Generation and Analysis of a Mouse Intestinal
Metatranscriptome through Illumina Based RNA-
Xuejian Xiong1., Daniel N. Frank2., Charles E. Robertson3, Stacy S. Hung1,4, Janet Markle5,6,
Angelo J. Canty7, Kathy D. McCoy8, Andrew J. Macpherson8, Philippe Poussier5,9, Jayne S. Danska5,6,10,
1Program in Molecular Structure and Function, The Hospital for Sick Children, Toronto, Canada, 2Division of Infectious Diseases, University of Colorado, Aurora, Colorado,
United States of America, 3Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, Colorado, United States of America,
4Department of Molecular Genetics, University of Toronto, Toronto, Canada, 5Department of Immunology, University of Toronto, Toronto, Canada, 6Program in
Genetics and Genomic Biology, The Hospital for Sick Children, Toronto, Canada, 7Department of Mathematics and Statistics, McMaster University, Hamilton, Canada,
8Department Klinische Forschung, University of Bern, Bern, Switzerland, 9Sunnybrook Health Sciences Centre Research Institute, University of Toronto, Toronto, Canada,
10Department of Medical Biophysics, University of Toronto, Toronto, Canada, 11Department of Biochemistry, University of Toronto, Toronto, Canada
With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our
understanding of the relationships of microbial communities with their environments. While metagenomics aims to
catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can
provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to
be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use
an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of
eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis
of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using
multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based
search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the
mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species.
To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli
homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with
components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the
economy of HTS platforms for metatranscriptomics.
Citation: Xiong X, Frank DN, Robertson CE, Hung SS, Markle J, et al. (2012) Generation and Analysis of a Mouse Intestinal Metatranscriptome through Illumina
Based RNA-Sequencing. PLoS ONE 7(4): e36009. doi:10.1371/journal.pone.0036009
Editor: Ramy K. Aziz, Cairo University, Egypt
Received December 30, 2011; Accepted March 29, 2012; Published April 27, 2012
Copyright: ? 2012 Xiong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors would like to acknowledge support from the following sources: Canadian Institute of Health Research operating grant – CTP-82940 (to JP,
SSH and XX); Genome Canada and the Ontario Genomics Institute -‘‘Genome-Environment Interactions in type 1 diabetes’’ (to JSD, AJC and PP); Juvenile Diabetes
Research Foundation - #17-2011-520 (to JSD, AJC, DNF, JP and PP); National Institutes of Health Grant R21HG005964 (to DNF and CER); Frederick Banting and
Charles Best Canada Graduate Scholarship Doctoral Award (to JM); and the Hospital for Sick Children Research Training Centre (to SSH). 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.
* E-mail: email@example.com
. These authors contributed equally to this work.
Boosted by the advent of high through-put sequencing (HTS)
platforms, metagenomics has emerged as a powerful approach for
analyzing complex bacterial communities [1,2]. Typically such
studies focus on the random (shotgun) sequencing of DNA to
define the relative abundance of genes within a community [3,4].
Further inferences on community structure can be gleaned
through 16S rRNA gene surveys which provide information on
relative species abundance [5,6]. As such, functional insights are
limited to cataloguing genes within a sample, either through direct
sequence identification (shotgun sequencing) or inference through
reference genomes (16S rRNA gene surveys). Understanding the
functional relationships within bacterial communities would be
significantly enhanced by the analysis of bacterial gene/protein
expression . Previous metatranscriptome studies have relied on
cDNA-RFLP and microarray approaches [8–10] and have been
largely limited to the analysis of known genes. More recently,
several groups have reported metatranscriptomic studies with
HTS platforms [11–17]. These studies report several challenges
for optimizing the yield of informative reads, HTS data analyses
and presentation of these complex data.
Choice of HTS platform has a significant impact on the yield of
informative reads. In most recent RNA HTS (RNA-Seq) studies,
mRNA are mapped onto reference genomes for a single species
PLoS ONE | www.plosone.org1April 2012 | Volume 7 | Issue 4 | e36009
(e.g. mouse, human) [18,19]. For complex bacterial communities,
such as the rodent or human intestine, complete sets of reference
genomes are lacking. Furthermore, given the extensive sequence
diversity that can occur even between related bacterial species,
generation of a complete set of reference genomes is currently
beyond current sequencing capabilities. To increase the probabil-
ity of obtaining a significant match to previously identified
sequences, metatranscriptomic studies have relied on the 454
Titanium (Roche) platform [12,14,20] which generates longer
reads than the Illumina  and SOLiD (Applied Biosystems,
Carlsbad, Ca) platforms. However, the SOLiD and Illumina
platforms offer a significant improvement in read coverage over
454 for the same unit cost enhancing interest in their application
to metatranscriptomics. One solution is to use a hybrid approach
in which 454 is used first to assemble a series of reference DNA
sequences onto which Illumina- or SOLiD-generated transcript
data can be mapped . A second factor affecting yields of
informative reads is RNA sample preparation. In addition to
purification of high quality RNA, it is desirable to reduce the
proportion of ribosomal RNA (rRNA) sequences with commercial
kits that remove 16S and 23S rRNA molecules providing
enrichment for the high complexity mRNA . However,
questions remain about the effectiveness and potential bias of
the rRNA removal approaches.
Bioinformatics analyses of metatranscriptomics datasets are an
intensive area of research and development. Previous approaches
have focused on matching sequences to gene families as defined by
knowledgebases such as the Clusters of Orthologous Groups
(COG), Gene Ontology (GO) and SEED resources [23–25]. Such
annotation schemes provide high-level comparisons across broad
functional categories. To provide more detailed, molecular level
insights, recent studies are beginning to explore the use of
biochemical pathway analyses [3,26]. For example, the iPath tool
 has been used to map reads onto metabolic pathways defined
by the Kyoto Encyclopedia of Genes and Genomes (KEGG )
to produce an integrated view of the metabolic capabilities of an
environmental sample . With the availability of high quality
protein interaction datasets, the opportunity exists to exploit these
exceptional datasets as functionally coherent scaffolds on which to
organize and interpret metatranscriptomic data. As such, these
datasets offer the possibility to extend systems-based analyses
beyond metabolism to capture additional bacterial subsystems
such as protein complexes or signaling and transport pathways.
Here we are interested in exploring the feasibility of using the
Illumina HTS platform to perform metatranscriptomics on a
model gut microbiome. Our choice of experimental system is
guided by the recent appreciation of the considerable role that
commensal flora in the human gut play in human health and
disease. For example, metagenomic surveys have already illumi-
nated potential mechanisms by which inflammatory bowel diseases
(IBD), metabolic syndrome and type 2 diabetes develop and persist
[3,29–31]. Furthermore, studies of IBD patient microbiota, reveal
loss of potentially beneficial commensal microorganisms compared
to control subjects [31–33]. At the same time, the role of intestinal
bacteria in other inflammatory diseases such as type 1 diabetes is
just beginning to be explored . Motivated by the need to
extend metagenomic analyses beyond the simple interrogation of
gene catalogues, this study seeks to establish the potential of
metatranscriptomics as a platform for investigating the mechanis-
tic contributions of the gut microbiome on health and disease.
