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Secretor Genotype (
gene) Is Strongly Associated
with the Composition of
in the Human
Pirjo Wacklin*, Harri Ma
¨kivuokko, Noora Alakulppi, Janne Nikkila
¨, Heli Tenkanen, Jarkko Ra
Partanen, Kari Aranko, Jaana Ma
Finnish Red Cross Blood Service, Helsinki, Finland
Intestinal microbiota plays an important role in human health, and its composition is determined by several factors, such as
diet and host genotype. However, thus far it has remained unknown which host genes are determinants for the microbiota
composition. We studied the diversity and abundance of dominant bacteria and bifidobacteria from the faecal samples of
71 healthy individuals. In this cohort, 14 were non-secretor individuals and the remainders were secretors. The secretor
status is defined by the expression of the ABH and Lewis histo-blood group antigens in the intestinal mucus and other
secretions. It is determined by fucosyltransferase 2 enzyme, encoded by the FUT2 gene. Non-functional enzyme resulting
from a nonsense mutation in the FUT2 gene leads to the non-secretor phenotype. PCR-DGGE and qPCR methods were
applied for the intestinal microbiota analysis. Principal component analysis of bifidobacterial DGGE profiles showed that the
samples of non-secretor individuals formed a separate cluster within the secretor samples. Moreover, bifidobacterial
diversity (p,0.0001), richness (p,0.0003), and abundance (p,0.05) were significantly reduced in the samples from the non-
secretor individuals as compared with those from the secretor individuals. The non-secretor individuals lacked, or were
rarely colonized by, several genotypes related to B. bifidum,B. adolescentis and B. catenulatum/pseudocatenulatum. In
contrast to bifidobacteria, several bacterial genotypes were more common and the richness (p,0.04) of dominant bacteria
as detected by PCR-DGGE was higher in the non-secretor individuals than in the secretor individuals. We showed that the
diversity and composition of the human bifidobacterial population is strongly associated with the histo-blood group ABH
secretor/non-secretor status, which consequently appears to be one of the host genetic determinants for the composition
of the intestinal microbiota. This association can be explained by the difference between the secretor and non-secretor
individuals in their expression of ABH and Lewis glycan epitopes in the mucosa.
Citation: Wacklin P, Ma
¨kivuokko H, Alakulppi N, Nikkila
¨J, Tenkanen H, et al. (2011) Secretor Genotype (FUT2 gene) Is Strongly Associated with the Composition of
Bifidobacteria in the Human Intestine. PLoS ONE 6(5): e20113. doi:10.1371/journal.pone.0020113
Editor: Michael Otto, National Institutes of Health, United States of America
Received January 28, 2011; Accepted April 12, 2011; Published May 19, 2011
Copyright: ß2011 Wacklin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Growing evidence shows that the composition and diversity of
the microbiota in the human intestine can have a surprisingly
strong impact on the well-being and health of the host. For
example, inflammatory bowel disease (IBD) has been associated
with the disturbance of the intestinal microbiota, resulting in the
modulation and dysregulation of the inflammatory responses in
the intestine . Microbiota composition has been shown to have
an effect on the energy harvest and storage of the host  and
thus, microbiota alterations associated with obesity may have a
role in weight-associated health problems.
The microbiota composition in the human intestinal tract is
determined by several factors, such as host genotype, health status,
age, microbial interactions, and diet . Based on numerous
intervention studies, there is convincing evidence for the influence
of the diet on the intestinal microbiota (e.g. , ). In contrast,
although growing evidence indicates that host genetic background
has a significant impact on the microbiota composition in the
intestine, no specific genetic factors determining the intestinal
microbiota composition have been established to date. Twin
studies applying plate counts, PCR-DGGE fingerprinting or DNA
microarrays have shown a higher similarity in the microbiota
composition between monozygotic twins than between dizygotic
twins, unrelated persons, marital couples and family members [6–
7], thus clearly pointing to a strong effect of host genetics. In the
study by Turnbaugh et al. , the pyrosequencing analysis also
showed a higher level of similarity in the microbiota composition
in twin pairs than between twins and their mothers or unrelated
persons, although in their study the similarity of the microbiota
between monozygotic twins did not differ from that of dizygotic
The human intestinal tract is colonised with highly diverse and
numerous microbiota which has an established role in maintaining
the intestinal homeostasis. A particularly interesting group is
bifidobacteria, which comprise the predominant intestinal micro-
biota in infants and are abundant also in the adult population
comprising up to 6% of the normal intestinal microbiota . An
adult intestine is typically colonised with one to four bifidobacterial
species , B. longum,B. adolescentis,B. bifidum and B. catenulatum
PLoS ONE | www.plosone.org 1 May 2011 | Volume 6 | Issue 5 | e20113
being the most prevalent , . Bifidobacteria have beneficial
properties, such as immunomodulatory and pathogen inhibition
effects (reviewed by ). They also are commonly incorporated
in probiotic products.
