ArticlePDF Available

Abstract and Figures

The human intestine is colonised with highly diverse and individually defined microbiota, which likely has an impact on the host well-being. Drivers of the individual variation in the microbiota compositions are multifactorial and include environmental, host and dietary factors. We studied the impact of the host secretor status, encoded by fucosyltransferase 2 (FUT2) -gene, on the intestinal microbiota composition. Secretor status determines the expression of the ABH and Lewis histo-blood group antigens in the intestinal mucosa. The study population was comprised of 14 non-secretor (FUT2 rs601338 genotype AA) and 57 secretor (genotypes GG and AG) adult individuals of western European descent. Intestinal microbiota was analyzed by PCR-DGGE and for a subset of 12 non-secretor subjects and 12 secretor subjects additionally by the 16S rRNA gene pyrosequencing and the HITChip phylogenetic microarray analysis. All three methods showed distinct clustering of the intestinal microbiota and significant differences in abundances of several taxa representing dominant microbiota between the non-secretors and the secretors as well as between the FUT2 genotypes. In addition, the non-secretors had lower species richness than the secretors. The soft clustering of microbiota into enterotypes (ET) 1 and 3 showed that the non-secretors had a higher probability of belonging to ET1 and the secretors to ET3. Our study shows that secretor status and FUT2 polymorphism are associated with the composition of human intestinal microbiota, and appears thus to be one of the key drivers affecting the individual variation of human intestinal microbiota.
Content may be subject to copyright.
Faecal Microbiota Composition in Adults Is Associated
with the
Gene Determining the Secretor Status
Pirjo Wacklin
*, Jarno Tuimala
, Janne Nikkila
, Sebastian Tims
, Harri Ma
, Noora Alakulppi
Pia Laine
, Mirjana Rajilic-Stojanovic
, Lars Paulin
, Willem M. de Vos
, Jaana Ma
1Finnish Red Cross Blood Service, Helsinki, Finland, 2Institute of Biotechnology, University of Helsinki, Helsinki, Finland, 3Department of Veterinary Biosciences and
Department of Bacteriology and Immunology, University of Helsinki, Helsinki, Finland, 4Laboratory of Microbiology, Wageningen University, Wageningen, The
The human intestine is colonised with highly diverse and individually defined microbiota, which likely has an impact on the
host well-being. Drivers of the individual variation in the microbiota compositions are multifactorial and include
environmental, host and dietary factors. We studied the impact of the host secretor status, encoded by fucosyltransferase 2
(FUT2) -gene, on the intestinal microbiota composition. Secretor status determines the expression of the ABH and Lewis
histo-blood group antigens in the intestinal mucosa. The study population was comprised of 14 non-secretor (FUT2
rs601338 genotype AA) and 57 secretor (genotypes GG and AG) adult individuals of western European descent. Intestinal
microbiota was analyzed by PCR-DGGE and for a subset of 12 non-secretor subjects and 12 secretor subjects additionally by
the 16S rRNA gene pyrosequencing and the HITChip phylogenetic microarray analysis. All three methods showed distinct
clustering of the intestinal microbiota and significant differences in abundances of several taxa representing dominant
microbiota between the non-secretors and the secretors as well as between the FUT2 genotypes. In addition, the non-
secretors had lower species richness than the secretors. The soft clustering of microbiota into enterotypes (ET) 1 and 3
showed that the non-secretors had a higher probability of belonging to ET1 and the secretors to ET3. Our study shows that
secretor status and FUT2 polymorphism are associated with the composition of human intestinal microbiota, and appears
thus to be one of the key drivers affecting the individual variation of human intestinal microbiota.
Citation: Wacklin P, Tuimala J, Nikkila
¨J, Tims S, Ma
¨kivuokko H, et al. (2014) Faecal Microbiota Composition in Adults Is Associated with the FUT2 Gene
Determining the Secretor Status. PLoS ONE 9(4): e94863. doi:10.1371/journal.pone.0094863
Editor: Christopher Quince, University of Glasgow, United Kingdom
Received December 19, 2013; Accepted March 20, 2014; Published April 14, 2014
Copyright: ß2014 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: Part of this work was supported by the Netherlands Organization for Scientific Research (Spinoza Grant 2008 to WMdV) and the Finnish Academy of
Sciences (141140 to WMdV). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: For conflict of interests, the authors declare that PW, HM and JM are inventors in patent applications (20110158950, Use of Blood group
status I; 20110158951, Use of Blood group status II; 20120020941 Use of Blood group status III). Of these, patent application 20120020941 ‘‘Use of Blood group
status III’’ is related to the data used in the manuscript. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.
* E-mail:
Microbiota colonising the human intestine maintains homeo-
stasis and has a marked effect on the human health. This vital role
of intestinal microbiota is carried out by a numerous and diverse
collection of microbial species that varies from person to person.
The drivers of high inter-individual variation are poorly known,
but contribution by diet, environment, and host genetics has been
proposed. The composition of this complex intestinal microbiome
is preserved in an individual for periods longer than a decade [1],
indicating that the host has mechanisms for regulating composi-
tion and activity of its microbiota. Further, monozygotic twins
have been found to carry a more similar intestinal microbiota than
dizygotic twins, unrelated persons or family members [2], [3],
suggesting a role for host genetics in maintaining the homeostasis.
However, other studies have reported that even though monozy-
gotic twins had more similar intestinal microbiota composition
than unrelated subjects, their microbiota composition does not
differ from that of dizygotic twins [4], [5]. While technical
explanations relating to the depth of the analysis cannot be
excluded, this has been interpreted as an indication of an effect of
shared environment. A better delineating of the impact of host
genetics on the intestinal microbiota is of great importance for
understanding the relation between microbiota and health as well
as for paving the way for personalised treatments for intestinal and
other disorders [6].
One candidate host gene affecting the human microbiota is
fucosyltransferase 2 (FUT2) gene, which encodes fucosyltransferase 2
enzyme. FUT2 is responsible for the synthesis of the type 1 H
antigens, which act as precursors for the ABO and Lewis b histo-
blood group antigens expressed on intestinal mucosa and other
secretions. Approximately 20% of individuals of European descent
represent the non-secretor phenotype, which is caused by the
FUT2 single nucleotide polymorphism (SNP) rs601338 (W143X,
G428A). Non-secretors are homozygous for non-functional FUT2
(genotype AA), and lack ABO histo-blood group antigens in
secretions. Secretor individuals have either one (genotype AG) or
two (genotype GG) functional FUT2 alleles, allowing the synthesis
of the ABO antigens in secretions. Recently, secretor status was
associated with the composition of human intestinal bifidobacteria
[7] and with microbiota composition of humanized gnotobiotic
mouse [8].
The non-secretor phenotype or the FUT2 genotype AA has
been associated with several diseases, for example with an
PLOS ONE | 1 April 2014 | Volume 9 | Issue 4 | e94863
increased risk for Crohn’s disease [9], type 1 diabetes [10],
experimental vaginal candidiasis [11], and urinary tract infections
[12]. Secretor status and carbohydrate availability in the intestine
have shown to influence the expansion of antibiotic treatment
related enteric pathogens, such as Salmonella and C difficile [13].
Further, Rausch et al. [14] showed that microbiota composition
differences related to the FUT2 polymorphism contributed to the
higher susceptibility of the non-secretors to Crohn’s disease. Thus,
knowledge of the influence of the FUT2 gene on microbiota
composition is highly relevant for the understanding of the
aetiology of diseases involving host-microbe interactions.
To determine the impact of the FUT2 polymorphism on the
intestinal microbiota, we analysed the faecal microbiota of non-
secretors and secretors by three methods; denaturing gradient gel
electrophoresis (DGGE), 16S rRNA gene pyrosequencing and
HITChip phylogenetic microarray analysis. The evidence from all
of these fundamentally different methods indicated that microbiota
compositions differed between the non-secretors and the secretors
as well as between the FUT2 genotypes. Further, the results
indicated that the soft clustering of microbiota composition into
our representation of enterotypes 1 and 3 was associated with the
secretor status/FUT2 genotype. The overall differences support
the conclusion that FUT2 is one of the host genetic factors
explaining individual variation in the microbiota composition.
Microbiota composition by DGGE analysis
The faecal samples of the 71 individuals were analysed by a
PCR-DGGE with several group-specific and universal bacterial
primers to profile the intestinal microbiota in the samples. The
RDA of the PCR-DGGE profiles revealed that the dominant
microbiota composition (ANOVA, F = 2.58, p,0.005) and the
composition of bifidobacteria (F = 4.07, p,0.005) and lactobacilli
(F = 2.35, p,0.01) differed between the 14 non-secretors and the
57 secretors as well as between the FUT2 genotypes AA (14), AG
(33), and GG (24) (Figure 1, S1, S2). Furthermore, a trend-like
pattern was observed for the difference in the Clostridium clusters
IV (F = 1.51, p,0.07) and XIVa (F = 1.88, p,0.08) between the
non-secretors and the secretors (Figure S1). The unconstrained
MDS clustering did not show clear clustering in regard to secretor
status or FUT2 genotype (Figure S3). Diversity estimates did not
differ between the non-secretor and the secretor phenotypes or the
FUT2 genotypes except for bifidobacteria. Bifidobacterial results
were reported previously [7]. The diversity estimates and
microbiota compositions evaluated by PCA and RDA (data not
shown) for a subset of 12 secretor samples included in the in-depth
microbiota profiling (see below), were comparable to all secretor
samples of the data set (n = 57).