Here we applied RNA-Seq on RNA preparations obtained from
the intestinal contents of aged-matched non-obese diabetic (NOD)
mice colonized with eight commensal bacteria. By focusing on a
relatively simple community, our investigation centers on
developing and optimizing experimental and analytical pipelines
targeted specifically for metatranscriptomics.
Results and Discussion
Metatranscriptomic data from the intestines of non-
obese diabetic (NOD) mice
To assess molecular protocols and computational analyses for
Illumina-based metatranscriptomics, we sequenced RNA prepared
from the flushed cecum and colon of age-matched NOD mice
weeks prior to diabetes onset. The mice were the progeny of NOD
animals that had been re-derived by embryo transfer into a
completely germ-free environment and then colonized with an
Altered Schaedler flora (ASF) containing eight known species
[35,36]: Lactobacillus acidophilus, L. salivarius, Parabacteroides distasonis,
Mucispirillum schaedleri, three members of Clostridium cluster XIV
and a relatively uncharacterized species of Firmicutes. All but P.
diastonis (a member of the phylum Bacteroidetes) and M. schaedleri
(a member of the phylum Deferribacteres) are members of the
phylum Firmicutes. Although reference genome sequences of
strains related to the two Lactobacillus spp. are available, we note
that the two reference genomes for L. acidophilus (strains 30SC and
NCFM) display considerable genomic variation, with 2037 and
1864 putative open reading frames (ORFs), respectively. Since the
objective of many research groups is to apply metatranscriptomic
methods to complex environmental and mammalian-host-associ-
ated bacterial communities that will exhibit diverse previously
uncharacterized species, we selected the altered ASF colonized
NOD intestinal samples, as a relatively simple pilot, to examine
our ability to agnostically assign HTS reads to samples for which
no reference genome is available.
We compared two RNA preparation methods: Qiagen
RNAEasy (Qiagen, Valencia, CA), which selects for longer RNAs;
and Invitrogen mirVANA (Invitrogen, Carlsbad, CA), which also
purifies microRNAs. In addition, we examined the use of the
Invitrogen RiboMinus Bacterial kit to selectively deplete rRNA,
thereby enriching the high complexity mRNA. Twelve combina-
tions of RNA preparation conditions were used and the samples
were multiplexed and sequenced in a single lane of an Illumina
Genome Analyzer IIx flow cell. Sequence reads have been made
available for download from the National Center for Biotechno-
logical Information (NCBI) Sequence Read Archive (SRA, http://
SRA051354). After filtering for HTS read quality, these samples
produced 21.7 million single-end 76-nt reads (Table 1). rRNA
sequences were identified through BLAST comparisons to an in-
house database of rRNA sequences including sequences represen-
tative of all eight ASF species. Depending on sample, the use of the
Qiagen RNAEasy kit alone resulted in 60–95% rRNA, while
application of the Invitrogen RiboMinus kit to the same RNA
samples reduced rRNA to 40–70% of total RNA. In contrast, the
Invitrogen mirVANA protocol reduced rRNA to 30–40% of total
RNA reads even in the absence of an rRNA depletion step.
Phylogenetic analysis of metatranscriptomic rRNA
Because the mice used in this study were colonized with a
defined set of bacteria, we could use the expected distribution of
bacteria in the sample set to evaluate the utility of using relatively
short rRNA reads to classify microorganisms. ASF comprises eight
bacterial species representing three phyla (Firmicutes, Deferribac-
teres, and Bacteroidetes) and five families (Firmicutes: Lachnospir-
aceae, Ruminococcaceae, and Lactobacillaceae; Deferribacteres: Deferri-
bacteraceae; and Bacteroidetes: Porphyromonadaceae). Although the
Mouse Intestinal Metatranscriptomics
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Table 1. Sequence yields for 12 different sample preparations.
Number of Reads Matching:
% of Non-Adaptor Reads Matching:
12 cecal and colon derived samples were prepared from four different NOD mice using a variety of RNA extraction protocols. Samples were multiplexed on a single Illumina sequencing run to generate 21.7 million sequences. Of
these ,1.5 million (,10%) could be mapped to a known bacterial transcript either via BWA against bacterial genomes (nt) or via BLASTX against the protein non-redundant database (peptide). Use of the Invitrogen extraction kit
resulted in the most consistent generation of a high proportion of bacterial transcripts.
‘*’indicates the additional use of the RiboMinus kit.
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three expected phyla were most often represented in the sequence
dataset (.99% of reads), other phyla not representative of ASF
species were detected, including Actinobacteria and Spirochaetes
(Table 2). These aberrant classifications could have arisen from 1)
small levels of other microorganisms in the germ-free colonies; 2)
contamination of gut content samples following collection; or 3)
misclassification. In both the cecum and colon, members of the
phylum Bacteroidetes were the most abundant microorganisms,
accounting for .85% of the SSU and LSU rRNA reads.
However, at the family-level the expected and observed results
differed drastically. Although all five ASF families were observed
(Table 2), other related families were abundant in the dataset. For
example, 40.2% of sequences were classified as Bacteroidaceae,
rather than Porphyromonadaceae (19.6%). Similarly, 16.8% of
sequences were classified as Clostridiaceae, rather than the related
families Lachnospiraceae (17.5%) or Ruminococcaceae (0.12%). We
interpret these results as evidence that short reads (50–76 nt)
obtained by shotgun sequencing are inherently noisy markers of
phylogeny; since shotgun reads may be generated from any sub-
sequence of an rRNA transcript, reads originating from moder-
ately to highly conserved gene segments may not contain sufficient
numbers of unique characters to distinguish among low-level
The RiboMinus protocol uses biotinylated antisense oligonu-
cleotides with broad-specificities for bacterial small- and large-
subunit rRNAs to selectively remove bacterial rRNA from
samples. Because sequence heterogeneity and the kinetics of
oligonucleotide hybridization with highly structured rRNA
molecules potentially could result in biased rRNA depletion, we
compared the phylogenetic distributions of mRNA and rRNA
transcripts in depleted and non-depleted samples. Although
ostensibly universal oligonucleotides were used to hybrid-capture
and remove rRNA molecules, the frequencies with which bacteria
were identified differed greatly following rRNA-depletion. For
example, the phyla Bacteroidetes and Firmicutes were over- and
under-represented following rRNA depletion of samples from
animals 502 and 504 (Table 2). Thus, bacterial community
composition within a sample cannot be reliably ascertained if prior
removal of rRNA transcripts is performed.
Mapping metatranscriptomic reads to transcripts
requires a peptide-centric approach
Next we attempted to map the 5 million putative mRNA reads
to known sequences. First we applied the sequence mapping tool,
BWA , to filter and assign mouse-derived sequences
(transcripts and genome). In total, 1,240,692 (,24%) of the
putative mRNA reads mapped to a mouse sequence. For most
samples, we identified approximately twice as many mouse
sequences through comparisons to the mouse genome compared
Table 2. Family-level distribution of small-subunit RNA sequences1.