The A, B and H blood group antigens are a1,2-linked fucose
containing glycans present on glycoproteins and glycolipids of
erythrocytes (red blood cells) in individuals representing A, B and
H blood groups, respectively. The enzyme fucosyltransferase 1
encoded by the FUT1 gene is responsible for the synthesis of ABH
antigens on erythrocytes. The ABH antigens are also expressed in
mucus and other secretions, where their expression is generated by
another enzyme, fucosyltransferase 2 (secretor type a1,2-fucosyl-
transferase) encoded by the FUT2 gene. In ABH secretor
individuals (80% of Caucasians) fucosyltransferase 2 converts type
1 N-acetyllactosamine glycan chains to H antigen, which functions
as a precursor for the A, B and Lewis b antigens. Non-secretor
individuals do not express active fucosyltransferase 2 enzyme due
to a non-sense mutation in the FUT2 gene and therefore they are
not able to express the ABH antigens in their mucus and other
secretions. The FUT2 gene together with the FUT3 gene encoding
fucosyltransferase 3 (Lewis type a1,3/4-fucosyltransferase), is also
required for the synthesis of Lewis b histo-blood group (Le a
antigens in secretions. In non-secretor individuals, the FUT3 gives
rise to the Lewis a histo-blood group (Le a
) antigen due to non-
functional fucosyltransferase 2. Lewis negative (Le a
uals have a mutation in FUT3 gene leading to Lewis null
phenotype, irrespective of the secretor status or FUT2 gene. These
mucosal ABH and Lewis histo-blood group antigens are known to
serve as an energy source  and adhesion receptors for many
microbes , and thus could play a role in shaping the
microbiota composition of the host.
In the present study, we report that the genetic variation in the
human fucosyltransferase 2 (FUT2) gene determining the presence
of mucosal a1,2-fucosylated glycan structures, such as ABH and
Lewis b histo-blood group antigens, is strongly associated with the
microbiota composition, in particular that of bifidobacteria, in the
human intestinal tract. We studied the association of the secretor
status (determined by the FUT2 gene) with the intestinal
microbiota composition by comparing the dominant bacterial
and bifidobacterial populations in faecal samples of non-secretor
and secretor individuals. The denaturing gradient gel electropho-
resis (PCR-DGGE) and qPCR analysis showed that the compo-
sition of the intestinal microbiota and particularly bifidobacteria
was strongly associated with the host’s secretor status. Secretor
status determining the expression of the ABH and Lewis b glycan
epitopes in the human intestine seems to be one of the host
features significantly shaping the composition of bifidobacteria in
Blood group analysis
Fourteen of the study individuals were non-secretors and 57
secretors. Twelve of the individuals had Lewis a blood group and
48 had Lewis b blood group (Table 1). In addition, 11 individuals
represented Lewis negative blood group, expressing neither Lewis
a nor Lewis b antigens. For the Lewis negative samples, secretor
status could not be determined by the hemagglutination assay.
The secretor status determination of the Lewis negative individuals
was based on the sequencing of the coding exon of the FUT2 gene.
The sequencing of the FUT2 exon showed that 9 of the Lewis
negative individuals with unknown secretor phenotype turned out
to be secretors and two were non-secretors, that is, they were
homozygous for the 428G.A mutation leading to a FUT2 null-
allele (428G.A, se
, W143X, rs601338) (Table 2). All
individuals with the non-secretor phenotype were homozygous
for the same FUT2 null-allele caused by the 428G.A mutation.
Individuals with a secretor phenotype were either homozygous
(GG) or heterozygous (GA) at the position 428 (Table 2)
generating a functional FUT2 gene. There were no discrepancies
between the serological and gene level determinations of the
secretor status (Table 2).
PCR-DGGE gel-to-gel variation
In order to estimate the gel-to-gel variation in the PCR-DGGE
analysis, we repeated 18 samples two or three times in the
bifidobacterial–DGGE. The correlation of the replicate samples
was 0.99 when PCR-DGGE band intensity values were used and
0.91 when the presence/absence of the bands was used, indicating
that profiles of the replicate samples were highly similar and the
gel-to-gel variation was low.
PCR-DGGE of dominant intestinal bacteria
The richness (i.e. number of bands) of dominant bacteria
obtained by PCR-DGGE with universal bacterial primers, was
significantly higher in the non-secretor than in the secretor
individuals (ANOVA; p = 0.03)(Fig. 1), showing that the non-
secretor individuals had on average more bacterial genotypes over
the detection limit. The bacterial diversity in the samples was
measured by Shannon diversity index using the DGGE band
intensity values. The Shannon diversity index showed a trend
towards an increased bacterial diversity in the non-secretor
individuals (p = 0.07). In addition, the individuals with the Lewis
negative blood group had lower richness (ANOVA, p = 0.02) and
Table 1. Distribution of ABH and Lewis Blood groups in the
Blood group Non-secretor (14) Secretor(57) All (71)
A (%) 8 (57%) 20 (34%) 28 (39%)
AB (%) 2 (14%) 9 (16%) 11 (15%)
B (%) 1 (7%) 9 (16%) 10 (14%)
O (%) 3 (21%) 19 (33%) 22 (31%)
Lewis a (Le a
)12 0 12
Lewis b (Le a
)0 48 48
)2 9 11
Secretor status of the Lewis negative individuals was determined by
sequencing the coding exon of the FUT2 gene.
