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ORIGINAL PAPER
Barcoded pyrosequencing analysis of the microbialcommunity in a simulator of the human gastrointestinaltract showed a colon region-specific microbiota modulationfor two plant-derived polysaccharide blends
Massimo Marzorati • Lois Maignien • An Verhelst • Gabriela Luta •
Robert Sinnott • Frederiek Maarten Kerckhof • Nico Boon •
Tom Van de Wiele • Sam Possemiers
Received: 10 July 2012 / Accepted: 18 September 2012! Springer Science+Business Media Dordrecht 2012
Abstract The combination of a Simulator of the
Human Intestinal Microbial Ecosystem with ad hocmolecular techniques (i.e. pyrosequencing, denaturing
gradient gel electrophoresis and quantitative PCR)
allowed an evaluation of the extent to which two plantpolysaccharide supplements could modify a complex
gut microbial community. The presence of Aloe veragel powder and algae extract in product B as compared
to the standard blend (product A) improved its
fermentation along the entire simulated colon. Thepotential extended effect of product B in the simulated
distal colon, as compared to product A, was confirmed
by: (i) the separate clustering of the samples before andafter the treatment in the phylogenetic-based dendro-
gram and OTU-based PCoA plot only for product B;
(ii) a higher richness estimator (?33 vs. -36 % of
product A); and (iii) a higher dynamic parameter(21 vs. 13 %). These data show that the combination of
well designed in vitro simulators with barcoded
pyrosequencing is a powerful tool for characterizingchanges occurring in the gut microbiota following a
treatment. However, for the quantification of low-abundance species—of interest because of their
relationship to potential positive health effects (i.e.
bifidobacteria or lactobacilli)—conventional molecu-lar ecological approaches, such as PCR–DGGE and
qPCR, still remain a very useful complementary tool.
Keywords Gastrointestinal resource management
(GRM) ! Pyrosequencing ! Ambrotose ! SHIME !Intestinal bacteria
Introduction
In recent years, the increased use of moleculartechniques in the field of gastrointestinal microbial
ecology has allowed a much deeper sampling of the
microbial community diversity. It became possible toperform comparative analysis of the human gut
microbiota, investigating the association of the mic-
robiota with different diets in human populations, andto describe the minimal gut metagenome and the
minimal gut bacterial genome in terms of functions
present in all individuals and most bacteria, respec-tively (Andersson et al. 2008; De Filippo et al. 2010;
Electronic supplementary material The online version ofthis article (doi:10.1007/s10482-012-9821-0) containssupplementary material, which is available to authorized users.
M. Marzorati ! L. Maignien ! F. M. Kerckhof !N. Boon ! T. Van de Wiele (&) ! S. PossemiersLaboratory of Microbial Ecology and Technology(LabMET), Ghent University, Coupure Links 653,9000 Ghent, Belgiume-mail: tom.vandewiele@ugent.be
A. Verhelst ! S. PossemiersProDigest, Technologiepark 3, 9052 Ghent, Belgium
G. Luta ! R. SinnottMannatech Inc, 600 S. Royal Lane, Suite 200, Coppell,TX 75019, USA
123
Antonie van Leeuwenhoek
DOI 10.1007/s10482-012-9821-0
Qin et al. 2010). The application of these techniquesto in vivo studies created new opportunities to evaluate
the effect of functional ingredients (i.e. pre- and
probiotics) in relation to human health. However, thepossibility of understanding the mechanism of action
of these ingredients in the different areas of the
gastrointestinal tract (GIT) is frequently limited due toclear sampling issues (Marzorati et al. 2009). In this
respect, the application of well-designed in vitro
continuous models of the GIT may represent a usefulcomplementary tool to study the intestinal microbial
processes in terms of gastrointestinal resource man-
agement (GRM), i.e. the management of the complexgut microbiota and its metabolism with the aim of
improving the host health (Manning and Gibson 2004;
Gibson et al. 2004; Possemiers et al. 2009).We made use of a Simulator of the Human
Intestinal Microbial Ecosystem (SHIME") to assess
the effect of two blends of a commercially availableplant-polysaccharide supplement—Ambrotose" com-
plex and Advanced Ambrotose" powder (Mannatech
Inc., Coppell, Texas, USA)—on the microbial ecologyof the different areas of the GIT (Marzorati et al.
