Metagenomic analysis of the Rhinopithecus bieti fecal microbiome reveals a broad diversity of bacterial and glycoside hydrolase profiles related to lignocellulose degradation
Xu et al. BMC Genomics
Metagenomic analysis of the Rhinopithecus bieti fecal microbiome reveals a broad diversity of bacterial and glycoside hydrolase profiles related to lignocellulose degradation
Bo Xu 0 1 2 3
Weijiang Xu 0 1 2 3
Junjun Li 0 1 2 3
Liming Dai 1
Caiyun Xiong 1
Xianghua Tang 0 1 2 3
Yunjuan Yang 0 1 2 3
lin Mu 0 1 2 3
i Zhou 0 1 2 3
i Ding 0 1 2 3
n Wu 0 1 2 3
ng 0 1 2 3
0 Engineering Research Center of Sustainable Development and Utilization of Biomass Energy, Ministry of Education , Kunming 650500 , China
1 School of Life Science, Yunnan Normal University , Kunming 650500 , China
2 Key Laboratory of Yunnan for Biomass Energy and Biotechnology of Environment , Kunming 650500 , China
3 Key Laboratory of Enzyme Engineering, Yunnan Normal University , Kunming 650500 , China
Background: The animal gastrointestinal tract contains a complex community of microbes, whose composition ultimately reflects the co-evolution of microorganisms with their animal host and the diet adopted by the host. Although the importance of gut microbiota of humans has been well demonstrated, there is a paucity of research regarding non-human primates (NHPs), especially herbivorous NHPs. Results: In this study, an analysis of 97,942 pyrosequencing reads generated from Rhinopithecus bieti fecal DNA extracts was performed to help better understanding of the microbial diversity and functional capacity of the R. bieti gut microbiome. The taxonomic analysis of the metagenomic reads indicated that R. bieti fecal microbiomes were dominated by Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria phyla. The comparative analysis of taxonomic classification revealed that the metagenome of R. bieti was characterized by an overrepresentation of bacteria of phylum Fibrobacteres and Spirochaetes as compared with other animals. Primary functional categories were associated mainly with protein, carbohydrates, amino acids, DNA and RNA metabolism, cofactors, cell wall and capsule and membrane transport. Comparing glycoside hydrolase profiles of R. bieti with those of other animal revealed that the R. bieti microbiome was most closely related to cow rumen. Conclusions: Metagenomic and functional analysis demonstrated that R. bieti possesses a broad diversity of bacteria and numerous glycoside hydrolases responsible for lignocellulosic biomass degradation which might reflect the adaptations associated with a diet rich in fibrous matter. These results would contribute to the limited body of NHPs metagenome studies and provide a unique genetic resource of plant cell wall degrading microbial enzymes. However, future studies on the metagenome sequencing of R. bieti regarding the effects of age, genetics, diet and environment on the composition and activity of the metagenomes are required.
Gastrointestinal microbiota; Rhinopithecus bieti; Metagenomics; Lignocellulose degradation; Pyrosequencing
The Yunnan snub-nosed monkey (Rhinopithecus bieti) is
an endangered colobine endemic to high-altitude forests
ranging from 3000 to 4400 meters in southwestern China
and southeastern Tibet . Overall, these colobines can be
classified as herbivores, ingesting flowers, fruits, leaves,
and seeds to varying degrees . The R. bieti possesses
specialized S-shaped and partitioned stomachs where
microbial fermentation of cellulose takes place [3,4]. This
adaptation enables them to eat food containing high levels
of structural polysaccharides, i.e., cellulose and related
compounds. Given the above features, this species has
received significant attention from researchers and serves as
an important model organism for studying the evolution
of the primate diet. However, researches on R. bieti have
mostly focused on aspects of taxonomy, ecology, anatomy
and conservation genetics.
