community fingerprinting. TTGE analysis of biopsy samples of
ulcerated and adjacent nonulcerated mucosa of 15 patients with active CD did
not reveal qualitative differences in the dominant bacterial population
profiles (V6–V8 region of the 16S rRNA gene) within a given patient although a high biodiversity was retained in both cases  [92]. Mean similarity values between TTGE
profiles of ulcerated and nonulcerated mucosa expressed with the Pearson correlation coefficient did not differ significantly across the
different intestinal segments (ileum, right colon, left colon, and rectum)
analyzed and ranged from 95.2±4.2% to 97.9±1.7%.
Solely based on TTGE analysis using universal primers, it thus appears that
local ulceration is not associated with pronounced variation in local bacterial
diversity. This
conclusion was further substantiated in a later study by the same group on the
basis of V3-V4-16S rDNA TTGE profiling and FISH analysis  [94]. Also in other
studies applying SDE-based population fingerprinting, no particular
mucosa-associated microbial pattern could be linked to the (etio)pathogenesis
of IBD  [91, 123]. Possibly, local dysbiosis among less predominant species may play a
role in the pathogenesis of ulceration. Because
these minor differences in diversity will largely remain undetected in SDE
fingerprinting using universal PCR primers or are difficult to reveal by
routine methods for band pattern analysis, future studies should employ
group-specific primers to focus on the composition of specific subpopulations
and/or should use more indepth mathematical approaches for differential profile
analysis.Whereas most studies concentrated on the
inventorization and monitoring of bacterial groups potentially associated with
CD, very few studies aimed to address the same question within the
metabolically active compartment of the gut microbiota. Sokol et al.  [93] analyzed the biodiversity of active bacteria in the dominant fecal
microbiota of UC patients (n = 9) in comparison with that of healthy subjects
(n = 9) by applying DNA- and RNA-based TTGE analysis of V6–V8 ribosomal
amplicons. The
number of bands in DNA-derived TGGE profiles were significantly higher than in RNA-derived profiles for UC
patients (15.3±3.2 and 9.1±2.8 bands, resp.) but not for controls (18.3±5.0 and 14.7±5.1 bands, resp.) which indicated a reduction in the
biodiversity of the active portion of the fecal microbiota in UC patients
relative to healthy controls. Irrespective of the initial
template (RNA or DNA), Pearson-UPGMA clustering analysis of TGGE profiles
tended to group the samples on the basis of their clinical affiliation (UC
versus controls) suggesting that each group has its specific bacterial
signature. Interindividual comparison of the “active” microbiota (RNA-derived
profiles) revealed a band that was significantly associated with UC patients
(89% versus 22% for controls). Sequence analysis attributed this band to E.
coli or related enterobacteria. Clearly, the possible pathophysiological
role of this overrepresentation in the active microbiota of UC patients should
be further assessed during remission and within the mucosa-associated
microbiota.


### 3.2.2. Other Intestinal Disorders

Irritable bowel syndrome (IBS) is an intestinal
disorder that is characterized by bowel dysfunction and pain  [161, 162]. IBS is
a very heterogeneous condition and includes three symptom categories: (i) diarrhoea-dominant,
(ii) constipation-dominant, and (iii) alternating type  [163, 164]. Although the
pathophysiology of IBS is not fully understood, it is highly probable that
alterations in the diversity and stability of intestinal microbiota play a role
in the development and/or maintenance of this disorder  [165]. In a Finnish
study  [121], culture-based techniques and DGGE analysis were employed to
compare the composition and temporal stability of the fecal microbiota of 21
IBS patients and 17 healthy controls. Culturing revealed slightly higher
coliform numbers as well as an increased aerobe/anaerobe ratio in the IBS
group. DGGE analysis of 16S rRNA gene V6–V8 amplicons revealed considerable
biodiversity and subject specificity of the predominant microbiota in both
study groups, but did not identify IBS-specific bacterial groups. Visual
comparison of DGGE fingerprints revealed a higher frequency of temporal
instability in the predominant bacterial population of IBS subjects (43%)
compared to controls (29%). However, profile similarity analysis using the Pearson correlation
coefficient revealed comparable interindividual similarity percentages
for both groups with a mean similarity of 87.5±11.2% for the IBS group and
85.7±12.7% for the control group. Still, the instability in some of the IBS subjects could
partly be explained by disturbances of the intestinal microbiota due to
antibiotic therapy during the study. Moreover, the authors suggested
that these findings could be associated with a subset of IBS subjects sharing
specific symptoms and thus not necessarily reflect the general microbial status
of all IBS patients. In this regard, future studies should include subject
groups with well-defined symptom-based IBS parameters to evaluate the association of intestinal instability with
specific IBS symptoms or with specific bacterial groups and species. In a subsequent
study of the same group  [71], the predominant and clostridial fecal microbiota of IBS patients and
healthy controls were compared to reveal possible differences in the composition, abundance,
and stability of selected groups by applying DNA- and RNA-based DGGE analyses and transcript analysis with the aid of affinity capture, a
multiplexed and quantitative hybridization-based technique. Clostridia, that is, C. histolyticum, C. coccoides-Eubacterium rectale, C. lituseburense, and C. leptum, were shown to represent the dominant fecal microbiota in 26
of the 32 subjects under study, contributing altogether 29–87%. The proportion of the C. coccoides-E. rectale group was found to be significantly
lower in the constipation-type IBS subjects compared to the controls. Although DNA- and
RNA-derived predominant community profiles showed considerable biodiversity and
subject-specificity, RNA-based DGGE profiles contained significantly fewer
amplicons (16±5 compared to 22±5 amplicons). In addition, only RNA-based DGGE profiles
of the IBS subjects indicated higher instability of the bacterial population
compared to the control subjects. Although intraindividual temporal instability of the
predominant microbiota was observed in both IBS and control subjects (with both
DNA- and RNA-based DGGE), only RNA-derived DGGE profiles of IBS subjects showed
a broader range in similarity values (39–95%) compared to control subjects (68–94%). When considering symptomatic
IBS subgroups, the
largest intraindividual variability in DGGE similarity values was observed in
the diarrhoea-type subgroup. These observations suggest that clostridial microbiota, in
addition to the instability of the active predominant fecal bacterial
population (RNA-derived profiles), may be involved in IBS. For future
research, the use of group-specific
primers in SDE analysis focussing on apparently affected groups (e.g.,
coliforms and clostridia) could be a valuable and effective approach to
identify potential IBS indicator organisms.The
use of SDE-based methodologies to determine the diversity and stability of
microbiota in inflammatory diseases has meanwhile expanded from IBD and IBS to
other intestinal diseases in which dysbiosis of the human microbiome is thought
to play a role such as neonatal necrotizing enterocolitis  [116] and coeliac
disease  [130] or diseases beyond the intestinal system such as (atopic) allergies  [82, 85, 166] and ankylosing spondylitis  [113].