Microbiota composition analysed by pyrosequencing
The microbiota association with the secretor status and FUT2
genotype was studied in more detail for the subset of 24 samples
(12 non-secretors, AA genotype and 12 secretors of which 5
carried genotype GG and 7 carried genotype AG) by pyrose-
quencing. Out of the 245 804 generated sequences, 127 352
sequences (52% of reads) passed the quality criteria. The average
total read number, Good’s coverage and the rarefaction curves
were similar between the non-secretors, secretors, and FUT2
genotypes (Table S1, Figure S4).
RDA based on the observed genera (ANOVA, F = 2.37,
p = 0.02) and OTUs (F = 1.34, p = 0.02) showed statistically
significant differences in the microbial composition between the
non-secretors and the secretors (Figure 2 and S5). Similar
microbiota differences in relation to secretor status was observed
in RDA with subsampled dataset (F = 1.93, p = 0.02) (Figure S6),
which standardize the sequencing coverage between the samples,
indicating that the differences were not due to variation in
sequence coverage. In addition, AMOVA indicated a statistical
significance for the difference between the non-secretors and the
secretors (p = 0.02). Between the FUT2 genotypes, a trend-like
difference (p = 0.08) was found by AMOVA and RDA on the
genera (F= 1.47, p = 0.09) (Figure 2). RDA based on the OTUs or
the subsampled dataset showed no significant differences between
the FUT2 genotypes (Figures S5 and S6). Neither did MDS show
clear clustering between the secretors and the non-secretors or
between the FUT2 genotypes (Figure S3).
To reveal the bacterial taxa contributing to the altered
microbiota composition, the relative abundances of the taxa were
compared in relation to the secretor status and the FUT2
genotypes. As expected, the microbiota composition varied greatly
in both the non-secretors and secretors (Figure. S7). At the phylum
level, the non-secretors, secretors and FUT2 genotypes shared
similar microbiota compositions, dominated by Firmicutes (on
average 93% of the total microbiota) (Figure S7). Only 4%, 2%
and 0.4% of the sequences were classified into Bacteroidetes,
Actinobacteria and Proteobacteria, respectively. Yet relative
abundances of the six bacterial genera, such as Clostridium, and
genera belonging to families Ruminococcaceae, and unclassified
Clostridiales varied notably between the non-secretor and the
secretors by both the ANOVA test and the indicator species
analysis (Figure 3). Several other genera, including the mucus-
degrader Akkermansia [15], showed a trend towards a lower relative
abundance in the non-secretors (ANOVA, F = 3.05, p = 0.09)
(Figure 3). The abundance of Akkermansia (13 sequences) and
several other taxa were close to the detection level with our
sequencing depth, and thus a single sample may contribute greatly
to the effect. The associations of genera with the FUT2 genotypes/
secretor status were weak, and their significances disappeared
when the p-values were corrected for the number of performed
statistical tests.
The average richness of genera was lower in the non-secretors
(on average 71 genera/sample) in comparison to the secretors (on
average 84 genera/sample) by c2m randomization test (p = 0.04),
which was verified with subsampled data (on average 58 and 69
genera/sample in the non-secretors and the secretors, p = 0.02).
The average richness of OTUs did not differ between the non-
secretors and the secretors in c2m test (1298 vs. 1498, p = 0.104).
At the genera level, mean inverse Simpson and Shannon diversity
indeces were lower in the non-secretors than in the secretors
(inverse Simpson: 9.0 vs. 11.8, ANOVA, F = 4.24, p = 0.05;
Shannon: 2.6 vs. 2.9, ANOVA, F = 4.54, p = 0.04), but these
border line differences were not verified using the subsampled or
OTU datasets. Diversity, average richness or rarefaction curves
(Figure S4) did not differ between the FUT2 genotypes, nor were
such differences detected in the rarefaction analysis based on
genera or OTUs between the non-secretors and the secretors
(Figure S4). In total, 215 bacterial genera were detected in the
whole dataset. No statistically significant co-occurrences were
identified in the analysis of pyrosequencing data.
Phylogenetic microarray analysis
The 24-sample subset was analysed additionally by the
phylogenetic microarray, the HITChip. The RDA on relative
abundances of HITChip level 2/genus-like taxa, showed signifi-
cant differences in microbiota compositions between the non-
secretors and the secretor samples (F = 1.99, p,0.03) as well as
between the FUT2 genotypes (F = 1.81, p,0.05) (Figure 4).
Intestinal Microbiota and FUT2
PLOS ONE | 2 April 2014 | Volume 9 | Issue 4 | e94863
Figure 1. RDA plots of dominant microbiota composition in the secretors (black) and the non-secretors (white) (A) and the
genotypes, AA (white), AG (grey) and GG (black) (B). The plots are based on the PCR-DGGE analysis with universal primers. The centroids of
each group are indicated by triangles. P-values show statistical significance in ANOVA test. The RDA plots of DGGE analysis with the specific bacterial
groups are shown in Figures S1 and S2.
Intestinal Microbiota and FUT2
PLOS ONE | 3 April 2014 | Volume 9 | Issue 4 | e94863
Similar results were obtained with level 3 taxa (species-like level)
(secretor status F = 1.51, p,0.06; genotypes F = 1.40, p,0.05)
(Figure S8). Moreover, unconstrained clustering by MDS based on
level 2 taxa showed clustering of the samples in relation to the
FUT2 genotypes/secretor status (Figure S3). The soft classification
of the samples into enterotypes, and the effect of the secretor
Figure 2. RDA plots of microbiota composition in the non-secretors (white) and the secretors (black) (A) and among
genotypes, AA (white), AG (grey) and GG (black) (B). Plots are calculated from the relative abundances of taxa obtained by the 16S rRNA gene
pyrosequencing. The centroids of each group are indicated by triangles. P-values show statistical significance in ANOVA test.
Intestinal Microbiota and FUT2
PLOS ONE | 4 April 2014 | Volume 9 | Issue 4 | e94863
status/genotype on the classification were assessed with HITChip
level 2 data. No samples belonging to the ET2 were present in our
dataset but 6 samples had microbiota composition resembling the
ET1 and 18 samples had composition resembling the ET3. The
dependency between secretor status/genotype and the soft
classification of samples into ET1 (Kruskal-Wallis; secretor status
(1) = 7.84, p = 0.005; genotype x
(2) = 7.86, p = 0.02) and ET3
(secretor status x
(1) = 7.52 p = 0.006; genotype x
(2) = 7.56,
p = 0.03) was statistically significant. The abundances of enter-
otype indicator species in the secretors, non-secretors and FUT2
genotypes are shown in Table S2.
Based on the HITChip hybridisation signals, a significant
reductions in the mean inverse Simpson and Shannon diversity
indices were evident for the non-secretors, when compared with
the secretor phenotype (ANOVA, Inverse Simpson index F = 13.4,
p = 0.001; Shannon index F = 14.47, p = 0.001) (Figure S9).
By HITChip, intestinal microbiota of both the non-secretors
and the secretors was dominated by Firmicutes (84%), followed by
Bacteroidetes (11%) and Actinobacteria (2.4%). The relative
abundances of several levels 1, 2, and 3 taxa, mainly belonging
to the dominant phyla Firmicutes, were significantly influenced by
the secretor phenotype and/or the FUT2 genotypes (Table S3, S4,
S5 and S6). The abundances of the taxa differed mainly between
the FUT2 genotype AA (non-secretor genotype) and the homo-
zygous FUT2 genotype GG, and less between the genotype AA
and the heterozygous genotype AG (Tables S3, S4, S5 and S6).
These included the taxa of bifidobacteria, B. angulatum,B.
catenulatum and Bifidobacterium spp., which were significantly more
abundant in the FUT2 genotype GG than in the genotype AA, a
non-secretor genotype (Table S6). The histogram indicated a clear
accumulation of significant p-values, increasing the evidence for
the association of the secretor status or the FUT2 genotype with
microbiota (Figure S10). Similarly to the sequencing data, the
associations of the taxa with the FUT2 genotype or the secretor
phenotype were rather weak in the HITChip analysis and the
correction for the number of performed statistical tests resulted in
the disappearance of the significant p-values.
The analysis of co-occurrences of taxa based on Spearman
correlation in the secretors and the non-secretors revealed that
seven bacterial taxa pairs co-occurred in the secretors, even when
applying a stringent threshold to the statistical significance (q-
values ,0.05) (Figure S11). In the non-secretors, a single positive
co-occurrence was observed (Figure S11). The correlations
between the microbial groups calculated as Pearson correlation
coefficients (on log transformed data) showed the same trends as
those with Spearman (data not shown), the only difference being
that fewer anti-correlations where detected using Pearson.
