Review Article |
Open Access |
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Exploring Microbial Diversity
Using 16S rRNA High-Throughput Methods |
Fabrice Armougom and Didier Raoult * |
URMITE - UMR CNRS 6236, IRD 3R198, Université de la Méditerranée, Faculté
de Médecine, 27 Boulevard Jean Moulin, 13005 Marseille, France |
*Corresponding author: |
URMITE - UMR CNRS 6236, IRD 3R198,
Université de la Méditerranée, Faculté de Médecine,
27 Boulevard Jean Moulin,
13005 Marseille, France,
Phone : (33).04.91.38.55.17,
Fax : (33).04.91.83.03.90,
Email : didier.raoult@gmail.com |
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Received January 21, 2009; Accepted February 24, 2009; Published February 27, 2009 |
Citation: Fabrice A, Didier R (2009) Exploring Microbial Diversity Using 16S rRNA High-Throughput Methods. J Comput
Sci Syst Biol 2: 074-092. |
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Copyright: © 2009 Fabrice A, 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. |
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As a result of advancements in high-throughput technology, the sequencing of the pioneering 16S rRNA gene
marker is gradually shedding light on the taxonomic characterization of the spectacular microbial diversity that
inhabits the earth. |
16S rRNA-based investigations of microbial environmental niches are currently conducted using several technologies,
including large-scale clonal Sanger sequencing, oligonucleotide microarrays, and, particularly, 454
pyrosequencing that targets specific regions or is linked to barcoding strategies. Interestingly, the short read
length produced by next-generation sequencing technology has led to new computational efforts in the taxonomic
sequence assignment process. |
From a medical perspective, the characterization of the microbial composition of the skin surface, oral cavity,
and gut in both healthy and diseased people enables a comparison of microbial community profiles and also
contributes to the understanding of the potential impact of a particular microbial community. |
Introduction |
Until recently, the vast majority of global microbial diversity
was inaccessible or largely underestimated by culturedependent
methods, since the cultivated fraction of the 4-
6x1031 prokaryotic genomes moving around the biosphere
( Whitman et al, 1998) is currently estimated to be 1%
( Giovannoni and Stingl, 2005). However, the development
of culture-independent methods and the commercialization
of next-generation sequencing technology ( Mardis, 2008,
Rothberg and Leamon, 2008) have yielded powerful new
tools in terms of time savings, cost effectiveness, and data
production capability. These new tools allow for the gradual
characterization of the unseen majority of environmental
microbial communities. Microbial diversity has recently been explored in a great variety of environments, including soil
( Roesch et al, 2007, Yergeau et al, 2008), sea ( Huber et al,
2007, Sogin et al, 2006), air ( Wilson et al, 2002), and the
human body, including from a medical perspective, the gastrointestinal
tract ( Andersson et al, 2008, Ley et al, 2006),
oral cavity ( Jenkinson and Lamont, 2005), vaginal tract (Zhou
et al, 2004), and skin surface ( Fierer et al, 2008). These
microbial communities have been characterized in terms of
community structure, composition, metabolic function, and
ecological roles. Investigations of environmental microbial
diversity have employed the 16S rRNA (16S) gene marker,
which offers phylogenetic taxonomic classification without
requiring isolation and cultivation. Although the use of the 16S phylogenetic marker is often criticized, due to its heterogeneity
among operons of the same genome ( Acinas et
al, 2004) or its lack of resolution at the species level (Pontes
et al, 2007), it is still considered as a ‘gold standard’ for
bacterial identification. The use of next-generation sequencing
technology has increased the size of 16S sequence databases
at an impressive speed ( Tringe and Hugenholtz,
2008). |
Supported by new high-throughput methods (454
pyrosequencing, PhyloChip microarrays) and strategies
(barcoding); the surveys of 16S gene in the human microbiota
attempt to provide a comprehensive picture of the community
differences between healthy and diseased states. In
this review, we focus on the 16S-gene-based characterization
of microbial communities using clonal Sanger sequencing,
phylogenetic oligonucleotide microarrays, and 454
pyrosequencing strategies as applied to medical research.
Propelled by the launch of the Human Microbiome Project,
16S high-throughput methods show tremendous potential for
identifying uncultivated or rare pathogenic agents, finding
shifts in the bacterial community associated with disease
states (Ley et al, 2006), understanding how microbiota are
affected by environmental factors of the human host (diet,
lifestyle, sex, age) (Fierer et al, 2008), and differentiating
between a core human microbial community and inter-individual
variability (Gao et al, 2007, Turnbaugh et al, 2008).
These advances will contribute to a more comprehensive
picture of both healthy and diseased states and will lead to
the use of more appropriate medical treatments, such as
targeted antibiotic therapy rather than the use of broad-range
antibiotics. |
Bacterial Taxonomic Classification |
The 16S rRNA Gene: A Phylogenetic Marker |
In the mid-1980s, major enhancements in bacterial typing
and characterization of phylogenetic relationships were
achieved, using new molecular approaches based on fulllength
sequencing of ribosomal genes. Pioneering work by
Woese and colleagues (Woese, 1987) described bacterial
rRNA genes as ‘molecular clocks’, due to their uncommon
features such as universality, activity in cellular functions,
and extremely conserved structure and nucleotide sequence.