Anatomy2: Cec CecCol CecCec ColCecCecCecCecCecCol
RNA Prep3:QiaQia Qia Qia QiaQia InvQiaInvQiaQia Qia
Total Reads ASF Species6
Microbacteriaceae5620 0.40.10.2 0.30.20.2 0.4 0.20.30.20.1 0.1
Deferribacteraceae145410.10.10.00.0 0.20.00.2 0.00.20.00.1 0.1
Lachnospiraceae 434676313.211.017.917.69.8 17.2 27.521.4 19.4220.127.116.11
Ruminococcaceae30551 0.10.10.1 0.20.00.10.18.104.22.168.10.1
Lactobacillaceae3790322.214.171.124 126.96.36.199 188.8.131.52.21.01.8
Spirochaetaceae27640.1 0.10.00.1 0.20.00.50.10.40.00.1 0.1
Total reads:2473483251317 45213245426 413910 50317171243 153079 341282 129166 352836 105089 214605
1Values are percents of total reads for sample.
2Anatomic location of sample. Cec=Cecum contents. Col=Colon contents.
3RNA extraction kit. Qia=Qiagen RNEasy. Inv=Invitrogen.
4RiboMinus removal of bacterial rRNA from sample.
5Phylum and family classifications of rRNA reads.
6Number of Altered Schaedler Flora species belonging to bacterial family.
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to mouse transcripts, suggesting the majority of mouse derived
sequences represent unspliced introns, 39 or 59 UTR’s or other
non-coding RNAs. In the absence of reference genomes for any of
the eight ASF species, we then applied BWA to map reads to 1078
microbial genomes. Only 92,594 of the 5 million putative bacterial
mRNA reads could be mapped by this approach. The less
stringent sequence similarity search tool, megaBLAST, mapped
only an additional 128,059 non-mouse reads. Since these tools rely
on the identification of nucleotide matches, the low frequency of
mapped reads presumably reflects the high level of sequence
diversity that can occur even between different strains of the same
species [38–40]. Such diversity may result from differences in
codon usage that do not impact peptide sequences.
Consequently, we performed BLASTX comparisons (translated
nucleotide v. protein) of putative mRNA reads against the protein
non-redundant database (protein-nr). This strategy was more
successful, with 1,234,400 of non-mouse-derived reads (32%)
mapping to 237,570 unique bacterial transcripts. Most matches
(.70–80% depending on sample) were high quality ($85%
sequence similarity over .65% of the read length - Figure S1).
Notably, minimal overlap (,0.1%) occurred between reads
matching bacterial transcripts through BLASTX comparisons
and reads mapping to the mouse genome through BWA. Two
samples (NOD501CecQN and NOD502CecQN) displayed con-
siderably fewer high-quality mapped transcripts compared to the
other 10 samples (48% and 62% matches .85% sequence
similarity over .65% of sequence length respectively). These
observations suggest that analysis of read matches may represent a
useful quality control step. Of the other 10 samples, one prepared
with the RiboMinus kit (NOD501CecQY) provided the highest
proportion of mapped bacterial reads (28.2%), while the two
Invitrogen mirVANA preparations also resulted in a relatively
high proportion of reads mapped to a bacterial transcript (9.7 and
18.1% respectively - see Table 1).
Due to relatively short read lengths, a concern in these analyses
is that matches to known transcripts are not meaningful. For a 25-
residue peptide, allowing for only a single mismatch results in a bit
score ,50, while matches even with 60% identity result in E-
values in excess of 10. To ensure that the bacterial transcripts
identified through the BLASTX comparisons were consistent with
the types of transcripts expected from the ASF bacteria, we
examined the phylogenetic distribution of the assignable tran-
scripts (Figure 1). To control for differences in transcript length
, we converted raw read abundance to Reads Per Kilobase of
transcript per Million reads mapped (RPKM - See Methods).
Consistent with the known ASF bacterial species, the majority
of mapped transcripts derived from Bacteroidetes or Clostridia
(Figure 1). Interestingly, many of the Bacteroidetes transcripts derive
from the Porphyromonadaceae which includes the ASF species
Parabacteroides distasonis. Furthermore, the phylogenetic distribution
of the reads differed significantly from that of the entire set of
bacterial proteins in protein-nr, which is dominated by Proteobacteria.
Comparisons to the taxonomic distribution of the samples using
the previously filtered small- and large-subunit rRNA reads
revealed that, while the same three dominant taxa were observed
in ORF and rRNA datasets, their relative representation in the
datasets differed. For example, the representation of ‘Bacteroi-
detes/Others’ within the transcript dataset ranged from ,5–28%
in the mRNA dataset compared with ,50–73% in the rRNA
dataset. Although it is possible that this may reflect differences in
relative activities between these taxa, these differences may also be
a consequence of the relative paucity of mRNA sequence data that
has previously been generated for this taxon. Alternatively as
noted above, this discrepancy may also be driven at least in part by
biases in the rRNA-depletion protocol. Finally, as noted above,
inaccuracies in rRNA classifications may skew the comparisons of
mRNA- and rRNA-based taxonomic distributions. For example,
the mapped transcript dataset also contained sequences of
actinobacterial or proteobacterial origin even though these were
not represented within the ASF bacteria resident in the NOD
mouse intestinal samples. We speculate that these transcripts may
represent orthologs of Bacteroidetes or Clostridium genes for which no
sequence representation exists in protein-nr. The dominance of
expected taxa in the mapped transcript datasets suggests that they
reflect the underlying distribution of mRNAs in the samples.
Functional analysis of metatranscriptome datasets
RNA-Seq data derived from organisms with a reference genome
can yield detailed gene-by-gene analysis of expression patterns. In
contrast, for metatranscriptomic datasets, BLASTX-based map-
ping requires alternative methods that avoid the need to identify
the precise source of a read. Instead, focus must shift to analyzing
differences in expression levels of functional classes of genes .
Previous metatranscriptomics analyses have relied on broad
functional categories such as those defined by the Clusters of
Orthologous Groups (COG) of proteins database and the Gene
Ontology (GO) resource [25,43]. However, the generality of the
functional vocabulary in these resources provide limited functional
insights so that recent efforts have focused on specific functional
classes such as gene families [26,44]. Mapping reads to transcripts
and, transcripts to gene families, reduces the need for gene-centric
based approaches that require accurate transcript mappings to
reference genomes. Reasoning that a gene family-based mapping
approach may result in functional insights, we have analyzed the
mouse metatranscriptome dataset in terms of gene families as well
as three types of functional entities: COG functional categories;
metabolic networks; and a protein-protein interaction network
derived for E. coli.
