Bacterial Community Mapping of the Mouse Gastrointestinal Tract
Citation: Gu S, Chen D, Zhang J-N, Lv X, Wang K, et al. (
Bacterial Community Mapping of the Mouse Gastrointestinal Tract
Shenghua Gu 0
Dandan Chen 0
Jin-Na Zhang 0
Xiaoman Lv 0
Kun Wang 0
Li-Ping Duan 0
Yong Nie 0
Xiao-Lei Wu 0
Colin Dale, University of Utah, United States of America
0 1 College of Engineering, Peking University , Beijing , China , 2 Kunming Medical University , Chengong, Kunming, Yunnan , China , 3 Yunnan University of Traditional Chinese Medicine , Chengong, Kunming, Yunnan , China , 4 Peking University Third Hospital, Peking University , Beijing , China
Keeping mammalian gastrointestinal (GI) tract communities in balance is crucial for host health maintenance. However, our understanding of microbial communities in the GI tract is still very limited. In this study, samples taken from the GI tracts of C57BL/6 mice were subjected to 16S rRNA gene sequence-based analysis to examine the characteristic bacterial communities along the mouse GI tract, including those present in the stomach, duodenum, jejunum, ileum, cecum, colon and feces. Further analyses of the 283,234 valid sequences obtained from pyrosequencing revealed that the gastric, duodenal, large intestinal and fecal samples had higher phylogenetic diversity than the jejunum and ileum samples did. The microbial communities found in the small intestine and stomach were different from those seen in the large intestine and fecal samples. A greater proportion of Lactobacillaceae were found in the stomach and small intestine, while a larger proportion of anaerobes such as Bacteroidaceae, Prevotellaceae, Rikenellaceae, Lachnospiraceae, and Ruminococcaceae were found in the large intestine and feces. In addition, inter-mouse variations of microbiota were observed between the large intestinal and fecal samples, which were much smaller than those between the gastric and small intestinal samples. As far as we can ascertain, ours is the first study to systematically characterize bacterial communities from the GI tracts of C57BL/6 mice.
Funding: This study was supported by the National Twelfth Five-Year Plan for Science & Technology of China (2012BAI06B02). The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
The adult mammalian gastrointestinal (GI) tract is home to
microorganisms with the number around 10 times greater than the
total number of mammalian somatic and germ cells . Host
microbe interactions are now regarded as essential to many aspects
of normal mammalian physiology, ranging from metabolic
activity to immune homeostasis . Recent studies on GI
microbiota confirmed that a balance in GI microbial communities
is crucial for host health maintenance; perturbation of this
microbial composition has been hypothesized to be involved in a
range of diseases outside the gut, such as diabetes , obesity ,
fatty liver, inflammatory bowel diseases , anxiety  and
even cancer . Although increasing research has been performed
on mammalian gastrointestinal tract microbial ecology, most of
the samples used in these studies were from the feces.
Consequently, our understanding of the characteristic microbiota
in different sections along with the GI tract is still very limited,
especially for C57BL/6 mice, which are one of the most
commonly used animals for studying gut microbiota related
diseases . Because the comprehensive characterization of
normal mouse GI tract microbial communities is a critical
prerequisite to understanding and predicting alterations in these
communities in relation to disease, we conducted a study to
characterize the GI tract microbiota of specific pathogen free
(SPF) C57BL/6 mice using a recently developed high-throughput
Materials and Methods
Animals and sample collection
Six male SPF C57BL/6 mice aged 10 weeks were used in this
study. All animal care procedures were approved by the
Institutional Animal Care and Use Committee of Peking
University prior to initiation of the experiment. All mice were
housed in one cage in a standard animal laboratory with a 12 h
lightdark cycle and were fed with a standard diet. Commercial
mouse chow (Academy of Military Medical Sciences,
jun2007005) and water were autoclaved before use. Feces were collected in
advance of all experimental procedures. All mice were transferred
to fresh sterilized cages and the feces were collected within two
hours from the cages. Mice were then euthanized, before the
contents of the stomach, duodenum, jejunum, ileum, cecum and
colon were sampled, weighed and immediately frozen in liquid
nitrogen. After the samples (42 in total) were thoroughly frozen,
they were stored at 280uC until DNA extraction. The mean
lengths of the murine small intestine (including duodenum,