## 3.2.1. Inflammatory Bowel Disease

Although the
exact etiology of inflammatory
bowel disease (IBD) is not known to date, it is generally assumed to
result from an inappropriate response of the mucosal immune system to the
normal enteric microbiota in a genetically susceptible individual  [156]. It has
been hypothesized that specific genetic polymorphisms, such as those in
intracellular NOD2 sensors with abnormal function, results in a failure to
efficiently regulate expression of Paneth cell-derived antimicrobial peptides  [157, 158]. The partial loss of this
protective function may allow commensals to damage epithelial cells hereby
inducing an inflammatory response. Crohn’s disease (CD) and ulcerative colitis (UC) are the two
major IBD phenotypes and are characterized by chronic inflammation of the
intestinal tract lining which causes severe watery and bloody diarrhoea and
abdominal pain  [156]. Whereas CD can virtually affect any segment of the intestinal tract, UC
is usually confined to the colon and rectum.The majority of SDE-based studies on IBD have primarily attempted to find
differences between CD/UC and healthy fecal or mucosal populations. As such,
V3–V5-16S rDNA DGGE profiling and subsequent band sequencing analysis of fresh
mucosal biopsy samples revealed a significantly higher prevalence ofClostridium spp., Ruminococcus torques, and E. coli in samples from CD
patients (n = 19) compared to healthy specimens (n = 15)  [159]. In turn, the
butyrate-producing Faecalibacterium prausnitzii was more
frequently encountered in the latter group. Overall, DGGE fingerprints of
mucosal CD populations displayed a higher patient-to-patient variability
compared to healthy subjects. The authors postulated that this difference may
reflect the difficulty of patients genetically predisposed to CD to maintain
and regulate a stable intestinal microbiota. A study of Bibiloni et al.  [160]
showed that the phylogenetic composition of biopsy-associated bacteria differed
between newly diagnosed untreated CD (n = 20) and UC patients (n = 15) and healthy
subjects (n = 14). Biopsies collected from inflamed and noninflamed sites of the terminal ileum and
various colonic regions were analyzed by DGGE, 16S rRNA gene clone
libraries, and qualitative
and quantitative PCR for detection of selected bacterial groups. DGGE profiles of universal
V3-16S rRNA gene amplicons were very similar within each subject (mean 85.0±2.4%),
irrespective of the intestinal region. However, enumeration by quantitative PCR
revealed approximately double numbers of biopsy-associated bacteria for UC patients than CD patients
and healthy subjects. In addition, the clone library composition indicated that the composition of
biopsy populations in UC and CD patients (P < .05), and those from healthy subjects
(P = .05) were statistically different. This comparison highlighted a significantly higher prevalence
of unclassified members of the phylum Bacteroidetes in CD patients, which may indicate
that UC and CD are bacteriologically distinct diseases.Depending on the
individual effectiveness, IBD patients undergoing immunomodulatory
therapy continuously balance between active disease and remission status. However, it is unclear if and in
what way the intestinal microbiota of these patients undergoes compositional
changes during these subsequent transitions. In this context, Seksik et al. [24]
monitored the fecal microbiota of patients with active colonic CD (n = 8),
patients in remission (n = 9), and healthy volunteers (n = 16). TTGE profiles of universal 16S rRNA gene V6–V8 amplicons were very stable over time in the healthy controls but varied markedly for a number of patients
(n = 4) who were monitored during both active and quiescent phase of CD. Fecal
TTGE profiles of these four patients revealed only a slight decrease in the
number of bands during the active phase (mean
loss of 1.7±2.7 bands), which indicated that the predominant fecal microbiota
retained a high degree of diversity in both phases. Based on TTGE band profile composition, no specific
bacterial groups could be assigned to active or quiescent CD state. In
contrast, quantitative dot blot hybridization of stool samples showed that the fecal microbiota in patients with CD (both active and inactive)
differed considerably from those of healthy subjects. Both the Bacteroides group (including the genera Bacteroides, Prevotella, and Porphyromonas)
and the bifidobacteria tended to be less represented in CD patients whereas
significantly more enterobacteria could be detected. In addition, approximately
30% of the endogenous microbiota of CD patients did not belong to the dominant
phylogenetic groups commonly found in healthy controls.Although currently
available data from SDE profiling and other molecular tools implicate a role of
intestinal bacteria in CD pathogenesis, a detrimental effect of localized
qualitative dysbiosis in CD-associated ulceration has so far not be
demonstrated by community fingerprinting. TTGE analysis of biopsy samples of
ulcerated and adjacent nonulcerated mucosa of 15 patients with active CD did
not reveal qualitative differences in the dominant bacterial population
profiles (V6–V8 region of the 16S rRNA gene) within a given patient although a high biodiversity was retained in both cases  [92]. Mean similarity values between TTGE
profiles of ulcerated and nonulcerated mucosa expressed with the Pearson correlation coefficient did not differ significantly across the
different intestinal segments (ileum, right colon, left colon, and rectum)
analyzed and ranged from 95.2±4.2% to 97.9±1.7%.
Solely based on TTGE analysis using universal primers, it thus appears that
local ulceration is not associated with pronounced variation in local bacterial
diversity. This
conclusion was further substantiated in a later study by the same group on the
basis of V3-V4-16S rDNA TTGE profiling and FISH analysis  [94]. Also in other
studies applying SDE-based population fingerprinting, no particular
mucosa-associated microbial pattern could be linked to the (etio)pathogenesis
of IBD  [91, 123]. Possibly, local dysbiosis among less predominant species may play a
role in the pathogenesis of ulceration. Because
these minor differences in diversity will largely remain undetected in SDE
fingerprinting using universal PCR primers or are difficult to reveal by
routine methods for band pattern analysis, future studies should employ
group-specific primers to focus on the composition of specific subpopulations
and/or should use more indepth mathematical approaches for differential profile
analysis.Whereas most studies concentrated on the
inventorization and monitoring of bacterial groups potentially associated with
CD, very few studies aimed to address the same question within the
metabolically active compartment of the gut microbiota. Sokol et al.  [93] analyzed the biodiversity of active bacteria in the dominant fecal
microbiota of UC patients (n = 9) in comparison with that of healthy subjects
(n = 9) by applying DNA- and RNA-based TTGE analysis of V6–V8 ribosomal
amplicons. The
number of bands in DNA-derived TGGE profiles were significantly higher than in RNA-derived profiles for UC
patients (15.3±3.2 and 9.1±2.8 bands, resp.) but not for controls (18.3±5.0 and 14.7±5.1 bands, resp.) which indicated a reduction in the
biodiversity of the active portion of the fecal microbiota in UC patients
relative to healthy controls. Irrespective of the initial
template (RNA or DNA), Pearson-UPGMA clustering analysis of TGGE profiles
tended to group the samples on the basis of their clinical affiliation (UC
versus controls) suggesting that each group has its specific bacterial
signature. Interindividual comparison of the “active” microbiota (RNA-derived
profiles) revealed a band that was significantly associated with UC patients
(89% versus 22% for controls). Sequence analysis attributed this band to E.
coli or related enterobacteria. Clearly, the possible pathophysiological
role of this overrepresentation in the active microbiota of UC patients should
be further assessed during remission and within the mucosa-associated
microbiota.


## 3.2.2. Other Intestinal Disorders

Irritable bowel syndrome (IBS) is an intestinal
disorder that is characterized by bowel dysfunction and pain  [161, 162]. IBS is
a very heterogeneous condition and includes three symptom categories: (i) diarrhoea-dominant,
(ii) constipation-dominant, and (iii) alternating type  [163, 164]. Although the
pathophysiology of IBS is not fully understood, it is highly probable that
alterations in the diversity and stability of intestinal microbiota play a role
in the development and/or maintenance of this disorder  [165]. In a Finnish
study  [121], culture-based techniques and DGGE analysis were employed to
compare the composition and temporal stability of the fecal microbiota of 21
IBS patients and 17 healthy controls. Culturing revealed slightly higher
coliform numbers as well as an increased aerobe/anaerobe ratio in the IBS
group. DGGE analysis of 16S rRNA gene V6–V8 amplicons revealed considerable
biodiversity and subject specificity of the predominant microbiota in both
study groups, but did not identify IBS-specific bacterial groups. Visual
comparison of DGGE fingerprints revealed a higher frequency of temporal
instability in the predominant bacterial population of IBS subjects (43%)
compared to controls (29%). However, profile similarity analysis using the Pearson correlation
coefficient revealed comparable interindividual similarity percentages
for both groups with a mean similarity of 87.5±11.2% for the IBS group and
85.7±12.7% for the control group. Still, the instability in some of the IBS subjects could
partly be explained by disturbances of the intestinal microbiota due to
antibiotic therapy during the study. Moreover, the authors suggested
that these findings could be associated with a subset of IBS subjects sharing
specific symptoms and thus not necessarily reflect the general microbial status
of all IBS patients. In this regard, future studies should include subject
groups with well-defined symptom-based IBS parameters to evaluate the association of intestinal instability with
specific IBS symptoms or with specific bacterial groups and species. In a subsequent
study of the same group  [71], the predominant and clostridial fecal microbiota of IBS patients and
healthy controls were compared to reveal possible differences in the composition, abundance,
and stability of selected groups by applying DNA- and RNA-based DGGE analyses and transcript analysis with the aid of affinity capture, a
multiplexed and quantitative hybridization-based technique. Clostridia, that is, C. histolyticum, C. coccoides-Eubacterium rectale, C. lituseburense, and C. leptum, were shown to represent the dominant fecal microbiota in 26
of the 32 subjects under study, contributing altogether 29–87%. The proportion of the C. coccoides-E. rectale group was found to be significantly
lower in the constipation-type IBS subjects compared to the controls. Although DNA- and
RNA-derived predominant community profiles showed considerable biodiversity and
subject-specificity, RNA-based DGGE profiles contained significantly fewer
amplicons (16±5 compared to 22±5 amplicons). In addition, only RNA-based DGGE profiles
of the IBS subjects indicated higher instability of the bacterial population
compared to the control subjects. Although intraindividual temporal instability of the
predominant microbiota was observed in both IBS and control subjects (with both
DNA- and RNA-based DGGE), only RNA-derived DGGE profiles of IBS subjects showed
a broader range in similarity values (39–95%) compared to control subjects (68–94%). When considering symptomatic
IBS subgroups, the
largest intraindividual variability in DGGE similarity values was observed in
the diarrhoea-type subgroup. These observations suggest that clostridial microbiota, in
addition to the instability of the active predominant fecal bacterial
population (RNA-derived profiles), may be involved in IBS. For future
research, the use of group-specific
primers in SDE analysis focussing on apparently affected groups (e.g.,
coliforms and clostridia) could be a valuable and effective approach to
identify potential IBS indicator organisms.The
use of SDE-based methodologies to determine the diversity and stability of
microbiota in inflammatory diseases has meanwhile expanded from IBD and IBS to
other intestinal diseases in which dysbiosis of the human microbiome is thought
to play a role such as neonatal necrotizing enterocolitis  [116] and coeliac
disease  [130] or diseases beyond the intestinal system such as (atopic) allergies  [82, 85, 166] and ankylosing spondylitis  [113].


## 3.3. Intervention Studies

Apart from
components naturally occurring in a normal diet, also functional foods
(including pre- and probiotics) and antimicrobial agents are able to induce
beneficial or detrimental changes in intestinal ecosystems. Starting from the
first weeks upon birth, the human diet is able to modulate the composition and
balance of the intestinal microbiota  [7, 153]. The
SDE approach is routinely applied in administration studies to monitor the
effects on the intestinal microbiota upon consumption of various active
components.