2010). The SHIME" is a dynamic model of the human
gastrointestinal tract used to study physicochemical,enzymatic and microbial parameters in the GIT in a
controlled in vitro setting. It has been extensively used
for more than 15 years and has been validated withseveral in vivo experiments (Molly et al. 1994;
Marzorati et al. 2009). This technology platform
allows to evaluate the effect of repeated doses of
selected food ingredients under gut representativeenvironmental conditions (Van de Wiele et al. 2007;
Sanchez et al. 2009; Grootaert et al. 2009; Marzorati
et al. 2010).The test supplements were already shown to be able
to affect serum glycosylation profiles (Alavi et al.
2011a, b) and to induce an anti-inflammatory effectagainst sulfate sodium-induced colitis in rats (Koetz-
ner et al. 2010; Ramberg et al. 2010). Ambrotose"
complex was shown to support cognitive health (Bestet al. 2010) in clinical trials. Moreover, it was already
shown that the intake of the supplements influenced
the composition and the metabolism of the residentmicrobial community in the different areas of the colon
(i.e. ascending, transverse and descending colon)
(Table 1). By means of a size exclusion chromatog-raphy analysis, it was shown that the fermentability of
Ambrotose" complex was limited to the proximal
colon [ascending (AC) ? transverse (TC)] while thatof Advanced Ambrotose" powder was extended to the
entire colon, as shown by the residual high-molecular
weight fraction still available for fermentation afterpassage in the simulated transverse colon (Fig. S1)
(Marzorati et al. 2010).
The aim of the present work was to investigatethe effect of the test products on the structure and
composition of the microbial community by means of
16S rRNA genes-based pyrosequencing, comparingthe obtained results with the denaturing gradient
gel electrophoresis (DGGE) and quantitative PCR
(qPCR)-based approach previously applied (Marzorati
Table 1 Composition and summary of the long-term effects of Ambrotose" complex and Advanced Ambrotose" powder on theactivity and composition of the colon’s microbial community, as compared to the control period (adapted from Marzorati et al. 2010)
Ambrotose" complex Advanced Ambrotose" powder
Composition Larch arabinogalactan (83.7 % galactose), Aloe vera gelextract (15.6 % galacturonic acid, 14.8 % mannose),glucosamine HCl, and tragacanth gum (9.9 %arabinose, 7.8 % xylose), rice starch (97 % glucose),ghatti gum (26 % arabinose, 16.6 % galactose)
Basic formula of Ambrotose" complex in which A. veraextract was substituted with A. vera gel powder (12 %glucose, 9.2 % mannose) and wakame algae extract(32.1 % fucose) was added
Fermentability Limited to the proximal colon (AC ? TC) Extended to the entire colon
SCFAproduction
Increased acetate and propionate, decreased butyrate Starch-like fermentation with no mayor changes
Ammoniumproduction
Slight increase Slight increase
Bifidobacteria Statistically significant increase in AC ? qualitativechanges
Statistically significant increase in the entirecolon ? qualitative changes
Lactobacilli No major quantitative/qualitative changes No major quantitative/qualitative changes
AC ascending colon, TC transverse colon, DC descending colon
Antonie van Leeuwenhoek
123
et al. 2010). The generation of a huge amount of data—as occurs with pyrosequencing—can also induce some
problems related to the final ecological interpretation
and the underpinning of what occurs to the structureand composition of a given microbial community. For
the first time, we will adapt some ecological param-
eters—originally developed to provide an interpreta-tion to molecular fingerprintings (Marzorati et al.
2008)—to be used on data generated with barcoded
pyrosequencing. The toolbox, based on range-rich-ness, dynamics and community organization, has been
theoretically re-engineered and adapted to provide a
universal platform, allowing to compare these data interms of richness and evenness (Read et al. 2011).
Materials and methods
SHIME study and sample collection
In Marzorati et al. (2010), two SHIME" experiments
have been described, which aimed to compare theeffect of Ambrotose" complex (product A in SHIME
1) and Advanced Ambrotose" powder (product B in
SHIME 2). The full experimental design is describedin Marzorati et al. (2010).