We applied the Markov clustering algorithm
(MCL) to the 237,570 unique transcripts identified from our 12
samples to define 12,784 gene families on the basis of sequence
similarity scores (see Methods). Summing the RPKM values for
each transcript in a class was used to derive the relative abundance
of each gene family. The size of the 500 gene families with the
highest number of transcripts assigned did not correlate well with
their relative expression (Figure S2). This suggests that gene family
expression patterns did not merely reflect the abundance of
transcripts identified in the initial mapping effort. Note here we
use the term ‘expression’ to represent transcript abundance; as
such it is important to consider that in addition to rates of
transcription, ‘expression’ may also be driven through overall
abundance of specific species. Table 3 shows the top 20 most
represented gene families for the NOD503CecMN sample. The
greatest correspondence in rankings of relative expression between
samples occurred between samples that produced the largest
number of reads that were reliably mapped to a known bacterial
transcript (NOD504CecMN; NOD504ColQN, NOD501CecQY
and NOD504CecQY). Eight of the top 20 most abundant families
are annotated as hypothetical, of which three (GF8057, GF5525
and GF3794) are ranked in the top three across the majority of
samples. GF8057, consisted of two members, both from
Parabacteroides spp. (gi|154490247 and gi|218259679) while
GF5525 contained five members, all from the order Clostridiales
(gi|153812042, gi|154482409, gi|154482396, gi|154482831 and
gi|169343363). Given their predicted relative expression, these
hypothetical proteins merit further functional investigation.
Among gene families that could be ascribed a putative function,
eight were implicated in regulatory roles (e.g. GF1 - TonB-
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Figure 1. Phylogenetic distribution of transcripts mapped to known bacterial genes. (A) Distribution of mRNA transcripts. (B) Distribution
of rRNA transcripts. Results are shown for the 12 independent samples described in Table 1. Consistent with 16S rRNA studies, the vast majority of
identified mRNA transcripts derive from Parabacteroides, Bacteroides or Clostridial species.
Mouse Intestinal Metatranscriptomics
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dependent receptor; GF3 - RagB/SusD domain protein; GF2 and
GF9 - two component response regulators). The ability to map
RNA-Seq data onto a defined set of gene families thus provides a
surrogate for elucidatingfunctional
COG functional categories.
reads assigned to general functional categories, we assigned
transcripts to COG categories on the basis of best BLAST
matches to the COG database. RPKM values were then used to
calculate the frequency of representation for each COG category.
It should be noted in these analyses that as for the phylogenetic
analyses, we observed consistency across samples (Figure 2). For
many categories, the distribution for each sample reflected the
underlying distribution of COG category assignment for all
proteins in the COG database highlighting the limitations of
using such a broad-category approach. Nonetheless, in addition to
the ‘Uncharacterized’ category, three categories show significant
(Z-score.2) differences between the samples and the COG
library: [M] - Cell wall/membrane/envelope biogenesis; [C]
Energy production and conversion; and [G] Carbohydrate
transcriptome enrichment of these categories may therefore
reflect exploitation of carbohydrates for the production of
biomass. On the other hand, in these analyses it is important to
note that observed differences represent relative (as opposed to
absolute) enrichments that could simply reflect decreases in other
categories (e.g. ‘Uncharacterized’).
representation within our datasets by representation of metabolic
representations of metabolic pathways [3,7] which may not
capture alternative pathways that utilize different suites of
enzymes to achieve similar functions . Here we adopted a
functionally related genes (Figure 3A). From these analyses we
identified several pathways that were well represented across
datasets. These pathways included starch and sucrose metabolism,
amino acid biosynthesis, glycerolipid metabolism, peptidoglycan
biosynthesis as well as components of purine metabolism. These
latter components are largely associated with the production of
RNA and DNA from purine precursors (data not shown).
Reassuringly, pathways specific to eukaryotes such as N-glycan
biosynthetic pathways , were poorly represented across our
datasets. As for the COG analyses, we found a high degree of
correlation in the expression of enzymes that were inferred across
samples. Perhaps surprisingly, high correlations were observed
between cecum and colon-derived samples despite the presumably
different environment to which the microbes in these sites were
exposed (Figure 3B).
To demonstrate how a network-based approach may transcend
traditional KEGG defined pathways, we constructed a subnetwork
of enzymes for the top 100 expressed enzymes (in terms of RPKM)
represented in the NOD503CecMN dataset (Figure 3C). Within
this network we identified a link between enzymes in the valine,
leucine and isoleucine biosynthetic pathway, which appear to feed
acetyl-CoA, produced from the synthesis of alpha-isopropylmalate
by isopropylmalate synthase (EC:184.108.40.206), into part of the TCA
cycle. In a second example, components of glycosphingolipid,
sphingolipid, starch and sucrose metabolism are linked to
glycolysis and the pentose phosphate pathway, suggestive of the
breakdown of compounds associated with the former to feed
glycolysis and the production of ribose. In a final example,
components of nucleotide metabolism are linked to alanine,
aspartate and glutamate metabolism through the production of
To examine the distribution of
adenylosuccinate by adenylosuccinate synthase (EC:220.127.116.11).
Together these pathways are indicative of the biochemical routes
adopted by the microbiome driving the production of biomass.
Adopting a network approach facilitates the identification of
nodes that mediate important roles within the network. Between-
ness centrality is a metric that assesses the importance of the node
to the network through determining the proportion of shortest
path lengths that pass through that node . Focusing on nodes
of high betweenness centrality, we observe differences between
samples (Figure 4). For example, although both the NOD502-
CecQN and NOD503CecMN samples show similar levels of
expression of beta-galactosidase (EC:18.104.22.168), the former has
higher levels of aldehyde reductase (EC:22.214.171.124) and aspartate
transaminase (EC:126.96.36.199). On the other hand, the NOD503-
CecMN sample has higher levels of 59-nucleotidase (EC:188.8.131.52);
glucosamine–fructose-6-phosphate aminotransferase (EC:184.108.40.206)
(EC:220.127.116.11). Such differences may indicate subtle changes in
the reliance of these key enzymes for directing flux within the
Consistent with previous metagenomic analyses, these studies
have highlighted the importance of metabolic activities involved in
the production of biomass. More importantly, the use of the
network for mapping metatranscriptomic datasets shows how
groups of functionally related enzymes, differentially expressed
across samples, can be readily identified.
E. coli protein-protein interaction networks.
of physical and functional interactions are now available for
bacteria that can be exploited as scaffolds onto which RNA-Seq
data may be mapped [48–53]. Although the focus of these datasets
on E. coli will undoubtedly preclude the identification of systems
specific to particular taxa (e.g. Bacteroides and/or Clostridiales),
many processes such as cell wall biogenesis, transcription and
translation are well conserved throughout bacteria. We may
therefore expect these analyses to yield insights into the activity of
basic core processes. First, we identified E. coli homologs of each
transcript on the basis of InParanoid-derived relationships .