### 3.3.1. Functional Foods

The fact that diet is a major factor controlling the
human intestinal balance has triggered the development of a new generation of foods specifically designed
to strengthen the gut microbiota via modulation. Functional foods include foods
and food products with a clearly identifiable health benefit in addition to
their basic nutritional value  [167]. In functional foods, the addition
or incorporation of pro- and/or prebiotic components as active ingredients
plays a key role in functional applications aiming at modulation of intestinal
microbiota. According to the FAO/WHO definition  [168], probiotics are live
microorganisms which, when administered in adequate amounts, confer a health
benefit for the host. An extended version of this definition is still under
debate, including the question whether the live status is truly required for
probiotic action  [169, 170]. Beneficial effects induced by probiotic activities are mediated
either through modulation of the indigenous microbiota or through the
immunomodulatory potential of the probiotic strains used. Bacterial
cultures incorporated in probiotic products for human consumption commonly—but
not exclusively—originate from the intestinal system of healthy (human)
subjects and most frequently
belong to the bifidobacteria and to LAB such as Lactobacillus spp. A
prebiotic, on the other hand, is a nondigestible selectively fermented compound
that induces specific changes both in the composition and/or the activity of
the gastrointestinal microbiota thereby conferring benefits upon host
well-being and health  [171]. Essentially, the functionality of a prebiotic
compound is determined
by its potential to stimulate beneficial bacteria indigenous to the gut
ecosystem. Complex oligosaccharides are most commonly used as prebiotics
including lactulose, galactooligosaccharides (GOS) and fructooligosaccharides
(FOS; e.g., oligofructose and inulin). A wide range of beneficial effects have
been attributed to probiotics, prebiotics, or a combination thereof (i.e.,
synbiotics), including modulation of the gut immune system, resistance to microbial
infections, antimutagenic/anticarcinogenic
effects, reduction of blood ammonia and cholesterol levels, prevention
and/or alleviation of diarrhoea and constipation, prevention and reducing
symptoms of intestinal chronic disorders, relief of lactose intolerance and
increased mineral absorption as reviewed in  [172–178].SDE-based methods have
played a key role in human dietary intervention studies aiming at demonstrating
the efficacy of functional food components and to substantiate potential health
claim. A selection of relevant studies that have contributed to this field is listed
in Table3  [68, 69, 72, 73, 75, 76, 88, 90, 98, 100, 105, 110, 111, 120, 127, 179, 180]. Solely based on findings from SDE analysis, it appears that
prebiotic administration can potentially affect the predominant bacterial
population of healthy human subjects, whereas most probiotic interventions only
seem to induce marked effects in patient groups. This could indicate that some probiotic components may have
more of a therapeutic effect in subjects with a disturbed intestinal
balance but less effective as general health promoting agents. On the other hand, it should be
kept in mind that SDE-based approaches focus on diversity and dynamics of
predominant intestinal microbiota, and are as such unsuitable to monitor
probiotic interventions that are based on the immunomodulatory potential of the
administered organism(s). Bibiloni et
al.  [110] used DGGE to evaluate the safety and efficacy of the mixed
probiotic preparation VSL#3® (http://www.vsl3.com/) consisting of three Bifidobacterium strains and five LAB strains (i.e., four Lactobacillus strains and one Streptococcus
thermophilus strain) in patients with active mild to moderate UC. DGGE analysis of V3-16S rRNA
gene amplicons generated from biopsies collected from seven patients
before and after 6-week VSL#3 administration revealed considerable variation of
the predominant microbiota in four out of five patients in remission (mean dice similarity coefficient (Ds) of 69.9±12.7%). In
contrast, the DGGE profiles of the two patients with continued active
disease remained relatively stable after VSL#3 consumption (mean Ds  of 92.3±4.1%).
Importantly, it should be noted that the study did not report on the temporal
stability of biopsy profiles in the absence of probiotic treatment. In another
study, the effect of a 4-week administration of the candidate prebiotic di-D-fructofuranose-1,
2′ : 2, 3′-dianhydride (DFA III) on human fecal microbiota was studied by DGGE
analysis using universal V3-16S rRNA primers and Bacteroides fragilis subgroup-specific primers  [73]. Visual and numerical analysis of the DGGE profiles generated with both
primer sets revealed no pronounced changes related to DFA III administration in
healthy subjects. In a followup long-term human feeding trial (2 to 12 months)
with DFA III, however, DGGE profiles of the predominant bacterial population
revealed a marked increase in the intensity of bands related to Bacteroides spp.  [72]. In a study on the effect
of 3-week consumption of a GOS-containing probiotic yogurt on the diversity and
temporal stability of fecal microbiota in elderly  [69], DGGE revealed that the
predominant bacterial population and the Clostridium coccoides-Eubacterium
rectale group remained relatively stable during the study period. In
contrast, the Lactobacillus group showed temporal variation which
confirms previous observations under basal conditions  [64].Table 3
Selection of dietary intervention studies using SDE-based community fingerprinting.
ComponentaAdministered componentReference(s)bpLevan-type
exopolysaccharides, levan, inulin and FOS[111]pGOS and FOS[75]pDifructose
anhydride III (DFA III)[72, 73]PLactobacillus rhamnosus DR20[76, 88]PLactobacillus paracasei F19[98]PVSL#3® (probiotic
mixture of eight strains)[110]PBifidobacterium longum (Bifina®)
and yogurt with Bifidobacterium animalis DN-173 010[179]yLactobacillus
delbrueckii subsp. bulgaricus and Streptococcus thermophilus[68]p, PInulin orB. longum (Bifina®)[180]pPInulin-containing probiotic
yogurts[127]pPGOS-containing probiotic yogurt[69]p, P, pPGOS and/orBifidobacterium lactis Bb-12[100]p, P, pPLactulose
and/orSaccharomyces boulardii[90]oBlack tea[120]o, op, oPIsoflavones and FOS orB. animalis DN-173 010[105]ap: prebiotic; P: probiotic; pP: synbiotic; y, yogurt; o:
other.
bAll studies used DGGE as SDE method except in  [105], where
TTGE was used.In the course of probiotic
intervention studies, DGGE and related fingerprinting techniques have been used
to verify if the administered strain(s) is (are) detectable in intestinal
samples  [76, 88, 98, 100, 105, 110, 125, 179]. Based on a combination of
culture-based methods and 16S rDNA DGGE, Wall et al.  [86] even reported the
recovery of probiotic strains Lactobacillus
paracasei NFBC 338
and B. animalis subsp. lactis Bb12
in ileostomy effluents of two infants without a history of probiotic intake. In this context, it should
be noted that SDE fingerprinting is not the most optimal tool for detection of administered
strains because of the relatively poor detection limit (especially when using
universal primers) and the lack of resolution to discriminate the introduced
strain(s) from other strains of the same or highly related autochthonous member
species of the intestinal microbiota. More suitable approaches are those applying strain-specific primers
(e.g., conventional or real-time PCR) or probes (e.g., fluorescent FISH probes)
which will not only provide a higher sensitivity but may also allow relative
quantification of the probiotic target  [181–184]. On the other hand, it should
be kept in mind that all aforementioned DNA-based approaches do not allow to
discriminate between living and dead cells and thus do not provide information
on probiotic survival throughout the gastrointestinal tract.In recent years, SDE-based community fingerprinting has been integrated
in larger polyphasic studies in combination with conventional culture methods
and/or with other molecular culture-independent methods to detect and monitor
changes in human intestinal ecosystems upon administration of probiotic,
prebiotic, or other (in)organic compounds with claimed functionalities. As
such, DGGE and FISH approaches were combined with selective culture methods to
evaluate the impact of a 3-week diet supplementation with prebiotic GOS or FOS on the composition and
activities of the fecal microbiota of 15 healthy human volunteers  [75]. V3-16S rRNA gene
DGGE profiles remained relatively stable during the study, whereas clear
alterations in response to dietary supplementation were observed in rRNA-DGGE
profiles as evidenced by the detection of additional fragments or increased
staining intensity of band fragments attributed to Bifidobacterium adolescentis and/or Collinsella aerofaciens. In contrast, DGGE analysis using
genus-specific primers derived from the transaldolase gene generated relatively stable profiles
for fecal bifidobacteria. Although the taxonomic composition of the bifidobacterial population
was not substantially different and both DGGE and FISH revealed that the Bifidobacterium and Collinsella populations
remained relatively unchanged, rRNA-DGGE provided evidence of increased
metabolic activity in
response to prebiotic consumption. A combination of DGGE and FISH was
also used to investigate the effect of black tea drinking on the fecal
microbiota of healthy volunteers with hypercholesterolemy  [120]. DGGE of 16S rRNA gene V6–V8
amplicons showed that each subject harboured a specific predominant bacterial
population that exhibits little change over time and that was not significantly
changed by drinking black tea. Even though black tea did not affect the
specific bacterial groups analyzed by FISH (i.e., Bifidobacterium, Bacteroides and Prevotella, Clostridium phylogenetic clustersIV and XIVa, Atopobium group, Faecalibacterium-like
species and E. coli), it did decrease the total amount of bacteria
detected by the universal bacterial probe. In a study that combined the use of
TGGE and FISH analysis, it was demonstrated that isoflavone supplementation with and without pro- or
prebiotics induced significant dynamic changes on the composition of the dominant
intestinal microbiota of 39 postmenopausal women  [105]. Results of FISH analysis
indicated that several of the dominant fecal groups were stimulated by isoflavones alone,
whereas TGGE
profiling of 16S rRNA gene V6–V8 amplicons revealed marked changes in the
predominant intestinal microbiota. Intraindividual comparison of TGGE
fingerprints showed a mean Pearson similarity value of 73% before and after one
month of isoflavone supplementation. In combination with a pro- or prebiotic
compound, isoflavones triggered comparable population changes as evidenced by
mean fingerprint similarity values of 71 ± 18% and 68 ± 16% obtained for the
probiotic (Bifidobacterium
animalis DN-173
010) and the
prebiotic (FOS) test groups, respectively.
In addition, FISH results showed a bifidobacterial increase following prebiotic supplementation, often
referred to as the bifidogenic effect. Amongst others  [76, 125, 180], the aforementioned studies
have demonstrated the potential of using SDE fingerprinting and FISH analyses
in a complementary approach to characterize basic interactions between
intestinal microbiota and functional food compounds and to quantify
subpopulations responding to the introduced component(s).Next to FISH, also real-time PCR has been used in combination with DGGE
to verify and substantiate compositional changes in a semiquantitative manner.
The latter two methods were used in an integrated approach to monitor and
quantify pronounced changes in fecal microbiota of healthy subjects upon
long-term administration of a prebiotic (lactulose), a probiotic (Saccharomyces boulardii), and their synbiotic combination  [90]. Although the DGGE profiles obtained
with the universal V3-16S rRNA gene primers as well as those generated using group-specific
primers targeting the Bacteroides fragilis subgroup, the genus Bifidobacterium and the Clostridium lituseburense and Clostridium coccoides-Eubacterium rectale groups remained fairly stable, one pronounced change was
observed in the universal fingerprint profiles after lactulose ingestion. The DGGE band appearing or intensifying in 27
of the 30 subjects could be assigned to Bifidobacterium adolescentis by band position analysis and band
sequencing. In subsequent real-time PCR analysis, this finding was
correlated to a statistically significant stimulation of total bifidobacteria
and of B. adolescentis. In contrast, the probiotic yeast S. boulardii did not
display any detectable universal changes in the DGGE profiles nor influenced
bifidobacterial levels. In a double-blind crossover study on the qualitative
and quantitative effects of fresh and heat-treated yogurt on the bacterial
intestinal microbiota from healthy subjects  [68], DGGE profiling revealed overall stability of
the predominant bacterial population and the LAB population at baseline, after
fresh yogurt intake and after heat-treated yogurt intake. However, real-time
PCR with group-specific primers indicated a significantly higher density of LAB
and Clostridium perfringens and a significant decrease in the density of Bacteroides after consumption of both types of yogurt.