Samples for DNA analyses were collected once a
week from each colon compartment of the SHIME,simulating respectively AC, TC and descending (DC)
colon (Van de Wiele et al. 2007). The metagenomic
DNA was extracted from the samples using the CTABprotocol (Boon et al. 2003). qPCR for the Firmicutes
and Bacteroidetes phyla were conducted according
to the protocols described by Guo et al. (2008).Samples for pyrosequencing analysis were collected
at the end of the control and treatment periods from
each colon compartment. The resulting 12 sampleswere labeled as follows: S1 (product A) or S2 (product
B) to indicate the SHIME system; AC, TC or DC to
indicate respectively ascending, transverse anddescending colon; and, finally, ‘c’ for the control and
‘t’ for the treatment period.
Pyrosequencing and pre-processing
of the sequences
The V5 and V6 regions of the 16S rRNA genes of
each sample were amplified using a primer set corre-sponding to primers 784F and 1061R described by
Andersson et al. (2008). The forward primer containedthe sequence of the Titanium A adaptor (50-CCATCT
CATCCCT-GCGTGTCTCCGACTCAG-30) and a
barcode sequence. The specific PCR conditions arethose reported in De Filippo et al. (2010). Amplicons
were then cleaned using the HighPure Cleanup kit
(Roche) and DNA concentrations were determinedusing the Quant-iT PicoGreen dsDNA reagent and kit
(Invitrogen). Both procedures were conducted accord-
ing to the manufacturer’s instructions. The final poolsof cleaned amplicons combined in equimolar ratios
into a single tube were prepared as described in De
Filippo et al. (2010). Pyrosequencing was carriedout using primer A on a 454 Life Sciences Genome
Sequencer FLX instrument (Roche), following tita-
nium chemistry by DNAVision (Charleroi, Belgium).The pyrosequencing of the 12 amplicon libraries
produced a total of 134,844 16S rDNA reads.
Processing and clustering analysis of these sequenceswas carried out using the Mothur software (Schloss
et al. 2009; Schloss 2010) and the external tools
implemented in Mothur. Primers and MID sequenceswere removed from the 50 end of the sequences.
Sequences that did not meet the following quality
criteria were filtered out of the dataset: no ambiguousbase calling, at most 8 homopolymers, minimum
length of 100 bp and at most 2 mismatches in the
primer sequence. The resulting dataset contained68,551 high quality sequences. Sequences were
trimmed at the 30 end of a 50 bp window when the
average quality score within this window droppedbelow 35. The dataset was then dereplicated for
downstream analysis, resulting in 16,034 unique
sequences. These were then aligned using the align-ment tool included in Mothur (Schloss 2009; De Santis
et al. 2006) and the Silva core alignment as a template
(Schloss 2009; Pruesse et al. 2007). Sequences that didnot span the V5 region (i.e. position 22,598–25,298 in
the SILVA alignment) were filtered out, and longer
reads were trimmed down to fit this alignment length.Rare OTUs were merged with abundant OTUs when
differing by only a single mistmatch, according toHuse et al. (2010). Putative chimeric sequences were
filtered out using Uchime (Edgar et al. 2011; Schloss
et al. 2009). This preprocessing resulted in a highquality dataset of filtered and aligned sequences with
a mean average of 1,985 sequences per library. In order
to avoid possible biases due to library size differences(Gihring et al. 2012), these were subsampled to the
Antonie van Leeuwenhoek
123
smallest library size, i.e. 790 reads. Subsampling hasbeen performed using the Mothur package.
Sequence analysis and phylogenetic classification
The high-quality dataset was analyzed with three
different methods. In a first ‘‘OTU-based’’ approach,sequences were clustered into OTU (97 % similarity
threshold). OTU composition was used to evaluate the
alpha diversity (observed richness, inverse Simpsonindex and rarefaction curves—Fig. S2) within each
sample, and the Jaccard similarity index for beta
diversity estimation between the 12 samples. Thelatter was used for a principal coordinate analysis of
the variance (PCoA). The dataset was also evaluated
using a ‘‘phylogenetic-based’’ approach: a dendro-gram was made by UPGMA based on the sample
pairwise comparison using Jaccard presence/absence
index, constructed with the Clearcut software basedon the pairwise distance matrix (Evans et al. 2006;
Sheneman et al. 2006). Node support was conducted
with a jackkniffed resampling (based on 100 trees)with qiime (www.qiime.org). Finally, each sequence
was assigned at taxonomy down to the genus level
using ribosomal database project taxonomic classifi-cation (Wang et al. 2007). When required, the closest
relative of sequences representing dominant OTUs in
Gene Bank nucleotide collection were retrieved usingBLASTn (http://blast.ncbi.nlm.nih.gov/).