Table 2. Comparison of FUT2 genotypedeterminations with
secretor phenotype determinations.
FUT2 genotype based
A SNP Phenotype Total
Non-secretor Secretor Unknown
Non-secretor (AA) 12 0 2 14
Secretor (GA) 0 27 6 33
Secretor (GG) 0 21 3 24
Secretor phenotype could not be determined for the Lewis negative
individuals by hemagglutination assay (phenotypic assay) applied here.
FUT2 Associated with Intestinal Bifidobacteria
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diversity (ANOVA, p = 0.02) of dominant bacteria than the Lewis
a individuals and lower diversity (ANOVA, p = 0.02) than the
Lewis b individuals (Fig. 1).
Eight DGGE band positions, or ‘‘DGGE genotypes’’, were
statistically (Fisher’s exact test, p-value between 0.0005 and 0.05)
more commonly detected in the non-secretor than in the secretor
individuals (Table 3). However, no clustering between the secretor
and non-secretor samples was detected in the PCA of DGGE
profiles of the dominant microbiota suggesting that other factors
dominate the variation in microbiota.
PCR-DGGE analysis of intestinal bifidobacterial
The non-secretor individuals formed a separate cluster within the
secretor individuals in the PCA analysis of bifidobacterial DGGE
profiles (Fig. 2), indicating that the bifidobacterial population was
different in the non-secretor individuals in comparison with the
secretor individuals. The Lewis negative individuals did not cluster
separately from the Lewis a or Lewis b individuals.
The bifidobacterial band positions that mainly contributed to the
PCA clustering were 17.70%, 20.4%, 26.6%, 62.2% and 63.70%
(Fig. 2). Three of these band positions (26.6%, 63.70% and 17.70%)
were significantly more common in the secretor individuals than in
the non-secretor individuals (Fisher’s exact test, p,0.01) (Table 4).
None of the 14 non-secretors had band in positions 17.70% and
63.70%. Furthermore, the band position 26.60% was clearly less
frequent in the non-secretor individuals (N =2/14; 14%)than in the
secretor individuals (N = 38/57; 67%) (Table 4). These band
Figure 1. Richness and diversity of dominant bacteria in faecal samples of the non-secretor and secretor individuals (A, B) and of
the Lewis a, b and negative individuals (C, D) based on the DGGE profiles. A significant difference between the groups by ANOVA:
p,0.05. In addition, a trend (p = 0.07) towards higher diversity in the non-secretor than in secretor individuals and towards higher richness in Le b
individuals than in Le negative individuals was detected.
Table 3. The significantly differing band positions and the
incidence of bands in secretor (14) and non-secretor samples
(57) by PCR-DGGE with universal bacterial primers.
% of non-
25.20 % 22 43 28 0.02 0.05
60.20 % 19 36 25 0.01 ns
56.60 % 17 64 14 ns 0.0005
39.00 % 11 36 11 0.004 0.01
42.40 % 9 29 9 0.02 0.07
47.00 % 7 21 7 0.05 ns
50.50 % 6 29 4 0.001 0.01
61.10 % 4 21 1.8 0.0002 0.03
Only band positions, which were statistically significantly different between
the groups by ANOVA or/and Fisher exact test are shown.
ns = non-
FUT2 Associated with Intestinal Bifidobacteria
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positions were also among the most commonly detected genotypes
in the secretor individuals (Table 4). Forty bands in the
bifidobacterial DGGE gels, which represented 10 band positions,
were excised from the DGGE gel and sequenced to identify which
species/groups they represent (Table S1). With three exceptions, the
band positions present in more than 10% of the samples, could be
identified by sequencing (Table 4). The band position 26.60% was
related to B. adolescentis and the position 63.70% to B. catenulatum/
pseudocatenulatum, respectively (Table 4). Sequencing of the band
position 17.70% was unsuccessful despite several attempts and was
therefore not identified. The remaining band positions were related
to B. longum, B. bifidum, uncultured bifidobacteria, and another B.
Figure 2. PCA plot based on the bifidobacterial DGGE profiles of faecal samples from the non-secretor (open circles) and secretor
(closed circles) individuals (A) and DGGE bands contributing to the principal components 1 and 2 (B). In panel B, the numbers in bold
indicate the band positions, which were significantly less commonly (Fisher’s exact test, p,0.01) detected in the non-secretor individuals than in the
secretor individuals (See Table 4).
Table 4. Identification of the bifidobacterial DGGE band positions by sequencing and the incidence of the bands in secretor (14)
and non-secretor samples (57).