jejunum and ileum) and murine large intestine (including cecum
and colon) were 42.5 and 11.3 cm, respectively. The murine
jejunum is defined as the terminal transverse part of the murine
DNA Extraction, PCR amplification, amplicon
quantization, pooling, and pyrosequencing
Total genomic DNA from each sample (100 mg) was extracted
using the QIAamp DNA Stool Mini Kit according to the
manufacturers instructions. A region of about 180 bp, in the
16 S rRNA gene and covering the V3 region, was selected to
construct a community library through tag pyrosequencing. The
broadly conserved primers, 340F
(59-CCTACGGGAGGCAGCAG-39) and 533R (59-TTACCGCGGCTGCTGGCAC-39),
containing the A and B sequencing adaptors (454 Life Sciences)
were used to amplify this region. In addition, these primers also
contained an 11 nt barcode sequence that allowed for multiple
samples to be analyzed in a single sequencing run. The PCRs were
carried out in triplicate using 20 ml reactions with 0.6 mM each
primer,1050 ng of template DNA, 4 ml of the PCR reaction
buffer and 2.5 U of Phusion DNA Polymerase. The amplification
program consisted of an initial denaturation step at 94uC for
4 min, followed by 22 cycles, where 1 cycle consisted of 94uC for
10 s (denaturation), 55uC for 10 s (annealing) and 72uC for 15 s
(extension), and a final extension of 72uC for 10 min. Negative
controls were always performed to verify the lack of Taq
performance without the DNA template. Replicate PCR products
of the same sample were mixed within a PCR tube. They were
then visualized on agarose gels (2% in TBE buffer) containing
ethidium bromide, and purified with a DNA gel extraction kit
(Axygen, China). Prior to sequencing, the DNA concentration of
each PCR product was determined using a Quant-iTPicoGreen
double stranded DNA assay (Invitrogen, California, USA) and was
quality controlled on an Agilent 2100 bioanalyzer (Agilent, USA).
Following quantization, the amplicons from each reaction mixture
were pooled in equimolar ratios based on concentration and
subjected to emulsion PCR to generate amplicon libraries, as
recommended by 454 Life Sciences . Amplicon
pyrosequencing was performed from the A-end using a 454/Roche A
sequencing primer kit on a Roche Genome Sequencer GS FLX
Titanium platform at Majorbio Bio-Pharm Technology,
Pyrosequencing reads with more than one ambiguous
nucleotide or within correct barcodes or primers were removed and
excluded from further analysis. Sets of sequences with $97%
identity were defined as an Operational Taxonomic Unit (OTU).
OTUs were assigned to a taxonomy using the Ribosomal
Database Project (RDP) Naive Bayes classifier .
Representative sequences from each cluster were aligned with the
PyNAST aligner  to the greengenes core set in QIIME
. A phylogeny was constructed within QIIME using FastTree
. Rarefaction curves, alpha diversity, and beta diversity
calculations were also performed using QIIME. Phylogenetic
diversity (PD) and Shannon diversity index (SI) were estimated to
evaluate the ecological diversity of microbiota from each sample.
SI is a quantitative measure that reflects how many different
types (such as species) there are in a dataset, and simultaneously
takes into account how evenly the basic entities (such as
individuals) are distributed among those types. The value of a
diversity index increases both when the number of types
increases and when evenness increases. But the interpretation is
hindered by uncertain species definitions and the lack of a
statistical framework for comparing values. In contrast to SI,
phylogenetic diversity (PD) takes into account the taxonomic
breadth of samples without relying on morphotaxa, species or
sequence-type designations . To analyze the relationships
between samples, dual hierarchal dendrograms were calculated,
based on bacterial composition information at taxonomic levels.
An analysis was performed with the NCSS 2007 software using
weighted pair clustering which was based upon Manhattan
distance measurements. The similarity among the microbial
communities was determined using UniFrac analysis  in
which weighted and unweighted principal coordinate analysis
(PCoA) were performed. The OTU network was constructed by
QIIME and visualized using Cytoscape  to map gut
microbial community composition and structure onto the mouse
GI tract, thereby complementing phylogeny-based microbial
community comparisons. These analyses were used to bin 16S
rRNA V3 gene sequences into OTUs and to display microbial
genera partitioning across mouse GI tracts. OTUs and each
sample were designated as nodes in a bipartite network, in which
OTUs are connected to the samples in which their sequences
were found. A spring-embedded algorithm was used to cluster
the OTUs and samples.