Ecological interpretation based on microbialresource management indices
The phylotype data were analyzed at the family andgenus level as shown in Marzorati et al. (2008) and
Read et al. (2011). Briefly, the richness parameter
(R) corresponded to the number of observed OTUs foreach sample. The dynamics parameter (Dy)—how
deeply a microbial community changes—was based
on the calculation of the similarity values betweensamples belonging to the same simulated colon
compartment before and after the treatment, inaccordance to the following formula: % change =
100 % - similarity. The matrix of similarity was
based on the Pearson product-moment correlation, asdescribed in Marzorati et al. (2008). In order to
graphically represent the evenness of the bacterial
communities, the Pareto–Lorenz (PL) distribution
curves (Lorenz, 1905) were set up, based on thephylotypes distribution, and the Community organi-
zation parameter (Co) (Gini coefficient 9 100) was
calculated (Marzorati et al. 2008). The cumulative Cocurve was calculated as follows. Consider a dataset of
N phylotype classes (family or genus), sall phylotype
classes are first sorted from high to low abundance(with phylotype-1 having the most sequences and
phylotype-N the fewest). Secondly, the different Co
(C0i, with i = 1 to N) values are calculated for a
progressing window of phylotypes, starting from 1 to
N. These C0i values can then be plotted against the
amount of classes (i) (Read et al. 2011). The values ofthe treatment period are subtracted to those of the
control period thus providing the final DCo curve
(Fig. S6). This analysis pipeline was implemented inthe open-source statistical programming language
R (www.r-project.org) and graphical visualizations
were made using the ggplot2 package (http://had.co.nz/ggplot2/).
Results
In-depth analysis of the colonic microbialcommunity by pyrosequencing
Samples collected from each colon compartment, atthe end of the control and treatment periods, were
subjected to multiplex pyrosequencing of the V5 and
V6 hypervariable regions of 16S rRNA gene. Morethan 92.2 % of the total reads in the dataset containing
all of the samples belonged to Actinobacteria
(1.1 ± 2.0 %), Bacteroidetes (56.0 ± 12.9 %), Firmi-cutes (29.8 ± 9.3 %), and Proteobacteria (5.3 ± 4.7 %).
On average, Bacteroidetes and Firmicutes—the most
abundant bacterial phyla in the human gut microbi-ota—accounted for 85.8 ± 8.7 % of the total
sequences. Relevant differences were found in the
proportions of the four phyla following the treatmentwith both dietary supplements (Fig. 1). Product A
induced an increase in the concentration of Bacteroi-detes with a concomitant decrease of Firmicutes and/or
Proteobacteria in the ascending and descending colon.
In the transverse colon the trend was different: theconcentration of Bacteroidetes slightly decreased
while Actinobacteria and Firmicutes increased. Prod-
uct B led to an increase of Bacteroidetes and a decreaseof Firmicutes in all the simulated colon compartments.
Antonie van Leeuwenhoek
123
As a consequence, the ratio Firmicutes/Bacteroidetes
decreased for both products during the treatment as
also shown by the qPCR data, with the exception of theDC of the SHIME treated with product A (Fig. S3).