The gastrointestinal tract of animals harbors a complex
microbial community, and the composition of this
community ultimately reflects the co-evolution of
microorganisms with their animal host and the diet adopted by the
host . Its well known that herbivores lack the enzymatic
capacity needed to degrade plant polysaccharides,
particularly cellulose, and instead rely on community of
microorganisms that have this capacity. Therefore, R. bieti are
expected to have a well-adapted gut microbiota. Indeed,
the limited studies published to date suggest the
microbiomes of R. bieti possess a large number of bacteria that
may be involved in degradation of cellulose ; however,
very little is currently known about the genetic potential
and structure-function relationships intrinsic to these
Recently, next-generation sequencing technologies have
been used to characterize the microbial diversity and
functional capacity of a range of microbial communities in the
gastrointestinal tracts of humans [7-10] as well as in
several animal species [11-25]. The most important
advantages of this cloning-independent approach are the
avoidance of cloning bias and the bias introduced by PCR
amplification. To the best of our knowledge, this study
was the first to apply a random sample pyrosequencing
approach to analyze the metagenome of R. bieti, an
herbivore whose habitat and diet are very different compared to
the herbivores studied so far.
Results and discussion
The analysis of the reads yielded a high percentage of
species identification in complex metagenomes and even
higher in less complex samples. Long sequence reads from
454 GS FLX Titanium pyrosequencing provided the high
specificity needed to compare the sequenced reads with the
DNA or protein databases and allowed the unambiguous
assignment of closely related species. The initial
pyrosequencing runs yielded 97,942 reads containing 37,482,416
bases of sequence, with an average read length of 382 bp.
Prior to further processing, the raw read data were
subjected to the Metagenome Rapid Annotation using
Subsystem Technology (MG-RAST) v.3.0 online server quality
control pipeline  to remove duplicate and low quality
reads (Additional file 1). The filtering step removed 9.6% of
reads in the sample. The unique sequence reads that passed
the quality control (QC) filtering step were then subjected
to further analysis focusing on biodiversity and functional
annotation. All reads were deposited in the National Center
for Biotechnology Information (NCBI) and can be accessed
in the Short Read Archive (SRA) under the accession
Phylogenetic analysis of R. bieti fecal bacteria, eukaryota,
archaea, and viruses
The overview of the phylogenetic computations
provided 97.81% bacteria, 1.27% eukaryota, 0.73% archaea,
and 0.17% viruses. In the R. bieti intestinal
metagenome, Firmicutes was the most predominant phylum
(39.36%), followed by Bacteroidetes (27.6%),
Proteobacteria (19.41%), Actinobacteria (3.61%) and Spirochaetes
(2.01%) (Figure 1). Compared with the previous 16S
rRNA gene-based data , Firmicutes were the
dominant bacteria phylum. In addition, higher percentages of
Bacteroidetes and lower percentages of Spirochaetes in
the R. bieti intestinal metagenome were observed. This
discrepancy may have been caused by the biases
associated with the primers, PCR reaction conditions, or
selection of clones .
Within the Firmicutes group, Clostridiales were most
predominant, among which Clostridium was
overrepresented (Additional file 2), which is consistent with previous
16S rRNA gene-based data . The presence of a large
proportion of Clostridia was likely to be important for
lignocellulosic biomass degradation . Several cellulolytic
microbes such as C. phytofermentans, Ruminococcus albus,
C. thermocellum, C. cellulolyticum, R. flavefaciens and
C. cellulovorans were abundant in the R. bieti metagenome
(Additional file 2). Among clostridial genomes sequenced
to date, C. phytofermentans has the highest number of
genes encoding enzymes for the modification and
breakdown of complex carbohydrates. It contains genes for 161
carbohydrate-active enzymes (CAZy), which include 108
glycoside hydrolases (GH) spreading across 39 families
. C. thermocellum is an anaerobic thermophilic
bacterium that exhibits one of the highest rates of cellulose
utilization among described microorganisms . R.
flavefaciens is a specialist cellulolytic bacterial species
characterized in the rumen, other herbivorous animals and humans.
Currently it is the only rumen bacterium known to produce
a defined cellulosome  which is usually associated with
improved cellulolytic efficiency . These cellulolytic
Clostridia, which are ubiquitous in cellulosic anaerobic
Figure 1 Bacterial phylum profiles of the R. bieti microbiome. The percentage of the R. bieti fecal metagenomic sequences assigned to M5NR
database is shown. Through the Organism Abundance tool in MG-RAST, the R. bieti fecal sequencing runs were determined from the M5NR database
with the BLASTx algorithm. The e-value cutoff for the metagenomic sequence matches to the M5NR database was 1 105, with a minimum alignment
length of 30 bp.
environments, represent a major paradigm for efficient
biological degradation of cellulosic biomass .
