FEMS Microbiology Ecology, 91, 2015, fiv107
doi: 10.1093/femsec/fiv107
Advance Access Publication Date: 11 September 2015
Research Article
RESEARCH ARTICLE
Metatranscriptomic discovery of plant
biomass-degrading capacity from grass
carp intestinal microbiomes
Shangong Wu1,2 , Yi Ren3 , Chun Peng1 , Yaotong Hao1 , Fan Xiong1 ,
Guitang Wang1,∗ , Wenxiang Li1 , Hong Zou1 and Esther R. Angert4
1
Institute of Hydrobiology, Chinese Academy of Sciences, and the Key Laboratory of Aquatic Biodiversity and
Conservation of Chinese Academy of Sciences, Wuhan, Hubei Province 430072, P. R. China, 2 Freshwater
Aquaculture Collaborative Innovation Center of Hubei Province, Wuhan 430070, P. R. China, 3 Shanghai
Majorbio Bio-Pharm Technology Co., Ltd, Shanghai 201203, P. R. China and 4 Department of Microbiology,
Cornell University, Ithaca, NY 14853, USA
∗ Corresponding author: Institute of Hydrobiology, Chinese Academy of Sciences, and the Key Laboratory of Aquatic Biodiversity and Conservation of
Chinese Academy of Sciences, Wuhan, Hubei Province 430072, P. R. China. Fax: +86-027-68780123; E-mail: [email protected]
One sentence summary: Grass carp intestinal microbiome functions in carbohydrate turnover and fermentation.
Editor: Julian Marchesi
ABSTRACT
Despite the economic importance of fish, the ecology and metabolic capacity of fish microbiomes are largely unknown.
Here, we sequenced the metatranscriptome of the intestinal microbiota of grass carp, Ctenopharyngodon idellus, a freshwater
herbivorous fish species. Our results confirmed previous work describing the bacterial composition of the microbiota at the
phylum level as being dominated by Firmicutes, Fusobacteria, Proteobacteria and Bacteriodetes. Comparative
transcriptomes of the microbiomes of fish fed with different experimental diets indicated that the bacterial transcriptomes
are influenced by host diet. Although hydrolases and cellulosome-based systems predicted to be involved in degradation of
the main chain of cellulose, xylan, mannan and pectin were identified, transcripts with glycoside hydrolase modules
targeting the side chains of noncellulosic polysaccharides were more abundant. Predominant ‘COG’ (Clusters of
Orthologous Group) categories in the intestinal microbiome included those for energy production and conversion, as well as
carbohydrate and amino acid transport and metabolism. These results suggest that the grass carp intestinal microbiome
functions in carbohydrate turnover and fermentation, which likely provides energy for both host and microbiota. Grass carp
intestinal microbiome thus reflects its evolutionary adaption for harvesting nutrients for an herbivore with a
high-throughput nutritional strategy that is not dominated by cellulose digestion but rather the degradation of intracellular
polysaccharides.
Keywords: CAZy; grass carp; microbiome; metatranscriptome; plant cell wall polysaccharide
INTRODUCTION
Certain plant polysaccharides are nutritionally unavailable to
most animals without the help of symbionts in the gastroin-
testinal tract (Warnecke et al. 2007; Brulc et al. 2009; Suen et al.
2010). Cellulolytic bacteria, for example, are common in gastrointestinal microbiomes, and microbial glycoside hydrolase
Received: 26 April 2015; Accepted: 27 August 2015
C FEMS 2015. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 10
(GH) genes and carbohydrate-binding modules (CBMs) are abundant in digestive tract metagenomes (Ley et al. 2005; Brulc et al.
2009; Pope et al. 2010; Martens et al. 2011; Zhu et al. 2011; Engel, Martinson and Moran 2012), suggesting that microbial symbionts are involved in the hydrolysis of plant material to help
extract nutrients (Hess et al. 2011). However, these bacteria are
widely distributed in natural environments (Allgaier et al. 2010;
Wu et al. 2012), and it is sometimes difficult to determine if microbes are only incidental visitors in the gastrointestinal tract of
host animals (Clements et al. 2014). In addition, it is not possible to tell if genes detected in a metagenomic survey are even
expressed. Therefore, previous gene-based studies only characterize species composition, abundance and the metabolic potential of intestinal bacteria related to plant cell degradation.
RNA-Seq is a recently developed approach to metatranscriptome
or transcriptome profiling that uses deep-sequencing technologies (Wang, Gerstein and Snyder 2009; Mason et al. 2012) to provide a far more precise and comprehensive measure of gene
expression patterns. This technology has the potential to shed
new light on the metabolic and functional capabilities of intestinal microbiomes.