Especially at the higher level phylogeny, similar amounts of
connections were formed in both the secretors and the non-
Our findings based on the microbiota profiling with three
methods (PCR-DGGE, pyrosequencing and the HITChip)
showed that the composition of the intestinal microbiota was
associated with the secretor status and with the determinant of the
secretor status, the FUT2 gene polymorphism. The secretor
status/FUT2 appeared to be one of the host genetic features
contributing to the human microbiota and inter-individual
microbiota variation. This demonstrates that a single host gene
can have a significant effect on the intestinal microbiota
composition in healthy adults.
The human intestinal microbiota has recently been tentatively
clustered into three main types, called enterotypes, suggested to be
characterised by a few key genera [16]. The drivers of these
clusters, which are still under debate [17], [18], are not fully
known, but diet, environment and host genetics have been
proposed. Indeed, long-term diet has been reported to explain part
of the enterotypes [19] and immunity-related host gene GTPase
family M gene (IRGM) (SNP rs1747270) has been associated to
Figure 3. Mean relative abundances of the bacterial genera showing significant (ANOVA/indicator species analysis, p
0.05) or
trend-like differences (p
0.09) between the non-secretors (n = 12) and the secretors (n = 12) or between the
genotypes AA
(n = 12), AG (n = 7), GG (n = 5). The statistical significance is indicated for a comparison of the secretors/FUT2 genotypes AG and GG to the non-
secretors/FUT2 genotype AA. The taxa significantly differing in both indicator species analysis and ANOVA test are underlined.
Intestinal Microbiota and FUT2
PLOS ONE | 5 April 2014 | Volume 9 | Issue 4 | e94863
Figure 4. The RDA plots of microbiota composition in the non-secretors (white) and the secretors (black) (A) and among the
individuals with
genotypes, AA (white), AG (grey) and GG (black) (B). The RDA plots were based on genus-like/level 2 intensities of
HITChip analysis. The centroids of each group are indicated by triangles. P-values show statistical significance in ANOVA test.
Intestinal Microbiota and FUT2
PLOS ONE | 6 April 2014 | Volume 9 | Issue 4 | e94863
enterotype clustering (Quince et al, 2013). In the study of
microbiota in obese and lean monozygotic twins [3], showed that
twin pairs, even when discordant in terms of body mass index,
shared highly similar microbiota, and concluded that human
microbiota was strongly linked to the host genotype. Moreover,
about half of the genera representing the core microbiota of twins
(so called structural core) overlapped with the reported key genera
of enterotypes, indicating that genotypic factors may be drivers of
enterotype clustering [3]. We treated the enterotypes here as
examples of certain observable states in microbiota, since their
very existence and interpretation as true universal discrete
representations of microbiota are still under on-going research.
Nevertheless, using HITChip data, we evaluated the role of
secretor status, which was shown to associate with microbiota
composition in this study, as one potential driver of the enterotypes
clustering of human microbiome. Samples of this dataset belonged
to only two (ET1 and ET3) of the three reported enterotypes. We
resorted to a soft classification, in concordance with Huse et al.
[17] and Koren et al. [18], who have argued that the enterotypes
are more likely to form continuous than distinct clusters. An
analysis of the classification results indicated that the non-secretors
had a higher probability of belonging to ET1 and the secretors to
ET3, observed with a supervised classification method. Although
the secretor status did not exclusively explain the enterotype
classification, it indicated that secretor status supposedly together
with other factors, such as long-term diet [19] and IRGM [20]
may affect the intestinal microbiota composition among individ-
uals. Recently, several methodological aspects (eg. clustering
method and distance metric) were shown to affect enterotype
clustering strength [18]. This may also have influence by clustering
of Arumugam et al. [16], which our classification of the samples
was based on. Summarizing, this analysis provides further
evidence that microbiota and genotype, in this case secretor
status, are strongly linked.
Several autoimmune diseases such as the inflammatory bowel
disease (IBD) [9], [21], and type I diabetes [10] have been
associated with the FUT2 polymorphism. By studying microbiota
of IBD patients with known FUT2 genotypes Rausch et al. showed
that the FUT2 genotype could contribute to the susceptibility for
IBD through altered microbiota composition [14]. The present
study comparing the fecal microbiota in 14 non-secretors and 57
secretors showed that even healthy non-secretors and secretors
have differences in their intestinal microbiota. Similarly, differ-
ences in composition of mucosal microbiota between healthy non-
secretors and secretors were suggested in study by Rausch et al. on
the basis of only three healthy non-secretors [14]. Actually, we
observed that the bacteria belonging to Blautia et rel., Dorea
formicigenerans et rel., Ruminococcus gnavus et rel., and Clostridium
sphenoides et rel., were significantly more abundant in the non-
secretors than in the secretors. Interestingly, all these taxa have
been associated with IBS and/or IBD in several studies [22–24]
and may thus indicate that non-secretors have propensity for
intestinal aberrations. In addition to IBD, higher susceptibility to
e.g. coeliac disease [25] and diabetes type 1 [26] has been
associated with the dysbiosis of microbiota and in separate studies
also with non-secretor status [10], [27]. Based on these examples it
is tempting to speculate that the FUT2 genotype may be a relevant
factor that induces alterations in the microbiota composition and
plays a role in aetiology of the several diseases involving host-
microbiota interactions.
Intestinal bacterial species are commonly equipped with varying
glycan degrading enzymes and abilities to access mucus glycans,
allowing them to target and benefit from the complex glycan
structures, such as the ABO histo-blood group antigens, present in
the intestine [28]. The interaction between the bacteria and the
fucosylated glycan antigens of the host has been elegantly
demonstrated for different Bacteroides species [29]. The two
Bacteroides taxa co-occurring in the secretors in this study, B.
plebeius et rel. and B. fragilis et rel., have a very high number of
genes coding for a-L-fucosidase (.10 genes/genome) and (8–10
genes/genome) b-galactosidase enzymes, which potentially en-
ables them to utilize of ABO glycan structures and take advantage
of this constant source of growth substrates on intestinal mucosa.
Enzymes for the degradation of the ABO glycan structures have
also been identified in Bifidobacterium species and the mucus
degrading genera Ruminococcus,Clostridium and Akkermansia [30–35],
which were among the significantly abundant bacteria in the
secretors in this study. In secretors, these genera co-occurred with
bacteria (Sporobacter, Lachnobacillus, and Oxalobacter), which do not
have enzymes for the ABO antigen degradation in their genomes.
For example Akkermansia have a-L-fucosidases, and b-galactosi-
dases [35] allowing them to remove the terminal and other sugar
moieties of the ABO histo-blood group antigens, whereas
Oxalobacter, unable to grow on sugars [36], could benefit from
end products of Akkermansia. The co-occurrences indicated that the
ABO histo-blood group antigens may benefit through food-web
also bacteria incapable of ABO degradation. Besides growth
substrates, the ABO histo-blood group antigens provide adhesion
receptors for bacteria as described for several pathogens such as
Helicobacter pylori [37] and Staphylococcus aureus [38] as well as for
commensal microbes such as Lactobacillus gasseri [39]. In addition to
the secretor status/FUT2, other genetic factors are likely to
influence the intestinal microbiota composition, but the informa-
tion is still limited. Recent report showed that NOD2 genotypes
have an influence also on human microbiome [40]. Several
metabolism and immunity related genes, such as leptin, NOD and
TLR encoding genes, have shown to co-vary with the mouse
microbiome [41].
We showed the difference in the dominant microbiota
composition between the secretors and the non-secretors by three
profiling techniques. We have previously indicated that in general
the HITChip and the pyrosequencing produce comparable results,
but the resolution of HITChip has been reported to be higher for
intestinal bacterial taxa with low abundance than the resolution of
routine pyrosequencing [42], [43]. As an example of the impact of
a microbiota profiling technique, we observed different levels of
changes in the bifidobacterial population by applying different
methods. Our previous results by PCR-DGGE and qPCR
methods showed that the composition and diversity of intestinal
bifidobacteria was strongly associated with the secretor status [7].
Indeed, the abundances of several bifidobacterial taxa differed
between the non-secretor genotype (AA) and the secretor genotype
GG (but not AG) according to the HITChip, confirming our
previous results on the association of bifidobacteria with the FUT2
[7]. However, no difference was detected in the diversity of
bifidobacteria. The bifidobacterial 16S rRNA genes are closely
related, which may cause cross-hybridization and thus, the
masking of some differences between non-secretors and secretors.