Bacteroidetes were the second predominant phylum in
the R. bieti gastrointestinal tract with Bacteroidales as the
primary contributor to the Bacteroidetes populations,
followed by Flavobacteria, Sphingobacteriales and
Cytophagales. The major genus in the Bacteroidetes phylum
was Bacteroides. Bacteroides are commonly found in the
human intestine where they have a symbiotic
hostbacterial relationship with humans. They assist in breaking
down food and producing valuable nutrients and energy
that the body needs. B. vulgatus, B. fragilis and
Flavobacterium johnsoniae comprise about 1.1%, 1.1%, and 1.08%
of the reads analyzed respectively (Additional file 2);
therefore, it is considered the predominant species in the R.
bieti metagenome. B. fragilis is a ubiquitous
Gramnegative anaerobic bacterium that inhabits the lower
gastrointestinal tract of most mammals . Recent
findings have revealed that this organism possesses the ability
to direct the cellular and physical maturation of the host
immune system and protect its host from experimental
colitis [33-35]. B. vulgatus is among the most commonly
isolated microbes from the human gastrointestinal tract,
and it has been found to constitute part of the core gut
microbiota in healthy humans [10,36]. According to the
CAZy classification scheme, B. vulgatus is the only
sequenced gut Bacteroidetes with a gene encoding a
xylanase . F. johnsoniae digests many polysaccharides and
proteins, but it is best known for its ability to rapidly
digest insoluble chitin . F. johnsoniae and other
members of the Bacteroidetes phylum are thought to play
important roles in the turnover of this compound in many
environments . Perhaps the major metabolic function
of these dominant intestinal bacteria is the fermentation
of nondigestible carbohydrates including large
polysaccharides (i.e., pectins and cellulose), which are key sources of
energy in the R. bieti colon.
Similarly, Burkholderiales were the primary contributors
to the Proteobacteria populations, followed by
Enterobacteriales and Pseudomonadales. The major genus in the
Proteobacteria phylum was Pseudomonas. P. fluorescens
was the predominant species among the Pseudomonas in
the R. bieti metagenome. P. fluorescens is a common
Gram-negative bacterium that can be found in the low
section of the human digestive tract .
A distinctive feature of the R. bieti metagenome is the
abundance of phylum Fibrobacteres and Spirochaetes, and
this abundance is unexpected and far greater than in other
animals (Figure 2). Fibrobacter succinogenes was the only
species existed in the R. bieti gut (Additional file 2) that is
recognised as a major bacterial degrader of lignocellulosic
material in the herbivore gut . It was originally thought
that members of the genus Fibrobacter were restricted to
the mammalian intestinal tract, but the occurrence and
distribution of members of the Fibrobacteres phylum has
recently been extended to include termite intestinal contents
where cellulose is again the primary carbon source for the
host organisms . Spirochaetes form a distinct
monophyletic phylum of bacteria, and contain four genera that
contain important pathogenic species, these being
Treponema, Borrelia, Leptospira and Brachyspira, which were all
exist in the R. bieti metagenome (Additional file 2).
Morphologically diverse Spirochaetes are consistently
present in the hindgut of all termites , and are found
out as ectosymbionts attached to the surface of
cellulosedigesting protists .
Eukaryota were a minor constituent (1.27%) in the
R. bieti metagenome. Species of Blastocystis which have
Figure 2 Phylogenetic clustering of R. bieti, pygmy loris, human, mouse, canine, cow, and chicken gastrointestinal metagenomes. A
double hierarchical dendrogram was established through weight-pair group clustering methods based on the non-scaling Manhattan distance. The
dendrogram shows the phylogenetic distribution of the microorganisms among the eleven metagenomes from the seven different hosts, including
R. bieti (JSH), pygmy loris (WFH), human (HSM and F1S), mouse (LMC and OMC), dog (K9C and K9BP), cow (CRP), and chicken (CCA and CCB). The
linkages of the dendrogram do not show the phylogenetic relationship of the bacterial phylum and are based on the relative abundance of taxonomic
profiles. The heat map depicts the relative percentage of each phylum of microorganism (variables clustering on the y axis) in each sample (x axis
clustering). The heat map color represents the relative percentage of the microbial descriptions in each sample, with the legend indicated at the upper
left corner. Branch length indicates the Manhattan distances of the samples along the x axis (scale at the upper right corner) and of the microbial phyla
along the y axis (scale at the lower left corner).
been reported as the most commonly occurring
microeukaryote in human feces [45,46] were also represented
in small quantities (<0.01%) in the R. bieti metagenome.