The grass carp, Ctenopharyngodon idellus, is a freshwater herbivorous fish species of the family Cyprinidae. The fish is native to rivers, lakes, reservoirs and ponds in eastern Asia, especially in China, and has been introduced to more than 100
countries since the middle of last century (Ni and Wang 1999;
Song et al. 2009). The grass carp has now become an important commercial aquaculture species, with a steadily increasing global production output, reaching 4.57 million tons in 2011
(ftp://ftp.fao.org/fi/stat/summary/a-6.pdf). In the wild, the fish
feeds mainly on submersed aquatic vegetation by using comblike pharyngeal teeth to grind plant material (Cui et al. 1991;
Ni and Wang 1999). The amount of plant material consumed
by an individual fish may reach 40–100% of its body weight
each day (Li, Yang and Lu 1980; Laird and Page 1996; Ni and
Wang 1999). The grass carp lacks a stomach and pyloric caeca
and has an intestine only twice its body length (Ni and Wang
1999). Food transits through the gut in 8–18 h (Stevens and
Hume 1998; Ni and Wang 1999) and some foraged materials pass
through undigested (Ni and Wang 1999), suggesting that any
processing of plant biomass by intestinal microbes must occur
quickly.
Previous studies detected limited cellulolytic activity and
metabolism of carbohydrates in the grass carp intestine (Lesel,
Fromageot and Lesel 1986; Das and Tripathi 1991; Ni et al. 2014).
Further work found short-chain fatty acids in the gut of grass
carp (Stevens and Hume 1998), suggesting the presence of bacterial fermentation. Culture-based studies on selective media
incubated under aerobic conditions recovered Aeromonas, Bacillus, Vibrio and Enterobacter spp. as the major cellulose-degrading
bacteria isolated from the fish intestinal contents (Saha et al.
2006; Feng et al. 2008; Jiang et al. 2011; Wang et al. 2014). More
recently, deep sequencing of 16S rRNA gene amplicons from the
grass carp intestine has widened our views on intestinal microbiota diversity, implicated Anoxybacillus, Leuconostoc, Clostridium, Actinomyces and Citrobacter as potential polysaccharidedecomposing bacteria in the fish intestine, and also revealed
the abundant bacteria which might be related to feed digestion
(Wu et al. 2012), suggesting that the grass carp intestinal microbiota plays important roles in host nutrition as reported for other
vertebrates (Ley et al. 2008). Little is known about the genetic capability of the grass carp gut microbiome, especially its role on
plant biomass degradation considering the short period of time
that feed stays in the intestine.
Table 1. Formulation and chemical composition of the experimental
diets (% dry matter).
Ingredients
Soybean meal
Rapeseed meal
Cottonseed meal
Corn starch
Fish meal
Fish oil
Cholinechloride
Monocalcium phosphate
Mineral premix1
Vitamin premix2
Proximate chemical composition
Crude fiber
Crude protein
Crude lipid
Crude ash
FM∗
97.2
CF†
SG‡
35
20
20
10
10
2.64
0.3
1.5
0.5
0.5
1.5
0.5
0.5
0.49
62.76
5.45
11.09
6.30
40.45
4.26
6.58
29.00
10.37
3.75
6.33
Note: 1 Mineral premix (mg kg−1 diet): Zn, 80.00; Fe, 150.00; Cu, 4.00; Mn, 20.00; I,
0.40; Co, 0.10; Se, 0.10; Mg, 100.00.
2
Vitamin premix (mg kg−1 diet): thiamin, 20; riboflavin, 20; pyridoxine, 20;
cyanocobalamine, 2; folic acid, 5; calcium patotheniate, 50; inositol, 100; niacin,
100; biotin, 5; starch, 3 226; vitamin A (ROVIMIX A-1000), 110; vitamin D3, 20; vitamin E, 100; vitamin K3, 10.
∗
Group contains FM93, FM96 and FM98, † group includes CF05, CF06 and CF07,
and ‡ group contains SG6, SG7 and SG9.
Here, we report analyses of metatranscriptomic data from
hindgut microbiomes of farmed and wild-caught grass carp.
These data were used to investigate the composition of the active microbiota of the grass carp hindgut and examine differences in the microbiomes of fish fed different diets. The primary
goal of this study was to explore the possible role of intestinal
microbiome on polysaccharide and oligosaccharide digestion.
MATERIALS AND METHODS
Experimental animals and sample collection
The farmed grass carp was raised in artificial ponds in Wuhan
City, Hubei Province, China from 6 April to 5 July, 2012. During
this time, the pond water temperature ranged from 22 to 32◦ C.
The ponds are made of cement and brick, and are located in
the middle reaches of the Yangtze River, within the major production region of the fish. The water depth and coverage of the
ponds were approximately 1.5 m and 60 m2 , respectively. The
fishes were added specifically for this experiment and the three
groups of grass carp were cultured using three different diets
(Table 1). During the experimental period, the fishes were fed
to apparent satiation twice a day (7:30, 16:00 h). At the end of
the feeding experiment, when water temperatures were high
(∼32◦ C) and conditions would support more active digestion and
high growth rates (Ni and Wang 1999), fishes were harvested by
net. Three fishes were taken from an experimental pond each
day over the course of 3 days (July 5–7). All fishes were caught
at the same time of day (∼13:30 h) and all were approximately
30 cm fork length. The fishes were then euthanized in the laboratory with MS-222. Fish designated FM93, FM96 and FM98 fed
exclusively on fishmeal, a second group (CF05, CF06 and CF07)
was fed a normal compound feed with 6.3% crude fiber and
the last group (SG6, SG7 and SG9) was fed fresh Sudan grass
(Sorghum spp.). The fishes were dissected immediately with sterile scissors. The intestines were aseptically removed from the
Wu et al.