The three types of rRNA in prokaryotic ribosomes are classified
as 23S, 16S, and 5S, according to their sedimentation
rates, and have sequence lengths of about 3300, 1550, and
120 nucleotides, respectively (Rossello-Mora and Amann,2001). Initially, microbial diversity studies involved sequencing
the 5S rRNA gene obtained from environmental samples
(Lane et al, 1985, Stahl et al, 1985). However, the relatively
short sequence length of the 5S gene contains few phylogenetically
informative sites, which limits its usefulness for
taxonomic classification purposes. In addition, although the
information content of the 23S rRNA gene is larger than
that of the 16S gene, it is the 16S gene that has become a
standard in bacterial taxonomic classification because it is
more easily and rapidly sequenced (Spiegelman et al, 2005).
It is widely accepted that a compelling classification of
prokaryotes should be based on a ‘polyphasic approach’
that includes genomic, phenotypic, and phylogenetic information
(Vandamme et al, 1996). However, most bacterial
diversity surveys exclusively target the 16S gene in a singlestep
phylogenetic approach (Pace, 1997). |
The 1550 base pairs of the 16S gene are a structural part
of the 30S ribosomal small subunit (SSU) and consist of
eight highly conserved regions (U-U8) and nine variable
regions across the bacterial domain (Jonasson et al, 2002).
As no lateral gene transfer seems to occur between 16S
genes (Olsen et al, 1986) and as their structure contains
both highly conserved and variable regions with different
evolution rates, the relationships between 16S genes reflect
evolutionary relationships between organisms. A comparison
of 16S gene sequence similarities is usually used as the
‘gold standard’ for taxonomic identification at the species
level. Although thresholds are arbitrary and controversial, a
range of 0.5% to 1% sequence divergence is often used to
delineate the species taxonomic rank (Clarridge, III, 2004).
Sequencing the 16S gene is currently the most common
approach used in microbial classification as a result of its
phylogenetic properties and the large amount of 16S gene
sequences available for comparison analyses. |
16S Gene Sequence Databases |
Accurate identification of organisms by comparative analysis
of 16S gene sequences is strongly dependent on the quality
of the database used. The curated Ribosomal Database
Project (RDP-II, http://rdp.cme.msu.edu/) provides 623,174
bacterial and archaeal small subunit rRNA gene sequences
in an aligned and annotated format and has achieved major
improvements in the detection of sequence anomalies (Cole
et al, 2007). Notably, among all of the online tools provided
by the RDP-II web site, the RDP classifier tool has demonstrated
effective taxonomic classification of short sequences
produced by the new pyrosequencing technology. The Greengenes project (http://greengenes.lbl.gov/) offers annotated,
chimera-checked, full-length 16S gene sequences
in standard alignment formats (DeSantis et al, 2006) and
has a particularly useful tool for 16S microarray design. The
Silva project (http://www.arb-silva.de) (Pruesse et al, 2007)
provides SSU as well as large subunit (LSU) rRNA sequences
from Bacteria, Archaea, and Eukarya in a format
that is fully compatible with the ARB package (Ludwig et
al, 2004). The ARB package (www.arb-home.de) has been
used in major 16S surveys (Eckburg et al, 2005, Ley et al,
2005, Ley et al, 2006, McKenna et al, 2008, Turnbaugh et
al, 2006) and notably allows phylogenetic tree constructions
by insertion of partial or near-full sequences into a pre-established
phylogenetic tree using a parsimony insertion tool. |
However, the lack of quality control of sequence entries
(ragged sequence ends and outdated or faulty entries) in
these major public sequence databases has led to the development
of high quality commercial databases, including
MicroSeq 500 and the RIDOM Mycobacteria project
(http:/
/www.ridom-rdna.de) (Harmsen et al, 2002). The MicroSeq
500 database targets the first 527-bp fragment of the 16S
gene and is able to identify most of the clinically important
bacterial strains with ambiguous biochemical profiles (Woo
et al, 2003). The ribosomal differentiation of the medical
microorganism (RIDOM) database targets the 5’end of the
16S sequence and is dedicated to Mycobacteria family
analyses. However, although these commercial databases
are continually expanding, the current total number of 16S
entries remains uncertain, and the representation of taxonomic
divisions is limited. |
Measures of Microbial Diversity |
The assessment of microbial diversity in a natural environment
involves two aspects, species richness (number of
species present in a sample) and species evenness (distribution
of relative abundance of species) (Magurran, 2005).
In order to estimate species richness, researchers widely
rely on the assignment of 16S sequences into Operational
Taxonomic Unit (OTU or phylotype) clusters, for instance,
as performed by DOTUR (Schloss and Handelsman, 2005).