### 3.3.2. Antimicrobial Agents

Apart from their generally well-documented
therapeutic effects on the site of infection, antimicrobials can also exert a
detrimental effect on the microbial balance of the gut ecosystem. So far, studies analyzing
the effect of antibiotic therapy on the selection and transmission of
antibiotic resistance among pathogens and commensals within the human
intestinal microbiota have mainly relied on culture-dependent approaches  [185–187]
which are highly restricted by the selectivity of the media used. In this
respect, SDE-based techniques provide a more suitable approach to monitor the
effects of antimicrobial agents on the total community structure of intestinal
microbiota.In several human studies mostly focussing on infant
populations, DGGE analysis has revealed drastic alterations among the
indigenous bacterial diversity upon therapy with various antimicrobials  [77, 96, 101, 102]. Antimicrobial-induced disruptions of fingerprinting profiles
were generally accompanied by a reduction in band numbers suggesting an overall
decrease in predominant intestinal ecosystem diversity. Favier et al.  [96]
monitored bacterial succession of the intestine during the first four months of
life of five babies, including one infant who received continuous antibiotic therapy
consisting of Augmentin (a mixture of clavulanic acid and amoxicillin) for 13 days
followed by Bactrimel (a mixture of trimethoprim and sulfamethoxazol) to combat
urinary reflux. During
the first month after birth, the universal 16S rRNA gene V6–V8 DGGE profiles of
the
antibiotic-treated baby were highly unstable. Main bacterial groups were
identified as E. coli and Enterococcus spp., despite the fact that the
administered antibiotics were expected to suppress enteric bacteria. After one month, DGGE
patterns indicated the presence of a simple but remarkably stable community until the end of the study. The most
significant differences between the profiles from the antibiotic-treated baby and the other four
healthy babies related to the absence of Bifidobacterium bands in spite of a partly
breast-milk diet. Likewise, a study in which nine Japanese infants were
monitored during the first two months after birth demonstrated that antibiotic
treatment at the beginning of life exhibits a strong influence on the
establishment of a normal microbial ecosystem in the intestine  [102]. Two
infants who received Cefalex (a cephalosporin antibiotic) therapy in the first four
days of life showed a remarkably deviating developmental pattern from the
trends observed in the other nontreated subjects. DGGE analysis of V3-16S rRNA
gene amplicons generated profiles with an overall low complexity that lacked
bands corresponding to bifidobacteria and other strict anaerobes. In fact, band
sequence analysis and random sequencing of 16S rRNA gene clone libraries
indicated that Enterobacteriaceae were the most dominant group
throughout the entire study period. In contrast to the findings of the two
aforementioned studies, the SDE fingerprinting data reported by Schwiertz et
al.  [101] indicated that the bacterial composition in infants was not
necessarily influenced by antibiotic treatment. In the latter study, the
establishment and succession of the neonatal microbiota in the first month of
life of 29 preterm hospitalized infants was monitored, and included seven
antibiotic-treated infants receiving cefotaxime and piperazine during the first three days followed
by vancomycin and amikacin therapy until inflammation was reduced which ranged
up to 21 days. Overall, DGGE analysis with universal V6–V8-16S rDNA primers showed
relatively stable profiles during and after antibiotic treatment, although the
complexity of the banding pattern generally appeared to be lower compared to
nontreated infants.Not suprisingly, it has been shown that the human adult intestinal
microbiota is affected to a different degree during antimicrobial therapy
depending on the type and/or activity spectrum of the therapeutic component
[77]. In the latter study, DGGE analysis of fecal samples from one patient was
performed for 12 months during which different antimicrobials were administered. Visual and
numerical analysis of the V3-16S rRNA gene fingerprints representing the
predominant microbiota
remained stable over eight months in the absence of antimicrobials (Ds of 88–91%) and were
only minimally affected following one week ingestion of ciprofloxacin (Ds of 73%). In contrast, clindamycin markedly reduced the microbial complexity (Ds of 11–18%). However, once
clindamycin therapy ceased, recovery of some intestinal groups was evident within days
as indicated by the increasing similarity indices when compared to the pattern
prior to antibiotic treatment (Ds of 36–44%). In three other
patients, cefazolin
(i.e., a cephalosporin with relatively low activity against intestinal
anaerobes) caused only
minimal alteration of V3-16S rRNA gene patterns (Ds of 81–83%) whereas
amoxicillin/clavulanate triggered marked changes in profile compositions (Ds of 19–42%). Overall, the
relative degree of alterations in the universal DGGE patterns tended to
correspond to the relative activity spectrum of the antimicrobials against intestinal anaerobes.In order to reduce the possible side-effects of antimicrobial therapy,
probiotics are commonly administered in combination with antimicrobials during
and after the period of intake  [188]. In such combinatorial approaches, the
absence of potentially transferable antibiotic resistance genes in the
administered strain has been recognized as one of the major safety critera for
human probiotics  [189]. In this context, the survival and stability of
probiotic strains during antimicrobial therapy are particularly relevant but
have not been studied into large detail. Upon combined doxycycline (a
tetracycline) and probiotic therapy, Saarela et al.  [190] found that the
complexity of V6–V8-16S rDNA DGGE profiles of fecal microbiota was lower (mean
number of bands, 14–25) compared to those of the (control) group only taking
probiotics (mean number of bands, 25–42). Probiotic strains Lactobacillus
acidophilus LaCH-5 and Bifidobacterium animalis subsp.
lactis Bb-12 from the administered commercial
preparation Trevis were recovered from fecal samples,
and phenotypically and genotypically characterized for their tetracycline (Tc)
resistance. The Tc-susceptible strain LaCH-5 remained so during therapy,
whereas recovered isolates of the Tc-resistant strain Bb-12 containing the tet(W)
resistance gene were not found to have acquired additional Tc resistance genes.
Although these observations evidence the stability of the probiotic strains as
such, however, the authors did not investigate the possible effect of
introducing a tet(W)-carrying strain during doxycycline therapy on the
dissemination of this gene throughout the intestinal microbiota.