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abdominal cavity and the contents of hindgut were gently
squeezed out, placed in a sterile tube and deposited in liquid
nitrogen. The grass carp intestine is arranged in the abdomen
in eight bends defined by hairpin loops. Only the most posterior segment described previously as the hindgut was used in
this study (Ni and Wang 1999). The hindgut was used in these
analyses as it most likely harbors bacterial symbionts actively
invoved in digestion and smaller proportion of allochthonous
bacteria compared to more anterior sections. In addition, two
wild fishes (DR and LL), with fork length of 40.5 and 37.5 cm, respectively, were caught from Danjiangkou Reservoir and Liangzi
Lake, in June 2012. The temperatures of these bodies of water
ranged from 20 to 25◦ C in June. Aquatic grass was found in the
digesta in the hindgut of each wild-caught fish but not observed
in any of the farmed fish. The methods used in this study were
reviewed and approved by the ethics committee of the Institute
of Hydrobiology, Chinese Academy of Sciences.
Taxonomic assignment, gene prediction and gene
functional classification
RNA extraction and sequencing
Carbohydrate-active enzyme (CAZy) family annotation
and analysis
For total RNA extraction, frozen gut contents were preserved in
RNAprotect Bacteria Reagent (Qiagen, Germany), to help stabilize RNA before bacterial cells were lysed. Samples were ground
in liquid nitrogen. Thereafter, total RNA was extracted using the
RNeasy Mini Kit (Qiagen, Germany) following the manufacturer’s
protocol with a minor modification, where RNA-free sterile zirconia beads were added to the samples to improve extraction
yield (Yu and Morrison 2004). For each gut sample, RNA was extracted in duplicate and the extracts from the same sample were
pooled. Finally, any residual DNA was removed from the RNA
samples using the RNase-Free DNase Set (Qiagen, Germany) according to the manufacturer’s manual. The quality and quantity of total RNA produced was estimated with a NanoDrop 2000
spectrophotometer (Thermo Scientific, USA) and Agilent 2100
Bioanalyzer (Agilent Technologies, Palo Alto, CA). Thereafter,
rRNA was depleted using the Ribo-Zero rRNA Removal Kit (MetaBacteria) and Ribo-Zero rRNA Removal Kit (Plant Leaf) (Epicenter
Biotechnologies, Madison, WI). Poly(A) RNA was removed using
Oligotex mRNA Kit (Qiagen, Valencia, CA). The TruSeq RNA Sample Prep Kit was used for sample preparation according to the
TruSeq Sample preparation guide (Illumina, San Diego, CA). Sequencing was performed on the Illumina Genome Analyzer IIx
(Illumina, San Diego, CA).
Sequence processing
Poor quality bases of raw Illumina metatranscriptomic reads
were removed by iterating a 5 nt window across the length of
each sequence and removing nucleotides in windows with a
mean quality score <20; iteration was stopped when the mean
quality score was >20. Reads with ‘N’ bases and lengths below 20 nt were also discarded. Adaptor sequences were removed by using Cross˙match (http://www.phrap.org) to search
a database of Illumina adaptor sequences. Following trimming
and adaptor removal, paired reads and single reads with high
quality were preserved. Reads were assembled using the SOAPdenovo assembler (Luo et al. 2012) at a range of k-mers (25, 27,
29 and 31). Default settings for all SOAPdenovo assemblies were
used. The longest contig length, highest read utilization rate
and N50 and N90 lengths were used to access the best assembly
results.
Putative mRNA reads were characterized with BLASTX (Altschul
et al. 1997) comparisons against the integrated NCBI nr (nonredundant) protein database (E-values <10−5 ). The LCA-based algorithm implemented in MEGAN (Huson et al. 2007) was used to
determine the taxonomic level of each gene.
MetaGene Annotator (Noguchi, Park and Takagi 2006) was
applied to assembled contigs to identify ORFs longer than 100
bp. ORFs were translated using the Bacterial Genetic Code (NCBI
translation table 11). BLASTP (Altschul et al. 1997) was used to
query the predicted protein sequences against the integrated
nr protein database. COG (Clusters of Orthologous Group) category (Tatusov et al. 2003) assignments were performed through
BLAST-based similarity searches to identify the closest matching
sequence in the STRING database (Search Tool for the Retrieval
of Interacting Genes, http://string-db.org/) (E-value < 10−6 ).
For CAZy family annotation (http://www.cazy.org/) (Cantarel
et al. 2009), contigs originating from scaffolds longer than
500 bp were extracted and ORFs predicted using the EMBOSS
suite (http://www.sanger.ac.uk/Software/EMBOSS/) (Rice, Longden and Bleasby 2000). To find CAZy genes, translated ORFs
were used to search dbCAN (http://csbl.bmb.uga.edu/dbCAN/)
(Yin et al. 2012), and the following parameters were applied: if
alignment >80 aa, use E-value < 10−5 , otherwise use E-value <
10−3 ; covered fraction of HMM > 0.3.