Best Blast hit (best cultured hit, similarity)
Detected bands % of non-secretors % of secretors
B. longum 53.5% 56 79 79
Uncultured bifidob. (B. adolescentis, 99%) 62.2% 41 50 60
B. adolescentis 26.6% 40 14 67**
not sequenced 17.7% 18 0 32**
B. catenulatum/pseudocatenulatum 63.7% 18 0 32**
B. bifidum 29.7% 17 7 28
not sequenced 20.4% 16 7 26
B. adolescentis 22.3% 13 14 19
Uncultured bifidob. (B. adolescentis/ruminantium, 98–99%) 43.8% 13 14 19
Uncultured bifidob. (B. catenulatum, 99%) 47.3% 9 7 14
Uncultured bifidob. (B. adolescentis, 99%) 55.0% 9 7 14
Uncultured bifidob. (B. ruminantium, 99%) 44.5% 8 7 12
Not sequenced 39.3% 7 7 11
The similarity of the best Blast hit for a cultured strain is shown in parentheses, in cases where an uncultured bacterium was the best hit. Detailed data for the
identification of each position is shown in table S1.
Only the band positions that were detected at least in 10 % of the samples are shown.** Significant differences:
Band positions 26.6%, 17.70% and 63.7% were more frequently detected in secretor than non-secretor samples (Fisher’s exact test, p,0.01), and in Lewis b than Lewis
a samples (Fisher’s exact test, p,0.01 for 17.70% and 26.60% ; p,0.02 for 63.70%).
FUT2 Associated with Intestinal Bifidobacteria
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adolescentis genotype (Table 4). In total, 26 band positions were
detected in the Bifidobacterial-DGGE gels, 13 (50%) of these were
detected at least in one non-secretor individual and all at least in one
The lower incidence of bifidobacterial DGGE genotypes in the
non-secretor individuals was also reflected in the bifidobacterial
diversity in the samples. Bifidobacterial diversity and richness in the
non-secretor individuals was significantly reduced in comparison
with the secretor individuals (Fig. 3). On average, the number of
bands per sample in the non-secretor individuals was almost twice
(1.9) as low as that in the secretor individuals. The mean number of
bands was 2.5 (range from 0 to 5) in the non-secretor samples and
4.7 (range from 0 to 11) in the secretor samples. The bifidobacterial
diversity and richness did not significantly differ between the Lewis-
negative individuals and the Lewis a or Lewis b individuals (Fig. 3).
The inter-individual variation in bifidobacterial profiles was
high in both the non-secretor and the secretor samples, as
indicated by relatively low similarity values for the DGGE profiles
(on average 41% between the non-secretor individuals and on
average 48% between the secretor individuals).
Using a qPCR approach, bifidobacterial 16S rRNA genes were
detectable in over 90% of the faecal samples. The total number of
bifidobacterial 16S rRNA gene copies was lower (Wilcoxon test,
p =0.05) and fewer bifidobacterial groups were present in the non-
secretor individuals in comparison with the secretor individuals
(Fig. 4). All the bifidobacterial groups, B. bifidum,B. longum group,
B. catenulatum/pseudocatenulatum and B. adolescentis were detected less
frequently in faecal samples of the non-secretor than in samples of
the secretor individuals, confirming the PCR-DGGE results. In
faecal samples with detectable amounts of B. adolescentis group, a
trend towards lower abundance of B. adolescentis in the non-secretor
than in secretor individuals (p = 0.06) was found. The abundances
of other bifidobacterial groups in samples were not significantly
different. The total bacterial numbers did not differ between the
secretor and non-secretor individuals (Fig. 4).
Effect of GG homozygocity versus GA heterozygocity in
FUT2 gene 428G.A (W143X, se
, rs601338) SNP on the
We also assessed whether homozygocity (GG) or heterozygocity
(GA) of the FUT2 gene 428G.A SNP is associated with the
composition of bifidobacteria or dominant bacteria. Neither the
composition nor the diversity of dominant bacteria or bifidobacteria
was significantly different between the secretor individuals homo-
zygous or heterozygous for the position 428 in the codon 143.
To study the association of the intestinal microbiota with the
histo-blood group secretor status (defined by the FUT2 gene), we
Figure 3. Bifidobacterial richness and diversity in faecal samples of the non-secretor and secretor individuals (A, B) and Lewis a, b
and negative individuals (C, D) based on the DGGE profiles. Significant differences by ANOVA: **** p,0.0001, *** p,0.001.
FUT2 Associated with Intestinal Bifidobacteria
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analysed the faecal microbiota in 71 individuals, of which 14 were
non-secretors. We observed that the diversity and amount of faecal
bifidobacteria was considerably reduced in the non-secretor
individuals. In addition to bifidobacteria, indications that the
composition of dominant bacteria differed between the non-secretor
and secretor individuals were discovered. Altogether these results
suggest that the FUT2 gene, which determines the presence of ABH
histo-blood group glycans in mucus lining of the intestine, is a host
genotypic feature significantly affecting the bacterial composition,
particularly the bifidobacterial composition, in the intestine.