In both SHIME systems, the positive effect on the
Bacteroidetes phylum in the ascending colon wasmainly due to an increase in the concentration of
bacteria belonging to the Prevotellaceae family
(Fig. 2). In contrast, this increase was mainly relatedto bacteria of the Bacteroidaceae family in the other
simulated colon compartments. Another common
effect of the treatments with the two different products
was that the decrease in Firmicutes was mainly relatedto a decrease in the Veillonellaceae family, while the
slight increase in Proteobacteria in the simulated distal
colon was associated with an increase of Desulfovib-rionaceae (Fig. 2). Finally, the strong increase in
Actinobacteria in the TC of the SHIME treated with
product A was due to an increase of the Nocardiaceaefamily (mainly in the genus Rhodococcus). By means
Fig. 1 Relative abundance of the identified phyla across thesamples. Samples coding: S1 (Product A) or S2 (Product B) toindicate the SHIME system; AC, TC or DC to indicate
respectively ascending, transverse and descending colon; and,finally, ‘c’ for the control and ‘t’ for the treatment period
Fig. 2 Relative abundance of the identified families across thesamples. Groups: I Bacteroidetes, II Firmicutes, III Actinobac-teria, IV Lentisphaerae, V Proteobacteria, VI Verrucomicrobia.Samples coding: S1 (Product A) or S2 (Product B) to indicate
the SHIME system; AC, TC or DC to indicate respectivelyascending, transverse and descending colon; and, finally, ‘c’ forthe control and ‘t’ for the treatment period
Antonie van Leeuwenhoek
123
of the pyrosequencing approach it was possible to
observe only a slight increase in the concentration ofBifidobacterium spp. in the simulated distal colon of
the SHIME treated with product B (i.e. B. longum sub.
infantis and B. adolescentis). However, when observ-ing the data obtained with specific DGGE and qPCR
protocols (Fig. 3), both products stimulated an addi-
tional Bifidobacterium spp. in the microbial commu-nity following the treatment (arrow on the DGGE
profiles) although only Pruduct B led to a statisticallysignificant bifidogenic effect in all the simulated colon
compartments (Fig. 3b).
In order to compare the composition of the microbialcommunities in the different areas of the simulated
colon, before and after the treatment, the OTU-based
data were clustered in a dendrogram and in a PCoA(Fig. 4). The latter is a geometric technique that converts
a matrix of distances between points in a multivariate
space into a projection that maximizes the amount ofvariation along a series of orthogonal axes (Liu et al.
2007). The dendrogram showed a clear clustering along
the experiment (Fig. 4a): samples from the AC of bothSHIME systems (control and treatment) clustered
together (cluster I). Samples from TC and DC clustered
separately (with the exception of S1TCt which clusteredin the cluster I), forming some sub-clusters within the
cluster II. Samples collected during the control periodfrom the simulated descending colon of both SHIME
systems clustered together with sample S1DCt in sub-
cluster II.1. Also the samples from the simulatedtransverse colon of the two SHIME systems clustered
together (II.2). Finally, the samples derived from the
simulated distal colon (TC ? DC) of SHIME 2 formed aseparate cluster (II.3), as an indication of the selective
a
b
Fig. 3 qPCR and DGGE data generated from the DNA samplescollected during the control and treatment periods of the SHIMEsystems treated with product A (a) and B (b). The arrows on the
DGGE profiles indicate a Bifidobacterium sp. that couldspecifically benefit of the treatment with the test products(adapted from Marzorati et al. 2010)
Antonie van Leeuwenhoek
123
effect of the applied treatment. The pronounced effect ofthe treatment with product B in the simulated distal colon
of the SHIME 2 was confirmed by the PCoA plot, which
showed the respective control and treatment samplesfalling apart in the 2D graph (please confront S2TCc and
S2DCc with the respective treatments).
Calculation of different ecological parameters
on the dataset
In Marzorati et al. (2010), we applied a specific
toolbox with the aim of providing an ecological
interpretation to the raw data originated from conven-tional fingerprinting analyses (Marzorati et al. 2008).