The criterion used to define an OTU at the species level is
the percentage of nucleotide sequence divergence; the cutoff
values vary between 1%, 3%, or 5%, depending on the
study. As a result of these inconsistencies, reliable statistical
comparisons or descriptions of species richness across
studies are restricted (Martin, 2002). The total community
diversity of a single environment, or the α-diversity, is often represented by rarefaction curves. These curves plot the
cumulative number of OTUs or phylotypes captured as a
function of sampling effort and, therefore, indicate only the
OTU richness observed in a given set of samples (Eckburg
et al, 2005). In contrast, nonparametric methods, including
Chao1 or ACE, are richness estimators of overall α-diversity
(Roesch et al, 2007). In addition, quantitative methods
such as the Shannon or Simpson indices measure the evenness
of the α-diversity. However, although these estimators
can describe the diversity of the microbiota associated
with a healthy or diseased state, they are not informative of
the (phylo)genetic diversity of an environmental sample
(Martin, 2002). |
Contrary to the α-diversity, the β-diversity measure offers
a community structure comparison (taxon composition
and relative abundance) between two or more environmental
samples. For instance, β-diversity indices can compare
similarities and differences in microbial communities in
healthy and diseased states. A broad range of qualitative
(presence/absence of taxa) and quantitative (taxon abundance)
measures of community distance are available using
several tools, including LIBHUFF, P-test, TreeClimber
(Schloss and Handelsman, 2006b), SONS (Schloss and
Handelsman, 2006a), DPCoA, or UniFrac (Lozupone et al,
2006, Lozupone and Knight, 2005); these methods have been
thoroughly detailed in a previous review (Lozupone and
Knight, 2008). For example, the robust unweighted UniFrac
tool (Liu et al, 2007) measures the phylogenetic distance
between two communities as the fraction of phylogenetic
tree branch lengths leading to a descendant from either one
community or the other. While UniFrac efficiently detects
differences in the presence or absence of bacterial lineages,
the recently developed weighted UniFrac is the qualitative
version of original UniFrac and provides an efficient detection
of differences in the relative abundance of bacterial
lineages (Lozupone et al, 2007). |
16S High-throughput Methods |
16S Clonal Sanger Sequencing |
Until the appearance in the two last decades of sequencing-
by-synthesis methods (Ronaghi et al, 1996) such as those
used in pyrosequencing, the Sanger sequencing method
(Sanger et al, 1977) was the cornerstone of DNA sequence
production. The Sanger method is based on DNA synthesis
on a single-stranded template and di-deoxy chain-termination
(Hall, 2007, Pettersson et al, 2008). Improvements in cost-effectiveness and the development of high-throughput
techniques (e.g, fluorescent-labeled terminators, capillary
separation, template preparation) (Hunkapiller et al, 1991)
have enabled direct sequencing of clones, without laborious
prior screening by restriction analysis. As a result, Sanger
sequencing has produced the earliest in-depth analyses of
microbial communities (Eckburg et al, 2005, Ley et al, 2006,
Turnbaugh et al, 2006, Zhou et al, 2004). All 16S genes in a
sample are amplified using a universally conserved primer
pair that targets most of the species that have been sequenced
and deposited in the ribosomal databases. After
the cloning of amplified PCR products into specific vectors,
the inserts are sequenced. Sanger sequencing offers high
phylogenetic resolution power, as the method yields the longest
sequencing read available, up to 1000 base pairs (bp)
(Shendure and Ji, 2008). |
16S Microarray-based Approach |
The DNA microarray is a powerful technology that can
simultaneously detect thousands of genes on a single glass
slide or silicon surface (Gentry et al, 2006). Mainly used in
gene expression profiling (DeRisi et al, 1997, Schena et al,
1996), DNA microarray technology has also been employed
in bacterial identification and, more recently, has been
adapted for exploring microbial community diversity in environmental
niches. Microarrays used for microbial identification
rely on the 16S gene and use short 20- to 70-mer
oligonucleotide probes (Bae and Park, 2006) for multi-species
detection. These are referred to as phylogenetic oligonucleotide
microarrays (POAs). |
Due to the phenomenal microbial diversity that might be present in an environmental sample and the lack of prior
knowledge of its population composition, the oligonucleotide
probe design strategy has been modified for use in community
diversity analysis. Instead of using one unique probe
that targets a specific region of one taxon, multiple probe
sets are employed to target microorganisms at different taxonomic
levels. An efficient probe design based on a hierarchical
phylogenetic framework was established in 2003, using
a database of curated and aligned 16S sequences known as
ProkMSA (DeSantis et al, 2003). Desantis et al. defined
9,020 OTUs in the ProkMSA alignment using sequence identity
clustering and demonstrated the capability of microarrays
to correctly assign two species to their OTU using multiple
probe sets (DeSantis et al, 2003). |
High-throughput microorganism detection by microarray
technology was first achieved with high-density photolithography
microarrays using 31,179 20-mer oligonucleotide
probes in a deep air investigation (Wilson et al, 2002). These
high density microarrays present an alternative to clone library
sequencing, since they can more deeply assess microbial
diversity (DeSantis et al, 2007) without a cloning
bias. With advancements in technology, the PhyloChip platform,
an Affymetrix microarray product, was developed by
the Lawrence Berkeley National Laboratory. The PhyloChip