The secretor status determined by the FUT2 gene was strongly
associated with the bifidobacterial diversity and composition. The
non-secretor individuals only had about half of the bifidobacterial
diversity and richness present in the secretor individuals based on
the PCR-DGGE analysis. In addition, the non-secretor individuals
had significantly reduced bifidobacterial abundance in comparison
with the secretor individuals as measured by qPCR. Moreover, the
non-secretor individuals lacked, or were rarely colonised by,
several bifidobacterial DGGE genotypes, which were related to
species B. adolescentis,P. catenulatum/pseudocatenulatum and B. bifidum,
and were common in the secretor individuals. We applied the
PCR-DGGE to compare the bacterial diversity and community
structure between the secretor and non-secretor individuals. The
PCR-DGGE method is known to detect only the predominant
part of the bacteria present in a complex sample. Bifidobacterial
population is usually composed of limited number (0–4) of species
 and thus, could be captured by the PCR-DGGE analysis with
bifidobacterial specific primers. We also showed that bifidobacter-
ial-DGGE profiles were highly reproducible. Moreover, we
isolated bifidobacterial strains from the non-secretor and secretor
individuals and analysed their 16S rRNA gene fragments in a
DGGE gel along with faecal samples. The isolated strains
corresponding to the most common (present in .10% samples)
band positions in bifidobacterial DGGE gels were found (data not
shown), reducing the likelihood that the detected DGGE bands
originate from PCR artefacts sometimes occurring in the PCR-
DGGE. In contrast to the bifidobacteria-specific DGGE, it is likely
that methodological limitations hinder the interpretation of the
PCR-DGGE targeted at dominant bacteria (universal PCR-
DGGE). It is probable that several secretor-status associated
genotypes, which are present at low levels, are missed in the PCR-
DGGE using universal primers and their associations with the
secretor status could, thus, not be detected. Nevertheless, our
finding on the differences in the dominant microbiota between the
non-secretor and secretor individuals suggests that the association
between microbiota and secretor status is not limited to
bifidobacteria only. It remains to be seen which other bacterial
groups and species are associated with the secretor status.
Figure 4. Incidence (% of samples) (left) and Box-and-Whisker plots (right) (based on log
16S rRNA gene copies per g faeces) of
total bacteria, bifidobacteria and bifidobacterial groups in the faecal samples of the non-secretor and secretor individuals by
qPCR. A significant difference by Wilcoxon test:
p,0.05. In addition, a trend (p = 0.06) towards higher number of the 16S rRNA gene copies of B.
adolescentis in the secretor individuals than in the non-secretor individuals was detected.
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The altered microbiota composition in the non-secretor
individuals shown in this study may be an important factor
contributing to the non-secretor disease susceptibility. Polymor-
phism of the FUT2 gene, determining the secretor status, has been
suggested to modulate innate immune responses and even have an
evolutionary role in humans’ survival during different pathogen
outbreaks ,. The non-secretor phenotype has been
genetically associated with increased risk for Crohn’s disease
, , and necrotizing enterocolitis . Secretor status is also
associated with the susceptibility to several infectious diseases.
Non-secretors have an increased risk for urinary tract infections
,  and vaginal candidiasis , , but a reduced risk
for diarrhoea caused by certain genotypes of Norovirus .
Interestingly, many of these secretor status associated diseases,
such as Crohn’s disease , , urinary tract infection , and
NEC  have also been connected to changes in the intestinal
microbiota composition or activity. Bifidobacteria have been
shown to have health promoting effects on humans . Bifido-
bacteria or bifidobacteria-containing strain mixtures have shown
promising results e.g. in the alleviation of the symptoms of irritable
bowel syndrome (IBS) , inflammatory bowel disease , and
diarrhoea , although the mechanism of action is largely
unknown. Reduced bifidobacterial abundance has been connected
to intestinal disorders such as irritable bowel syndrome  and
inflammatory bowel disease . Taken together, the properties of
bifidobacteria and the results of this study suggest that the secretor
status, by effecting bifidobacterial diversity, may also play a role in
susceptibility to the diseases associated with the decreased bifido-
bacterial abundance in the intestine.
Metagenomics studies indicate that a considerable number of
intestinal microbiota genes are involved in carbohydrate metab-
olism. Kurokawa et al.  reported that the carbohydrate
metabolism genes of microbiota are enriched in the intestine (24%
of genes in adults and 34% in children) in comparison with the
microbiota originating from other environments, such as soil and
sea. Both plant polysaccharides and host derived glycans are
important energy sources for intestinal bacteria. Fucosylated histo-
blood group antigens, such as the ABH and Lewis b histo-blood
group antigens, are terminal epitopes of glycan chains in
glycoproteins and glycolipids mediating the interaction between
host and both commensal and pathogenic intestinal bacteria ,
. Non-secretor individuals have null-allele of FUT2 gene and
do not express such a1,2-fucose containing glycan structures in
their intestinal mucosa. Bacteria that can interact with these
epitopes and compete for adhesion sites or to use them as energy
sources have a better colonisation ability in secretor individuals
than in non-secretor individuals (e.g. bifidobacteria in this study).