In general, the abundance of Firmicutes, Bacteroidetes, and
Actinobacteria differed between Hitchip and pyrosequencing
methods applied in this study (84%, 11% and 2,4% by HITChip;
93%, 4% and 2% by pyrosequencing, respectively). The low
detection frequency of Bacteroidetes and Actinobacteria by
pyrosequencing may be due forward primer F27, which does
not allow optimal recovery of Bacteroidetes and Actinobacteria in
comparison to Firmicutes. The same DNA extraction method was
applied for both methods. A low number of bifidobacteria
sequences (2%) by pyrosequencing also indicates that more
Intestinal Microbiota and FUT2
PLOS ONE | 7 April 2014 | Volume 9 | Issue 4 | e94863
sequencing effort would have been necessary in order to reveal the
bifidobacteria-related differences. The recent mouse study of
Kashyap et al. [8] showed that the FUT2/secretor status
associated changes of intestinal microbiota were diet-dependent.
In the present study, information on the dietary habits was not
collected and the effect of diet could thus not be investigated.
This study shows that non-secretors have an altered intestinal
microbiota community and strengthens the evidence indicating
that the FUT2 polymorphism influence on the intestinal micro-
biota. The secretor status is one of the drivers of host-associated
variation in the microbiota composition and, together with other
factors it may contribute to the clustering of microbiota into
Materials and Methods
To study the association of FUT2 on intestinal microbiota of
healthy non-secretor and secretor individuals, faecal and blood
samples were collected from 71 adults of European descent, of
whom 14 were non-secretors (FUT2 SNP FUT2 rs601338
genotype AA, 12 Lewis a phenotype, 2 Lewis negative phenotype)
and 57 were secretors (33 had genotype AG and 24 genotype GG,
all had Lewis b phenotype). Based on the exclusion criteria
confirmed by interviewing the volunteers, subjects with clinically
diagnosed intestinal diseases or regular intestinal disturbances or
antibiotic therapy two months prior to the sampling were
excluded. The participants consumed mixed diet without any
restrictions, except that the consumption of probiotics was
restricted one week before faecal sampling. No other dietary
information was collected. The study had the approval of the
ethical committee of the Helsinki University Hospital and all the
subjects signed a written informed consent. The age, gender and
ABO distribution of the participants is shown in [7]. The faecal
samples for the microbiota profiling 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 antico-
agulated peripheral blood samples by centrifugation and stored at
280uC until DNA extraction.
Determination of secretor status and genotype
Human DNA was extracted from the buffy coat preparations
using the QIAamp DNA Blood Mini Kit (QIAGEN Inc, CA, US).
ABO blood groups were determined by serology and by
genotyping the FUT2 SNP rs601338 as described in Wacklin et
al. [7].
Microbiota profiling
DNA for the DGGE analysis was extracted from the faecal
samples using the FastDNA SPIN Kit for Soil and the FastPrep
Instrument (MP Biomedicals, CA, USA) as described by Wacklin
et al. [7]. DNA for pyrosequencing and HITChip analyses was
extracted using the method of Apajalahti et al. [44] as described in
Ma¨kivuokko et al. [45].
DGGE. All 71 faecal samples were analysed by PCR-DGGE
using universal bacterial primers, and bifidobacteria, lactobacilli,
B. fragilis group, Clostridium cluster IV, and cluster XIVa specific
primers using Dcode universal mutation detection system (Bio-
Rad, CA, USA) as described in [45]. Despite several attempts,
PCR was unsuccessful for 7 samples with bifidobacteria primers,
two samples with B. fragilis specific primers, and one sample with
Clostridium cluster IV and lactobacilli primers. The DGGE gels
were analysed and principle component analyses (PCA) were
performed for the secretors and the non-secretor as well as for the
individuals with FUT2 genotypes AA, AG and GG with
Bionumerics (Applied Numerics) as described by Wacklin et al.
[7]. Diversity estimates, redundancy analysis (RDA) and Multidi-
mensional scaling (MDS) with Bray-Curtis distance and statistical
tests (ANOVA) were based on intensity matrixes of the secretors
and the non-secretor, and the individuals with different FUT2
genotype and analysed using R 2.14.2 (R Development Core
team, 2012) and its extension package vegan version 2.0–3 [46].
The 16S rRNA gene pyrosequencing. For in-depth profil-
ing of microbiota, the bar-coded pyrosequencing method was
applied to 24-sample subset, containing the samples of 12 non-
secretors (AA genotype carriers) and 12 secretors (5 GG or 7 AG
genotype carriers). The subset included all non-secretor samples,
except those two Lewis negative individuals. The secretor
individuals were matched with the non-secretors according to
their ABO blood group, age, and gender. The V1-V3 region of the
16S rRNA gene was PCR amplified in three replicates using a
universal bacterial primer pair (F27 59-AGAGTTT-
TACCGCGGCTGCTGG-39). The PCR products were quanti-
fied and pooled in equal amounts and sequenced using a Genome
sequencer FLX Titanium (Roche) in the Institute of Biotechnology
(University of Helsinki, Finland). The raw sequences (245 806)
were trimmed using Mothur v.1.19.0 [47]. The sequences with
averaged quality score over 30, length over 300 bases, exact
matches to barcode tags and the forward primer, no ambiguous
bases, no homopolymers longer than 8 bp, and which were non-
chimeric were included in the analysis. The high-quality sequences
were binned to the samples according to barcode tags, and into
operational taxonomic units (OTUs) applying the furthest
neighbor algorithm, threshold of certainty over 60%, and
sequence similarity over 97%. The sequences were classified into
bacterial taxa using the nearest neighbor method and SILVA
release 111 as a reference database [48] and the classify.seqs tool
implemented in Mothur [47]. The diversity and richness in the
non-secretors and the secretors as well as in the individuals with
different FUT2 genotypes was estimated with several methods:
The average species richness was assessed with the R extension
package rich version 0.1 [49] using a c2m randomization test with
999 randomisations [50]. Inverse Simpson and Shannon diversity
indices were calculated in R extension package vegan. Indicator
species analysis [51] using untransformed data was carried out in
R using the extension package labdsv version 1.4.-1 [52].
Abundances of OTUs and taxa between the non-secretors and
the secretors as well as between the different FUT2 genotypes were
Hellinger transformed [51], [53] and used for RDA with 999
permutations and for MDS applying Bray-Curtis distance using R
with package vegan. An analysis of molecular variance (AMOVA)
[54], which is a distance-based, non-parametric approach based
on permutations to derive the sum of squares and a pseudo F
statistic, was conducted in R using the package pegan [55] for the
non-secretors, the secretors and the different FUT2 genotypes.
Additionally, RDA, MDS and diversity estimates were analysed
using a 2500 sequence subsample from each sample (total 60 000
sequences). Random subsampling was performed in Mothur.
HITChip. A phylogenetic microarray analysis by the Human
Intestinal Tract (HIT) chip [56], which contains probes for over
1000 known intestinal species and allows also the detection of the
taxa present in low numbers [22], was applied to the 24-sample
subset. The hybridisation of the samples was performed two times
with a reproducibility of .98% (assessed by Pearson’s correlation
coefficient). The HITChip analysis was performed as earlier
described in [56]. In short, the data was quality controlled, within-
Intestinal Microbiota and FUT2
PLOS ONE | 8 April 2014 | Volume 9 | Issue 4 | e94863
array spatial normalization was performed, outliers were removed,
and quantile normalisation was applied to the results of two
hybridisations of the same sample [56]. Min-max normalization as
described in [56] was used for the calculation of the inversed
Simpson and Shannon diversity indices on level 3 data in R
package vegan. For all the other analyses between-array normal-
ization was performed with quantile normalization [57]. The
differences in each bacterial group between the non-secretors and
the secretors as well as between the different FUT2 genotypes were
analysed with linear models and ANOVA tests, transforming the
array intensities into logarithmic scale. RDA and MDS analyses
were based on Hellinger transformed data and analysed similarly
to pyrosequencing data. The enterotype classification was
performed based on the HITChip data of the samples, combined
with the HITChip data for all the MetaHIT samples (n = 124)
classified originally by Arumugam et al. [16], which was used as a
training set. Three distinct groups, so called Enterotypes, were
found [16]. The abundances of genera in the non-secretor and
secretor samples were compared against a set of predictive models
(ntree = 1000), which were built while using the training set, in a
random forest approach [58]. Probability scores of the classifica-
tion were estimated from random forest models from the training
set. The association of enterotypes with secretor status and
genotypes was measured with Kruskal-Wallis test using the
probability of belonging to a certain enterotype and the host
secretor status/FUT2 genotype. Co-occurrence networks were
built using Spearman correlations based on relative abundances
and Pearson correlations based on log transformed relative
abundances of the level 2 taxa, using thresholds of r.0.7, relative
abundance of the taxa .0.1%, and presence in .50% of the non-
secretors or secretors. The networks were visualized using the
Gephi network visualization and exploration platform [59]. The q-
value threshold for significant co-occurrence was set at 0.05. The
original HITChip data that was used for our analysis is in table S7.