has rapidly identified up to 8,900 distinct environmental microorganisms
at different taxonomy levels from soil (Yergeau
et al, 2008) , air (Brodie et al, 2007), or uranium-contaminated
site samples (Brodie et al, 2006) in a single experimental
run. The PhyloChip is a glass slide with a small surface
area, containing a high density microarray of hundreds
of immobilized oligonucleotides (15- to 25-mer length). The
method employs parallel hybridization reactions, using a flow
of fluorescently labeled DNA targets. The microarray slide
is analyzed with a fluorescence microscope equipped with
a cooled CCD camera. |
Pyrosequencing Technology |
Sequencing Chemistry |
Pyrosequencing is a DNA sequencing method (Clarke,
2005, Ronaghi, 2001, Ronaghi and Elahi, 2002) based on the
sequencing-by-synthesis principle, which was first described
in 1985 (Melamede and Wallace, 1985). This method relies
on efficient detection of the sequential incorporation of natural
nucleotides during DNA synthesis (Ronaghi et al, 1996,
Ronaghi et al, 1998) (Figure 1). The pyrosequencing technique
includes four enzymes that are involved in a cascade reaction system (Figure 1). |
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Figure1: Principle of pyrosequencing technology
The primer for the sequencing step is hybridized to a single-stranded DNA template, and incubated with the enzymes, DNA
polymerase, ATP sulfurylase, luciferase and apyrase, and the substrates. Deoxyribonucleotide triphosphate (dNTP) is added,
one at a time, to the pyrosequencing reaction. The incorporation of a nucleotide is accompanied by release of pyrophosphate
(PPi). The ATP sulfurylase quantitatively converts PPi to ATP. The signal light produced by the luciferase-catalyzed reaction
in presence of ATP is detected by a charge coupled device (CCD) camera and integrated as a peak in a Pyrogram. The
nucleotide degrading Apyrase enzyme continuously degrades ATP excess and unincorporated dNTPs. The process continues
with addition of the next dNTP and the nucleotide sequence of the complementary DNA strand is inferred from the signal
peaks of the pyrogram.
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During the reaction, the Klenow fragment of DNA polymerase
I releases inorganic pyrophosphate molecules (PPi)
upon the addition of one nucleotide to a primer hybridized to
a single-stranded DNA template. The second reaction, catalyzed
by ATP sulfurylase, produces ATP, using the released
PPi as a substrate. The ATP molecules are then converted
to a luminometric signal by the luciferase enzyme. Therefore,
the signal light is detected only if a base pair is formed
with the DNA template, and the signal strength is proportional
to the number of nucleotides incorporated in a single
nucleotide flow. Finally, the unincorporated nucleotides and
excess ATP are degraded between base additions by a nucleotide-
degrading enzyme such as apyrase (Ronaghi et al,
1998); at this point, another dNTP is added and a new cycle
begins. The earliest attempts at pyrosequencing were performed
using the PSQ96 system (Biotage AB, Upsala,
Sweden) and targeted the short 16S variable regions V1
and V3 (Jonasson et al, 2002, Tarnberg et al, 2002) or the
human pathogen H. pylori (Hjalmarsson et al, 2004). This
system produced reads of an average length of around 20-
40 bases. |
The 454 Life Sciences Pyrosequencing Platform |
In 2005, Margulies et al. first described a highly parallel
sequencing platform (GS 20 454 Life Sciences) using a
pyrosequencing protocol optimized for solid support. The
authors demonstrated the ability of the system to assemble
complete genomes (Mycoplasma genitalium and Streptococcus
pneumoniae) from short sequencing reads
(Margulies et al, 2005). In 454 pyrosequencing, the DNA
template is fragmented, and the resulting fragments are individually
immobilized onto a bead by limiting dilution. Emulsion
PCR is performed for the DNA amplification step, in
which each DNA fragment is independently confined into a
droplet of oil and water containing PCR reagents. The
clonally amplified fragments are distributed into a picotiter
plate, which contains ~1.6 million picoliter wells, with a well
diameter allowing one bead per well. |
Using the pyrosequencing protocol previously described,
the chemiluminescent signal obtained from an incorporated
nucleotide is recorded by a charge-coupled device (CCD)
camera, and data analysis, such as image processing or de
novo sequence/genome assembly, is performed with provided
bioinformatics tools. Other massively parallel platforms
(Solexa and SOLID) using flow cell and other sequencing chemistries were reviewed previously (Holt and Jones,
2008). |
While the first generation 454 Life Sciences apparatus
(GS20) provided up to 25 megabases of data with an average
read length of 100 base pairs (bp), the new GS FLX
Titanium provides up to 400 megabases of data with an average
read length of 400 bp. The 454 Life Sciences highthroughput
sequencing platform outperforms the clonal
Sanger sequencing method in terms of time and cost per
sequenced nucleotide (Hugenholtz and Tyson, 2008), capacity
of sequencing per run, as well as time savings in
library preparation and data analysis. The contribution of
454 pyrosequencing to the literature is considerable, as 283
publications since 2005 have reported the use of this technology
(Figure 2), and more than 20% of those publications
belong to Science or Nature group journals. When applied
to the field of microbial diversity, 454 pyrosequencing offers
major insights, particularly in the investigation of human
gut flora (Dethlefsen et al, 2008, Turnbaugh et al, 2006,
Turnbaugh et al, 2008), vaginal microbiota (Spear et al, 2008),
hand surface bacteria (Fierer et al, 2008), soil diversity
(Roesch et al, 2007), deep sea ecosystems (Huber et al,
2007, Sogin et al, 2006), and viral and phage populations
(Desnues et al, 2008, Eriksson et al, 2008, La et al, 2008).