In addition, the presence of Blastocystis has been linked
to a number of gut-related diseases. Some of these
diseases could be the outcome of the predation of
beneficial bacteria by Blastocystis in light of the similar
observations in ruminant cattle and their communalistic
Fungi have very low abundance sequences (0.26%), with
Ascomycota being the primary contributor (Additional file
3). Compared with humans, more diverse fungi species
belonging to 21 different genera exist in the R. bieti
metagenome. The most abundant fungi genera in the R. bieti
metagenome were Aspergillus (0.06%), Gibberella (0.03%),
Magnaporthe (0.02%) and Neurospora (0.02%) (Additional
file 3). Fungi in the intestinal ecosystem of NHPs have not
yet been studied extensively. Comparative analyses
indicate that both fungal populations in the R. bieti and pygmy
loris metagenomes  were predominated by Aspergillus.
However, the most abundant fungi species within
Aspergillus of the R. bieti metagenome, A. fumigatus, has not
been detected in pygmy loris. A. fumigatus is one of the
most common human pathogen. The whole genome
sequence of A. fumigatus shows that up to 18 different genes
encoding endoglucanases have been annotated, which
indicates that this species own a good capacity in
lignocellulose hydrolysis . Moreover, two fungi species
(Neurospora crassa and Gibberella zeae) have been
identified predominantly both in the R. bieti (Additional file 3)
and pygmy loris metagenome , which were also
identified in feline , canine (K9C and K9BP) , and mouse
metagenomes (OMC) .
Unlike other NHPs, R. bieti are herbivorous mammals
that have a specialized gut in which plant materials
degraded by microbial processes apparently similar to those
that occur in the rumen. In rumen ecosystems, fungi
interact with other microbes to take part in decomposing
cellulose. Thus, it seems likely that anaerobic fungi may
play significant role in fiber degradation in the R. bieti.
Future studies are required by next-generation sequencing
to gain further insight into the fungal diversity in the
R. bieti gastrointestinal tract.
Archaea are a minor component of the R. bieti
metagenome, comprising 0.73% of the total sequencing
reads. Archaea consist of two phyla, Euryarchaeota and
Crenarchaeota, which diverged into night classes and
eleven orders (Additional file 4). Among the groups of
archaea, methanogenic archaea is the most predominant
and diverse group. Methanogenic archaea is also
widespread in chicken, dogs, felines, mice, ruminants, NHPs
and humans [12,16,19,25,49-51]. In the R. bieti fecal
metagenome, Methanocorpusculum labreanum is the major
component of archaea, having a percentage of 0.05% in all
the analytic sequences (Additional file 4), which is
consistent with pygmy loris . The majority of the Archaea in
the rumen are methanogens which provide
thermodynamically favorable conditions for ruminal microbial fibre
degradation . Although methanogenic archaea make up
only a small part of the R. bieti microbial population, they
may play an important role in microbial fermentation like
in the rumen system. Archaea are considered commensals;
however, they contribute to pathogeny in humans because
of mutual interactions with other microorganisms .
For instance, methanogens consume hydrogen and
create an environment that enhances the growth of
polysaccharide fermenting bacteria, leading to higher energy
utilization. Higher numbers of methanogenic archaea have
been observed in obese humans . However, the
prevalence and medical importance of archaea in R. bieti need
to be determined.
Only 0.17% of the total reads have viral origin, with only
the order Caudovirales being identified. Three families
were observed (Myoviridae, Podoviridae and Siphoviridae)
within the Caudovirales order, and all sequences were
classified as bacteriophages (Additional file 5).
Bacteriophages influence food digestion by regulating microbial
communities in the human gastrointestinal tract through
lytic and lysogenic replication . Bacteriophages also
contribute to human health by controlling invading
pathogens . Recent metagenomic analyses of the DNA
viruses from human feces have revealed that the majority of
DNA viruses in human feces are novel, and most of the
recognizable sequences also belong to bacteriophages .
The close phylogenetic relationship between humans and
NHPs, coupled with the exponential expansion of human
populations and human activities within the primate
habitats, has resulted in the exceptionally high
possibility of pathogen exchange . Therefore, studies on the
viral community of NHPs and the potential for
crosstransmission between humans and NHPs are needed.