Supporting Information
Figure S1 RDA plots of bifidobacteria, lactobacilli,
Clostridium cluster IV and XIVa and Bacteroides fragilis
populations in the non-secretors (white) and the secre-
tors (black). The RDA analyses were based on PCR-DGGE
profiles of the samples. The centroids of each group are indicated
by triangles. P-values show statistical significance in ANOVA test.
Figure S2 RDA plots of bifidobacteria, lactobacilli,
Clostridium cluster IV and XIVa and Bacteroides fragilis
populations in the individuals with FUT2 genotypes AA
(white), AG (grey) and GG (black). The RDA analysis based
on the PCR-DGGE profiles of the samples. The centroids of each
group are indicated by triangles. P-values show statistical
significance in ANOVA test.
Figure S3 MDS plots of intestinal microbiota composi-
tions in the individuals with FUT2 non-secretor geno-
type AA (white), secretor genotype AG (grey) and GG
(black) based on band intensities of the DGGE analysis
(A), on abundances of genera by pyrosequencing (B), on
abundances of level 2 taxa by HITChip (C), on abun-
dances of OTUs by pyrosequencing (D) and on abun-
dances of genera in subsampled pyrosequencing data.
Figure S4 Rarefaction curves for the non-secretor and
the secretor samples based on detected OTUs using 0.97
similarity threshold (A) and on the genera (B). Blue line =
non-secretors/FUT2 genotype AA, red line = FUT2 genotype
AG, green line = FUT2 genotype GG.
Figure S5 RDA plots based on the OTUs detected in the
non-secretors (white) and the secretors (black) (A) and
among the FUT2 genotypes, AA (white), AG (grey) and
GG (black) (B). Threshold of 97% similarity was used for
clustering of the sequences into OTUs. The centroids of each
group are indicated by triangles. P-values show statistical
significance in ANOVA test.
Figure S6 RDA plots of microbiota compositions at
genus level in the non-secretors (white) and the secretors
(black) (A) and among the FUT2 genotypes, AA (white),
AG (grey) and GG (black) (B). Plots are based on random
subsample of the pyrosequencing data set (2500 sequences per
sample, total 60 000 sequences). The centroids of each group are
indicated by triangles. P-values show statistical significance in
ANOVA test.
Figure S7 Relative abundances of bacterial phyla in the
secretors/FUT2 genotypes AG (n = 7) and GG (n = 5) and
in the non-secretors/genotype AA (n = 12). The abundances
were based on the 16S rRNA gene pyrosequencing.
Figure S8 RDA plots of intestinal microbiota composi-
tions in the non-secretors (white) and the secretors
(black) (A) and among individuals with the FUT2
genotypes AA (white), AG (grey) and GG (black) (B). Plots
were based on Hellinger transformed level 3/species-like taxa
obtained by HITChip analysis. The triangles indicate centroids of
the study groups. P-values show statistical significance in ANOVA
Figure S9 Bacterial diversity in the individuals with
FUT2 non-secretor genotype AA (n = 12), and with
secretor genotypes AG (n = 7) and GG (n = 5). The results
were based on the HITChip analysis.
Figure S10 ANOVA p-value histogram for level 3/
species –like level differences between the non-secretor
and the secretors (A) and different FUT2 genotypes (B) in
HITChip analysis.
Figure S11 Co-occurrences based on the relative abun-
dances of the level 2/genus-like level bacterial taxa for
the secretors (A) and the non-secretors (B). Co-occurrences
with r.0.7, relative abundance of the bacterial group .0.1%,
and present .50% of the non-secretor or secretor samples are
shown. Light green indicate positive and yellow negative co-
occurrences. Statistically significant positive and negative co-
occurrences (q-value ,0.05) are shown in blue and red,
Table S1 The number of the 16S rRNA gene sequences
obtained from the non-secretor samples, the secretor
samples and the samples with different FUT2 genotypes
by pyrosequencing.
Intestinal Microbiota and FUT2
PLOS ONE | 9 April 2014 | Volume 9 | Issue 4 | e94863
Table S2 Average relative abundances (%) of enterotype
indicator species in the non-secretors, the secretors and
the individuals with FUT2 genotypes AA, AG or GG
belonging to the enterotype 1 (ET1) or 3 (ET3).
Table S3 The bacterial level 1 and 2 taxa, whose
relative abundances were significantly different (p-value
0.05 in ANOVA) between the non-secretor (NSS) and
the secretor (SS) individuals by the HITChip analyses.
Table S4 The bacterial level 1 and 2 taxa, whose were
relative abundances were significantly different between
the samples with different FUT2 genotypes based on
HIT Chip analyses.
Table S5 The bacterial taxa at the level 3/species-like
level, whose relative abundances were significantly
different (p-value
0.05 in ANOVA) between the non-
secretor (NSS) and the secretor (SS) individuals by the
HITChip analysis.
Table S6 The bacterial taxa at the level 3/species-like
level, whose relative abundances were significantly
different (p-value
0.05 in ANOVA) between the non-
secretor genotype AA and the secretor genotypes AG and
GG by the HITChip analyses.
Table S7 Original HITChip data.
The volunteers are thanked for their sample donations. Ms. Sisko
Lehmonen and Ms. Paula Salmelainen are thanked for skilful technical
Author Contributions
Conceived and designed the experiments: HM JM PW MR-S WdV.
Performed the experiments: PW NA MR-S PKL LP. Analyzed the data:
PW JN JT ST JM WdV PKL NA. Wrote the paper: PW JM ST JT JN
WdV. Designed and conducted the collection of the faecal samples: HM
1. Rajilic-Stojanovic M, Heilig HG, Tims S, Zoetendal EG, de Vos WM (2013).
Long-term monitoring of the human intestinal microbiota composition. Environ
Microbiol 15: 1146–1159.
2. Zoetendal EG, Akkermans ADL, Akkermans-van Vliet WM, de Visser, J. Arjan
G M., et al. (2001). The Host Genotype Affects the Bacterial Community in the
Human Gastronintestinal Tract. Microbial Ecology in Health and Disease 13:
3. Tims S, Derom C, Jonkers DM, Vlietinck R, Saris WH, et al. (2013). Microbiota
conservation and BMI signatures in adult monozygotic twins. ISME J 7: 707–
4. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, et al.
(2009). A core gut microbiome in obese and lean twins. Nature 457: 480–484.
5. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, et al.
(2012). Human gut microbiome viewed across age and geography. Nature 486:
6. de Vos WM, de Vos EA (2012). Role of the intestinal microbiome in health and
disease: from correlation to causation. Nutr Rev 70: S45–56.
7. 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: e20113.
8. Kashyap PC, Marcobal A, Ursell LK, Smits SA, Sonnenburg ED, et al. (2013).
Genetically dictated change in host mucus carbohydrate landscape exerts a diet-
dependent effect on the gut microbiota. Proc Natl Acad Sci U S A 110: 17059–
9. McGovern DP, Jones MR, Taylor KD, Marciante K, Yan X, et al. (2010).
Fucosyltransferase 2 (FUT2) non-secretor status is associated with Crohn’s
disease. Hum Mol Genet 19: 3468–3476.
10. Smyth DJ, Cooper JD, Howson JM, Clarke P, Downes K, et al. (2011). FUT2
nonsecretor status links type 1 diabetes susceptibility and resistance to infectio n.
Diabetes 60: 3081–3084.
11. Hurd EA, Domino SE (2004). Increased susceptibility of secretor factor gene
Fut2-null mice to experimental vaginal candidiasis. Infect Immun 72: 4279–
12. Sheinfe ld J, Schaeffer AJ, Cordon-Cardo C, Rogatko A, et al. (1989).
Association of the Lewis blood-group phenotype with recurrent urinary tract
infections in women. N Engl J Med 320: 773–777.
13. Ng KM, Ferreyra JA, Higginbottom SK, Lynch JB, Kashyap PC, et al.(2013)
Microbiota-liberated host sugars facilitate post-antibiotic expansion of enteric
pathogens. Nature 502: 96–99.
14. Rausch P, Rehman A, Kunzel S, Hasler R, Ott SJ, et al. (2011). Colonic
mucosa-associated microbiota is influenced by an interaction of Crohn disease
and FUT2 (Secretor) genotype. Proc Natl Acad Sci U S A 108: 19030–1 9035.
15. Belzer C, de Vos WM (2012). Microbes inside—from diversity to function: the
case of Akkermansia. ISME J 6: 1449–1458.
16. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, et al. (2011).
Enterotypes of the human gut microbiome. Nature 473: 174–180.
17. Huse SM, Ye Y, Zhou Y, Fodor AA (2012). A core human microbiome as
viewed through 16S rRNA sequence clusters. PLoS One 7: e34242.
18. Koren O, Knights D, Gonzalez A, Waldron L, Segata N, et al. (2013). A Guide
to enterotypes across the human body: Meta-analysis of microbial community
structures in human microbiome datasets. PLoS Comput Biol 9: e1002863.
19. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, et al. (2011). Linking
long-term dietary patterns with gut microbial enterotypes. Science 334: 105–
20. Quince C, Lundin EE, Andreasson AN, Greco D, Rafter J, et al. (2013). The
impact of Crohn’s disease genes on healthy human gut microbiota: a pilot study.