Due to the short read length generated by the 454 platform
and in order to increase sequencing capacity, new strategies
for exploring microbial diversity by 16S pyrosequencing
were developed. |
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Figure 2: Number of publications enabled by the 454 pyrosequencing technology since 2005.
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16S Pyrosequencing Strategies |
Targeting Specific Regions |
Due to the short sequencing read length generated by
pyrosequencing technology (e.g, 100 bp for GS 20 and 200
bp for GS FLX) and due to the small amount of nucleotide
variability in the 16S gene throughout the bacterial domain,
full 16S gene sequence assembly and the taxonomic assignment
of species present in a mixed microbial sample
remain a computational challenge (Armougom and Raoult,
2008). One strategy of addressing this problem consists of
targeting a specific variable 16S region that exhibits a sufficient
phylogenetic signal to be accurately assigned at the
genus level or below. Surprisingly, short sequence fragments
(including 100 bases) can provide substantial phylogenic
resolution (Liu et al, 2007). By choosing appropriate 16S
regions and simulating the read length obtained with GS FLX
(250 bases), Liu et al. reproduced the same results obtained
from full length 16S sequences using the UniFrac clustering
tool (Liu et al, 2007). The authors suggested that the F8-
R357 primer set, which amplifies a sequence spanning the
V2 and V3 hypervariable regions and generates a 250-bp
amplicon, could be the optimal primer set for exploring microbial
diversity using 16S 454 pyrosequencing with GS FLX.
These primers were also used in a study of the macaque
gut microbiome (McKenna et al, 2008). A more recent study
reported by Liu et al, focused on the capabilities of different
taxonomic assignment methods (as a function of se- lected 16S regions), confirmed their previous conclusions
recommending the F8-R357 primer set. The authors also
suggested that regions surrounding the V6 hypervariable
region were not appropriate for taxonomic assignment using
a 16S 454 pyrosequencing strategy (Liu et al, 2008). |
Whereas Liu et al. laud the use of the F8-R357 primer
pair the study that introduced the concept of tag
pyrosequencing was performed using the V6 hypervariable
region amplified from deep water samples of the North Atlantic
(Sogin et al, 2006). Based on the Shannon entropy
measure, the 16S gene shows high variability in the V6 region.
This region was selected in an analysis of the human
gut microbiome combined with a barcoding strategy
(Andersson et al, 2008). In addition, in their recent study of
a gut microbial community, Huse et al. showed that the use
of tags of the V3 and V6 regions in 454 pyrosequencing
notably provides taxonomic assignments equivalent to those
obtained by full length sequences generated using clonal
Sanger sequencing (Huse et al, 2008). |
The taxonomic assignment of pyrosequencing reads is sensitive
to the classification method employed (Liu et al, 2008).
Wang et al. showed a classification accuracy map of their
RDP classifier tool along the 16S sequence position (Wang
et al, 2007), which revealed that variable V2 and V4 regions
of 16S provided the best taxonomic assignment results
at the genus level. No true consensus seems to emerge
among the 16S 454 pyrosequencing studies that target variable
regions (Table1). However, this is not surprising since
the power of phylogenetic resolution of variable 16S regions
might differ depending on the taxa present in the microbial community studied; these results may also be due to
the under-representation of 16S sequences of certain environments
in the reference databases (Huse et al, 2008). |
Table 1: Choice of primer set in microbial diversity investigations using 16S 454 pyrosequencing
A Mixture of Forward (F) or Reverse (R) primers is indicated by “&”. In italic, primer set targeting the Archaeal domain.
Studies that used the barcoding strategy are indicated with an asterisk (*). The 16S rRNA variable region (Vx) targeted by
the study is indicated in the last column.