However, these approaches cannot be used for large
datasets originated from pyrosequencing, and there-fore we re-engineered this toolbox as described by
Read et al. (2011). The calculated values are summa-
rized in Table 2. Evaluation of the richness parameterR, according to the observed OTUs, showed that
product A only induced an increase in richness
(?21 %) in the TC and a decrease (-36 %) in theDC. On the contrary, the positive effect of product B
on the richness parameter was mainly localized in the
simulated descending colon with a 33 % increase.The treatments also induced a change on the
relative abundance of the different bacterial groups,
as shown by the calculation of the Co values (based onGini coefficient) at level of phylum, family and genus
a
b
Fig. 4 Phylogenetic-based dendrogram (a) and 2D view of betadiversity OTU-based PCoA (b). Samples coding: S1 (product A)or S2 (product B) to indicate the SHIME system; AC, TC or DCto indicate respectively ascending, transverse and descendingcolon; and, finally, ‘c’ for the control and ‘t’ for the treatmentperiod. The symbol asterisk indicates that the specific node hada 75–100 % support according to a jack-kniffed resampling
Table 2 Data for the total number of sequences after filtering, coverage, Observed OTUs, inverse Simpson index, dynamics (Dy),and community organization (Co) at phylum, family and genus level
Index Product A Product B
S1ACc S1ACt S1TCc S1TCt S1DCc S1DCt S2ACc S2ACt S2TCc S2TCt S2DCc S2DCt
n. seqsa 2861 1299 1146 822 790 1269 1895 2605 1304 3330 1343 5163
Coverage 0.45 0.52 0.50 0.76 0.44 0.45 0.38 0.31 0.27 0.44 0.43 0.24
Obs. OTU 49 37 44 54 83 52 40 43 75 74 61 81
Inv. Simpson 6.51 2.81 6.82 9.71 12.08 5.63 7.04 3.34 7.49 3.49 6.63 6.73
Dy (family) 80 % 18 % 14 % 60 % 6 % 21 %
Dy (genus) 78 % 25 % 13 % 60 % 6 % 21 %
Co phylum 70.2 76.6 64.7 56.7 59.5 69.8 71.3 75.5 72.7 69.9 61.6 58.7
Co family 89.4 92.3 87.7 83.7 81.5 87.9 88.5 90.5 89.2 87.0 87.2 83.4
Co genus 92.0 93.6 90.1 86.0 85.2 91.1 91.6 92.7 91.3 90.0 90.5 87.3
S1 (product A) or S2 (product B) refer to the SHIME system; AC, TC or DC to indicate respectively ascending, transverse anddescending colon; ‘c’ indicates the control and ‘t’ the treatment period. The ecological meaning of these indices is reported in TableS1 in SIa All the libraries have been normalized to a number of sequences equal to 790
Antonie van Leeuwenhoek
123
(Table 2). The treatment with product A increased the
Co parameter—at all the investigated phylogenetic
levels—in the AC and DC (lower evenness) while itdecreased it in the TC. Product B increased the Co
parameter, at all the investigated phylogenetic levels
in the simulated ascending colon compartment. On thecontrary, it decreased it in the simulated distal colon
(TC ? DC). As the pyrosequencing analysis not only
provides the sequences of the dominant microorgan-isms, but also of all those less abundant microorgan-
isms which are normally not detected with the
fingerprinting techniques, this alters the shape of theLorenz curve towards complete dominance (Fig. S4).
Under these conditions, information is lost during the
calculation of the Pareto-Lorenz curve, especially atthe lower phylogenetic level (genus). In order to
overcome this limitation, we calculated the
cumulative Co parameter as proposed in Read et al.(2011). The resulting Fig. 5—calculated at genus
level—is the difference of the cumulative Co curve of
the treatment minus that of the control (the full figuresare reported in Fig. S5). According to Fig. 5, product
A led to a change in the structure of the microbial
community both in the dominant and in the low-abundant genera. On the contrary, product B mainly
increased the evenness among the most dominant
genera. Moreover, following the treatment with prod-uct B—as compared to product A—the main peak of
the curves points in the same direction, indicating that
the microbial sub-community, composed by the 10most dominant phylotypes (at genus level), was more
uneven in all colon compartments. More precisely,
with both treatments the enhanced phylotypes in theAC were mainly associated to the genus Prevotella.
On the contrary, bacteria belonging to Ruminococc-aceae, Porphyromonadaceae and mainly to the generaBacteroides and Mitsuokella decreased. In the simu-
lated distal colon (TC ? DC), the OTUs related to
Bacteroides spp. increased their importance in themicrobial community.
All the changes in richness and evenness of the
microbial communities at genus level in the differentareas of the colon were also reflected in the dynamic
(Dy) parameter, calculated on a similarity matrix
based on Pearson’s correlation coefficients (Marzoratiet al. 2008). Product A induced a main change in the
GIT microbial community in the AC (78 %) with a
decreasing effect along the colon (13 % in the DC).Product B, induced a Dy of 60 % in the AC (lower as
compared to product A) but, always compared to A,
had a higher impact in the DC (21 %) (Table 2).