Proc Natl Acad Sci U S A 110: 654–659.
21. Miyoshi J, Yajima T, Okamoto S, Matsuoka K, Inoue N, et al. (2011). Ectopic
expression of blood type antigens in inflamed mucosa with higher incidence of
FUT2 secretor status in colonic Crohn’s disease. J Gastroenterol 46: 1056–1063.
22. Rajilic-Stojanovic M (2007). Diversity of human gastrointestinal microbiota.
Novel perspestives from high throughput analyses. Wageningen: Wageningen
University and Research Centre. 214 p.
23. Rajilic-Stojanovic M, Biagi E, Heilig HG, Kajander K, Kekkonen RA, et al.
(2011). Global and deep molecular analysis of microbiota signatures in fecal
samples from patients with irritable bowel syndrome. Gastroenterology 141:
24. Willing BP, Dicksved J, Halfvarson J, Andersson AF, Lucio M, et al. (2010). A
pyrosequencing study in twins shows that gastrointestinal microbial profiles vary
with inflammatory bowel disease phenotypes. Gastroenterology 139: 1844–1854.
25. Sanz Y, De Pama G, Laparra M (2011). Unraveling the ties between celiac
disease and intestinal microbiota. Int Rev Immunol 30: 207–218.
26. Wen L, Ley RE, Volchkov PY, Stranges PB, Avanesyan L, et al. (2008). Innate
immunity and intestinal microbiota in the development of Type 1 diabetes.
Nature 455: 1109–1113.
27. Parmar AS, Alakulppi N, Paavola-Sakki P, Kurppa K, Halme L, et al. (2012).
Association study of FUT2 (rs601338) with celiac disease and inflammatory
bowel disease in the Finnish population. Tissue Antigens 80: 488–493.
28. Koropatkin NM, Cameron EA, Martens EC (2012). How glycan metabolism
shapes the human gut microbiota. Nat Rev Microbiol 10: 323–335.
29. Marcobal A, Barboza M, Sonnenburg ED, Pudlo N, Martens EC, et al. (2011).
Bacteroides in the infant gut consume milk oligosaccharides via mucus-
utilization pathways. Cell Host Microbe 10: 507–514.
30. Anderson KM, Ashida H, Maskos K, Dell A, Li SC, Li YT (2005). A clostridial
endo-beta-galactosidase that cleaves both blood group A and B glycotopes: the
first member of a new glycoside hydrolase family, GH98. J Biol Chem 280:
31. Hata DJ, Smith DS (2004). Blood group B degrading activity of Ruminococcus
gnavus alpha-galactosidase. Artif Cells Blood Substit Immobil Biotechnol 32:
32. Henry S, Oriol R, Samuelsson B (1995). Lewis histo-blood group system and
associated secretory phenotypes. Vox Sang 69: 166–182.
33. Hoskins LC, Boulding ET (1976). Degradation of blood group antigens in
human colon ecosystems. I. In vitro production of ABH blood group-degrading
enzymes by enteric bacteria. J Clin Invest 57: 63–73.
34. Katayama T, Sakuma A, Kimura T, Makimura Y, Hiratake J, et al. (2004).
Molecular cloning and characterization of Bifidobacterium bifidum 1,2-alpha-L-
fucosidase (AfcA), a novel inverting glycosidase (glycoside hydrolase family 95).
J Bacteriol 186: 4885–4893.
35. van Passel MW, Kant R, Zoetendal EG, Plugge CM, Derrien M, et al. (2011).
The genome of Akkermansia muciniphila, a dedicated intestinal mucin
degrader, and its use in exploring intestinal metagenomes. PLoS One 6: e16876.
Intestinal Microbiota and FUT2
PLOS ONE | 10 April 2014 | Volume 9 | Issue 4 | e94863
36. Stewart CS, Duncan SH, Cave DR (2004). Oxalobacter formigenes and its role
in oxalate metabolism in the human gut. FEMS Microbiol Lett 230: 1–7.
37. Boren T, Falk P, Roth KA, Larson G, Normark S (1993). Attachment of
Helicobacter pylori to human gastric epithelium mediated by blood group
antigens. Science 262: 1892–1895.
38. Nurjadi D, Lependu J, Kremsner PG, Zanger P (2012). Staphylococcus aureus
throat carriage is associated with ABO-/secretor status. J Infect 65: 310–317.
39. Uchida H, Kinoshita H, Kawai Y, Kitazawa H, Miura K, et al. (2006).
Lactobacilli binding human A-antigen expressed in intestinal mucosa. Res
Microbiol 157: 659–665.
40. Rehman A, Sina C, Gavrilova O, Ha¨ sler R, Ott S, et al. (2011). Nod2 is essential
for temporal development of intestinal microbial communities. Gut 2011 60:
41. Spor A, Koren O, Ley R (2011). Unravelling the effects of the environment and
host genotype on the gut microbiome. Nat Rev Microbiol 9: 279–290.
42. Claesson, O’Sullivan O, Wang Q, Nikkila¨ J, Marchesi JR, et al. (2009).
Comparative Analysis of Pyrosequencing and a Phylogenetic Microarray for
Exploring Microbial Community Structures in the Human Distal Intestine.
PLoS ONE 4(8): e6669.
43. van den Bogert B, de Vos WM, Zoetendal EG, Kleerebezem M (2011).
Microarray analysis and barcoded pyrosequencing provide consistent microbial
profiles depending on the source of human intestinal samples. Appl Environ
Microbiol 77: 2071–2080.
44. Apajalahti JH, Sa¨ rkilahti LK, Ma¨ki BR, Heikkinen JP, Nurminen PH, et al.
(1998). Effective recovery of bacterial DNA and percent-guanine-plus-cytosine-
based analysis of community structure in the gastrointestinal tract of broiler
chickens. Appl Environ Microbiol 64: 4084–4088.
45. Ma¨ kivuokko H, Lahtinen SJ, Wacklin P, Tuovinen E, Tenkanen H, et al. (2012).
Association between the ABO blood group and the human intestinal microbiota
composition. BMC Microbiol 12: 94.
46. Oksanen J, Blanchet FG, Kindt R, Legendre P, O’Hara RB, et al. (2012).
Vegan: Community Ecology Package. R package, version 2.0–3.
47. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, et al. (2009).
Introducing mothur: open-source, platform-independent, community-supported
software for describing and comparing microbial communities. Appl Environ
Microbiol 75: 7537–7541.
48. Pruesse E, Quast,C, Knittel K, Fuchs BM, Ludwig W, et al. (2007). SILVA: a
comprehensive online resource for quality checked and aligned ribosomal RNA
sequence data compatible with ARB Nucl Acids Res 35: 7188–7196.
49. Rossi J (2011). Rich: Species richness estimation and comparison. R package
version 0.1.
50. Manly BFJ (1997). Randomization and Monte Carlo methods in biology.
London: Chapman & Hall. 399 p.
51. Dufrene M, Legendre P (1997). Species Assemblages and Indicator Species: The
Need for a Flexible Asymmetrical Approach. Ecol Monogr 67: 345–366.
52. Roberts DW (2010). Labdsv: Ordination and Multivariate Analysis for Ecology.
R package version 1.4–1.
53. Legendre P, Gallagher E (2001). Ecologically meaningful transformations for
ordination of species data. Oecologia 129: 271–280.
54. Excoffier L, Smouse PE, Quattro JM (1992). Analysis of molecular variance
inferred from metric distances among DNA haplotypes: application to human
mitochondrial DNA restriction data. Genetics 131: 479–491.
55. Paradis E (2010). Pegas: an R package for population genetics with an
integrated-modular approach. Bioinformatics 26: 419–420.
56. Rajilic-Stojanovic M, Heilig HG, Molenaar D, Kajander K, Surakka A, et al.
(2009). Development and application of the human intestinal tract chip, a
phylogenetic microarray: analysis of universally conserved phylotypes in the
abundant microbiota of young and elderly adults. Environ Microbiol 11: 1736–
57. Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003). A comparison of
normalization methods for high density oligonucleotide array data based on
variance and bias. Bioinformatics 19: 185–193.
58. Breiman L (2001). Random Forests. Mach Learn 45: 5–32.
59. Bastian M, Heymann S, Jacomy M (2009). Gephi: an open source software for
exploring and manipulating networks. International AAAI Conference on
Weblogs and Social Media. Available:
Intestinal Microbiota and FUT2
PLOS ONE | 11 April 2014 | Volume 9 | Issue 4 | e94863
... Additionally, the fecal microbiota of nonsecretor rs601338 AA subjects were found to carry a decreased abundance of probiotic Bifidobacteria, a genus capable of producing immunoregulatory short chain fatty acids (SCFA) and promoting gut barrier integrity critical for preventing commensal-induced autoimmunity (Figure 2). 66 FUT2 is an interesting example of genetics determining the ability for environmental factors to potentially contribute to T1D. ...