To account for expression bias due to transcript length, each
sample transcript expression was normalized to provide values
of reads per kilobase of transcript per million reads mapped
(RPKM) using the formula:
R = 109 C /NL
where C = the number of reads that could be mapped in
that sample to the specific bacterial transcript, L = the length
of the transcript and N = the total number of reads that could be
mapped to bacterial transcripts in that sample (Mortazavi et al.
2008).
If a contig was assigned to a cellulase gene family,
the related transcript reads were extracted using Bowtie 2
(http://bowtie-bio.sourceforge.net/index.shtml) with default parameters (Langmead and Salzberg 2012). For each sample, the
relative expression of each gene family was derived from the
sum of RPKM values for each transcript associated with that
gene family.
Multidimensional scaling statistical analyses
Nonmetric multidimensional scaling (NMDS) was performed
with Primer 5 to group the different sample communities (Clarke
1993). In general, considering the accuracy of Illumina short
reads (Krause et al. 2008), class level was used to classify each
bacterial mRNA gene sequence, and the percent contribution
from each class was calculated by dividing the number of sequences in each class by the total number of mRNA gene sequences in the respective community. Secondly, the similarity/differences in the abundances of bacterial CAZy proteins
among the present samples were determined.
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Data accessibility
All the RNA-Seq datasets have been submitted to the DDBJ
Sequence Read Archive (SRA) under accession number:
PRJDB1893 (https://trace.ddbj.nig.ac.jp/BPSearch/bioproject?acc
=PRJDB1893), and LA000001-LA328022 (328022 entries) (https://
www.ddbj.nig.ac.jp/whatsnew/wn140609-e.html).
RESULTS AND DISCUSSION
Many recent studies have highlighted the coevolutionary adaptation between a host animal and its alimentary tract microbiome (Ley et al. 2005, Warnecke et al. 2007, Brulc et al. 2009,
Muegge et al. 2011). However, these works have focused on terrestrial animals. The present study used metatranscriptome
analyses of grass carp hindgut samples to characterize the taxonomic composition, metabolic activities and expressed GHs of
the intestinal microbiome, and to compare how metabolic capabilities differ between animals fed different diets. The hindgut
microbiota was specifically targeted for these studies due to the
high numbers of bacteria in the hindgut compared to more anterior regions (Zhou et al. 1998). In addition, these hindgut microbes most likely represent indigenous populations of symbiotic microorganisms and are less likely to represent ingested
populations (Clements et al. 2014).
Figure 1. Phylogenetic distribution of mRNA transcripts mapped to known bacterial and archaeal genes. Results are shown as the relative abundance of reads
assigned at the phylum level in the 11 grass carp samples. Fish designated FM93,
FM96 and FM98 were fed exclusively on fishmeal, CF05, CF06 and CF07 were fed
a normal compound feed with 6.3% crude fiber, and SG6, SG7 and SG9 were fed
fresh Sudan grass (Sorghum spp.). The category presented as ‘Others’ includes
the sum of different phyla which are less than 1% in the samples.
Metatranscriptomic data from the intestinal
microbiomes of grass carp
The various metatranscriptomic datasets generated from this
study are summarized in Table S1 (Supporting Information). After filtering read quality, the 11 intestinal samples produced
676 million reads (total 62 Gb) with average length of 77–98 bp.
For most samples, small subunit rRNAs accounted for no more
than 5.0% of the reads except in FM96 (7.26%), FM98 (11.9%) and
DR (11.11%). Total coding sequences identified in all samples
were more than 28.12% of the reads except SG7 (7.38%) and DR
(10.80%), and most of the coding sequences in each library were
related to bacteria. Crenarchaeota, Aquificae, Chlorobi and Thermotogae sequences were detected in low abundance (not more
than 0.15%) and only as mRNA reads from a few samples. Although there were some differences in the abundances of different phyla in different samples, mRNA reads revealed that the
11 communities were dominated by Fusobacteria, Firmicutes,
Proteobacteria and Bacteroidetes (Fig. 1), which accounted for
57.11–82.37% of the combined bacterial and archaeal coding sequences. These results are generally consistent with those from
previous studies characterizing the microbiota of fishes (Wu et al.
2010, 2012; Roeselers et al. 2011; Xing et al. 2013; Ni et al. 2014; Xia
et al. 2014).