Changes in bacterial abundance were compared using repeated
measures ANOVA analysis with the Tukeys honestly significant
difference (HSD) post hoc test. Relationships between sequences
and diversity and coverage were examined by Pearsons
correlation. Statistical analyses were performed using Graohoad prism
Program (version5.0.1, Graphpad software Inc., San
Diego,CA,USA). Significance was accepted at P,0.05.
Figure 2. Relative abundance of sequences belonging to different bacterial phyla. Sequences that could not be classified into any known
group were assigned as Unknown. Sto: Stomach samples; Duo: Duodenum samples; Jej: Jejunum samples; Ile: Ileum samples; Cec: Cecum samples;
Col: Colon samples; Fec: Feces samples. The number following the abbreviations stands for the mouse number. For example, Cec1, Cec2, Cec3, Cec4,
Cec5, and Cec6 stands for the Cecum sample from the 1st, 2nd, 3rd, 4th, 5th and 6th mouse.
Figure 3. Relative abundance of sequences belonging to different bacterial Class. Sto: Stomach samples; Duo: Duodenum samples; Jej:
Jejunum samples; Ile: Ileum samples; Cec: Cecum samples; Col: Colon samples; Fec: Feces samples. The number following the abbreviations stands for
the mouse number. For example, Cec1, Cec2, Cec3, Cec4, Cec5, and Cec6 stands for the Cecum sample from the 1st, 2nd, 3rd, 4th, 5th and 6th mouse.
Figure 4. Relative abundance of sequences belonging to different bacterial Class. (q, P,0.05, compared to Cecum; #, P,0.05, compared
to Colon; *, P,0.05, compared to Feces, by Tukeys honestly significant difference (HSD) post hoc test).
Diversity of the bacterial community along the mice GI
After removed reads containing incorrect primer or barcode
sequences and sequences with more than one ambiguous base, a
total of 283,234 valid reads were obtained from the 42 samples
through 454 pyrosequencing analysis. Each sample was covered by
an average of 6743 reads (Table S1 in File S1). Goods coverage of
all samples averaged 96.461.6% (mean6s.d., ranging from 91%
to 96%) (Table S1 in File S1). The individual rarefaction curves
tended to approach the saturation plateau except in one of the
duodenal samples (Figure S1 in File S1). No significant correlation
(Pearsons correlation, P.0.2) was found between the number of
reads per sample, the number of OTUs, and the estimated
number of OTUs.
PD and SI were estimated to evaluate the ecological diversity of
microbiota from each sample. Generally, jejunal and ileal samples
had the lowest diversity, while samples from cecum, colon, and
feces had the highest PD (Figure 1A) and SI (Figure 1B) values. In
addition, the PD and SI values of gastric and duodenal samples
showed much higher inter-mouse variation than those from other
Changes in bacterial community structure along the
mouse GI tract
Taxonomically, 21 different bacterial phyla or groups were
identified (Figure 2). The majority of the sequences obtained
belonged to Bacteroidetes (61.94%) and Firmicutes (30.55%) with
the rest distributed among Proteobacteria (5.39%), Cyanobacteria
(0.63%), Tenericutes (0.165%), Actinobacteria (0.13%),
Deferribacteres (0.10%) and unclassified bacteria (0.95%). However, only
Bacteroidetes, Firmicutes and Proteobacteria were found in all
samples. Among the 7 GI sites, the duodenum harbored most of
the phyla and groups, including the duodenum-unique Chlorobi,
Chloroflexi, Nitrospirae, SM2F11, SPAM, TM6 and WS3 groups.