Given the type of methodology (shotgun DNA
pyrosequencing approach) that we utilized, our study could
only determine the dsDNA virus. Future studies need to
provide a richer understanding of both RNA and dsDNA
viruses to complete human knowledge of the viral
Studies on cow , cats , mice , rats  as well
as humans  and NHPs  revealed a correlation
between host diet and microbial community composition.
However, the characterization of the dietary-induced
changes in NHPs microbiomes through high-throughput
sequencing technologies has not been performed thus far.
Hence, more attention should be given to it in future
Metagenomics-based metabolic profiles
Protein and carbohydrate metabolism are the most
abundant functional categories, representing 9.87% and 9.59%
of the R. bieti fecal metagenomes respectively (Figure 3).
Genes associated with amino acids and derivatives, DNA
metabolism, RNA metabolism, cofactors (vitamins,
prosthetic groups, pigments), cell wall and capsule and
membrane transport are also very abundant in the R. bieti
metagenomes. Approximately 16.85% of the annotated
reads from the R. bieti fecal metagenomes were
categorized within the clustering-based subsystems, most of
which have unknown or putative functions.
Compared with NHPs pygmy loris gastrointestinal
microbiomes , protein metabolism was more enriched
in the gut microbiomes of R. bieti. On further analysis, it
seemed that R. bieti had enriched protein biosynthesis in
which universal GTPases were the most abundant.
Because R. bieti eat foods rich in fibre, it is likely R. bieti have
more available substrate for bacterial fermentation. This
increase in protein biosynthesis may simply be the result
of higher metabolic activity and/or growth of microbial
Diversity of fibrolytic enzymes in the R. bieti metagenome
To obtain a more in-depth view of the carbohydrate
enzymes present in R. bieti fecal microbiome, we subjected
our samples to the CAZy database (http://www.cazy.org),
as described by Cantarel et al. . The comparison of the
88,514 metagenome reads post-QC processing based on
the CAZy database provided 2,315 hits at an e-value
restriction of 1 106. Candidate sequences that belong
to the glycosyl transferase (GT) families GT2 (268) are
Figure 3 Functional composition of the R. bieti microbiome. The percentage of the R. bieti fecal metagenomic sequences assigned to the
general SEED subsystems is shown. Through the Functional Abundance tool in MG-RAST, the R. bieti fecal sequencing runs were determined
from the SEED database with the BLASTx algorithm. The e-value cutoff for the metagenomic sequence matches to the SEED subsystem database
was 1 105 with a minimum alignment length of 30 bp.
the most abundant, followed by members of the GT4
(189) and glycoside hydrolase (GH) families GH13 (165)
(Additional files 6 and 7).
In the R. bieti fecal metagenomes there is a wide
diversity of GH catalytic modules with >1,300 sequences
belonging to 79 GH families. GHs are a prominent group of
enzymes that hydrolyze the glycosidic bond among the
carbohydrate molecules. The most frequently occurring
GH families in the R. bieti metagenome were GH13 (165;
12.61% of the total GH matches), 2 (106; 8.1%), 3 (83;
6.35%) and 43 (61; 4.66%) (Additional files 6 and 7).
Family GH13 is the major glycoside hydrolase family acting on
substrates containing -glucoside linkages. The majority
of the enzymes acting on starch, glycogen, and related
oligo- and polysaccharides, are found within family GH13,
which represents the largest family of glycoside hydrolases.
In adition to -amylases, it contains pullulanase,
cyclomaltodextrin glucanotransferase (CGTase),
cyclomaltodextrinase, neopullulanase, -glucosidase, etc. (www.cazy.org).
Some members of family GH13 bearing a variable number
of supplemental N- or C-terminal extensions such as
starch-binding modules (carbohydrate-binding modules
(CBM) 26, CBM41 and CBM34 in CAZy)  were all
annotated in the R. bieti metagenome (Additional file 7).
Families GH2 and GH3, which contain a large range
of glycosidases cleaving nonreducing carbohydrates in
oligosaccharides and the side chains of hemicelluloses
and pectins, were abundant less than GH13. GH2
components are -D-galactosidases, -glucuronidases,
-Dmannosidases, and exo--glucosaminidases. The most
common activities of GH3 include -D-glucosidases,
-L-arabinofuranosidases, -D-xylopyranosidases, and
N-acetyl--D-glucosaminidases . GH43 shows
-xylosidase, -1,3-xylosidase, -L-arabinofuranosidase,
arabinanase, xylanase, and galactan 1,3--galactosidase
activity (www.cazy.org). Glycosyl transferases are
ubiquitous enzymes that catalyze the attachment of sugars
to a glycone . Candidate genes that belong to the
glycosyl transferase families GT2 (268; 36.12% of the
total GT matches) and GT4 (189; 25.47%) are the most
abundant (Additional file 6).