Intestinal bifidobacteria, whose abundance and diversity were
higher in the intestine of secretor individuals than non-secretor
individuals in this study, are adapted to utilise glycans present in
mucins and human milk . It is known that some microorgan-
isms secrete glycosidases capable of degrading histo-blood group
antigens . Comparatively small populations of human faecal
bacteria produce a-glycosidases capable of degrading terminal
ABH and/or Lewis groups in glycans . Among them are
bifidobacteria with 1,2-a-fucosidase to hydrolyse a-1,2-fucosyl
linkages present in various glycans, such as the above-mentioned
histo-blood group antigens . Recently, Turroni et al. 
showed, using genomic, proteomic and transcriptomic analysis of
B. bifidum PRL2010, the existence of enzymes allowing further
degradation of many core glycan chains and they concluded that
the property is important for intestinal colonisation of B. bifidum.
Such degradation of glycan cores may require the initial removal
of terminal a-fucose, enabling subsequent processing of glycan
chain by b-galactosidase and/or b-N-acetylhexosaminidase and
endo-a-N-acetylgalactosaminidase, which catalyses the release of
GalNAc from serine/threonine residues of various mucin-type
glycoproteins, all of these enzymes being encoded in B. bifidum
PRL2010 genome . In addition, bifidobacteria but typically
not the other common commensals, have lacto-N-biosidase
degrading type 1 glycan chains, which are precursors of
fucosylated histo-blood group antigens in the human intestine
. Therefore, it can be concluded that bifidobacteria have very
specific strategies for the utilization of host glycans.
In this study we present evidence that the FUT2 gene, which
defines the secretor status and thus, the expression of the ABH and
Lewis histo-blood group antigens in intestinal mucus, is one of the
host genotypic features determining the composition of intestinal
microbiota, particularly bifidobacterial population. We showed
that bifidobacterial diversity and composition is strongly associated
with the secretor status of the host. These results increase our
understanding of the factors explaining inter-individual variations
in intestinal microbiota composition and help us to evaluate the
role of intestinal microbiota in health and disease.
Materials and Methods
Altogether 82 healthy adult individuals were recruited to the
study from Helsinki metropolitan area, Finland. Individuals with
clinically diagnosed intestinal diseases or regular intestinal
disturbances were excluded from the study. The individuals had
not received antibiotic therapy within two months of the faecal
sampling time. Probiotic consumption was restricted one week
before sampling and alcohol consumption was limited to one
portion per day during three days before faecal sampling. All
individuals consumed mixed diet. The study had the approval of
the ethical committee of the Helsinki University Hospital and all
subjects signed a written informed consent.
Both faecal and blood samples were collected from 71 subjects
(7 males and 64 females). The distribution of blood groups was
balanced towards the blood groups (Lewis a, Lewis negative or AB
and B) rare in Finland by excluding 11 secretor individuals
representing common blood groups A or O and Lewis b from
faecal donation. The age of the volunteers who donated faecal
samples ranged from 31 years to 61 years and was on average 44.7
Faecal samples for the determination of the microbiota
composition were frozen at 280uC within 5 hours of defecation.
EDTA anticoagulated peripheral blood samples for blood group
analysis were kept at +4uC and analysed within 24 hours. Buffy
coats were extracted from citrate anticoagulated peripheral blood
samples by centrifugation and stored at 280uC until DNA
Determination of ABH and Lewis blood group and
ABO blood groups were determined by hemagglutination assay
with Olympus PK 7300 according to standard blood banking
practise. Lewis a and Lewis b typings were performed in tubes by
monoclonal antisera (Sanquin, the Netherlands). Determination of
secretor status was based on Lewis antigens. Secretor status could
not be determined by phenotyping for the samples of Lewis
negative individuals and their secretor status assignments were
based on genotyping of the FUT2 gene.
In addition to phenotyping, secretor status was genotyped by
sequencing the coding exon of FUT2 as described in  and .
Briefly, the FUT2 exon was amplified with PCR and sequenced
FUT2 Associated with Intestinal Bifidobacteria
PLoS ONE | www.plosone.org 7 May 2011 | Volume 6 | Issue 5 | e20113
with ABI3100 in the Haartman Institute, Sequencing Core
Facility (University of Helsinki, Finland) using primers described
in Table 5. Individual’s secretor genotype was defined as non-
secretor, when the FUT2 428G.A SNP (se
, W143X, rs601338)
was AA and as secretor when the FUT2 428G.A SNP was GA or
GG. Based on the sequence analyses of the FUT2 exon in a
separate Finnish cohort consisting of 184 secretor and non-
secretor individuals, no other non-secretor alleles than the FUT2
, W143X, rs601338) were found in Finnish
population (data not shown).