Full-text available
The conceptual basis for a genetic predisposition underlying risk for type 1 diabetes (T1D) development predates modern human molecular genetics. Over half of genetic risk has been attributed to the human leukocyte antigen (HLA) class II gene region and the insulin (INS) gene locus – both thought to confer direction of autoreactivity and tissue specificity. Notwithstanding, questions still remain regarding the functional contributions of a vast array of minor polygenic risk variants scattered throughout the genome that likely influence disease heterogeneity and clinical outcomes. Herein, we summarize the available literature related to the T1D‐associated coding variants defined at the time of this review, for the genes PTPN22,IFIH1,SH2B3,CD226,TYK2,FUT2,SIRPG,CTLA4,CTSH, and UBASH3A. Data from genotype‐selected human cohorts are summarized, and studies from the non‐obese diabetic (NOD) mouse are presented to describe the functional impact of these variants in relation to innate and adaptive immunity as well as β‐cell fragility, with expression profiles in tissues and peripheral blood highlighted. Each variant’s contribution to progression through T1D staging, including environmental interactions, are discussed with consideration of how their respective protein products may serve as attractive targets for precision‐medicine based therapeutics to prevent or suspend T1D development.
... However, since the FUT2 gene also affects the fucosylation of the intestinal mucin, an effect was to be expected on adult microbiome. This was indeed observed in a study with Finnish adults [95] but not in a well-studied UK cohort [96]. However, the impact of antibiotics or other confounders may have affected the outcome of the latter study. ...
Full-text available
It is known for more than 100 years that the intestinal microbes are important for the host's health and the last decade this is being intensely studied with a focus on the mechanistic aspects. Among the fundamental functions of the intestinal microbiome are the priming of the immune system, the production of essential vitamins and the energy harvest from foods. By now, several dozens of diseases, both intestinal and non-intestinal related, have been associated with the intestinal microbiome. Initially, this was based on the description of the composition between groups of different health status or treatment arms based on phylogenetic approaches based on the 16S rRNA gene sequences. This way of analysis has mostly moved to the analysis of all the genes or transcripts of the microbiome i.e. metagenomics and meta-transcriptomics. Differences are regularly found but these have to be taken with caution as we still do not know what the majority of genes of the intestinal microbiome are capable of doing. To circumvent this caveat researchers are studying the proteins and the metabolites of the microbiome and the host via metaproteomics and metabolomics approaches. However, also here the complexity is high and only a fraction of signals obtained with high throughput instruments can be identified and assigned to a known protein or molecule. Therefore, modern microbiome research needs advancement of existing and development of new analytical techniques. The usage of model systems like intestinal organoids where samples can be taken and processed rapidly as well as microfluidics systems may help. This review aims to elucidate what we know about the functionality of the human intestinal microbiome, what technologies are advancing this knowledge, and what innovations are still required to further evolve this actively developing field.
... For example, the family Christensenellaceae, which are the most heritable bacteria in the study of Goodrich et al. in human, show a heritability of 0.39, while other studies reported an heritability of 0.62 for this family [48,49]. Candidate genes in human that might be associated with Christensenellaceae abundance, such as ILR23 or FUT2,were also identi ed [50,51]. In chicken, studies identi ed differences in microbiota composition between genetic lines, for instance in two chicken lines that differ in their susceptibility to bacterial infections [52] or in two divergent genetic lines for body weight [53]. ...
Full-text available
Background Salmonella Enteritidis (SE) is one of the major causes of human foodborne intoxication through the consumption of contaminated poultry products. Genetic selection of animals more resistant to Salmonella carriage and the modulation of gut microbiota are two promising ways of decreasing individual Salmonella carriage. This study aims to identify the main genetic and microbial factors controlling the individual levels of Salmonella carriage in chickens (Gallus gallus) in controlled experimental conditions. Two-hundred and forty animals from the White Leghorn inbred lines, N and 61, were infected by SE at 7 days of age. After infection, animals were kept in isolators to reduce the recontamination of birds by Salmonella. Caecal contents were sampled at 12 days post-infection and used for DNA extraction. Microbiota DNA was used to measure individual counts of SE by digital PCR and to determine the bacterial taxonomic composition through a 16S rRNA gene high-throughput sequencing approach. Results Results confirmed that the N line is more resistant to Salmonella carriage than the 61 line, and that intra-line variability is higher for the 61 line. Furthermore, the 16S analysis showed strong significant differences in microbiota taxonomic composition between the two lines. Out of 617 Operational Taxonomic Units (OTUs), over 390 were differentially abundant between the two lines. Furthermore, within the 61 line, we found a difference in the microbiota taxonomic composition between high and low Salmonella carriers, with 39 differentially abundant OTUs. Finally, via metagenome functional prediction based on 16S data, we identified several metabolic pathways potentially associated to microbiota taxonomic differences (e.g. butyrate metabolism) between high and low carriers. Conclusions Overall, this study demonstrates that the caecal microbiota composition of the N and 61 lines is influenced by the host genetics, which could be one of the reasons why these lines differ for their Salmonella carriage in experimental infection conditions.
... Citizen science initiatives, such as the American Gut Project, can help evaluate the effects of interactions among host genetics, diet, and environment on microbiome composition on larger scales. Analyzing patient sequencing data along with selfreported metadata may also help elucidate possible associations between the non-secretor phenotype and increased risk of certain diseases, including Crohn's disease, type 1 diabetes, vaginal candidiasis, and urinary tract infections (13). ...
Full-text available
The microbiome has sparked interest in both research and clinical applications in the past few decades. Public interest has led to the development of numerous interventions targeting the microbiome, including the use of probiotics, prebiotic fibers, and in some cases, fecal microbiota transplantation. Personalized approaches to microbiome modulation aim to minimize potential harm and enhance efficacy. The effects of dietary fiber, probiotics, and FMT may all depend on an individual’s baseline microbiome composition, and taking these factors into account can allow for more precise interventions that prevent potential adverse effects. Collection of citizen science data on larger scales may help further elucidate the complex crosstalk among genetic, environmental, and microbial factors in order to continue advancing research towards personalized microbiome modulation strategies.
... 69 Several studies also have shown secretor status to be linked to microbiota composition. 15,70 In a study by Rodriguez-Diaz et al., human patients were shown to have a correlation between their gut microbiota diversity, secretor status and susceptibility to HuNoV infection. 15 Host anti-HuNoV salivary IgA titers were obtained from the participants and were used as an indicator of previous infection susceptibility. ...
The influence of the microbiota on viral infection susceptibility and disease outcome is undisputable although varies among viruses. The purpose of understanding the interactions between microbiota, virus, and host is to identify practical, effective, and safe approaches that target microbiota for the prevention and treatment of viral diseases in humans and animals, as currently there are few effective and reliable antiviral therapies available. The initial step for achieving this goal is to gather clinical evidences, focusing on the viral pathogens—from human and animal studies—that have already been shown to interact with microbiota. The subsequent step is to identify mechanisms, through experimental evidences, to support the development of translational applications that target microbiota. In this chapter, we review evidences of virus infections altering microbiota and of microbiota enhancing or suppressing infectivity, altering host susceptibility to certain viral diseases, and influencing vaccine immunogenicity in humans and farm animals.
Full-text available
Background: The FUT2 gene (Se gene) encoding the enzyme α-1,2-L-fucosyltransferase 2 seems to have a significant effect on the number and type of bacteria colonizing the intestines. Methods: In a group of 19 patients after bariatric surgery, the polymorphism (rs601338) of FUT2 gene was analyzed in combination with body mass reduction, intestinal microbiome (16S RNA sequencing), and short chain fatty acids (SCFA) measurements in stools. Results: Among the secretors (Se/Se polymorphism of the FUT2 gene rs601338, carriers of GG variant), correlations between waist-hip ratio (WHR) and propionate content and an increase in Prevotella, Escherichia, Shigella, and Bacteroides were observed. On the other hand-in non-secretors (carriers of GA and AA variants)-higher abundance of Enterobacteriaceae, Ruminococcaceae, Enterobacteriaceae, Clostridiales was recorded. Conclusions: The increased concentrations of propionate observed among the GG variants of FUT 2 may be used as an additional source of energy for the patient and may have a higher risk of increasing the WHR than carriers of the other variants (GA and AA).
Disruption of the intestinal microbiota occurs frequently in allogeneic hematopoietic cell transplantation (allo-HCT) recipients and predisposes them to development of graft-versus-host disease (GvHD). In a prospective, single-center, single-arm study, we investigated the effect of donor fecal microbiota transplantation (FMT) on symptoms of steroid-refractory or steroid-dependent, acute or late-onset acute intestinal GvHD in 15 individuals who had undergone allo-HCT. Study participants received a fecal suspension from an unrelated healthy donor via nasoduodenal infusion. Donor FMT was well tolerated, and infection-related adverse events did not seem to be related to the FMT procedure. In 10 of 15 study participants, a complete clinical response was observed within 1 month after FMT, without additional interventions to alleviate GvHD symptoms. This response was accompanied by an increase in gut microbial α-diversity, a partial engraftment of donor bacterial species, and increased abundance of butyrate-producing bacteria, including Clostridiales and Blautia species. In 6 of the 10 responding donor FMT recipients, immunosuppressant drug therapy was successfully tapered. Durable remission of steroid-refractory or steroid-dependent GvHD after donor FMT was associated with improved survival at 24 weeks after donor FMT. This study highlights the potential of donor FMT as a treatment for steroid-refractory or steroid-dependent GvHD, but larger clinical trials are needed to confirm the safety and efficacy of this procedure.