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16S Barcoding Strategy |
The currently available 454 GS FLX pyrosequencer can
accommodate a maximum of sixteen independent samples,
since a picotiter plate contains sixteen physically separated
regions. |
To overcome this limitation and expand the capacity by
pooling DNA from independent samples in a single sequencing
run, a barcoding approach was developed, which associates
a short unique DNA sequence tag (barcode) with
each DNA template origin. In contrast with Sanger sequencing,
pyrosequencing technology such as GS FLX generates
sequencing reads from the first position of the DNA template
fragment. Therefore, the sequencing reaction driven
by an oligonucleotide that is complementary to adaptor A
and B first reads the barcode sequence, allowing the identification
of the original DNA template source (Andersson et
al, 2008, McKenna et al, 2008). Using the DNA barcode
strategy, each primer is composed of the 5’-adaptor A or B
(required for the PCR emulsion), followed by the DNA
barcode and the primer targeting the DNA region
(McKenna et al, 2008) (Figure 3). By selecting a DNA target
with a nucleotide length inferior to the average read
length of pyrosequencing, an unidirectional barcoding strategy
can be achieved, adding the barcode sequence only to
the forward or reverse primer (Andersson et al, 2008). The
length of the barcode sequence varies from 2-4 nucleotides (Andersson et al, 2008, Binladen et al, 2007, McKenna et
al, 2008) to 10 nucleotides (Dowd et al, 2008b, Dowd et al,
2008a) or 12 nucleotides (Fierer et al, 2008, Turnbaugh et
al, 2008); the number of DNA samples that can be combined
in a single sequencing run increases with barcode
length. |
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Figure 3: The barcoding strategy used in 454 pyrosequencing
Each primer contains a region complementary to the 454 A or B adaptator sequencing primers, a variable barcode sequence
length (in red) and a region complementary to ends of the 16S rRNA region targeted (Primer R or F)
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Finally, the efficiency of barcoding strategy was investigated
using a set of eight nucleotide barcodes based on error-
correcting codes (called Hamming codes). The ability
to detect sequencing errors that change sample assignments
and to correct errors in the barcodes was evaluated to 92%
(Hammady et al, 2008). |
Taxonomy Assignment of Short Pyrosequencing Reads |
Taxonomy assignment using standard phylogenic methods
such as likelihood- or parsimony- based tree construction
is inconceivable, given the large amount of sequence
data (400,000 reads for GS FLX Titanium) generated by
high-throughput pyrosequencing (Liu et al, 2008). In addition,
due to the short read length resulting from
pyrosequencing (~ 100 bp for GS 20 and 250 bp for GS
FLX), full 16S sequence assembly is a computational challenge,
especially for closely related species in a mixed bacterial
sample. Therefore, current taxonomy classification
tools such as the naïve Bayesian RDP-II classifier (Wang
et al, 2007) or the Greengenes classifier (DeSantis et al,
2006) employ rapid but approximate methods. In contrast
to the RDP-II classifier, the Greengenes classifier requires
a pre-computed alignment for its taxonomic classification.
While, tree-based methods are subject to large variations in
assignment accuracy according to the DNA region examined
(Liu et al, 2008), the tree-independent methods used in
the RDP-II and Greengene classification tools yield stable
and accurate taxonomy assignment results. However, although
these methods provide satisfactory assignment results
at the genus level, they have a limited resolution power
at the species level. This limitation could be reduced, but
not yet solved, by the 400- to 500-base read length generated
by the new GS Titanium pyrosequencing platform. |
Pyrosequencing read classification based on a sequence
similarity search using the BLAST algorithm can yield reliable
results; however, the closest match is not inevitably the
nearest phylogenetic neighbor (Koski and Golding, 2001).
Sundquist et al. (Sundquist et al, 2007) recently proposed a
method based on a similarity search with BLAT, a BLASTlike
alignment tool (Kent, 2002), and inferred specific phylogeny placement using a preliminary set of best BLAT match
scores. Finally, a tag-mapping methodology has been recently
introduced with GAST (Global Alignment for Sequence
Taxonomy), which is a taxonomic assignment tool
combining BLAST, multiple sequence alignment, and global
distance measures. GAST has been used to identify taxa
present in deep sea vent and human gut samples (Huse et
al, 2008, Sogin et al, 2006). |
The accuracy rate of classification methods of short 16S
rRNA sequences is measured as the percentage of sequences
correctly classified from a representative set of
bacteria sequences of known classification. This accuracy
rate can be associated with the misclassified sequence rate.
Overall, the simulation tests showed efficient classification
down to the genus level. Simulating 200-base segments (such
as generated by the GS FLX), the RPD-II classifier tool
indicated an overall taxonomic assignment accuracy of 83.2
% at the genus level (Wang et al, 2007). Likewise, using the
methodology of Sundquist and co-workers (Sundquist et al.
2007), the simulation of read resolution for 200 base segments
in diverse and representative samples yield an accuracy
rate around 80% as obtained for the RDP-II classifier.