Discussion
In vitro approaches to study the gastrointestinal tract
and intestinal microbial processes offer an excellentexperimental setup to study possible prebiotic prop-
erties of selected food ingredients. In fact, theapplication of well-designed continuous models
allows the in-depth study of the biological activity of
selected products in the gut under representativeenvironmental conditions. In this study, we applied
for the first time a 16S rRNA gene pyrosequencing
approach to a SHIME experiment in order to inves-tigate the effect of two polysaccharide supplements
a
b
Fig. 5 DCo (community organization parameter) calculated atgenus level and expressed as the difference of the cumulative Cocurve of the treatment minus that of the control. The ecologicalinterpretation of this parameter is provided in Fig. S6. AC, TCand DC indicate respectively ascending, transverse anddescending colon
Antonie van Leeuwenhoek
123
on the composition of the gut microbial community.The aim was to compare the data previously obtained
with DGGE and qPCR with those of the deep
sequencing. It was already known that product Bwas fermented all over the simulated colon compart-
ments (Marzorati et al. 2010; Fig. S1) and therefore,
product B was expected to have a higher impact on themicrobiota in the distal part of the colon.
Pyrosequencing retrieved the presence of 2 main
phyla (i.e. Bacteroidetes, Firmicutes) that accountedfor more than 80 % of the total diversity of the gut
microbial community. These findings are in line with
what previously reported in humans (De Filippo et al.2010; Turnbaugh et al. 2009). The proportion between
the two phyla corresponds also to earlier findings with
the HITChip analysis on the microbial communities ofthe SHIME system (Van den Abbeele et al. 2010) and
of the I-Chip analysis of the dynamic intestinal model
of TNO (TIM2) (Rajilic-Stojanovic et al. 2010). Inboth simulators, a loss of Firmicutes as opposed to
Bacteroidetes was observed during the initial in vivo-
in vitro shift. Nevertheless, the microbial communityobtained in the different regions of the in vitro colon
resulted to be stable upon inoculation, colon region
specific, and relevant to the in vivo conditions whencompared to the fecal sample used to inoculate the
system (Van den Abbeele et al. 2010).
The dominance of Bacteroidetes is typical of ahealthy gut microbiota coevolved with a polysaccha-
ride-rich diet (De Filippo et al. 2010). Moreover, the
relative abundance of two dominant bacterial phyla,the Bacteroidetes and the Firmicutes, has been posi-
tively correlated with body weight control (obese
show a decrease in the abundance of Bacteroidetes anda proportional increase in Firmicutes). This should be
related to the ability of the microbiota to harvest
energy from the diet (Ley et al. 2005; Turnbaugh et al.2006). However, there is no real scientific consensus
and some reports show the opposite, as recently
thoroughly summarized in a review (Ley 2010) andreported in literature (Collado et al. 2008; Duncan
et al. 2008; Zhang et al. 2009; Schwiertz et al. 2010).In this study, both products were able to further
decrease the Firmicutes/Bacteroidetes ratio in the
microbial community of each colon compartment.Pyrosequencing allowed to effectively explore the
impact of the treatment on the whole microbial
community and showed that product B had a moreextended effect along the colon as compared to
product A. This was clearly supported by the phylo-genetic-based dendrogram and the OTU-based PCoA
shown in Fig. 4. Both products had a similar effect in
the AC (cluster I—Fig. 4) and the samples from thedistal colon clustered separately (cluster II). Within
cluster II, the treatment with product B was still able to
modify the microbiota in the distal colon as showed bythe separate clustering of samples S2TCt and S2DCt
(sub-cluster II.3), in contrast with the fact that the
samples from the simulated descending colon of theSHIME treated with product A grouped in the same
sub-cluster II.1. The same conclusions can be reached
analyzing the PCoA plot in which the samples of thesimulated distal colon of the SHIME 2 fell apart as
compared to the samples of the descending colon of
SHIME 1 that, on the contrary, clustered together. Theextended effect of product B, as compared to product
A, was also supported by a higher richness estimator, a
higher Dy parameter and a similar DCo profile—calculated at genus level—for all the colon compart-
ments. The DCo parameter in combination with
pyrosequencing—introduced for the first time in thiswork—is a very useful additional tool to study the
effect of a given treatment on the structure (in terms of
evenness) of a microbial community (see Fig. S6 forfurther explanations). In fact, it allows to identify
changes occurring in high/1ow abundant OTUs even
when the final Co values are very similar (i.e. controlvs. treatment in any colon compartment of the SHIME
2). In the specific case of product B, the DCo profile
pointed out that some phylotypes were selectivelyenhanced during the treatment and that they became
more dominant in the microbial community at the
expense of other phylotypes, while the final degree ofevenness for the whole community did not change.