## 3.3.1. Functional Foods

The fact that diet is a major factor controlling the
human intestinal balance has triggered the development of a new generation of foods specifically designed
to strengthen the gut microbiota via modulation. Functional foods include foods
and food products with a clearly identifiable health benefit in addition to
their basic nutritional value  [167]. In functional foods, the addition
or incorporation of pro- and/or prebiotic components as active ingredients
plays a key role in functional applications aiming at modulation of intestinal
microbiota. According to the FAO/WHO definition  [168], probiotics are live
microorganisms which, when administered in adequate amounts, confer a health
benefit for the host. An extended version of this definition is still under
debate, including the question whether the live status is truly required for
probiotic action  [169, 170]. Beneficial effects induced by probiotic activities are mediated
either through modulation of the indigenous microbiota or through the
immunomodulatory potential of the probiotic strains used. Bacterial
cultures incorporated in probiotic products for human consumption commonly—but
not exclusively—originate from the intestinal system of healthy (human)
subjects and most frequently
belong to the bifidobacteria and to LAB such as Lactobacillus spp. A
prebiotic, on the other hand, is a nondigestible selectively fermented compound
that induces specific changes both in the composition and/or the activity of
the gastrointestinal microbiota thereby conferring benefits upon host
well-being and health  [171]. Essentially, the functionality of a prebiotic
compound is determined
by its potential to stimulate beneficial bacteria indigenous to the gut
ecosystem. Complex oligosaccharides are most commonly used as prebiotics
including lactulose, galactooligosaccharides (GOS) and fructooligosaccharides
(FOS; e.g., oligofructose and inulin). A wide range of beneficial effects have
been attributed to probiotics, prebiotics, or a combination thereof (i.e.,
synbiotics), including modulation of the gut immune system, resistance to microbial
infections, antimutagenic/anticarcinogenic
effects, reduction of blood ammonia and cholesterol levels, prevention
and/or alleviation of diarrhoea and constipation, prevention and reducing
symptoms of intestinal chronic disorders, relief of lactose intolerance and
increased mineral absorption as reviewed in  [172–178].SDE-based methods have
played a key role in human dietary intervention studies aiming at demonstrating
the efficacy of functional food components and to substantiate potential health
claim. A selection of relevant studies that have contributed to this field is listed
in Table3  [68, 69, 72, 73, 75, 76, 88, 90, 98, 100, 105, 110, 111, 120, 127, 179, 180]. Solely based on findings from SDE analysis, it appears that
prebiotic administration can potentially affect the predominant bacterial
population of healthy human subjects, whereas most probiotic interventions only
seem to induce marked effects in patient groups. This could indicate that some probiotic components may have
more of a therapeutic effect in subjects with a disturbed intestinal
balance but less effective as general health promoting agents. On the other hand, it should be
kept in mind that SDE-based approaches focus on diversity and dynamics of
predominant intestinal microbiota, and are as such unsuitable to monitor
probiotic interventions that are based on the immunomodulatory potential of the
administered organism(s). Bibiloni et
al.  [110] used DGGE to evaluate the safety and efficacy of the mixed
probiotic preparation VSL#3® (http://www.vsl3.com/) consisting of three Bifidobacterium strains and five LAB strains (i.e., four Lactobacillus strains and one Streptococcus
thermophilus strain) in patients with active mild to moderate UC. DGGE analysis of V3-16S rRNA
gene amplicons generated from biopsies collected from seven patients
before and after 6-week VSL#3 administration revealed considerable variation of
the predominant microbiota in four out of five patients in remission (mean dice similarity coefficient (Ds) of 69.9±12.7%). In
contrast, the DGGE profiles of the two patients with continued active
disease remained relatively stable after VSL#3 consumption (mean Ds  of 92.3±4.1%).
Importantly, it should be noted that the study did not report on the temporal
stability of biopsy profiles in the absence of probiotic treatment. In another
study, the effect of a 4-week administration of the candidate prebiotic di-D-fructofuranose-1,
2′ : 2, 3′-dianhydride (DFA III) on human fecal microbiota was studied by DGGE
analysis using universal V3-16S rRNA primers and Bacteroides fragilis subgroup-specific primers  [73]. Visual and numerical analysis of the DGGE profiles generated with both
primer sets revealed no pronounced changes related to DFA III administration in
healthy subjects. In a followup long-term human feeding trial (2 to 12 months)
with DFA III, however, DGGE profiles of the predominant bacterial population
revealed a marked increase in the intensity of bands related to Bacteroides spp.  [72]. In a study on the effect
of 3-week consumption of a GOS-containing probiotic yogurt on the diversity and
temporal stability of fecal microbiota in elderly  [69], DGGE revealed that the
predominant bacterial population and the Clostridium coccoides-Eubacterium
rectale group remained relatively stable during the study period. In
contrast, the Lactobacillus group showed temporal variation which
confirms previous observations under basal conditions  [64].Table 3
Selection of dietary intervention studies using SDE-based community fingerprinting.
ComponentaAdministered componentReference(s)bpLevan-type
exopolysaccharides, levan, inulin and FOS[111]pGOS and FOS[75]pDifructose
anhydride III (DFA III)[72, 73]PLactobacillus rhamnosus DR20[76, 88]PLactobacillus paracasei F19[98]PVSL#3® (probiotic
mixture of eight strains)[110]PBifidobacterium longum (Bifina®)
and yogurt with Bifidobacterium animalis DN-173 010[179]yLactobacillus
delbrueckii subsp. bulgaricus and Streptococcus thermophilus[68]p, PInulin orB. longum (Bifina®)[180]pPInulin-containing probiotic
yogurts[127]pPGOS-containing probiotic yogurt[69]p, P, pPGOS and/orBifidobacterium lactis Bb-12[100]p, P, pPLactulose
and/orSaccharomyces boulardii[90]oBlack tea[120]o, op, oPIsoflavones and FOS orB. animalis DN-173 010[105]ap: prebiotic; P: probiotic; pP: synbiotic; y, yogurt; o:
other.
bAll studies used DGGE as SDE method except in  [105], where
TTGE was used.In the course of probiotic
intervention studies, DGGE and related fingerprinting techniques have been used
to verify if the administered strain(s) is (are) detectable in intestinal
samples  [76, 88, 98, 100, 105, 110, 125, 179]. Based on a combination of
culture-based methods and 16S rDNA DGGE, Wall et al.  [86] even reported the
recovery of probiotic strains Lactobacillus
paracasei NFBC 338
and B. animalis subsp. lactis Bb12
in ileostomy effluents of two infants without a history of probiotic intake. In this context, it should
be noted that SDE fingerprinting is not the most optimal tool for detection of administered
strains because of the relatively poor detection limit (especially when using
universal primers) and the lack of resolution to discriminate the introduced
strain(s) from other strains of the same or highly related autochthonous member
species of the intestinal microbiota. More suitable approaches are those applying strain-specific primers
(e.g., conventional or real-time PCR) or probes (e.g., fluorescent FISH probes)
which will not only provide a higher sensitivity but may also allow relative
quantification of the probiotic target  [181–184]. On the other hand, it should
be kept in mind that all aforementioned DNA-based approaches do not allow to
discriminate between living and dead cells and thus do not provide information
on probiotic survival throughout the gastrointestinal tract.In recent years, SDE-based community fingerprinting has been integrated
in larger polyphasic studies in combination with conventional culture methods
and/or with other molecular culture-independent methods to detect and monitor
changes in human intestinal ecosystems upon administration of probiotic,
prebiotic, or other (in)organic compounds with claimed functionalities. As
such, DGGE and FISH approaches were combined with selective culture methods to
evaluate the impact of a 3-week diet supplementation with prebiotic GOS or FOS on the composition and
activities of the fecal microbiota of 15 healthy human volunteers  [75]. V3-16S rRNA gene
DGGE profiles remained relatively stable during the study, whereas clear
alterations in response to dietary supplementation were observed in rRNA-DGGE
profiles as evidenced by the detection of additional fragments or increased
staining intensity of band fragments attributed to Bifidobacterium adolescentis and/or Collinsella aerofaciens. In contrast, DGGE analysis using
genus-specific primers derived from the transaldolase gene generated relatively stable profiles
for fecal bifidobacteria. Although the taxonomic composition of the bifidobacterial population
was not substantially different and both DGGE and FISH revealed that the Bifidobacterium and Collinsella populations
remained relatively unchanged, rRNA-DGGE provided evidence of increased
metabolic activity in
response to prebiotic consumption. A combination of DGGE and FISH was
also used to investigate the effect of black tea drinking on the fecal
microbiota of healthy volunteers with hypercholesterolemy  [120]. DGGE of 16S rRNA gene V6–V8
amplicons showed that each subject harboured a specific predominant bacterial
population that exhibits little change over time and that was not significantly
changed by drinking black tea. Even though black tea did not affect the
specific bacterial groups analyzed by FISH (i.e., Bifidobacterium, Bacteroides and Prevotella, Clostridium phylogenetic clustersIV and XIVa, Atopobium group, Faecalibacterium-like
species and E. coli), it did decrease the total amount of bacteria
detected by the universal bacterial probe. In a study that combined the use of
TGGE and FISH analysis, it was demonstrated that isoflavone supplementation with and without pro- or
prebiotics induced significant dynamic changes on the composition of the dominant
intestinal microbiota of 39 postmenopausal women  [105]. Results of FISH analysis
indicated that several of the dominant fecal groups were stimulated by isoflavones alone,
whereas TGGE
profiling of 16S rRNA gene V6–V8 amplicons revealed marked changes in the
predominant intestinal microbiota. Intraindividual comparison of TGGE
fingerprints showed a mean Pearson similarity value of 73% before and after one
month of isoflavone supplementation. In combination with a pro- or prebiotic
compound, isoflavones triggered comparable population changes as evidenced by
mean fingerprint similarity values of 71 ± 18% and 68 ± 16% obtained for the
probiotic (Bifidobacterium
animalis DN-173
010) and the
prebiotic (FOS) test groups, respectively.
In addition, FISH results showed a bifidobacterial increase following prebiotic supplementation, often
referred to as the bifidogenic effect. Amongst others  [76, 125, 180], the aforementioned studies
have demonstrated the potential of using SDE fingerprinting and FISH analyses
in a complementary approach to characterize basic interactions between
intestinal microbiota and functional food compounds and to quantify
subpopulations responding to the introduced component(s).Next to FISH, also real-time PCR has been used in combination with DGGE
to verify and substantiate compositional changes in a semiquantitative manner.
The latter two methods were used in an integrated approach to monitor and
quantify pronounced changes in fecal microbiota of healthy subjects upon
long-term administration of a prebiotic (lactulose), a probiotic (Saccharomyces boulardii), and their synbiotic combination  [90]. Although the DGGE profiles obtained
with the universal V3-16S rRNA gene primers as well as those generated using group-specific
primers targeting the Bacteroides fragilis subgroup, the genus Bifidobacterium and the Clostridium lituseburense and Clostridium coccoides-Eubacterium rectale groups remained fairly stable, one pronounced change was
observed in the universal fingerprint profiles after lactulose ingestion. The DGGE band appearing or intensifying in 27
of the 30 subjects could be assigned to Bifidobacterium adolescentis by band position analysis and band
sequencing. In subsequent real-time PCR analysis, this finding was
correlated to a statistically significant stimulation of total bifidobacteria
and of B. adolescentis. In contrast, the probiotic yeast S. boulardii did not
display any detectable universal changes in the DGGE profiles nor influenced
bifidobacterial levels. In a double-blind crossover study on the qualitative
and quantitative effects of fresh and heat-treated yogurt on the bacterial
intestinal microbiota from healthy subjects  [68], DGGE profiling revealed overall stability of
the predominant bacterial population and the LAB population at baseline, after
fresh yogurt intake and after heat-treated yogurt intake. However, real-time
PCR with group-specific primers indicated a significantly higher density of LAB
and Clostridium perfringens and a significant decrease in the density of Bacteroides after consumption of both types of yogurt.