Total bacterial DNA was extracted from faecal samples using
the FastDNAHSPIN Kit for Soil and the FastPrepHInstrument
(MP Biomedicals, CA, USA) according the manufactures
instruction with minor modifications. Shortly, 0.3 g faecal sample
was mixed by vortexing with sodium phosphate buffer and MT
buffer. Faecal slurry (1 ml) was homogenised and cells were lysed
in lysing matrix E tubes with FastPrepHInstrument three times for
60 at speed setting 6.0 m/s. DNA was purified using silica based
binding matrix, SPIN
filters, and SEWS-M wash solution. The
DNA was eluted in 250 ml Dnase/pyrogen free water. Human
DNA was extracted from buffy coat preparations using the
QIAamp DNA Blood Mini Kit (QIAGEN Inc, CA, US). The
DNA concentrations were determined with NanoDrop 1000
(Thermo Scientific, DE, USA). The extracted DNA samples were
stored at 220uC.
The similarity and diversity of microbiota in faecal samples of
the study subjects was analysed by the PCR-DGGE. The partial
16S rRNA gene was amplified by PCR with universal bacterial
primers and bifidobacterial specific primers. Amplification with
universal primers U968F+GC (59- CGCCCGGGGCGCGCCC-
CCTTA-39) and U1401R (59-CGGTGTGTACAAGACCC-39)
 was performed as described in Ma¨tto¨ et al. . Bifido-
bacteria were amplified with primers Bif164F (59- GGGTGGT-
AATGCCGGATG-39) and Bif662R+GC (59- CGCCCGCCGC-
GTTACACCGGGAA-39) as described in Satokari et al. ,
except elongation temperature of 72uC and Taq polymerase
(Invitrogen) were used. Template DNAs were diluted to con-
centration 20 ng/ml for both PCRs. PCR products were obtained
from all the samples with universal bacterial primers and from 64/
71 samples with bifidobacterial primers. A volume of 20 ml PCR
product was separated in 8% polyacrylamide gel with denaturing
gradient of urea and formamide ranging from 38% to 60%
(universal amplicons) or from 45% to 60% (bifidobacterial
amplicons). The DGGE gels were run at 70 V for 960 mins using
Dcode universal mutation detection system (Bio-Rad, CA, USA).
The gels were stained with SYRBHSafe DNA gel stain (Invitrogen,
Oregon, USA) for 30 mins and documented with SafeImager
Bluelight table (Invitrogen) and AlphaImager HP (Alpha Innotech,
South-Africa) imaging system. To estimate the gel-to-gel variation
and reproducibility of the bifidobacterial PCR-DGGE method, 18
of the samples were run two or three times in different gels.
The bands were excised from the bifidobacterial DGGE gels by
sterile Pasteur pipette and the DNA was eluted by incubating the
gel slices in 50 ml of sterile H
Oat+4uC overnight. The correct
positions and purity of the DNA fragments eluted from the bands
were checked by amplifying 1 ml of eluted DNA with primers
Bif164F-Bif662R and re-running the amplified fragments along
with the original samples in PCR-DGGE. Bands were sequenced
in Eurofins MWG (Germany) using primers Bif164F and Bif66R
without GC-rich clamp.
The qPCR method was applied to detect and quantify the 16S
rRNA gene copies of bacteria, bifidobacteria and 4 bifidobacterial
species/groups, B. bifidum,B. longum group, B. catenulatum/
pseudocatenulatum and B. adolescentis in faecal samples. The primers
and annealing temperature for each primer pair are shown in
Table 6. Reaction mixture (25 ml) was composed of 0.3 mMof
each primer (Sigma-Aldrich, UK), 1 x Power SYBR Green PCR
Master Mix (Applied Biosystems, CA, USA), 4 ml faecal DNA
diluted to the concentration of 1 ng/ml for bifidobacterial group/
species-specific primer pair and to the concentration of 0.1 ng/ml
for universal primers and bifidobacterial primers. The amplifica-
tion conditions in the ABI Prism 7000 instrument (Applied
Biosystems, CA, USA) were one cycle of 95 uC for 10 mins,
followed by 40 cycles of 95 uC for 15 s, and appropriate annealing
temperature (see Table 6) for 60 s. Melting temperature curves
from 60uCto95uC were analysed to determine the specificity of
the amplification. All the samples and standards were analysed in
three replicates. Standard curves from bacterial strains were
constructed for each bacterial group (Table 6) by 10-fold dilutions
of known concentrations of the bacterial genomic DNA (from
10 ng/ml to 0.0001 ng/ml). The Genomic DNA from the standard
strains was extracted by QIAmpHDNA mini kit (Qiagen)
combined with cell lysis in the FastPrepHInstrument (MP
Biomedicals, CA, USA). Bacterial cells were harvested from
plates, transferred to lysis tubes containing 0.1 g zirconia beads
(diameter of 0.1 mm) (BioSpec products, Inc., USA) and sterile
solid glass beads (diameter of 3 mm) (Sigma) in 180 ml ATL-buffer
from the QIAmpHDNA mini kit. Lysis tubes were treated with
FastPrepHInstrument for 60 s at speed setting 5.5 m/s. DNA
concentration measurements were performed similarly to the
samples (see above). GenEx Enterprise v.126.96.36.199 (MultiD
Analyses AB, Sweden) was used for the analysis of standard
curves and reverse quantification of the samples. The amplifica-
tion efficiencies were from 93% to 98% for all the other qPCR
primer pairs except for B. bifidum specific primers, in which
amplification efficiency varied from 80% to 92% and for B.
catenulatum/pseudocatenulatum, in which efficiency varied from 87%
Digitalised DGGE gel images were imported to the Bionumerics
program version 5.0 (Applied Maths, Belgium) for normalisation
Table 5. Primers used in sequencing of the FUT2 gene
encoding fucosyltransferase 2.