The enhanced phylotypes in the AC were mainly
associated to the genus Prevotella (some xylanolyticspecies belong to this genus—Dodd et al. 2010) while
in the distal colon they were associated to the genera
Bacteroides. The latter is a genus that includesmicroorganisms whose genome is enriched in carbo-
hydrate acting enzymes and also vitamin and cofactormetabolism, indicating that these bacteria have
adapted to a role of diet digestion and vitamin
production (Karlsson et al. 2011). Bacteroides spp.have been shown to be able to efficiently degrade
arabinogalactans and other plant-derived polysaccha-
rides (Salyers et al. 1977; Van Laere et al. 2000). In allcolon compartments, the enhanced growth of these
Antonie van Leeuwenhoek
123
bacteria occurred at the expenses of other bacteriabelonging to Ruminococcaceae, Porphyromonadaceae
and mainly to the genus Mitsuokella (with several
lactate producers).We have previously shown that products A and B
had an effect in modulating the structure and compo-
sition of the microbial community by, for instance,increasing the concentration of Bacteroidetes or
inducing a bifidogenic activity (Marzorati et al.
2010). However, only by means of a more in depthmolecular analysis (i.e. 16S rRNA gene pyrosequenc-
ing), we could discriminate the opposite trends
occurring in the bacteroides and prevotella genera inthe different areas of the simulated GIT. On the other
hand, pyrosequencing has also some limitations. Even
if it can detect a high percentage of low abundantOTUs in a complex sample, the accuracy in quanti-
fying the same rare species is limited (e.g. Lactoba-cillus spp. and Bifidobacterium spp.), at least workingwith a dataset containing 23,827 sequences. This value
represents a good balance between cost and depth of
analysis per sample and is in line with a similarecological survey previously conducted in mice
(Turnbaugh et al. 2009). If on the one hand pyrose-
quencing indicated a general decrease in the phylumFirmicutes, on the other hand, qPCR had previously
pointed out a specific positive effect on the lactobacilli
(belonging to the same phylum) concentration in theascending and transverse colon of SHIME 2 (Marzo-
rati et al. 2010). With respect to other low-abundant
groups pyrosequenging showed only a slight increaseof Bifidobacterium spp. (i.e. B. longum sub. infantisand B. adolescentis) in the simulated distal colon of
the SHIME treated with product B. On the contrary,specific DGGE and qPCR analyses (Fig. 3) showed
that Bifidobacterium spp. could benefit from both the
treatments, mainly in the simulated ascending colonwith product A and all along the GIT with product B.
The growth of bifidobacteria can be indeed stimulated
by Aloe vera whole leaf extract (Pogribna et al. 2008)while arabinogalactan supported the growth of Bifi-dobacterium longum in batch co-culture with Bacte-roides spp. (Degnan and Macfarlane, 1995).
Finally, with the available pyrosequencing instru-
ments, it is often not possible to provide a phyloge-netic classification of the obtained sequences at
species level. This is a major problem considering
that positive health effects from gut microorgan-isms are exclusively determined at strain level. Such
identification can only be reached with specific qPCR,FISH analysis or gene libraries of the full 16S rRNA
gene.
In conclusions, the combination of a SHIME exper-iment with the barcoded pyrosequencing approach is a
powerful tool for characterizing changes occurring in
the gut microbiota following a treatment. Minorchanges in the overall composition of the blends led
to a region- specific microbiota modulation potentially
extending the saccharolytic fermentation to the distalcolon. However, especially in the GIT, pyrosequenc-
ing may have some limitations related to the quanti-
fication of those species that are of key interest forGRM purposes—because related to potential positive
health effects (i.e. bifidobacteria or lactobacilli)—and
are, nevertheless, only a minor fraction. In this situa-tion, conventional molecular ecological approaches,
such as PCR–DGGE and qPCR (with primers specific
for the targeted microbial groups) still remain a veryuseful complementary tool.
Acknowledgments Tom Van de Wiele, Sam Possemiers andMassimo Marzorati are Postdoctoral Fellows of the ResearchFoundation—Flanders (FWO, Belgium).
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