## 3.3.2. Antimicrobial Agents

Apart from their generally well-documented
therapeutic effects on the site of infection, antimicrobials can also exert a
detrimental effect on the microbial balance of the gut ecosystem. So far, studies analyzing
the effect of antibiotic therapy on the selection and transmission of
antibiotic resistance among pathogens and commensals within the human
intestinal microbiota have mainly relied on culture-dependent approaches  [185–187]
which are highly restricted by the selectivity of the media used. In this
respect, SDE-based techniques provide a more suitable approach to monitor the
effects of antimicrobial agents on the total community structure of intestinal
microbiota.In several human studies mostly focussing on infant
populations, DGGE analysis has revealed drastic alterations among the
indigenous bacterial diversity upon therapy with various antimicrobials  [77, 96, 101, 102]. Antimicrobial-induced disruptions of fingerprinting profiles
were generally accompanied by a reduction in band numbers suggesting an overall
decrease in predominant intestinal ecosystem diversity. Favier et al.  [96]
monitored bacterial succession of the intestine during the first four months of
life of five babies, including one infant who received continuous antibiotic therapy
consisting of Augmentin (a mixture of clavulanic acid and amoxicillin) for 13 days
followed by Bactrimel (a mixture of trimethoprim and sulfamethoxazol) to combat
urinary reflux. During
the first month after birth, the universal 16S rRNA gene V6–V8 DGGE profiles of
the
antibiotic-treated baby were highly unstable. Main bacterial groups were
identified as E. coli and Enterococcus spp., despite the fact that the
administered antibiotics were expected to suppress enteric bacteria. After one month, DGGE
patterns indicated the presence of a simple but remarkably stable community until the end of the study. The most
significant differences between the profiles from the antibiotic-treated baby and the other four
healthy babies related to the absence of Bifidobacterium bands in spite of a partly
breast-milk diet. Likewise, a study in which nine Japanese infants were
monitored during the first two months after birth demonstrated that antibiotic
treatment at the beginning of life exhibits a strong influence on the
establishment of a normal microbial ecosystem in the intestine  [102]. Two
infants who received Cefalex (a cephalosporin antibiotic) therapy in the first four
days of life showed a remarkably deviating developmental pattern from the
trends observed in the other nontreated subjects. DGGE analysis of V3-16S rRNA
gene amplicons generated profiles with an overall low complexity that lacked
bands corresponding to bifidobacteria and other strict anaerobes. In fact, band
sequence analysis and random sequencing of 16S rRNA gene clone libraries
indicated that Enterobacteriaceae were the most dominant group
throughout the entire study period. In contrast to the findings of the two
aforementioned studies, the SDE fingerprinting data reported by Schwiertz et
al.  [101] indicated that the bacterial composition in infants was not
necessarily influenced by antibiotic treatment. In the latter study, the
establishment and succession of the neonatal microbiota in the first month of
life of 29 preterm hospitalized infants was monitored, and included seven
antibiotic-treated infants receiving cefotaxime and piperazine during the first three days followed
by vancomycin and amikacin therapy until inflammation was reduced which ranged
up to 21 days. Overall, DGGE analysis with universal V6–V8-16S rDNA primers showed
relatively stable profiles during and after antibiotic treatment, although the
complexity of the banding pattern generally appeared to be lower compared to
nontreated infants.Not suprisingly, it has been shown that the human adult intestinal
microbiota is affected to a different degree during antimicrobial therapy
depending on the type and/or activity spectrum of the therapeutic component
[77]. In the latter study, DGGE analysis of fecal samples from one patient was
performed for 12 months during which different antimicrobials were administered. Visual and
numerical analysis of the V3-16S rRNA gene fingerprints representing the
predominant microbiota
remained stable over eight months in the absence of antimicrobials (Ds of 88–91%) and were
only minimally affected following one week ingestion of ciprofloxacin (Ds of 73%). In contrast, clindamycin markedly reduced the microbial complexity (Ds of 11–18%). However, once
clindamycin therapy ceased, recovery of some intestinal groups was evident within days
as indicated by the increasing similarity indices when compared to the pattern
prior to antibiotic treatment (Ds of 36–44%). In three other
patients, cefazolin
(i.e., a cephalosporin with relatively low activity against intestinal
anaerobes) caused only
minimal alteration of V3-16S rRNA gene patterns (Ds of 81–83%) whereas
amoxicillin/clavulanate triggered marked changes in profile compositions (Ds of 19–42%). Overall, the
relative degree of alterations in the universal DGGE patterns tended to
correspond to the relative activity spectrum of the antimicrobials against intestinal anaerobes.In order to reduce the possible side-effects of antimicrobial therapy,
probiotics are commonly administered in combination with antimicrobials during
and after the period of intake  [188]. In such combinatorial approaches, the
absence of potentially transferable antibiotic resistance genes in the
administered strain has been recognized as one of the major safety critera for
human probiotics  [189]. In this context, the survival and stability of
probiotic strains during antimicrobial therapy are particularly relevant but
have not been studied into large detail. Upon combined doxycycline (a
tetracycline) and probiotic therapy, Saarela et al.  [190] found that the
complexity of V6–V8-16S rDNA DGGE profiles of fecal microbiota was lower (mean
number of bands, 14–25) compared to those of the (control) group only taking
probiotics (mean number of bands, 25–42). Probiotic strains Lactobacillus
acidophilus LaCH-5 and Bifidobacterium animalis subsp.
lactis Bb-12 from the administered commercial
preparation Trevis were recovered from fecal samples,
and phenotypically and genotypically characterized for their tetracycline (Tc)
resistance. The Tc-susceptible strain LaCH-5 remained so during therapy,
whereas recovered isolates of the Tc-resistant strain Bb-12 containing the tet(W)
resistance gene were not found to have acquired additional Tc resistance genes.
Although these observations evidence the stability of the probiotic strains as
such, however, the authors did not investigate the possible effect of
introducing a tet(W)-carrying strain during doxycycline therapy on the
dissemination of this gene throughout the intestinal microbiota.


## 4. Limitations and Potential Pitfalls

Despite
its increasing use in the field of molecular microbial ecology, it is clear
that SDE-based community profiling has a number of limitations that do not
allow indepth analysis of microbial communities as complex as the human
intestinal tract. Some of these limitations, such as detection level and
taxonomic resolution, can be regarded as potential pitfalls and should be
carefully taken into account during protocol development and data analysis. In
fact, many of these critical factors are situated along the stages prior to the
actual SDE step such as sampling and sample processing, nucleic acid extraction
and community PCR, and deserve specific attention when troubleshooting SDE
problems.


### 4.1. PCR Bias

As discussed
above, the choice of an efficient and reproducible nucleic acid extraction
method ensuring optimal cell lysis and maximal removal of various PCR
inhibitors present in intestinal samples is highly crucial. Likewise, possible
bias introduced during PCR amplification by differential or preferential
amplification of target genes from complex communities may prejudice the
analysis  [191]. As a result, SDE fingerprint profiles may not entirely reflect
the actual composition of the predominant microbiota in the sample because of a
(partial) lack of amplification of certain DNA/RNA templates. Nonproportional
amplification can be due to several factors  [192] including template and target
sequence properties (e.g., GC-content, presence of secondary structures and
template concentration)  [191, 193], efficiency of primer binding influenced by
primer preference, annealing temperature and primer mismatches, and the number
of PCR cycles  [104, 194]. Furthermore, it has also been reported that formation
of chimeric and heteroduplex molecules during the amplification process  [99]
may generate a distorted view of the actual microbial diversity  [137]. In this
context, Petersen and Dahllöf  [195] described a new protocol that makes use of internal standards
during DNA extraction and PCR-SDE in order to compensate for experimental
variability. This modification allows analyzing the relative abundance of
individual species back to the original sample, thereby facilitating relative comparative
analysis of diversity in complex microbial communities. Other authors have
proposed to incorporate an internal standard during PCR to compare fragment
staining intensities between profiles and allowing quantitative measurements of
fragment intensities  [75].


### 4.2. Taxonomic Resolution of the 16S rRNA Gene

Although every
functional gene can theoretically be used, target genes for SDE fingerprinting
should preferably (i) be present in a single copy in the bacterial genome; (ii)
contain conserved regions among the members of the population to allow rational
primer design; and (iii) comprise regions with sufficient sequence variation
amongst the members of the population to produce a fingerprint revealing
maximal diversity. Although the 16S rRNA gene is the prototype target in SDE
applications based on the above criteria, it should be kept in mind that the
possible occurrence of intraspecific multicopy operon heterogeneity  [196] and
the lack of a sufficient number of polymorphic regions between closely related
taxa are intrinsic limitations that may affect the taxonomic resolution and
complicate interpretation of SDE fingerprints. Although mostly not recognized
as such, both phenomena are sources of systematic error in community
fingerprinting analyses  [135, 197]. As a result of the multioperon effect, a
single species may appear as several bands instead of a single band in SDE
profiles thereby leading to an overestimation of the diversity. For example,
Satokari et al.  [99] distinguished three distinct DGGE bands when analyzing the
amplicon from Bifidobacterium adolescentis ATCC 15703T obtained with Bifidobacterium-specific PCR primers Bif164-f and
Bif662-GC-r (Table 2). Further examination revealed the presence of five rRNA
gene clusters in this strain, including two clusters exhibiting
microheterogeneity that were visualized as two separate bands. The third
visualized band appeared to be a heteroduplex of the former two fragments.
Similar observations were detected with the use of other group-specific primers
targetting the Lactobacillus group  [88] and the Bacteroides fragilis subgroup  [64]. On the other hand, an insufficient number of polymorphic regions
in the target gene may lead to an underestimation of the diversity because
bands of two or more species have identical positions in the community
fingerprint. For example, PCR primers Lac1 and Lac2 specific for the Lactobacillus group  [88, Table 2] do not allow to distinguish members of the Lactobacillus casei group as a result of
identical V3-16S rRNA gene sequences. Theoretically, the aforementioned
effects can be reduced by choosing the appropriate V region in the 16S rRNA gene  [131, 198]. Alternatively, single-copy housekeeping genes characterized by higher
substitution rates such as rpoB  [199–202] have recently been used as
targets for microbial community profiling, but still await implementation in
intestinal microbiology. To our knowledge, the use of the transaldolase gene in
one study for the detection of bifidobacterial populations in fecal samples  [74]
is the only application in intestinal microbiology using an alternative target
gene.