The relative abundance of each E. coli gene was then generated
from the sum of RPKM values of the transcripts that map to each
gene. Correlation across samples varied (Spearman correlation
coefficients 0.45–0.90) but was greatest between samples with the
most mapped reads (Figure 5A). Within a single sample, E. coli
gene abundance ranged from 1 to 12,936 reads, a dynamic range
of four orders of magnitude with the most highly expressed for
eight samples being FepA (an outer membrane receptor associated
with the ferric enterobactin transport system). As noted above, the
focus on E. coli suggests that the relative expression of each gene
may correlate with its relative level of conservation. This might
imply that our mapping simply reflects underlying conservation
biases and indeed we note some correlation between expression
and conservation (Figure 5A). However, mapping these data onto
a high quality protein-protein interaction template readily
identifies components of several diverse bacterial processes with
expression profiles that do not correlate with conservation
(Figure 5B–D). For example, we identify both components that
are poorly expressed but well conserved (e.g. murC, murF and
murG involved in cell wall biogenesis) as well as components that
are well expressed but poorly conserved (e.g. fepA, fecI and fecR
involved in iron transport). Furthermore, for a selected group of
transporters, although many are well conserved, we note that only
those components associated with spermidine transport (potA,
potB, potC and potD) are also well expressed, suggesting a
biologically meaningful role for this transport function within our
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Table 3. Relative abundance of gene families.
# of Members
RNA binding protein
ABC transporter, ATP
The 236,769 bacterial transcripts found to match reads were clustered into 13,278 gene families. The table indicates the relative abundance of each gene family as measured by summing RPKM values for each transcript in that
family. Shown are the top 20 most abundant families in the NOD503CecMN sample together with the rank of abundance across all 12 samples. ‘Descriptions’ represent annotations associated with the transcripts according to
definitions provided by Genbank. ‘Predominant taxon’ indicate the most frequent taxonomic group associated with the transcripts in the gene family. doi:10.1371/journal.pone.0036009.t003
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metatranscriptomic study that showed expressed genes to be
significantly more evolutionarily conserved than non-expressed
genes , demonstrating the biological relevance of the
relationship between conservation and expression.
Summary and Future Perspectives
Here we have shown that in the absence of reference genomes,
RNA-Seq technology may be applied to environmental samples.
Despite the low statistical significance associated with sequence
matches, the phylogenetic assignments of the mapped transcripts
were skewed towards those expected of ASF-colonized mice,
suggesting that this approach is biologically meaningful. By placing
transcripts into functional classes, functional insight can be gained
either on the basis of associated annotations of e.g. representative
gene families, or through more sophisticated systems-based
approaches that exploit network relationships. In this study, we
note several areas that would benefit from recent technological
developments as well as the development of new algorithms. First,
we were able to map only a limited number of reads to known
bacterial mRNA transcripts. The development of paired-end reads
allied to increases in read length associated with the Illumina HTS
platform should enhance homolog detection. In addition, given
sufficient depth of sequencing coverage, it may be possible to
combine reads into larger ‘contigs’ that would increase the
information content of the reads. However, currently available
short-read assembler algorithms [56,57] have not been optimised
for assembling metatranscriptomic data and our initial attempts
applying the Velvet short read assembler  resulted in few
Figure 2. Distribution of COG functional annotations. (A) Distribution of COG functional annotations of reads mapping to known bacterial
transcripts for the 12 samples analysed in this study. Also shown is the distribution of COG assignments for all proteins in the COG database
(background). Asterisk’s indicate COG categories with significant (Z-score.2) differences in relative frequency between samples and the background
(Z-score is indicated). (B) Pearson correlation coefficients of frequency of reads assigned to each COG category across the 12 samples.
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Figure 3. Metatranscriptome data mapped in the context of a global metabolic network. (A) Network map highlighting metabolic
enzyme expression for reads obtained from the NOD503CecMN sample. Size of node indicates relative expression. Colour of node indicates
functional category of enzyme as defined by KEGG superclasses (see key for details). Several example pathways are indicated. (B) Spearman rank
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contigs longer than individual reads (data not shown). A significant
challenge in the field is the expected shallow coverage of
transcripts found in samples, such as mammalian commensal
communities, containing hundreds or thousands of bacterial
species, and the need to avoid the generation of chimeric contigs.
An interesting question thus arises as to what is the depth of
sequencing required to inform on a specific community. Factors to
consider include diversity of species associated with the community
as well as the size of their genomes. On the other hand, while it is
clear as sequencing costs continue to fall, that high sequencing
depths will become readily attainable and further development of
analytical pipelines such as those presented here will be critical to
avoid major bottlenecks in evaluation of these datasets.
At present, there is no combination of metagenomic analyses
that can accurately and comprehensively define the composition
and function of a complex bacterial community. This limitation
reflects a nascent field characterised by a paucity of annotated
bacterial genomes, the inability to culture most environmental
species, error from PCR primer bias and high throughput
sequencing and reliance on models for mapping sequence reads.
For example, surveys of 16S rRNA gene sequences between
samples may be functionally informative if the abundances of
bacterial groups differ significantly. However, closely related
bacterial strains that differ in their gene expression between sets
of environments or disease states would not be detected by this
approach. While current computational tools for metatranscrip-
tomics analysis cannot yet assign genes to unique species, we show
that this approach has the potential to reveal the functional
architecture of all genes expressed by a defined community.
Moving to the future, we envisage that a complete characterization
of the organization and interrelationships of microbial communi-
ties will require the integration of several complementary datasets
including: metagenomics, metatranscriptomics, 16S rRNA gene
surveys as well as proteomics and metabolomics. On the other
correlation coefficients of relative enzyme expression across the 12 samples. In general there is a high degree of consistency in enzyme expression
across samples. (C) Top 100 expressed enzymes in the NOD503CecMN sample. For three subnetworks, links are apparent that extend beyond the
boundaries of KEGG defined pathways.
Figure 4. Samples display subtle differences in expression of enzymes of high betweenness centrality. For each enzyme in the network,
betweenness centrality was calculated and mapped to node colour. Between the two samples we identify differences in the expression of nodes of
high betweenness, suggesting an altered reliance on pathway flux within the network.
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hand, these studies will require considerable investment in
resources; consequently pilot studies such as the one presented
here are essential if we are to address issues of feasibility before
committing to large scale investments.
Materials and Methods
Housing and handling of mice
The mice used in the RNA sequencing study were born from
NOD dams that were the progeny of mating pairs born in a
completely germ-free environment following axenic (sterile)
embryo transfer into germ-free pseudo-pregnant females. The
germ-free status of mice in the facility is maintained by housing in
flexible-film isolators and monitored by qPCR analysis of fecal
DNA preparations using pan-specific bacterial 16S primers, and
culture of cecal contents under anaerobic (blood agar) and aerobic
(Luria broth) culture conditions. In addition DNA staining of cecal
contents with Sytox green is used to confirm the bacterial absence.
The absence of parasites, bacteria and virus contamination was
independently confirmed quarterly by shipment of sentinel mice
for analysis at a commercial facility.
Once GF status was confirmed as described, GF NOD mice
were colonized by co-housing with gnotobiotic mice colonized
with defined cultured bacterial species (Altered Schaedler’s flora;
ASF - ) which were prepared in the laboratory from cloned
bacteria using sterile technique. The ASF consists of Lactobacillus
acidophilus (ASF 360), Lactobacillus murinus (ASF 361), Bacteroides
distasonis, (ASF 519), Mucispirillum schaedleri (ASF 457), Eubacterium
plexicaudatum (ASF 492), a Fusiform-shaped bacterium (ASF 356)
and two Clostridium species (ASF 500, ASF 502). These ASF-
colonized gnotobiotic mice were then bred in isolators to ensure
no additional species were introduced. The presence of the ASF
species was confirmed by species-specific bacterial qPCR .