Similar to the previous studies on archaea in digestive tracts
(Brulc et al. 2009; Zhang et al. 2009; Ni et al. 2014), methanogens
were the dominant archaeal populations in the grass carp distal
gut (not less than 55.43% of archaeal transcripts, except sample SG6, where the proportion was 38.42%; Table S2, Supporting Information). However, the archaeal sequences were only
a small proportion of the reads compared with bacterial sequences, and were no more than 0.05% in any library (Table
S1, Supporting Information), which is considerably smaller than
those typically encountered in ruminants, where Archaea comprise 0.5–3.0% of the microbiotas (Lin, Raskin and Stahl 1997;
Ziemer et al. 2000; Brulc et al. 2009). Methanogenic archaea may
indirectly contribute to polysaccharide processing by lowering
the partial pressure of hydrogen by generating methane, and
Figure 2. NMDS plot comparing the taxonomic composition of cultured and wild
grass carp intestinal microbiomes from mRNA sequences assigned at the class
level.
thereby may improve microbial fermentation rates and increase
host energy extraction from indigestible polysaccharides (Backhed et al. 2005; Turnbaugh et al. 2006; Zhang et al. 2009). The lower
abundance of archaea found here may contribute to the low efficiency of plant feed utilization by grass carp (Li, Yang and Lu
1980; Ni and Wang 1999).
One-way ANOVA analysis was used to identify if there were
significant differences in bacterial phyla abundances in cultured fish fed different feeds. Our results showed that for
mRNA reads, bacterial abundances in Fusobacteria and Proteobacteria were significantly different (F = 5.528, P < 0.05, and
F = 5.994, P < 0.05, respectively). Fusobacterial mRNAs were
abundant in all but the fish fed Sudan grass while Proteobacterial transcripts were more abundant in fish fed Sudan grass
and the wild-caught individuals than in fish fed other diets.
Further, the phylogenetic compositions of 11 grass carp intestinal libraries reflected the type of food the fish had consumed
(Fig. 2). These results further support our model that diet influences the composition of the active microbiota in the grass carp
hindgut.
Wu et al.
5
Figure 3. Distribution of COG functional annotations of reads mapping to known bacterial transcripts for the 11 grass carp microbiome samples.
Functional analysis of metatranscriptome datasets
The distribution of transcripts to general functional categories
was assessed on the basis of best BLAST matches to the COG
database. In general, COG functional profiles were consistent
across different communities (Fig. 3). When the data for all
of the grass carp metatranscriptomes were combined, most of
the transcripts mapped to the category ‘Translation, ribosomal
structure and biogenesis’ [J] (22.8%), followed by ‘Energy production and conversion’ [C] (9.7%) and ‘Carbohydrate transport and
metabolism’ [G] (8.0%). The categories ‘Amino acid transport and
metabolism’ [E] and ‘Lipid transport and metabolism’ [I] were
3.6 and 1.0%, respectively. The COG functional category profiles from grass carp intestinal metatranscriptomes were similar to those found in gastrointestinal or rumen metagenomes
from panda, wallaby, cow, termite and human (Warnecke et al.
2007; Brulc et al. 2009; Pope et al. 2010; Qin et al. 2010; Zhu
et al. 2011), highlighting metabolic similarities of microbiomes
in these herbivorous and omnivorous animals. Further one-way
ANOVA analyses on the cultured fish samples indicated that category G was significantly different among the fish fed different
fiber diets (F = 4.97, P ≤ 0.05), while the other categories showed
no significant differences: C (F = 1.25, P = 0.35), E (F = 3.44, P =
0.10) and I (F = 2.23, P = 0.19). All this suggests significant divergence in the function of intestinal microbiomes in relation to
different diets and specifically the exploitation of carbohydrates.
Plant polysaccharide degradative enzymes
Grass carp uses comb-like pharyngeal teeth situated in the
throat to cut, tear and grind plant material, which assist in
the breakdown of the plant cell walls and release of cell contents (Ni and Wang 1999). The fishes prefer softer and more
succulent submersed aquatic macrophytes, and this tendency
has been used on a large scale for aquatic weed control
(Ni and Wang 1999; Pı́palová 2006). In cultured conditions, the
fish will consume Sudan grass (Sorghum spp.) and other terrestrial plant forage. These materials often contain a complex mixture of polysaccharides, with cellulose and hemicelluloses as
major constituents (Sun and Cheng 2002; Bayer, Shoham and
Lamed 2013; Berti et al. 2013). We used metatranscriptome data
to investigate shifts in expression of carbohydrate-active enzymes in fish fed different diets, including a diet of Sudan grass.
The de novo assembly of Illumina data resulted in a set of 195
Mbp of assembled contigs (Table S3, Supporting Information).
Predicted ORFs were compared against dbCAN (Yin et al. 2012) to
identify CAZy families. The sequences assigned to a CAZy family
were further filtered for microbial data sets. These analyses retrieved 8821 genes and modules from all of the grass carp intestinal microbiomes, distributed among 200 different CAZy families
(Table S4, Supporting Information). Of these genes and modules,
37.66% belonged to GH families, 30.20% were assigned to GT
(Glycosyl Transferase) families, 13.13% were attributed to CBM
families and 10.58% were in CE (Carbohydrate Esterase) families.
The GH catalytic modules contained 3322 sequences belonging
to 77 GH families, and CBM module included 1158 sequences
from 48 families. Previous metagenomic studies confirmed that
the microbiomes in the alimentary tract of cow, panda, wallaby,
human and termite have the metabolic potential to hydrolyze
carbohydrate components of the plant cell wall but the proportion of genes dedicated to these functions varied from one
metagenome to another (Warnecke et al. 2007; Brulc et al. 2009;
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 10
Pope et al. 2010; Qin et al. 2010; Hess et al. 2011; Zhu et al. 2011).