While the bacterial community structure varied from different
anatomical region along the mice GI tract. At the phylum level,
the relative abundance of Proteobacteria was significantly higher
(P,0.05) in cecum than that in other sites except for the
stomach(Figure S2 in File S1). There is no significant difference
along GI tract of Bacteroidetes, Firmicutes and other phyla or
groups. At the class level, Bacteroidia (belonging to Bacteroidetes)
dominate the GI tract, and there is no significant different along
the GI tract (Figure S2 in File S1). The relative abundance of
Bacilli (belonging to Firmicutes) was obviously higher in the
stomach and small intestine than that in the large intestine and
feces, though there is no significant difference (Figure 3, Figure S3
in File S1). In contrast, the relative abundance of Clostridia
(belonging to Firmicutes) was much higher in the large intestine and
feces than that in the small intestine and stomach (P,0.05,
Figure 3, Figure S3 in File S1). Interesting, the relative abundance
of Epsilon-proteobacteria (belonging to Proteobacteria) was much
higher in the cecum than that in other sites (P,0.05, Figure 3,
Figure S3 in File S1). At the family level, anaerobes including
Bacteroidaceae (belonging to Bacteroidetes), Prevotellaceae
(belonging to Bacteroidetes), Rikenellaceae (belonging to
Bacteroidetes), Lachnospiraceae (belonging to Firmicutes) and
Ruminococcaceae (belonging to Firmicutes) were enriched in the large
intestine and feces (P,0.05) while Lactobacillaceae was enriched
in small intestine and stomach (Figure 4). A large proportion of
unclassified Bacteroidales was no significant difference along the
GI tract. At the genus level, large intestine and feces had a higher
proportion of Bacteroides, Prevotella, Alistipes (P,0.05), while
Lactobacillus was obviously higher in the stomach and small
intestine than that in the large intestine and feces, though there is
no significant difference because of large inter-mouse variations
(Figure S4 in File S1).
Furthermore, inter-mouse variations were observed from the
phylum to the OTU levels: higher inter-mouse variations were
detected among the gastric and small intestinal samples than
among the large intestinal and fecal samples.
Clustering of the bacterial community among GI sites
Wards clustering based upon Manhattan distance suggested that
the large intestine and fecal bacterial communities were distinct
from the gastric and small intestinal ones at the phylum (Figure S5
in File S1), class and family levels (Figure S6 in File S1). Similarly,
principal coordinate analysis (PCoA) plots using both weighted
(Figure S7 in File S1) and unweighted (Figure 5) UniFrac distances
clustered samples mainly by sites, but not by individuals. Bacterial
communities in the large intestinal and fecal samples clustered
closely to one another while those from gastric and small intestinal
samples did not. These results also supported the observation that
inter-mouse variations of fecal and large intestinal microbiota were
lower than those of gastric and small intestinal samples.
The PCoA plot with the taxonomic information at the family
level revealed that the anaerobic Prevotellaceae, Lachnospiraceae,
and Rikenellaceae were particularly abundant and important in
clustering of fecal and large intestinal microbiota (Figure 5). In
contrast, Lactobacillaceae was contributed largely to the
community similarity of gastric and small intestinal samples.
OTUs network across different anatomic sites of the
mouse GI tract.
OTUs and intestine sites (Figure 6) or mice were designated as
nodes in bipartite network, in which OTUs were connected to the
samples or mice in which their sequences were found. The
network-based analyses (Figure 6) showed that samples were more
closely associated with one another from the same large intestine
sites (cecum and colon) as well as feces than that from same small
intestine sites. The results suggested the higher similarity of
samples from large intestine and feces than small intestine and
stomach. Moreover, the same GI site from different individuals
had its shared OTUs (Figure S8 in File S1). These shared taxa
might perform unique functions to a GI site from other sites.
Different sites shared different common core microbiota both in
amount and composition. The stomachs of the six mouse
individuals had a small core microbiota (11 OTUs) (Figure S8
in File S1) belonging to Bacteroidales (family unclassified),
Lactobacillaceae, Lachnospiraceae, and Desulfovibrionaceae
(Figure 7). The duodenum, jejunum and ileum of the six
individuals had a relatively bigger core microbiota (26, 21 and
20 OTUs) (Figure S8 in File S1), most of which were
Bacteroidales (family unclassified), Lactobacillaceae, and
Desulfovibrionaceae OTUs (Figure 7). The cecum, colon and feces had the largest
core microbiota (72, 74 and 84 OTUs) (Figure S8 in File S1),
which was composed of bacteria belonging to Bacteroidales (family
unclassified), Lachnospiraceae, Ruminococcaceae, Clostridiales,
Bacteroidaceae, Prevotellaceae, Rikenellaceae,
Deferribacteraceae, Desulfovibrionaceae, Lactobacillaceae, and unclassified
bacteria (Figure 8). The more shared OTUs in cecum and colon
Nucleotide sequence accession number
All sequences have been deposited in the GenBank Sequence
Read Archive under the accession number SRA061180.