Comparative analysis of GH families was done between
metagenomes of the R. bieti, human, Tammar, termite
hindgut, and cow rumen. Clustering analysis of the GH
family distribution implied that the R. bieti metagenome
was most closely related to cow rumen (Additional file 8).
The GH5 were the most abundant cellulases in all
metagenomes, which occurred at the highest frequency in the
termite metagenome. Similar to the termite  and
rumen  metagenomes, R. bieti was more evenly
balanced with GH5 and GH9 cellulases. However, GH9
cellulases occurred at a lower frequency in human 
metagenomes and were not even detected in the
macropod  metagenomic datasets. GH28 hemicellulases
were prevalent in the R. bieti microbiome; however, they
occurred at a lower frequency in other animal gut
microbiomes. Debranching enzymes profile in the R. bieti
microbiome was similar to those reported for wallaby
foregut microbiome . The R. bieti microbiome
possessed a large number of reads matching GH family
specific for oligosaccharides, in which the most abundant
oligosacchride-degrading enzymes were GH2, GH3, and
GH43. Although both R. bieti and humans belong to
primates, the GH profiles targeting plant structural
polysaccharides in the two metagenomes are not more
similar than other animals. This could be the result of the
diet differences. Overall, the distribution of GH family
enzymes in the micobiomes of R. bieti generally reflected its
adaptation to special food types.
Comparative metagenomic analysis
Despite the extensive variation among individuals, the
gut microbiota of members of the same species are
often more similar to one another compared with those
of other species. Thus, it is important to provide a
comparison between the gastrointestinal microbiomes of
primates and those of other animals. The results of this
study were compared with data sets from different
animals and even humans in the MG-RAST database. Paired
data from other studies were chosen, such as lean (LMC)
and obese (OMC) mouse cecal metagenomes , two
chicken cecal metagenomes (CCA, CCB) , two canine
intestinal metagenomes (K9C, K9BP) , and two
human fecal metagenomes (F1S, HSM). F1S was a healthy
human fecal metagenome , whereas HSM was defined
as human feces from a malnourished subject. In addition,a
cow rumen and a NHPs pygmy loris  fecal
metagenomes were also utilized for comparison. The
comprehensive overview of the data sets is shown in Additional file 9.
Clustering the metagenomes was carried out with
unscaled Manhattan variance distances and presented through
a double hierarchical dendrogram. The clustering-based
comparisons were demonstrated at the phylogenetic level
(Figure 2) and the metabolic level (Figure 4). In the
phylogenetic comparison, the R. bieti samples clustered with the
human fecal metagenomes (F1S), two chicken cecal
metagenomes (CCA and CCB), a cow rumen metagenome and
a pygmy loris fecal metagenome and separated from those
of the two mouse metagenomes (OMC and LMC).
Nonmetric multidimensional scaling (MDS) plot illustrated that
the distances among the R. bieti and cow rumen as well as
among HSM, K9C and K9BP were the nearest (Additional
file 10). This may be due to similar bacterial diversity
influenced by similar diet rich in fibre within R. bieti and cow.
In all the samples, the Actinobacteria, Bacteroidetes,
Firmicutes and Proteobacteria were the most abundant
(Figure 2). The heat map also demonstrates that the R. bieti
metagenome was most distinguished by the greater
prevalence of Fibrobacteres, an important phylum of
cellulosedegrading bacteria, compared with other animals.
Metabolism-based hierarchical clustering demonstrates
that the R. bieti, dog, chicken, human and pygmy loris
samples clustered together and separated from those of
the mice and cow (Figure 4). Non-metric MDS plot
illustrated that all metagenomics data have more than
80% similarity (Additional file 11). As expected, all the
gut metagenomes were dominated by carbohydrate
metabolism subsystems with amino acids, protein, and cell
We presented for the first-time the application of the
shotgun metagenomic pyrosequencing approach to study the
fecal microbiome of the R. bieti. The overall goal of this
study was to characterize the species composition and the
functional capacity of the R. bieti fecal microbiome.