The present study indicated that the microbiome in grass carp
hindgut can digest plant structural polysaccharides including
cellulose, hemicellulose and pectin based on the occurrence of
characterized GH families and CAZy modules (Tables 1; Tables
S4 and S5, Supporting Information).
Microbial cellulases fall into GH families 5, 6, 7, 9, 44, 45, 48,
74 and 124. A total of 23 genes with GH5 family and 11 GH74
family (β-1,4-endoglucanases) domains were identified from the
entire grass carp data, along with 12 genes with GH9 and 2 GH48
family (cellobiohydrolase) domains (Tables 2 and S5, Supporting
Information). Of these GH families, GH5 was present in seven
individuals; the other two families, GH74 and GH9, were both
found in five samples; GH48 was only detected from one microbiome (Table S5, Supporting Information). The results are congruent with previous studies that GH5 was the most frequent
cellulase gene in digestive tract microbiomes (Warnecke et al.
2007; Pope et al. 2010; Swanson et al. 2011; Zhu et al. 2011; Engel, Martinson and Moran 2012). Characterized GH48 exoglucanases are especially efficient in the digestion of cellulose, and
have been found in microbiomes of bovine rumen (Brulc et al.
2009; Hess et al. 2011) and pygmy loris intestine (Xu et al. 2013).
The following CBM families, CBM2, CBM3, CBM4, CBM6, CBM8,
CBM9, CBM30, CBM46, CBM49 and CBM64 (19, 6, 20, 17, 1, 21,
6, 15, 1 and 2 members, respectively), which likely bind cellulose, were detected, of which CBM46 and CBM49 were characterized with GH5 and GH9, respectively. In addition, 10 cohesin
and 22 dockerin modules were detected. Cohesin and dockerins are common in cellulosomes and are considered cellulosome ‘signature sequences’ (Bayer, Shoham and Lamed 2006).
In total, cellulosome-based systems would be expected to play
a role in plant cell wall hydrolysis in this community. To our
knowledge, the cellulosomes present in gut microbiome of
aquatic animals have not been characterized thus far and would
be a worthy focus of future studies. Phylogenetic analysis of
these contigs indicates that Firmicutes (56.25%), Proteobacteria
(16.67%) and Bacteroidetes (10.42%) were the dominant cellulolytic bacteria (Table S6, Supporting Information). Specifically,
Clostridium (22.92%), Cellulosilyticum (14.58%), Aeromonas (10.42%)
and Caldilinea (8.33%) or close relatives appear to be the main
types of cellulose-decomposing bacteria in grass carp intestine,
which is generally congruent with the results reported by Jiang
et al. (2011) and Wu et al. (2012).
A total of 14 sequences affiliated with the GH10 family, which
decomposes the main chains of xylan, were retrieved and 143
GH43 family sequences, which contain enzymes active on the
side chains of xylan, were recovered (Table 2). Additionally, all
individuals that had GH10 members possessed GH43 members,
which might indicate that the two catalytic modules act synergistically to degrade xylan. This combination is observed in diverse cellulolytic bacteria (Bayer, Shoham and Lamed 2013). Further, CE1, CE2, CE3, CE4, CE6, CE7 and CE12 (243, 7, 48, 189, 22,
48 and 16 members, respectively) including acetyl xylan esterase
activity, part of the system responsible for xylan hydrolysis, were
widely distributed among grass carp gut microbiomes (Table
S4, Supporting Information). CE1 also possesses ferulic acid esterase activity. The combination of a typical xylanase together
with a feruloyl esterase would allow the rapid cleavage of xylan
chain from the lignin, which enhances the digestion of plant cell
wall polysaccharides (Bayer, Shoham and Lamed 2013). In addition, several CBMs that modulate the action of xylanase catalytic modules (Bayer, Shoham and Lamed 2006) were detected
(Table S4, Supporting Information), including CBM2, CBM9 and
CBM22 (19, 21 and 9 members, respectively). The GH10 xylanase
and GH5 cellulase would likely be most effective on cellulosexylan junctions (Bayer, Shoham and Lamed 2006). Strikingly, the
present study found that both GH10 and GH5 catalytic modules
occurred simultaneously in different grass carp microbiomes,
which could reflect the synergy of these enzymes on plant cell
wall hydrolysis.
Our metatranscriptomic analyses uncovered enzymes
targeting pectic polysaccharides including 37 GH28 (rhamnogalacturonases) (Table 2), 18 PL1, 10 PL2, 27 PL9 and
6 PL10 (pectate lyases), 4 PL11 (rhamnogalacturonan
lyases), 36 CE8 (pectin methylesterase), 16 CE12 (pectin
acetylesterases/rhamnogalacturonan acetylesterases) and 6
CE13 (pectin acetylesterases) (Table S4, Supporting Information).
In addition, the present study detected a few mannanases, 3
GH26 and 1 GH113 (Tables 2 and S4, Supporting Information),
which hydrolyzed main-chain linkages of mannans, which are
polysaccharides found in hardwood and softwood.