Preparation of samples and sequencing
Four gnotobiotic NOD mice colonized with ASF were sacrificed
at 12 weeks of age. The intestinal tract was immediately removed
and splayed open through a longitudinal incision. Luminal
contents from the small intestine (SI), cecum, and colon were
collected individually, by scraping biomass from intestinal wall
with a sterile scalpel. Specimens were placed in separate
Figure 5. Metatranscriptome data mapped in the context of a global E. coli protein interaction network. (A) Spearman correlation
coefficients of abundance of reads mapping to E. coli homologs across samples. (B)–(D) Comparison between conservation, as defined by number of
bacterial genomes in which a putative ortholog has been identified and relative expression of E. coli homologs for three selected subsystems derived
from the transcriptome data for a single sample (NOD503CecMN). Subsystems are defined from a previously generated high quality protein-protein
interaction network . Colours of nodes indicate genes involved in common functional modules. Size of nodes indicate either number of genomes
or relative expression in terms of RPKM (largest=.900 genomes/transcripts). (B) Proteins involved in cell division and cell wall biogenesis. (C)
Proteins involved in iron transport and tryptophan metabolism. (D) Proteins involved in select transport pathways - note of the ones shown, despite
many being highly conserved, only components involved in spermidine transport (PotA-D) are abundant within our sample.
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microcentrifuge tubes, frozen at 280C, and shipped to the
laboratory of Dr. Frank on dry ice. Upon thawing, approximately
30 mg luminal biomass was suspended in two volumes (mass/vol)
of phosphate buffered saline (pH7.4), producing a 100 ml slurry of
biomass. Two RNA extraction protocols were compared: 1) Cell
lysis in RNAzol B Reagent (IsoTex Diagnostics, Inc., Friends-
wood, TX) followed by RNA purification using the RNeasy kit
(Qiagen Inc., Valencia, CA ); and 2) Cell lysis in mirVanaTM
lysis/binding buffer (Invitrogen, Inc. Carlsbad, CA) followed by
RNA purification using the mirVanaTMkit (using the total RNA
procedure). In both procedures, 100 ml of sample slurry was added
to 1 ml of lysis buffer and ca. 250 mg of 0.1 mm zirconium beads
(Biospec Inc. Bartlesville, OK). Specimens were disrupted by bead-
beating for 2 min in a Mini-Beadbeater-8 (Biospec Products Inc,
Bartlesville, OK) followed by purification following the manufac-
turers’ protocols. Furthermore, 3 mg aliquots of three total RNA
samples (from the Qiagen-based protocol) were depleted of
bacterial rRNA molecules by application of the RiboMinusTM
Transcriptome Isolation Kit and RiboMinusTMConcentration
Module (Invitrogen, Inc. Carlsbad, CA). Aliquots of total RNA
were separated by 1% agarose/TBE gel electrophoresis and
visualized by ethidium bromide staining. RNA prepared from
cecum and colon was dominated by distinct rRNA bands, whereas
samples prepared from SI appeared degraded. Consequently, SI
samples were not subjected to sequencing. A total of 12 RNA
samples were submitted for next-generation sequencing (Table 1).
Sequencing was performed with the Illumina Genome Analyzer
IIx (GaIIx) platform at the Center for Advanced Genomics
(TCAG - Hospital for Sick Children). After deconvolution of the
barcodes used for multiplexing, 21,680,028 76 bp reads were
generated on a single flow cell. Reads are available for download
from the National Center for Biotechnology Information (NCBI)
Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/
Traces/sra: Accession number: SRA051354). Poor quality bases
were removed by iterating a 5 nt window across the 59 and 39 ends
of each sequence and removing nucleotides in windows with a
mean quality score ,20; iteration was stopped when the mean
quality score was .20. Adaptor sequences were removed using
Cross_match (http://www.phrap.org) to search a database of
Illumina adaptor sequences. Following trimming and adaptor
removal, reads with lengths less than 50 nt were discarded. Due to
the poor performance obtained from applying the Ribosomal
Database Project (RDP) classifier  to relatively short reads,
putative rRNA transcripts were identified by BLAST sequence
similarity searches (bit score .50) against an in-house database of
rRNA sequences constructed from the All-species Living Tree
Project SSU database , supplemented with ASF SSU
sequences  and 5S and LSU sequences  representative
of intestinal microbes. Blast hits with bit scores .50 were removed
from mRNA datasets. In all, 5,096,278 reads of putative mRNA
transcript origin were identified and subjected to further analyses.
To identify potential host contaminants, putative mRNA
transcripts were mapped to: 1) a database of mouse derived
transcripts (Ensemble release V.59 – http://www.ensembl.org); 2)
the mouse genome; and 3) a database of 1078 bacterial genomes
downloaded from the NCBI (June, 2010), using the software tool
BWA . Subsequent searches were performed using BLASTX
 against the set of bacterial proteins extracted from the protein
non-redundant database. To account for expression bias due to
transcript length, each sample transcript expression was normal-
ized to provide values of Reads Per Kilobase of transcript per
Million reads mapped (RPKM - ) using the formula:
where C=number of reads that could be mapped in that sample
to the specific bacterial transcript, L=the length of the transcript
and N=total number of reads that could be mapped to bacterial
transcripts in that sample.
Assigning functional classes
To generate gene family assignments for each transcript, we
performed an all-vs-all BLAST search of the 236,769 unique
transcripts identified from our 12 samples (E-value,1025). The
Markov clustering algorithm (MCL - ) was then applied using
an inflation parameter of 2.6 to place each transcript into one of
13,278 gene families. 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. COG category
 assignments were performed through BLAST-based similarity
searches to identify the closest matching sequence in the COG
database (E-value,1023). Enzyme classification (EC) assignments
were assigned by performing a BLASTP (E-value,e210) search of
the 246,538 transcripts against a database of 127,478 enzyme
proteins annotated by SwissProt Version 57.0 . A slightly more
stringent E-value is used here to reduce the number of false
positives that arise when sequence similarity is used for enzyme
classification purposes . Metabolic networks were constructed
as previously described : enzymes (EC numbers) are
represented as nodes and substrates connecting two enzymes are
represented as edges in the network. Enzyme-substrate relation-
ships were inferred from KEGG . E. coli homolog mapping
was performed through BLAST-based similarity searches to
identify the closest matching sequence in the set of E. coli K12
transcripts (E-value,1023). The relative abundance of each COG
category, EC number and E. coli homolog was derived from the
sum of RPKM values for each transcript that maps to the specific
category, number or homolog in question. The relative conserva-
tion of E. coli genes was generated through the systematic
identification of putative orthologs of each E. coli gene in the set
of 1078 bacterial genomes using the tool InParanoid .
Network metrics were computed using the BGL library in MatLab
BLASTX searches against a set of bacterial transcripts.