C57BL/6 mice are one of the most common animals used for
studying gut microbiota related disease . However, the
characteristics and distribution of the microbial community along
the C57B/6 mouse GI tract is less clear. Therefore, the
investigation into microbiota composition and diversity along the
C57BL/6 mouse GI tract was carried out in the present study
using a high-throughput pyrosequencing approach.
The overall taxonomic groups represented within the mice GI
tract were similar to previous findings. Three bacterial phyla, the
Firmicutes, Bacteroidetes and the Proteobacteria dominate the GI
tract [18,19,20]. However, Ley et al  reported that there is a
dominance of Firmicutes over Bacteriodetes while Caricilli et al
 reported that Bacteriodetes dominated the WT mice gut.
Species difference and diet difference may help explain the
apparently different results.
For a long time bacterial diversity along the mammalian GI
tract was thought to increase from stomach to feces because the
stomach and upper small intestine were viewed as being too harsh
(due to the low pH) for microorganisms to grow and to maintain
greater diversity. However, our results do not support this
traditional concept. Greater diversity was found in both fecal
and large intestinal samples as well as duodenal and gastric
samples, leaving the least diversity in jejunal and ileal samples
(Figure 1). Moreover, duodenal samples contained the most
bacterial phyla, although some phyla were detected with very low
abundance. In fact, a diverse microbiota was also detected in
human and horse stomachs[22,23,24]. The detection of diverse
microbiota in the stomach and duodenum as well as the varying
diversity along the GI tract might be because of the existence and
vanishing of transient microbiota. With food and water from
diet, bacteria are continuously ingested from the outer
environment to the stomach. During their stay in the host stomach, they
are susceptible to death induced by low pH. It is possible that a
part of the transient microbiota did quickly escape from the
stomach to the duodenum where the neutral pH offers a more
conducive environment for bacteria to live than in the stomach. In
this study, chow and water were autoclaved before use. However,
the SPF environment is not germ free, and the surrounding
bacteria may contaminate the diet and water. The large intestine is
far from the stomach, receives the least influence from transient
microorganisms and offers better surroundings for bacteria to
grow. These could be the reasons why the large intestinal and fecal
samples had higher and stable PD and SI (Figure 1), as well as the
least inter-mouse variations, which is in consistent with the results
of Eckburg et al . Secondly, many of the bacteria in different
phyla may, in fact, be died with their DNA temporarily persevered
and therefore detectable with DNA-based approaches, leading to
the false positive detection of many bacterial phyla. In this case,
the debris DNA from the transient microorganisms vanished in
the jejunum and ileum, leading to the detection of lower diversity
and less bacterial phyla. However, further research is needed to
explain these phenomena.
The properties of the different GI sections also exert influences
on the microbiota. These include intestinal motility, pH, redox
potential, nutrient supplies and host secretions [26,27,28]. Since
the same GI sections from different individuals have relatively
similar physicochemical conditions, the microbiota clustering by
GI sections was significant (Figure 5, Figure S7 in File S1). The
small intestine is the major site for digestion and the absorption of
nutrients, water and electrolytes. The differences between the
individual physicochemical conditions in the small intestine are
bigger than that in the large intestine . These may be another
reason why the gastric and small intestinal microbiota showed
remarkable inter-mouse variation, which were also reported in dog
and human [30,31].
The variable dominance of bacteria in different GI sections
also supports the influence of GI environment on the microbiota.
For example, the oxygen availability of stomach and small
intestine were higher, therefore the facultative bacteria including
Bacilli (class), Lactobacillaceae (family), Lactobacillus (genus) were
enriched in stomach and small intestine. In contrast, strictly
anaerobic Clostridia (class), Lachnospiraceae (family),
Ruminococcaceae (family), Prevotellaceae (family), Rikenellaceae (family),
Bacteroidaceae (family) were enriched in the large intestinal and
fecal samples where less oxygen is available (Figure 4). But why
the relative abundance of Epsilon-proteobacteria (belong to
Proteobacteria) was highest in cecum samples remain unclear.