Although few enzymes for deconstructing the main chain
of cell wall polysaccharides and pectins in grass carp microbiomes were recovered, the metatranscriptomes revealed a high
abundance of enzymes specific for the side chains of noncellulosic polysaccharides (Tables 2; Tables S4 and S5, Supporting
Information). The most abundant GH families in grass carp microbiomes were oligosaccharide-degrading enzymes, including
GH1 (283), GH2 (130), GH3 (222), GH92 (101) (Tables 2 and S4, Supporting Information), as well as GH families typically targeted
side chains of polysaccharides and pectins containing families
GH16 (33 members), GH31 (56 members), GH51 (31 members) and
GH53 (19 members).
We compared cellulase and hemicellulase gene numbers
in different dietary groups and found no significant difference
among microbiomes (F = 1.1, P = 0.40 for cellulase gene, and
F = 1.5, P = 0.31 for and hemicellulase gene), although they
were higher in high cellulosic dietary groups (Table S5, Supporting Information). Further, when the reads of cellulase and
hemicellulase genes in different groups were considered (RPKM
value), we found significant differences for hemicellulase genes
(F = 14.23, P = 0.005), but no significant difference for cellulase genes (F = 3.30, P = 0.11) among different groups (Table
S7, Supporting Information). Despite no significant difference
for cellulase genes, RPKM values were obviously higher in the
high cellulosic dietary groups. NMDS analysis using CAZy profile data showed that grass carp individuals fed exclusively on
fish meal grouped together, and were distinct from those with
higher fiber diets (Fig. S1, Supporting Information). All of these
results suggest that the grass carp gut microbiome adapts to
feed.
When compared with those repertoires of CAZy families
from metagenomes of bee (Engel, Martinson and Moran 2012),
canine (Swanson et al. 2011), pygmy loris (Xu et al. 2013), tammar wallaby (Pope et al. 2010), termite (Warnecke et al. 2007),
panda (Zhu et al. 2011) and bovine rumen (Brulc et al. 2009), the
grass carp gut metatranscriptome contained the highest diversity of CAZy families. For the GH profiles (Table 2), the relative
abundances of cellulases (1.4% of the GH recovered from all of
the grass carp samples) and endohemicellulases (2.4%) in grass
carp microbiome were close to those in tammar wallaby (2.0 and
5.0%), bovine (1.4 and 3.7%), human (2.5 and 4.0%) and canine (1.9
and 3.6%), higher than those in panda (0.7 and 1.1%) and bee
(0.5 and 1.2%), but lower than those in pygmy loris (3.5 and
6.8%) and termite (11.6 and 13.9%). Comparing the ratio of
oligosaccharide-degrading enzymes to GH sequences from cellulases, xylanases, mannanases and pectinases, we found that
the number of side-chain GHs were 1.2, 3.1, 6.3, 6.5, 6.5, 9.0,
Wu et al.
7
Table 2. Comparison of predicted GH profiles targeting plant structural polysaccharides in the grass carp transcriptomes and 8 herbivore and
omnivore metagenomes.1
Host Animal
Grass carp
Wallaby
Termite
Steer rumen
Panda
Human
Honey bee
Pygmy loris
Canine
Cellulases
GH5
GH6
GH7
GH9
GH44
GH45
GH48
GH74
Total
23
0
0
12
0
0
2
11
48 (1.4)
10
0
0
0
0
0
0
1
11 (2.0)
56
0
0
9
6
4
0
7
82 (11.6)
20
0
0
17
0
0
1
0
38 (1.4)
3
0
0
0
0
0
0
0
3 (0.7)
155
0
0
1
0
0
0
0
156 (2.5)
3
0
0
0
0
0
0
0
3 (0.5)
21
2
0
28
0
0
1
3
55 (3.5)
49
0
0
2
0
0
0
0
51 (1.9)
Endohemicellulases
GH8
GH10
GH11
GH12
GH26
GH28
GH53
total
7
14
0
0
3
37
19
80 (2.4)
1
11
0
0
5
2
9
28 (5.0)
5
46
14
0
15
6
12
98 (13.9)
7
16
1
1
16
9
51
101 (3.7)
2
2
0
0
0
1
0
5 (1.1)
48
86
0
0
104
10
0
248 (4.0)
0
0
0
0
1
6
0
7 (1.2)
9
17
0
0
4
71
6
107 (6.8)
5
8
1
0
11
61
11
97 (3.6)
Debranching enzymes
GH51
GH54
GH62
GH67
GH78
total
31
0
0
4
31
66 (2.0)
12
0
0
5
25
42 (7.5)
18
0
0
10
0
28 (4.0)
184
4
0
0
93
281 (10.3)
5
0
0
2
2
9 (2.0)
0
0
0
60
0
60 (1.0)
4
0
0
0
17
21 (3.6)
23
0
0
2
29
54 (3.4)
29
0
0
7
18
54 (2.0)
22
23
69
0
3
11
3
24
16
3
174 (24.7)
704
31
527
497
79
27
46
7
35
176
0
1425 (52.4)
2720
101
1
18
1
4
10
9
18
0
0
162 (36.2)
448
229
94
1102
386
97
222
39
135
0
0
2304 (37.23)
6188
64
22
45
13
3
20
2
14
18
0
201 (34.1)
590
9
134
142
33
11
13
3
11
103
0
459 (29.0)
1585
56
319
178
76
58
17
2
17
130
0
853 (31.9)
2678
Oligosaccharide-degrading enzymes
GH1
283
61
GH2
130
24
GH3
222
72
GH29
54
2
GH35
25
3
GH38
29
3
GH39
12
1
GH42
19
8
GH43
143
10
GH52
0
0
Total
917 (27.6)
184 (33.0)
Total number of GHs
3322
557
1
GH families are grouped according to their major functional roles in the degradation of plant structural polysaccharides proposed by Pope et al. (2010) with a minor