For each sample, we show the frequency of reads that have a
match to a known bacterial transcript at a specific threshold of %
sequence identity and % of read length.
Distribution of sequence matches from
families. The graphs indicate the RPKM values associated with
each gene family as a function of the number of members
associated with that family. For each sample we observe only weak
correlation between the size of the family and its relative
expression within the datasets.
RPKM values of the top 500 largest gene
Conceived and designed the experiments: JP DNF PP JSD. Performed the
experiments: XX DNF CER JM. Analyzed the data: XX DNF CER JP.
Contributed reagents/materials/analysis tools: SSH AJC KDM AJM.
Wrote the paper: DNF PP JSD JP.
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1.Schloss PD, Handelsman J (2003) Biotechnological prospects from metage-
nomics. Curr Opin Biotechnol 14: 303–310.
Breitbart M, Hewson I, Felts B, Mahaffy JM, Nulton J, et al. (2003)
Metagenomic analyses of an uncultured viral community from human feces.
J Bacteriol 185: 6220–6223.
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, et al. (2010) A human gut
microbial gene catalogue established by metagenomic sequencing. Nature 464:
Yooseph S, Sutton G, Rusch DB, Halpern AL, Williamson SJ, et al. (2007) The
Sorcerer II Global Ocean Sampling expedition: expanding the universe of
protein families. PLoS Biol 5: e16.
Turnbaugh PJ, Quince C, Faith JJ, McHardy AC, Yatsunenko T, et al. (2010)
Organismal, genetic, and transcriptional variation in the deeply sequenced gut
microbiomes of identical twins. Proc Natl Acad Sci U S A 107: 7503–7508.
Gilbert JA, Field D, Swift P, Thomas S, Cummings D, et al. (2010) The
taxonomic and functional diversity of microbes at a temperate coastal site: a
‘multi-omic’ study of seasonal and diel temporal variation. PLoS ONE 5:
Poroyko V, White JR, Wang M, Donovan S, Alverdy J, et al. (2010) Gut
microbial gene expression in mother-fed and formula-fed piglets. PLoS One 5:
Booijink CC, Boekhorst J, Zoetendal EG, Smidt H, Kleerebezem M, et al.
(2010) Metatranscriptome analysis of the human fecal microbiota reveals
subject-specific expression profiles, with genes encoding proteins involved in
carbohydrate metabolism being dominantly expressed. Appl Environ Microbiol
Weckx S, Allemeersch J, Van der Meulen R, Vrancken G, Huys G, et al. (2011)
Metatranscriptome analysis for insight into whole-ecosystem gene expression
during spontaneous wheat and spelt sourdough fermentations. Appl Environ
Microbiol 77: 618–626.
10. Nam YD, Chang HW, Kim KH, Roh SW, Bae JW (2009) Metatranscriptome
analysis of lactic acid bacteria during kimchi fermentation with genome-probing
microarrays. Int J Food Microbiol 130: 140–146.
11. Frias-Lopez J, Shi Y, Tyson GW, Coleman ML, Schuster SC, et al. (2008)
Microbial community gene expression in ocean surface waters. Proc Natl Acad
Sci U S A 105: 3805–3810.
12. Gilbert JA, Field D, Huang Y, Edwards R, Li W, et al. (2008) Detection of large
numbers of novel sequences in the metatranscriptomes of complex marine
microbial communities. PLoS One 3: e3042.
13. Poretsky RS, Gifford S, Rinta-Kanto J, Vila-Costa M, Moran MA (2009)
Analyzing gene expression from marine microbial communities using environ-
mental transcriptomics. J Vis Exp.
14. Shi Y, Tyson GW, DeLong EF (2009) Metatranscriptomics reveals unique
microbial small RNAs in the ocean’s water column. Nature 459: 266–269.
15. Marchetti A, Schruth DM, Durkin CA, Parker MS, Kodner RB, et al. (2012)
Comparative metatranscriptomics identifies molecular bases for the physiolog-
ical responses of phytoplankton to varying iron availability. Proc Natl Acad
Sci U S A 109: E317–325.
16. Radax R, Rattei T, Lanzen A, Bayer C, Rapp HT, et al. (2012)
Metatranscriptomics of the marine sponge Geodia barretti: tackling phylogeny
and function of its microbial community. Environ Microbiol.
17. Valles SM, Oi DH, Yu F, Tan XX, Buss EA (2012) Metatranscriptomics and
Pyrosequencing Facilitate Discovery of Potential Viral Natural Enemies of the
Invasive Caribbean Crazy Ant, Nylanderia pubens. PLoS ONE 7: e31828.
18. Rowley JW, Oler AJ, Tolley ND, Hunter BN, Low EN, et al. (2011) Genome-
wide RNA-seq analysis of human and mouse platelet transcriptomes. Blood 118:
19. Xiong Y, Chen X, Chen Z, Wang X, Shi S, et al. (2010) RNA sequencing shows
no dosage compensation of the active X-chromosome. Nat Genet 42:
20. Stewart FJ, Ulloa O, DeLong EF (2012) Microbial metatranscriptomics in a
permanent marine oxygen minimum zone. Environ Microbiol 14: 23–40.
21. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, et al.
(2008) Accurate whole human genome sequencing using reversible terminator
chemistry. Nature 456: 53–59.
22. Turnbaugh PJ, Quince C, Faith JJ, McHardy AC, Yatsunenko T, et al. (2010)
Organismal, genetic, and transcriptional variation in the deeply sequenced gut
microbiomes of identical twins. Proceedings of the National Academy of
Sciences 107: 7503–7508.
23. Tatusov RL, Koonin EV, Lipman DJ (1997) A genomic perspective on protein
families. Science 278: 631–637.
24. Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, et al. (2008) The
metagenomics RAST server - a public resource for the automatic phylogenetic
and functional analysis of metagenomes. BMC Bioinformatics 9: 386.
25. The Gene Ontology Consortium (2010) The Gene Ontology in 2010: extensions
and refinements. Nucleic Acids Res 38: D331–335.
26. Mitra S, Rupek P, Richter DC, Urich T, Gilbert JA, et al. (2011) Functional
analysis of metagenomes and metatranscriptomes using SEED and KEGG.
BMC Bioinformatics 12 Suppl 1: S21.
27. Letunic I, Yamada T, Kanehisa M, Bork P (2008) iPath: interactive exploration
of biochemical pathways and networks. Trends Biochem Sci 33: 101–103.
28. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, et al. (2006) From
genomics to chemical genomics: new developments in KEGG. Nucleic Acids
Res 34: D354–357.
29. Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ, et al. (2006)
Metagenomic analysis of the human distal gut microbiome. Science 312:
30. Frank DN, Pace NR (2008) Gastrointestinal microbiology enters the
metagenomics era. Curr Opin Gastroenterol 24: 4–10.