However, it is affirmative that fecal samples cannot represent the
mouse gut microbiota. Because there are many differences
between feces and various gut regions. For example, that
epsilonProteobacteria are high in the cecum but low in the feces.
Therefore, selection of the sampling site along the GI tract is
crucially important for the investigation of microbiota-related
health and disease issues.
OTU network analyses revealed the existence of a common
microbial composition, the core microbiota, among the different
GI section. Cecum, colon and fecal samples shared more common
OTUs, both in terms of numbers and more diverse compositions,
than the stomach and small intestine did. These results would
support the hypothesis that anatomical regions, which have their
own physicochemical conditions, exert important selective
pressures on microbiota and play important roles in shaping the GI
microbiota. OTU network analyses also revealed that unique
microbiota along the GI tract, which could be regarded as the
microbial marker of GI sections. It was known that the majority of
microbes reside in the gut have a profound influence on human
physiology and nutrition, and most of the researches focused on
the microbial communities in feces or large intestine. In this study,
we found some core OTUs among these samples. Although it was
not clear that whether these shared microorganisms were the
permanent residents or the passengers form the foods, the
core microbiota in the small intestine should be paid more
In conclusion, the mouse GI tract harbors many distinct niches,
each containing a different microbial ecosystem that varies
according to the location within the GI tract. Attention should
therefore be paid to ensure that the proper GI samples are used to
represent each GI microbial community during microbiota-related
File S1 Table S1, Figure S1S8. Table S1. Overview of
pyrosequencing results of each sample. Figure S1 Rarefaction
analysis of the different GI sample. Sto:Stomach samples;
Duo:Duodenum samples; Jej:Jejunum samples; Ile: Ileum samples;
Cec:Cecum samples; Col: Colon samples; Fec: Feces samples.
Figure S2 Bacterial families different along the GI tract. q
compared VS Cecum P,0.05; # compared VS Colon P,0.05;
compared VS Feces P,0.05 Figure S3 Bacterial classes different
along the GI tract. q compared VS Cecum P,0.05; # compared
VS Colon P,0.05; compared VS Feces P,0.05 Figure S4
Bacterial genus different along the GI tract. q compared VS
Cecum P,0.05; # compared VS Colon P,0.05; compared VS
Feces P,0.05 Figure S5. Dual hierarchal dendrogram based upon
phylum classified using bacterial tag-encoded amplicon
pyrosequencing. Sto: Stomach samples; Duo: Duodenum samples; Jej:
Jejunum samples; Ile: Ileum samples; Cec: Cecum samples; Col:
Colon samples; Fec: Feces samples. The number following the
abbreviations stands for the mouse number. For example, Cec1,
Cec2, Cec3, Cec4, Cec5, and Cec6 stands for the Cecum sample
from the 1st, 2nd, 3rd, 4th, 5th and 6th mouse. Figure S6 Dual
hierarchal dendrogram based upon class classified using bacterial
tag-encoded amplicon pyrosequencing. Sto: Stomach samples;
Duo: Duodenum samples; Jej: Jejunum samples; Ile: Ileum
samples; Cec: Cecum samples; Col: Colon samples; Fec: Feces
samples. The number following the abbreviations stands for the
mouse number. For example, Cec1, Cec2, Cec3, Cec4, Cec5, and
Cec6 stands for the Cecum sample from the 1st, 2nd, 3rd, 4th, 5th
and 6th mouse. Figure S7 PcoA Score plot of weighted UniFrac
distances for all samples within mice digestive tract. Figure S8
Operational taxonomic unit (OTU) network analysis of bacterial
communities from each GI tract site of 6 mice for the V3 16S
rRNA region. A, stomach; B, Duodenum; C, Jejunum; D, Ileum;
E, Cecum; F, Colon; G, Feces.
Conceived and designed the experiments: XLW YN LPD. Performed the
experiments: SHG DDC JNZ XML. Analyzed the data: SHG JNZ.
Contributed reagents/materials/analysis tools: KW LPD. Wrote the paper:
SHG JNZ YN XLW.
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