### 4.3. Taxonomic Resolution of SDE Profiles

Several authors have identified cases of comigration
in SDE analysis of amplicons showing clear sequence variation  [203–206]. Even
for phylogenetically unrelated strains, it has been reported that the
corresponding amplicons might have a similar melting behavior resulting in poor
electrophoretic resolution in SDE  [207–209]. The phenomenon of comigration may
also cause problems to retrieve reliable sequence information from individual
band extracts. To some extent, comigration can be addressed by exploiting a
typical advantage of the SDE technology, that is, the use of more narrow
gradients in order to produce high-resolution SDE profiles with a particular
part of the original profile. This approach has been referred to as denaturing gradient gel
electrophoresis gel expansion  [210, DGGEGE].Especially for SDE profiles from complex ecosystems such as the
intestinal tract, band sequencing analysis may prove to be less straightforward
as anticipated for several reasons. First of all, there is the possibility of
multiple sequences being present in a single band due to comigration. In this
case, a cloning step should be introduced prior to actual sequencing of the
fragments. Furthermore, it has been reported that excised DNA fragments are
commonly contaminated with ssDNA originating from other organisms present in
the sample resulting in genetic contamination of the sequence profile. Elimination
of the ssDNA products through mung bean or S1 nuclease treatment of the
eluted DNA prior to amplification (and cloning) can increase the success rate
to obtain a pure DNA sequence of the SDE band target  [75, 211]. An alternative but more complex approach to overcome both
aforementioned problems simultaneously involves direct
cloning from the original PCR product followed by screening of individual
clones against the environmental sample. In this context, it should be kept in
mind that the size range of fragments that can be reliably separated by SDE is
limited to 100–600 bp (optimally 200 bp). Sequence analysis of such relatively
small fragments may impede reliable identification up to the species level. In
addition, in silico and DGGE
analysis have revealed cross-reactivity of V3- and V3–V5-16S rDNA primers with
the human 18S rRNA gene  [119]. Especially in case of biopsies or
blood-contaminated fecal samples, coamplification of nontarget eukaryotic DNA
with 16S rRNA gene primers may lead to an overestimation of bacterial
biodiversity in SDE analysis when no subsequent analysis of individual
community amplicons by cloning and sequencing is performed.Next
to their relative electrophoretic position and sequence composition, also the
intensity and sharpness of SDE bands require special attention. Artifactual
double bands, that is, the situation where each prominent
band is accompanied at close distance by a second, often less intense band,
have been reported in several SDE applications. Janse et al. [212] suggested
that an extension of the final PCR elongation step can be
sufficient to prevent the formation of artifactual second bands. The origin of
double bands in SDE was explained by the authors as the formation of a
secondary product due to prematurely terminated elongation during each PCR
cycle. Extended incubation at high temperature during final elongation should
disrupt such structures and at the same time allow the Taq DNA
polymerase to synthesize a complete amplicon. Another observation potentially
hampering the resolution of SDE analysis is linked to the phenomenon of
extended fuzzy bands. The source of this inconsistency is the existence of
multiple melting domains (MMDs) in the amplified fragments which results in a
stepwise increase in retardation and ultimately leads to the visualization of a
wide and diffuse band. Little is known about the distribution of MMD which is
dependent on the target fragment and the phylogenetic group. This phenomenon
has been observed when using universal 16S rRNA gene primers in SDE analysis of
different types of environmental samples including feces  [64], water  [204], and soil  [213]. Weak, fuzzy bands may erroneously be
considered as background smear leading to misinterpretation of the profile
richness. Curving down or smiling of bands in lanes near the edges of the gel
appears to be an intrinsic feature of any SDE protocol. Although its actual
cause is not entirely clear, the smiling effect is thought to result from
seeping of urea and/or formamide into the buffer during the run, thereby
lowering the concentration of the denaturing substances at the edges of the
gel. This effect can be avoided by skipping the outer side lanes during loading
and/or by applying silicone grease to the spacers  [214].


### 4.4. Detection Limit

The detection
limit of SDE-based methods, that is, the minimum (relative) concentration or
number in which any given member of a complex bacterial ecosystem needs to be
present in order to be visualized in the corresponding community fingerprint,
was initially estimated to approach 1% of the total population  [49]. This
estimation was later substantiated for TGGE analysis of intestinal samples
[95], whereas Vanhoutte et al.  [64] reported 106 CFU/g feces (wet
weight) as the detection level that could be reached by DGGE for predominant
members of the fecal microbiota. In this context, it should be stressed that
the detection limit is a relative value that may strongly depend on several
parameters including the taxonomic complexity of the ecosystem present in the
sample, the efficiency of DNA extraction, the total number of bacteria, and the
relative concentration of each organism in the sample. Human stool usually
contains 1010–1012 CFU/g feces, and it is thus possible
that the detection limit may improve if 1010 CFU/g compared to 1012 CFU/g is present due to a
lower competition among the constituting DNA templates during PCR
amplification. In
general, the potential to detect a specific taxon can be improved by using
group-specific primers that can narrow the size of the target population. Even when using a
genus-specific primer, however, the template DNA ratio may still affect
the DGGE-based detection of certain species that are underrepresented in a
mixed community sample  [128].
Although poorly studied for SDE-based community fingerprinting of human
microbiota, multiple displacement amplification (MDA) may provide another
strategy to enhance the detection level especially in biopsy samples with lower
bacterial counts. In MDA, the use of 3′ to 5′ exonuclease resistant
random oligonucleotide primers and bacteriophage Phi29 DNA polymerase
will enrich any DNA target  [215], and the resulting template pool can be used
for 16S rDNA PCR and subsequent SDE profiling, for example, using TTGE  [216].


## 4.1. PCR Bias

As discussed
above, the choice of an efficient and reproducible nucleic acid extraction
method ensuring optimal cell lysis and maximal removal of various PCR
inhibitors present in intestinal samples is highly crucial. Likewise, possible
bias introduced during PCR amplification by differential or preferential
amplification of target genes from complex communities may prejudice the
analysis  [191]. As a result, SDE fingerprint profiles may not entirely reflect
the actual composition of the predominant microbiota in the sample because of a
(partial) lack of amplification of certain DNA/RNA templates. Nonproportional
amplification can be due to several factors  [192] including template and target
sequence properties (e.g., GC-content, presence of secondary structures and
template concentration)  [191, 193], efficiency of primer binding influenced by
primer preference, annealing temperature and primer mismatches, and the number
of PCR cycles  [104, 194]. Furthermore, it has also been reported that formation
of chimeric and heteroduplex molecules during the amplification process  [99]
may generate a distorted view of the actual microbial diversity  [137]. In this
context, Petersen and Dahllöf  [195] described a new protocol that makes use of internal standards
during DNA extraction and PCR-SDE in order to compensate for experimental
variability. This modification allows analyzing the relative abundance of
individual species back to the original sample, thereby facilitating relative comparative
analysis of diversity in complex microbial communities. Other authors have
proposed to incorporate an internal standard during PCR to compare fragment
staining intensities between profiles and allowing quantitative measurements of
fragment intensities  [75].


## 4.2. Taxonomic Resolution of the 16S rRNA Gene

Although every
functional gene can theoretically be used, target genes for SDE fingerprinting
should preferably (i) be present in a single copy in the bacterial genome; (ii)
contain conserved regions among the members of the population to allow rational
primer design; and (iii) comprise regions with sufficient sequence variation
amongst the members of the population to produce a fingerprint revealing
maximal diversity. Although the 16S rRNA gene is the prototype target in SDE
applications based on the above criteria, it should be kept in mind that the
possible occurrence of intraspecific multicopy operon heterogeneity  [196] and
the lack of a sufficient number of polymorphic regions between closely related
taxa are intrinsic limitations that may affect the taxonomic resolution and
complicate interpretation of SDE fingerprints. Although mostly not recognized
as such, both phenomena are sources of systematic error in community
fingerprinting analyses  [135, 197]. As a result of the multioperon effect, a
single species may appear as several bands instead of a single band in SDE
profiles thereby leading to an overestimation of the diversity. For example,
Satokari et al.  [99] distinguished three distinct DGGE bands when analyzing the
amplicon from Bifidobacterium adolescentis ATCC 15703T obtained with Bifidobacterium-specific PCR primers Bif164-f and
Bif662-GC-r (Table 2). Further examination revealed the presence of five rRNA
gene clusters in this strain, including two clusters exhibiting
microheterogeneity that were visualized as two separate bands. The third
visualized band appeared to be a heteroduplex of the former two fragments.
Similar observations were detected with the use of other group-specific primers
targetting the Lactobacillus group  [88] and the Bacteroides fragilis subgroup  [64]. On the other hand, an insufficient number of polymorphic regions
in the target gene may lead to an underestimation of the diversity because
bands of two or more species have identical positions in the community
fingerprint. For example, PCR primers Lac1 and Lac2 specific for the Lactobacillus group  [88, Table 2] do not allow to distinguish members of the Lactobacillus casei group as a result of
identical V3-16S rRNA gene sequences. Theoretically, the aforementioned
effects can be reduced by choosing the appropriate V region in the 16S rRNA gene  [131, 198]. Alternatively, single-copy housekeeping genes characterized by higher
substitution rates such as rpoB  [199–202] have recently been used as
targets for microbial community profiling, but still await implementation in
intestinal microbiology. To our knowledge, the use of the transaldolase gene in
one study for the detection of bifidobacterial populations in fecal samples  [74]
is the only application in intestinal microbiology using an alternative target
gene.