Full-text available
Background/Purpose The FUT2 gene is a histo-blood group antigen (HBGA) that determines the susceptibility to Norovirus (NoV) infection. This study investigated the clinical significance of the FUT2 gene profile and HBGA expression in NoV infection. Methods Fecal specimens were collected from children in Chang-Gung Children's Hospital with acute gastroenteritis (AGE). The medical records were reviewed for clinical data. The viral etiology of gastroenteritis was validated using molecular methods. Genomic DNA was isolated from saliva or whole blood with the Puregene B Kit, according to the manufacturers' instructions. Single-nucleotide polymorphisms (SNPs) were determined by real-time PCR assays. Results FUT2 gene DNA was examined in 98 children with AGE. NoV was detected by RT-PCR in 44 patients (44.8%), while 54 (55.2%) had non-NoV AGE. Of the 44 NoV patients, 38 (86.3%) were secretors (no G428A mutation) and six (13.7%) were non-secretors (G428A mutation). Of the 54 non-NoV AGE patients, 28 (51.9%) were secretors and 20 (48.1%) were non-secretors. NoV-infected patients who were secretors had more frequent vomiting (P < 0.001), longer duration of diarrhea (P < 0.001), and greater overall disease severity score (P < 0.001) compared with non-secretors. Non-NoV infection secretor AGE patients had a longer duration of diarrhea (P < 0.001) than non-secretors. Conclusion FUT2 secretor status affects NoV AGE in children. Secretor patients have prolonged diarrhea, more frequent vomiting, more severe disease, and greater infection transmissibility than non-secretors.
Full-text available
The availability and repartition of fucosylated glycans within the gastrointestinal tract contributes to the adaptation of gut bacteria species to ecological niches. To access this source of nutrients, gut bacteria encode α-l-fucosidases (fucosidases) which catalyze the hydrolysis of terminal α-l-fucosidic linkages. We determined the substrate and linkage specificities of fucosidases from the human gut symbiont Ruminococcus gnavus. Sequence similarity network identified strain-specific fucosidases in R. gnavus ATCC 29149 and E1 strains that were further validated enzymatically against a range of defined oligosaccharides and glycoconjugates. Using a combination of glycan microarrays, mass spectrometry, isothermal titration calorimetry, crystallographic and saturation transfer difference NMR approaches, we identified a fucosidase with the capacity to recognize sialic acid-terminated fucosylated glycans (sialyl Lewis X/A epitopes) and hydrolyze α1–3/4 fucosyl linkages in these substrates without the need to remove sialic acid. Molecular dynamics simulation and docking showed that 3′-Sialyl Lewis X (sLeX) could be accommodated within the binding site of the enzyme. This specificity may contribute to the adaptation of R. gnavus strains to the infant and adult gut and has potential applications in diagnostic glycomic assays for diabetes and certain cancers.
While the genome contains genetic code that predisposes an individual to risks for a variety of health challenges, epigenetics or code that is “on top of the genome” dictates which of these genes are turned “on” or “off.” The use of nutritional genomics in clinical practice, and more specifically nutrigenetics, allows one to more accurately predict nutritional needs to help prevent or ameliorate diseases such as cardiometabolic, neurodegenerative, psychiatric, irritable bowel, cancer, and mitochondrial diseases. In this way, a clinician may choose appropriate dietary, lifestyle, or nutritional interventions to help prevent disease conditions and, if present, improve disease states and outcomes. For example, the presence of an ACE deletion is associated with an increased risk of salt-sensitive hypertension. For those without this deletion, salt restriction is unlikely to have an impact on hypertension, and for those with the deletion, salt restriction is an effective measure to control disease. One may also use nutrigenomics or the measurable effect on the genome through qaunitifying various biomarkers such as organic acids, amino acids, fatty acids and other laboratory measures to help improve metabolism with the use of vitamins and minerals that serve as cofactors in various biochemical pathways. Nutrigenomic SNPs and pathways serve as guidance as to which forms of a nutrient may be better tolerated and/or absorbed. For example, SNPs in the cobalamin transporters 1 and 2 (TCN1 and TCN2) predict the need for hydroxocobalamin and adenosylcobalamin, respectively. Finally, pharmacogenetics is one of the more studied areas of nutritional genomics. With the use of pharmacogenetics, a practitioner may interpret genetic information to predict a patient’s tolerance and requirements for various medications, herbals and substances. This allows the personalization of pharmaceuticals to an individual’s specific needs.
Full-text available
We investigate how host mucus glycan composition interacts with dietary carbohydrate content to influence the composition and expressed functions of a human gut community. The humanized gnotobiotic mice mimic humans with a nonsecretor phenotype due to knockout of their α1-2 fucosyltransferase (Fut2) gene. The fecal microbiota of Fut2(-) mice that lack fucosylated host glycans show decreased alpha diversity relative to Fut2(+) mice and exhibit significant differences in community composition. A glucose-rich plant polysaccharide-deficient (PD) diet exerted a strong effect on the microbiota membership but eliminated the effect of Fut2 genotype. Additionally fecal metabolites predicted host genotype in mice on a polysaccharide-rich standard diet but not on a PD diet. A more detailed mechanistic analysis of these interactions involved colonization of gnotobiotic Fut2(+) and Fut2(-) mice with Bacteroides thetaiotaomicron, a prominent member of the human gut microbiota known to adaptively forage host mucosal glycans when dietary polysaccharides are absent. Within Fut2(-) mice, the B. thetaiotaomicron fucose catabolic pathway was markedly down-regulated, whereas BT4241-4247, an operon responsive to terminal β-galactose, the precursor that accumulates in the Fut2(-) mice, was significantly up-regulated. These changes in B. thetaiotaomicron gene expression were only evident in mice fed a PD diet, wherein B. thetaiotaomicron relies on host mucus consumption. Furthermore, up-regulation of the BT4241-4247 operon was also seen in humanized Fut2(-) mice. Together, these data demonstrate that differences in host genotype that affect the carbohydrate landscape of the distal gut interact with diet to alter the composition and function of resident microbes in a diet-dependent manner.
Full-text available
The human intestine, colonized by a dense community of resident microbes, is a frequent target of bacterial pathogens. Undisturbed, this intestinal microbiota provides protection from bacterial infections. Conversely, disruption of the microbiota with oral antibiotics often precedes the emergence of several enteric pathogens. How pathogens capitalize upon the failure of microbiota-afforded protection is largely unknown. Here we show that two antibiotic-associated pathogens, Salmonella enterica serovar Typhimurium (S. typhimurium) and Clostridium difficile, use a common strategy of catabolizing microbiota-liberated mucosal carbohydrates during their expansion within the gut. S. typhimurium accesses fucose and sialic acid within the lumen of the gut in a microbiota-dependent manner, and genetic ablation of the respective catabolic pathways reduces its competitiveness in vivo. Similarly, C. difficile expansion is aided by microbiota-induced elevation of sialic acid levels in vivo. Colonization of gnotobiotic mice with a sialidase-deficient mutant of Bacteroides thetaiotaomicron, a model gut symbiont, reduces free sialic acid levels resulting in C. difficile downregulating its sialic acid catabolic pathway and exhibiting impaired expansion. These effects are reversed by exogenous dietary administration of free sialic acid. Furthermore, antibiotic treatment of conventional mice induces a spike in free sialic acid and mutants of both Salmonella and C. difficile that are unable to catabolize sialic acid exhibit impaired expansion. These data show that antibiotic-induced disruption of the resident microbiota and subsequent alteration in mucosal carbohydrate availability are exploited by these two distantly related enteric pathogens in a similar manner. This insight suggests new therapeutic approaches for preventing diseases caused by antibiotic-associated pathogens.
Symbiotic microorganisms that reside in the human intestine are adept at foraging glycans and polysaccharides, including those in dietary plants (starch, hemicellulose and pectin), animal-derived cartilage and tissue (glycosaminoglycans and N-linked glycans), and host mucus (O-linked glycans). Fluctuations in the abundance of dietary and endogenous glycans, combined with the immense chemical variation among these molecules, create a dynamic and heterogeneous environment in which gut microorganisms proliferate. In this Review, we describe how glycans shape the composition of the gut microbiota over various periods of time, the mechanisms by which individual microorganisms degrade these glycans, and potential opportunities to intentionally influence this ecosystem for better health and nutrition.
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.