Taxonomic analysis of the metagenomic reads showed
similarities among the gut microbiomes of the R. bieti, humans
and other animals. Four phyla dominated the
microbiomes, namely, Actinobacteria, Bacteroidetes, Firmicutes
and Proteobacteria. However, the relative proportion of
the phyla was different. At the genus-level taxonomic
resolution, Bacteroides species were the most abundant,
most of which were represented by B. vulgates and
B. fragilis. The organisms belonging to the same genus
also represent one of the most abundant microbial taxa in
the human intestinal microbiota [7,68]. The R. bieti faecal
samples contained more bacteria belonging to the phylum
Fibrobacteres, all of which were represented by the
lignocellulose-degrading bacterium F. succinogenes. The
high amount of F. succinogenes present in the R. bieti feces
indicates a high turnover of lignocellulose in this NHPs.
Archaea, fungi, and viruses are minor constituents of the
R. bieti fecal microbiome. All archaea are members of
Crenarchaeota and Euryarchaeota, with methanogens
being the most abundant and diverse. Two fungi phylotypes
were present in the R. bieti fecal microbiome, namely,
Ascomycota and Basidiomycota, with Ascomycota being the
primary contributor. Only about 0.17% of sequences were
of viral origin, and all sequences were classified as
bacteriophages. Overall, the microbial populations of the
R. bieti gut system appeared to consist of taxa with known
capacities for degradation and utilizing foods high in
The comparative metagenomic analyses identified
unique and/or overabundant taxonomic and functional
elements in the R. bieti distal gut microbiomes. Relatively
abundant bacteria of phylum Fibrobacteres and
Spirochaetes were found in the R. bieti metagenome compared
with all the other gut metagenomes. Primary functional
categories were similar to those of other gut microbiomes
and were associated mainly with protein, carbohydrates,
amino acids, DNA and RNA metabolism, cofactors
(vitamins, prosthetic groups, pigments), cell wall and capsule
and membrane transport. Comparing GH profiles of R. bieti
with those of herbivores, we found that the R. bieti
microbiome possessed a large number of GH family specific for
oligosaccharides, similar to that for the cow rumen. These
findings may reflect the evolutionary adaptations for the
highly specialized herbivory of R. bieti.
Figure 4 Metabolic clustering of R. bieti, pygmy loris, human, mouse, canine, cow, and chicken gastrointestinal metagenomes. A double
hierarchical dendrogram was established through a weight-pair group clustering method based on the non-scaling Manhattan distance. The
dendrogram shows the distribution of the functional categories among the eleven metagenomes from the seven different hosts, including R. bieti
(JSH), pygmy loris (WFH), humans (HSM and F1S), murine (LMC and OMC), canine (K9C and K9BP), cow (CRP), and chicken (CCA and CCB). The linkages
of the dendrogram are based on the relative abundance of metabolic profiles. The heat map depicts the relative percentage of each category of
function (variables clustering on the y axis) in each sample (x axis clustering). The heat map color represents the relative percentage of functional
categories in each sample, with the legend indicated at the upper left corner. Branch length indicates the Manhattan distances of the samples along
the x axis (scale at the upper right corner) and of the microbial classes along the y axis (scale at the lower left corner).
These results contribute to the limited body of primate
metagenome studies and provide a unique genetic
resource of plant cell wall degrading microbial enzymes.
More studies involving the deeper sequencing of
metagenomes are required to fully characterize the
gastrointestinal microbiome of the R. bieti and other NHPs in
healthy and diseased states, of varying ages or genetic
backgrounds, and in the wild or in captivity.
Fecal sample collection
Fresh fecal samples from eight R. bieti were collected at
a single time point from the Baimaxueshan National
Nature Reserve of Weixi, Yunnan Province, China, with the
permission of the authorities of Baimaxueshan National
Nature Reserve. We tracked the R. bieti until they
defecated; the fecal samples were immediately collected
aseptically. The fresh fecal samples were transported to
the laboratory on dry ice within 24 hours of collection,
and then stored at 80C until DNA extraction. We
brought no toxic substance that would have adverse
effects on the biotic community to minimize disturbance
in the animal habitats. The research complied with the
protocols established by the China Wildlife Conservation
Association and adhered to the American Society of
Primatologists (ASP) Principles for the Ethical
Treatment of Non-Human Primates as well as the legal
requirements of China.