## 4.3. Taxonomic Resolution of SDE Profiles

Several authors have identified cases of comigration
in SDE analysis of amplicons showing clear sequence variation  [203–206]. Even
for phylogenetically unrelated strains, it has been reported that the
corresponding amplicons might have a similar melting behavior resulting in poor
electrophoretic resolution in SDE  [207–209]. The phenomenon of comigration may
also cause problems to retrieve reliable sequence information from individual
band extracts. To some extent, comigration can be addressed by exploiting a
typical advantage of the SDE technology, that is, the use of more narrow
gradients in order to produce high-resolution SDE profiles with a particular
part of the original profile. This approach has been referred to as denaturing gradient gel
electrophoresis gel expansion  [210, DGGEGE].Especially for SDE profiles from complex ecosystems such as the
intestinal tract, band sequencing analysis may prove to be less straightforward
as anticipated for several reasons. First of all, there is the possibility of
multiple sequences being present in a single band due to comigration. In this
case, a cloning step should be introduced prior to actual sequencing of the
fragments. Furthermore, it has been reported that excised DNA fragments are
commonly contaminated with ssDNA originating from other organisms present in
the sample resulting in genetic contamination of the sequence profile. Elimination
of the ssDNA products through mung bean or S1 nuclease treatment of the
eluted DNA prior to amplification (and cloning) can increase the success rate
to obtain a pure DNA sequence of the SDE band target  [75, 211]. An alternative but more complex approach to overcome both
aforementioned problems simultaneously involves direct
cloning from the original PCR product followed by screening of individual
clones against the environmental sample. In this context, it should be kept in
mind that the size range of fragments that can be reliably separated by SDE is
limited to 100–600 bp (optimally 200 bp). Sequence analysis of such relatively
small fragments may impede reliable identification up to the species level. In
addition, in silico and DGGE
analysis have revealed cross-reactivity of V3- and V3–V5-16S rDNA primers with
the human 18S rRNA gene  [119]. Especially in case of biopsies or
blood-contaminated fecal samples, coamplification of nontarget eukaryotic DNA
with 16S rRNA gene primers may lead to an overestimation of bacterial
biodiversity in SDE analysis when no subsequent analysis of individual
community amplicons by cloning and sequencing is performed.Next
to their relative electrophoretic position and sequence composition, also the
intensity and sharpness of SDE bands require special attention. Artifactual
double bands, that is, the situation where each prominent
band is accompanied at close distance by a second, often less intense band,
have been reported in several SDE applications. Janse et al. [212] suggested
that an extension of the final PCR elongation step can be
sufficient to prevent the formation of artifactual second bands. The origin of
double bands in SDE was explained by the authors as the formation of a
secondary product due to prematurely terminated elongation during each PCR
cycle. Extended incubation at high temperature during final elongation should
disrupt such structures and at the same time allow the Taq DNA
polymerase to synthesize a complete amplicon. Another observation potentially
hampering the resolution of SDE analysis is linked to the phenomenon of
extended fuzzy bands. The source of this inconsistency is the existence of
multiple melting domains (MMDs) in the amplified fragments which results in a
stepwise increase in retardation and ultimately leads to the visualization of a
wide and diffuse band. Little is known about the distribution of MMD which is
dependent on the target fragment and the phylogenetic group. This phenomenon
has been observed when using universal 16S rRNA gene primers in SDE analysis of
different types of environmental samples including feces  [64], water  [204], and soil  [213]. Weak, fuzzy bands may erroneously be
considered as background smear leading to misinterpretation of the profile
richness. Curving down or smiling of bands in lanes near the edges of the gel
appears to be an intrinsic feature of any SDE protocol. Although its actual
cause is not entirely clear, the smiling effect is thought to result from
seeping of urea and/or formamide into the buffer during the run, thereby
lowering the concentration of the denaturing substances at the edges of the
gel. This effect can be avoided by skipping the outer side lanes during loading
and/or by applying silicone grease to the spacers  [214].


## 4.4. Detection Limit

The detection
limit of SDE-based methods, that is, the minimum (relative) concentration or
number in which any given member of a complex bacterial ecosystem needs to be
present in order to be visualized in the corresponding community fingerprint,
was initially estimated to approach 1% of the total population  [49]. This
estimation was later substantiated for TGGE analysis of intestinal samples
[95], whereas Vanhoutte et al.  [64] reported 106 CFU/g feces (wet
weight) as the detection level that could be reached by DGGE for predominant
members of the fecal microbiota. In this context, it should be stressed that
the detection limit is a relative value that may strongly depend on several
parameters including the taxonomic complexity of the ecosystem present in the
sample, the efficiency of DNA extraction, the total number of bacteria, and the
relative concentration of each organism in the sample. Human stool usually
contains 1010–1012 CFU/g feces, and it is thus possible
that the detection limit may improve if 1010 CFU/g compared to 1012 CFU/g is present due to a
lower competition among the constituting DNA templates during PCR
amplification. In
general, the potential to detect a specific taxon can be improved by using
group-specific primers that can narrow the size of the target population. Even when using a
genus-specific primer, however, the template DNA ratio may still affect
the DGGE-based detection of certain species that are underrepresented in a
mixed community sample  [128].
Although poorly studied for SDE-based community fingerprinting of human
microbiota, multiple displacement amplification (MDA) may provide another
strategy to enhance the detection level especially in biopsy samples with lower
bacterial counts. In MDA, the use of 3′ to 5′ exonuclease resistant
random oligonucleotide primers and bacteriophage Phi29 DNA polymerase
will enrich any DNA target  [215], and the resulting template pool can be used
for 16S rDNA PCR and subsequent SDE profiling, for example, using TTGE  [216].


## 5. Conclusions and Future Perspectives

This review has highlighted the broad application
spectrum of SDE-based techniques in the field of intestinal microbiology,
ranging from primary assessments of the bacterial complexity and diversity of
intestinal community structures to the monitoring of compositional changes at
different population levels upon dietary or therapeutic interventions. In more
advanced approaches, additional tools such as band sequence analysis, band
position analysis, and blotting analysis permit further taxonomic exploration
of the microbial
communities present in the gut. Overall, SDE techniques are technically fairly
simple, fast, flexible, and reproducible. Because they allow simultaneous
analysis of multiple samples, SDE-based methods may be highly suitable in the
selection of candidate subjects for human metagenome studies. Taken together
with the ability to visualize poorly or as yet unculturable bacterial groups,
these features have contributed to the current fame and reputation of SDE
technology.As is the case with any other methodology, however,
also the SDE approach has a number of intrinsic limitations. Besides the
general biases associated with sampling (including sample size), total DNA
extraction and PCR amplification, also more specific restrictions such as intraspecies
16S rRNA gene operon
heterogeneities, limited fragment length, or fuzzy bands can limit the applicability of SDE. On the other
hand, it is important to always consider the significance and possible
consequences of these drawbacks in the context of the study because some
limitations will not be equally important when monitoring community stability
compared to when assessing biodiversity. One of the most important steps in the
definition of a new SDE protocol is the choice of the primer target which can
already prevent several potential drawbacks related with SDE. A careful selection of the
target fragment with regard to sequence variability and the distribution of
multiple melting domains and a clear focus on the phylum of interest is
conducive to achieve the desired resolution. In addition, also the SDE
technology itself is constantly developing. Denaturing high-performance
liquid chromatography (DHPLC) has relatively recently been introduced to detect
genetic variation based on the SDE principle, but employs an HPLC column
instead of a polyacrylamide gel matrix for amplicon separation  [217]. When
integrated in fully automated instruments such as the Transgenomic WAVE systems
(http://www.transgenomic.com/), DHPLC analysis offers several advantages over
conventional SDE analysis including the lack of gel preparation, the higher
throughput, and the possibility to automatically collect sample fractions for
further (sequence) analysis. DHPLC has been successfully used to analyze
microbial communities with a low  [218, 219] or high  [220] complexity. One
particular study in the field of intestinal microbiology revealed that DHPLC
provides a number of technical benefits compared to DGGE but appears to have
the same limitations in taxonomic resolution for profiling 16S rRNA gene
amplicons  [97]. Other
emerging technologies such as the combination of isotopically labeled substrate
analysis with RNA-DGGE  [221] may offer a promising prospect for implementation
in functional studies on gut microbiota.In contemporary intestinal
microbiology, SDE-based methods are rarely used as a single or end-point
approach but are usually combined with culture methods and/or other molecular
methods such as clone libraries, FISH, real-time PCR, and microarrays in a complementary
research strategy. It is beyond doubt that these polyphasic study designs
should be further pursued and developed to broaden current insights in the
microbial diversity, dynamics, and interactions within the intestinal tract. In
this regard, one of the major challenges ahead lies in the combined analysis of
microbial presence and microbial activity. As an example of such an integrated
approach, parallel DGGE analysis targetting the 16S rRNA gene as taxonomic
marker and the adenosine-5′-phosphosulfate reductase subunit A
gene as functional gene has been used to study the succession and
diversity of sulfate-reducing bacteria in the mouse gastrointestinal tract
[222]. In this regard, the wealth of information expected from large-scale sequencing efforts
such as the Human Microbiome Project (http://www.nihroadmap.nih.gov/hmp/) may open new
avenues for the development of SDE primers targeting specific functional genes.
Considering all upcoming technological developments, it is expected that SDE
community profiling will maintain and even reinforce its position in the large
spectrum of molecular approaches currently employed to unravel host-microbe and
micromicrobe interactions within the human microbiome. The successful
incorporation of DGGE profiling in the recently launched concept of functional
metagenomics  [223], that is, the transgenomic characterization
of key functional members of the microbiome that most influence host metabolism
and hence health, brings forward a first line of evidence in that respect.



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*Source: 597603-2008-12-14.xml*