modification, where GH74 is included in the cellulase category. The numbers in parentheses represent the percentages of each category relative to the total number of
GHs identified in the microbiomic datasets [3322 genes for 11 grass carp hindgut samples, 448 genes for wild and domestic giant panda fecal samples (Zhu et al. 2011),
557 for foregut samples from 8 Tammar wallabies (Pope et al. 2010), 704 for termite hindgut (Warnecke et al. 2007), 2720 for fiber-adherent microbe samples of steer
rumen (Brulc et al. 2009), 6188 for human fecal samples from 6 random healthy humans (Qin et al. 2010), 590 for honey bee hindgut samples (Engel et al. 2012), 1585 for
wild pygmy loris feces (Xu et al. 2013) and 2678 for canine fecal samples (Swanson et al. 2011)]. A complete inventory of total GHs and GHs targeting plant structural
polysaccharides recovered from different grass carp intestinal microbiomes are presented in Table S4 and S5 (Supporting Information), respectively.
18, 20.1 and 27 times higher than main-chain GHs for termite,
pygmy loris, tammar wallaby, canine, human, grass carp, bovine,
bee and panda, respectively. These differences reflect the dietary profiles of the hosts: termites (wood), pygmy loris (fruit and
gum), wallaby (predominantly grass and forbs, with some commercial pellet mix), human and canine (omnivorous), grass carp
(grass and commercial feeds), bovine (grass and commercial forage), bee (pollen and nectar) and panda (bamboo). In addition
to the differences in nutritional ecology, grass carp has unique
anatomical characteristics, including no stomach and pyloric
caeca, and short intestine (only two times its body length); in ad-
dition to changes in its feeding activity and food transit time dictated by ambient temperature (Stevens and Hume 1998; Ni and
Wang 1999). The high ratio of side-chain enzymes to main-chain
enzymes suggested that the grass carp microbiome might utilize the saccharide hydrolysates from the main chains of plant
cell wall polysaccharides and pectins in a very short amount
of time and in a limited way. Instead, the nutrient content released from the plant cell once the cell wall is broken appears
to be more important. Strikingly, the COG categories C, E and I
in the grass carp microbiome were predominant, and amylase
GH13 (352 members) and inulinase GH32 (104 members) were
8
FEMS Microbiology Ecology, 2015, Vol. 91, No. 10
abundant (Table S4, Supporting Information), indicating the importance of the exploiting storage polysaccharides from the
plant for energy metabolism. Grass carp intestinal microbiome
thus reflects its evolutionary adaption.
CONCLUSION
Our metatranscriptomic analysis confirmed the general composition of the digesta-associated bacterial community previously
determined by culture-based and culture-independent methods and revealed a high diversity in CAZy families expressed in
grass carp intestine. We found evidence for active cellulosomebased systems for plant cell wall hydrolysis in that the grass
carp gut microbiome by the identification of transcripts coding for catalytic modules involved in binding and degrading the
main chain of cellulose, xylan, mannan and pectin polysaccharides. On the other hand, transcripts for other oligosaccharidedegrading enzymes were more abundant, particularly those acting on plant storage polysaccharides. In addition, transcripts
mapped to the COG categories C, E, G and I were dominant in
the intestinal microbiome. On the whole, our results agree with
Gevers et al. (2012) and Nicholson et al. (2012) as with terrestrial
vertebrates, an important function of grass carp intestinal microbiome is to offer energy for the host, and form a continuum
with its host supporting the nutritional needs of all.
SUPPLEMENTARY DATA
Supplementary data are available at FEMSEC online.
ACKNOWLEDGEMENTS
We thank Dr. Yanbin Yin in Northern Illinois University for assistance in Carbohydrate-Active enzyme family annotation.
AUTHOR CONTRIBUTIONS
SGW, GTW and EA designed research; SGW and CP performed
research; HZ, and WXL contributed new reagents/analytic tools;
YR, CP and YTH analyzed data; and SGW, EA and FX wrote the
paper.
FUNDING
The research was supported by grants from National Natural Science Foundation of China (No. 31372571 and No.31272706) and
National Basic Research Program of China (No.2009CB118705).
Conflict of interest. None declared.
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