DNA extraction and shotgun pyrosequencing
Genomic DNA were extracted from the fecal samples with
the QIAamp DNA stool mini kit (Qiagen, Valencia, CA,
USA) following the protocol provided by the supplier
(0.25 g of each fecal sample). The quality and quantity of
the DNA were determined with a nanodrop (ND-1000)
spectrophotometer (Nanodrop Technologies, Wilmington,
DE, USA) through agarose gel electrophoresis. DNA
samples were stored frozen (20C) until use.
A total of 500 ng of pooled DNA was subjected to
library preparation and shotgun pyrosequencing using the
Roche 454 GS FLX Titanium System (Roche, Basel,
Switzerland) as it was not feasible to distinguish which
fecal sample corresponded to which individual. The
obtained reads were uploaded to MG-RAST  under the
name JSH_Metagenome and were assigned the
Metagenome ID: 4452795.3. The MG-RAST v.3.0 online server
quality control pipeline was utilized to remove reads of
short length and poor quality before annotation and the
analysis of metagenomic data . The pipeline
parameters were kept at default settings.
Bioinformatics and statistical analysis
Comparative metagenomic analysis was performed with
MG-RAST pipelines. The metagenomic runs from the R.
bieti data were compared with the current publicly
available gut metagenomes in the databases. In the MG-RAST
metagenomic annotation pipeline, the R. bieti fecal
metagenomic datasets were compared with ten public sets of
data from animals, including chicken cecum A (CCA
4440283), chicken cecum B (CCB 4440284), two dog
metagenome data sets (K9C 4444164 and K9BP 4444165),
lean mouse cecum (LMC 4440463), obese mouse cecum
(OMC 4440464), cow rumen (CRP 4441682), human stool
metagenome (HSM 4444130), human F1-S feces
metagenome (F1S 4440939) and pygmy loris fecal metagenome
(WFH 4476304). The organisms in MG-RAST were
classified through the M5NR protein database (http://tools.
metagenomics.anl.gov/m5nr/). The functional annotation
and classification relied on the SEED subsystem (;
databases. The maximum e-value of 1e-5, minimum percent
identity of 60, and minimum alignment length of 30 were
applied as the parameter settings in the analysis. The
taxonomic and functional profiles were normalized to
determine the differences in the sequencing coverage by
calculating the percent distribution prior to downstream
statistical analysis. Clustering was performed using Wards
minimum variance with unscaled Manhattan distances
. Heat maps were drawn by hierarchal clustering
performed with NCSS 2007 (Kaysville, Utah). Non-metric
MDS analysis based on Bray-Curtis similarity and
Euclidean distance were performed using PRIMER 6 statistical
software (PRIMER-E Ltd., Plymouth Marine Laboratory,
Annotations based on the carbohydrate-active enzymes
database (; http://www.cazy.org) were provided for all
the reads that passed the MG-RAST QC filter. Sequences
are subject to Blast analysis against a library composed of
modules derived from CAZy at an e-value restriction of
1 106. The cluster analysis of metagenomes on the basis
of GH profile was carried out using the PAST v.2.17b data
analysis package .
NHPs: Non-human primates; MG-RAST: Metagenome Rapid Annotation using
Subsystem Technology; QC: Quality control; NCBI: National Center for
Biotechnology Information; SRA: Short Read Archive; CAZy: Carbohydrate-Active
enzymes; GH: Glycoside hydrolase; GT: Glycosyl transferase; CBM:
Carbohydratebinding modules; MDS: Multidimensional scaling; ASP: American Society of
BX participated with the study design, bioinformatic analyses and manuscript
preparation. WX contributed with bioinformatic and statistical analyses.
JL contributed reagents and materials for the study. LD and CX carried out
sample collection and sample processing. XT and YY participated with statistical
analyses. YM contributed analysis tools for the study. JZ participated with
bioinformatic analyses. JD and QW carried out sample processing.
ZH collaborated in the design and coordination and helped to draft the
manuscript. All authors read and approved the final manuscript.
This work was supported by the National Natural Science Foundation of
China (31360268 and 31160229). We thank Dr. Bernard Henrissat for his
assistance with parts of the data analysis based on CAZy. We also thank
Dr Hein Min Tun for his assistance with the non-metric MDS analysis.
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