31. Peterson DA, Frank DN, Pace NR, Gordon JI (2008) Metagenomic approaches
for defining the pathogenesis of inflammatory bowel diseases. Cell Host Microbe
32. Frank DN, St Amand AL, Feldman RA, Boedeker EC, Harpaz N, et al. (2007)
Molecular-phylogenetic characterization of microbial community imbalances in
human inflammatory bowel diseases. Proc Natl Acad Sci U S A 104:
33. Baumgart M, Dogan B, Rishniw M, Weitzman G, Bosworth B, et al. (2007)
Culture independent analysis of ileal mucosa reveals a selective increase in
invasive Escherichia coli of novel phylogeny relative to depletion of Clostridiales
in Crohn’s disease involving the ileum. ISME J 1: 403–418.
34. Giongo A, Gano KA, Crabb DB, Mukherjee N, Novelo LL, et al. Toward
defining the autoimmune microbiome for type 1 diabetes. ISME J 5: 82–91.
35. Dewhirst FE, Chien CC, Paster BJ, Ericson RL, Orcutt RP, et al. (1999)
Phylogeny of the defined murine microbiota: altered Schaedler flora. Appl
Environ Microbiol 65: 3287–3292.
36. Robertson BR, O’Rourke JL, Neilan BA, Vandamme P, On SL, et al. (2005)
Mucispirillum schaedleri gen. nov., sp. nov., a spiral-shaped bacterium
colonizing the mucus layer of the gastrointestinal tract of laboratory rodents.
Int J Syst Evol Microbiol 55: 1199–1204.
37. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-
Wheeler transform. Bioinformatics 25: 1754–1760.
38. Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R (2005) The
microbial pan-genome. Curr Opin Genet Dev 15: 589–594.
39. Peregrin-Alvarez JM, Parkinson J (2007) The global landscape of sequence
diversity. Genome Biol 8: R238.
40. Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, et al. (2005)
Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae:
implications for the microbial ‘‘pan-genome’’. Proc Natl Acad Sci U S A 102:
41. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and
quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5: 621–628.
42. Gifford SM, Sharma S, Rinta-Kanto JM, Moran MA (2010) Quantitative
analysis of a deeply sequenced marine microbial metatranscriptome. Isme J 5:
43. Tatusov RL, Galperin MY, Natale DA, Koonin EV (2000) The COG database:
a tool for genome-scale analysis of protein functions and evolution. Nucleic
Acids Res 28: 33–36.
44. Huson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC (2011) Integrative
analysis of environmental sequences using MEGAN4. Genome Res 21:
45. Green ML, Karp PD (2006) The outcomes of pathway database computations
depend on pathway ontology. Nucleic Acids Res 34: 3687–3697.
46. Peregrin-Alvarez JM, Sanford C, Parkinson J (2009) The conservation and
evolutionary modularity of metabolism. Genome Biol 10: R63.
47. Hahn MW, Kern AD (2005) Comparative Genomics of Centrality and
Essentiality in Three Eukaryotic Protein-Interaction Networks. Molecular
Biology and Evolution 22: 803–806.
48. Butland G, Peregrin-Alvarez JM, Li J, Yang W, Yang X, et al. (2005) Interaction
network containing conserved and essential protein complexes in Escherichia
coli. Nature 433: 531–537.
49. Parrish JR, Yu J, Liu G, Hines JA, Chan JE, et al. (2007) A proteome-wide
protein interaction map for Campylobacter jejuni. Genome Biol 8: R130.
50. von Mering C, Jensen LJ, Kuhn M, Chaffron S, Doerks T, et al. (2007)
STRING 7–recent developments in the integration and prediction of protein
interactions. Nucleic Acids Res 35: D358–362.
51. Yellaboina S, Goyal K, Mande SC (2007) Inferring genome-wide functional
linkages in E. coli by combining improved genome context methods: comparison
with high-throughput experimental data. Genome Res 17: 527–535.
52. Hu P, Janga SC, Babu M, Diaz-Mejia JJ, Butland G, et al. (2009) Global
functional atlas of Escherichia coli encompassing previously uncharacterized
proteins. PLoS Biol 7: e96.
53. Peregrin-Alvarez JM, Xiong X, Su C, Parkinson J (2009) The Modular
Organization of Protein Interactions in Escherichia coli. PLoS Comput Biol 5:
54. Remm M, Storm CEV, Sonnhammer ELL (2001) Automatic clustering of
orthologs and in-paralogs from pairwise species comparisons. Journal of
Molecular Biology 314: 1041–1052.
55. Stewart F, Sharma A, Bryant J, Eppley J, DeLong E (2011) Community
transcriptomics reveals universal patterns of protein sequence conservation in
natural microbial communities. Genome Biology 12: R26.
Mouse Intestinal Metatranscriptomics
PLoS ONE | www.plosone.org14 April 2012 | Volume 7 | Issue 4 | e36009
56. Cheang MC, Chia SK, Voduc D, Gao D, Leung S, et al. (2009) Ki67 index, Download full-text
HER2 status, and prognosis of patients with luminal B breast cancer. J Natl
Cancer Inst 101: 736–750.
57. Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly
using de Bruijn graphs. Genome Res 18: 821–829.
58. Smith K, McCoy KD, Macpherson AJ (2007) Use of axenic animals in studying
the adaptation of mammals to their commensal intestinal microbiota. Semin
Immunol 19: 59–69.
59. Bennett WE, Jr., Gonzalez-Rivera R, Shaikh N, Magrini V, Boykin M, et al.
(2009) A method for isolating and analyzing human mRNA from newborn stool.
J Immunol Methods 349: 56–60.
60. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for
rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl
Environ Microbiol 73: 5261–5267.
61. Yarza P, Richter M, Peplies J, Euzeby J, Amann R, et al. (2008) The All-Species
Living Tree project: a 16S rRNA-based phylogenetic tree of all sequenced type
strains. Syst Appl Microbiol 31: 241–250.
62. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, et al. (2007) SILVA: a
comprehensive online resource for quality checked and aligned ribosomal RNA
sequence data compatible with ARB. Nucleic Acids Res 35: 7188–7196.
63. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped
BLAST and PSI-BLAST: a new generation of protein database search
programs. Nucleic Acids Res 25: 3389–3402.
64. van Dongen S Graph clustering by flow simulation: University of Utrecht.
65. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, et al. (2003)
The COG database: an updated version includes eukaryotes. BMC Bioinfor-
matics 4: 41.
66. Bairoch A, Apweiler R (2000) The SWISS-PROT protein sequence database
and its supplement TrEMBL in 2000. Nucleic Acids Res 28: 45–48.
67. Hung SS, Wasmuth J, Sanford C, Parkinson J (2010) DETECT–a density
estimation tool for enzyme classification and its application to Plasmodium
falciparum. Bioinformatics 26: 1690–1698.
68. Peregrı ´n-Alvarez J, Sanford C, Parkinson J (2009) The conservation and
evolutionary modularity of metabolism. Genome Biol 10: R63.
69. Remm M, Storm CE, Sonnhammer EL (2001) Automatic clustering of orthologs
and in-paralogs from pairwise species comparisons. J Mol Biol 314: 1041–1052.
Mouse Intestinal Metatranscriptomics
PLoS ONE | www.plosone.org15April 2012 | Volume 7 | Issue 4 | e36009