Primer Sequence 59R39Reference
1_FUT2forward CCATCTCCCAGCTAACGTGTCC 
2_FUT2reverse GGGAGGCAGAGAAGGAGAAAAGG 
3_FUT2forward GGGGAGTACGTCCGCTTCAC This study
4_FUT2reverse AGGATCTCCTGGCGGAGGTG This study
1_FUT2PCRforward ACACACCCACACTATGCCTGCAC 
2_FUT2PCRreverse ACTTGCAGCCCAACGCATCTT 
1_FUT2SEQforward CCAGCTAACGTGTCCCGTTTTCC 
2_FUT2SEQreverse GGCACTCATCTTGAGGGAGGCA 
FUT2 Associated with Intestinal Bifidobacteria
PLoS ONE | www.plosone.org 8 May 2011 | Volume 6 | Issue 5 | e20113
and band detection. The bands were normalised in relation to a
marker sample composed of 7 common intestinal bacterial strains
(for the universal PCR-DGGE) or 5 bifidobacterial strains (for the
bifidobacterial PCR-DGGE). Band search and band matching
using band tolerance of 1% were performed as implemented in the
Bionumerics. The bands and band matching were manually
checked and corrected. Samples with no amplification in the
bifidobacterial PCR-DGGE were excluded from the analysis.
Similarity of the bifidobacterial profiles was calculated as
implemented in Bionumerics, version 5. Matrices based on band
intensities and presence /absence of bands were exported from
Bionumerics and used for calculation of Shannon diversity indexes
in Microsoft Excel. Shannon diversity index, H’, was calculated
using equation H’ = Spiln(pi), where pi was proportion of each
species (i.e. DGGE band intensity) in the sample. The richness was
calculated as a number of detected bands in the DGGE profile of
the sample. Principal component analysis (PCA) based on the
band intensities was calculated as implemented in Bionumerics,
version 5.0. Other statistical analyses (ANOVA, Fisher exact test
and two-sample Wilcoxon test) were computed with statistical
programming language R, version 2.10.1. (http://www.r-project.
org/). Gel-to-gel variation was measured by comparing the DGGE
profiles of the samples run in different gels. The DGGE profiles
based on either band intensities or presence/absence of bands
were used for calculation of the Pearson correlation between
replicate samples using statistical programming language R,
The sequences retrieved from the DGGE bands were trimmed
and aligned by ClustalW . The closest relatives of the
sequences were searched using Blast (http://blast.ncbi.nlm.nih.
gov/Blast.cgi) and NCBI nr database. Distance matrix of the
aligned sequences was used to compare the similarity of the
sequences. The accession numbers of the sequences are
Table S1 The best Blast hits of the sequences derived from
bifidobacterial DGGE bands
Lehmonen, Ms. Elina Pusa, Ms. Paula Salmelainen and the technicians
responsible for the blood group determinations, are thanked for skilful
technical assistance. Dr. Pertti Sistonen is thanked for blood group
Conceived and designed the experiments: PW JM KA. Performed the
experiments: PW HT NA. Analyzed the data: PW JN NA. Contributed
reagents/materials/analysis tools: HT HM JM JN. Wrote the paper: PW
JM JP JR NA HM. Significantly contributed the sample collection: PW
KA. Designed and conducted the collection of the faecal samples: JM HM.
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Primer sequence 59R39Anneling-T, 6C Standard strain
Bacteria p201, p1370 GAGGAAGGNGNGGANGACGT, AGNCCCGNGAACGTATTCAC 60 L. rhamnosus E-96666
Bifidobacteria qBifF, qBifR TCGCGTCYGGTGTGAAAG, CCACATCCAGCRTCCAC 59 B. bifidum E-97795
B. longum group BiLON-1, BiLON-2 CAGTTGATCGCATGGTCTT, TACCCGTCGAAGCCAC 62 B. longum E-96664
B. bifidum BiBIF-1, BiBIF-2 CCACATGATCGCATGTGATTG, CCGAAGGCTTGCTCCCAAA 58 B. bifidum E-97795
BiCAT-1, BiCAT-2 CGGATGCTCCGACTCCT, CGAAGGCTTGCTCCCGAT 62 B. catenulatum DSM 16992
B. adolescentis group BiADO-1
58 B. adolescentis E-981074
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