However, while the benchmark of the Sundquist method
identified the V1 and the V2 regions of the 16S as the best
targets for the pyrosequencing of 100 bases (such as generated
by the GS 20), the benchmark of the RDP-II classifier
method identified the V2 and the V4 regions. Finally,
the comparison of the classification methods is rather difficult
since the representative bacteria sequences of known
classification used for the benchmarking is different for each
method. A collection of reference sequences of known classification
(defined as gold standard) is required for classification
method comparisons. For instance, collections of reference
sequence alignments are generally used for the
benchmarking of multiple sequence alignment methods
(Armougom et al. 2006). |
Metagenomic Approach |
Metagenomic is a culture-independent genomic analysis
of entire microbial communities inhabiting a particular niche,
such as the human gut (Riesenfeld et al, 2004, Schmeisser
et al, 2007). Metagenomic investigations aimed at finding
“who’s there and what are they doing” (Board On Life
Sciences, 2007) are providing new insight into the genetic
variability and metabolic capabilities of unknown or uncultured
microorganisms (Turnbaugh et al, 2006). The inclusion
of the metagenomic approach in this review was required, since metagenomic studies include the analysis of
the 16S sequences contained in metagenome data, in order
to identify community composition and determine bacterial
relative abundance (Biddle et al, 2008, Edwards et al, 2006 Krause et al, 2008, Wegley et al, 2007). However, we distinguished
the metagenomic approach from 16S highthroughput
methods, as the principal purpose of
metagenomic is to explore the entire gene content of
metagenomes for metabolic pathways and to understand
microbial community interactions (Tringe et al, 2005), rather
than targeting a single gene such as 16S. In contrast with
some scientific literature examples and because it can only
answer “who is there”, 454 pyrosequencing investigations
based on the 16S gene should not be assimilated to a
metagenomic case. |
Before the commercialization of the 454 pyrosequencing
platform, microbial community sequencing in early
metagenomic studies, such as in the Sargasso sea (Venter
et al, 2004) or in acid mine drainage (Edwards et al, 2000),
involved preliminary clone library construction and capillary
sequencing. This approach limited the expansion of microbial
diversity knowledge due to cloning bias and the cost of
capillary sequencing. In contrast, the inexpensive next-generation
454 pyrosequencing technology can perform direct
sequencing without requiring preliminary PCR amplification
or library construction. By excluding cloning and PCR
bias, 454 sequencing revolutionized the metagenomic field
by capturing up to 100% (depending on the quality of DNA
extraction and environmental sampling) of the microbial diversity
present in a sample. |
Case Studies |
The Human Microbiome Project |
Launched by the NIH Roadmap for Medical Research,
the Human Microbiome Project (HMP) seeks to comprehensively
characterize the human microbiota in order to
better understand its role in human health and disease states
(http://nihroadmap.nih.gov/) The HMP project mainly focuses
on the gastrointestinal, oral, vaginal, and skin
microbiota. |
The Gastrointestinal Microbiota |
Until recently, characterization of the gut microbiota diversity
was restricted to culture-based methods (Simon and
Gorbach, 1986). While the cultivable fraction is currently
estimated to be 442 bacterial, 3 archaeal, and 17 eukaryotic species (Zoetendal et al, 2008), the species richness of the
gut microbiota is estimated to be 15,000 or 36,000 bacterial
species, depending on the similarity cut-off applied in OTU
clustering (Frank et al, 2007). With the development of culture-
independent methods, 16S gene surveys have deeply
enhanced the microbial diversity map of the human gut
microbiota (Eckburg et al, 2005, Gill et al, 2006, Ley et al,
2006, Palmer et al, 2007). A major large-scale 16S investigation
by Eckburg et al. indicated that the gut of healthy
individuals was mainly composed of the Bacteroidetes and
Firmicutes bacterial phyla (83% of sequences). The sequences
also included the archaeal Metanobrevibacter
species, as well as a majority of uncultivated species and
novel microorganisms (Eckburg et al, 2005). Surprisingly, at
the phylum level, the bacterial diversity of the gut microbiota
is low; only 8 of 70 known bacterial phyla are represented.
Despite the predominance of Firmicutes and Bacteroidetes
phylotypes, the gut microbiota displays a great inter-individual
specificity in its composition (Ley et al, 2006), especially
in newborn babies (Palmer et al, 2007). Within elderly
populations, the Bacteroidetes proportion can decline
(Woodmansey, 2007). Recently, a barcoded pyrosequencing
study of the gut microbiota of six elderly individuals showed
that Actinobacteria was the second most abundant phylum,
not Bacteroidetes as expected(Andersson et al, 2008).
Age, caloric intake, antibiotic treatment (Dethlefsen et al,
2008) and diet are a few environmental factors that can
influence the gut microbiota composition and thus affect
human health. |
Through the comparison of lean and obese individuals, a
possible relationship between obesity and the composition
of (and changes in) gut microbiota has been investigated
(Ley et al, 2006, Turnbaugh et al, 2008) and reviewed
(Dibaise et al, 2008). Ley et al. showed that obese subjects
have a higher Firmicutes/Bacteroidetes ratio than lean
controls (Ley et al, 2006). By testing dietary factors, the
authors demonstrated a shift in Bacteroidetes and
Firmicutes relative abundance that correlated with weight
loss. Reduced bacterial diversity and familial similarity of
gut microbiota composition within obese individuals have
also been recently reported (Turnbaugh et al, 2008). |
The Oral Cavity Microbiota |
The understanding of healthy oral cavity microflora is essential
for the prevention of oral diseases and requires unambiguous
identification of microorganism(s) associated with
pathology. For instance, it is accepted that Streptococcus mutans is the etiological agent in dental caries (Jenkinson
and Lamont, 2005). Limited by the cloning and sequencing
approach, the characterization of the diversity in human oral
microflora was radically enhanced by the first 16S 454
pyrosequencing of saliva and supragingival plaque (Keijser
et al, 2008). Keijer et al. revealed 3,621 and 6,888 phylotypes
in saliva and plaque samples, respectively, and estimated
the total microbial species richness to be 19,000 (3% similarity
cut-off). Within the 22 phyla identified, the main taxa
are Firmicutes (genus Streptococcus and Veillonella) and
Bacteroidetes (genus Prevotella) in saliva, while Firmicutes
and Actinobacteria (genus Corynebacterium and Actinomyces)
are the most common in plaque. |
The Vaginal Microbiota |
By its exposure to the external environment, the female
genital tract can be easily affected in its reproductive functions.
Previous surveys of the human vaginal microbiota
proposed that the normal vaginal microbiota can act as a
defense mechanism, playing an essential role in preventing
infections such as bacterial vaginosis or sexually transmitted
diseases in women. Zhou et al. first characterized the
vaginal microbiota by 16S clone library sequencing (Zhou
et al, 2004). As found in culture-dependent studies, the authors
showed that Lactobacilli and Atopobium were generally
the predominant organisms; they also reported the first
identification of a Megasphaera species in the vagina. By
employing a full 16S pyrosequencing strategy and developing
a classification method of short 16S pyrosequencing
reads, Sundquist et al. studied the human vagina during pregnancy
and corroborated previous results. The authors identified
Lactobacillus as the dominant genus and detected a
significant presence of other genera including
Psychrobacter, Magnetobacterium, Prevotella,
Bifidobacterium, and Veillonella (Sundquist et al, 2007).
However, Lactobacillus can be missing in healthy vaginal
microbiota and replaced by other predominant genera such
as Gardnerella, Pseudomonas, or Streptococcus (Hyman
et al, 2005). Although inter-individual variability of the vaginal
microbiota was demonstrated, the function of the communities
was conserved and was shown to be involved in
the production of lactic acid (Zhou et al, 2004). The maintenance
of the vaginal acidity preserves an unfriendly environment
for the growth of many pathogenic organisms (Zhou
et al, 2004). Important shifts in relative abundances and types
of bacteria, especially the decrease in lactic acid bacteria,
in the healthy vagina are associated with bacterial vaginosis
infections (Spiegel, 1991). Compared to the healthy vaginal microbiota, bacterial vaginosis-associated microbiota showed
greater specie richness, different bacterial community structures,
and a strong association with members of the
Bacteroidetes and Actinobacteria phyla (Oakley et al,
2008). To enhance the understanding of the important variations
in the incidence of bacterial vaginosis among racial or
ethnic groups, the vaginal microbiota of Caucasian and black
women were explored. Striking differences were demonstrated
in community abundance and also in composition;
for instance, the predominance of Lactobacillus in black
women is lower than in Caucasian women (Zhou et al, 2007). |
The Skin Microbiota |
The skin probably offers one of the largest human-associated
habitats and has a bacterial density of around 107
cells per square centimeter (Fredricks, 2001). The commensal
bacterial communities and pathogenic microorganisms
harbored by the skin’s surface suggest an association
with healthy, infectious or noninfectious (psoriasis, eczema)
(Grice et al, 2008) skin states. Until recently, because skin
infections generally involve rare pathogenic isolates, the
resident skin bacteria remained poorly described and limited
to culture-dependent studies that under-represented the
extent of bacterial diversity. |
A recent 16S survey of the resident skin microbiota of the
inner elbow region, an area subjected to atopic dermatitis,
from five healthy humans generated 5,373 nearly complete
16S rRNA sequences. These sequences were assigned into
113 phylotypes belonging to the Proteobacteria (49%),
Actinobacteria (28%), Firmicutes (12%), Bacteroidetes
(9.7%), Cyanobacteria (<1%), and Acidobacteria(<1%)
phyla (Grice et al, 2008). Most of the 16S sequences (90%)
belong to the Proteobacteria phylum and, more accurately,
to the Pseudomonas and Janthinobacterium genera. Finally,
the authors’ results indicated a low level of deep evolutionary
lineage diversity and a similar diversity profile for
all of the subjects, suggesting a common core skin microbiota
among healthy subjects (Grice et al, 2008). Interestingly,
the few cultivated commensal skin bacteria, including S.
epidermidis and P. acnes, accounted for only 5% of the
microbiota captured in the Grice et al. study (Grice et al,
2008). |
In contrast, a study examining the diversity of skin
microbiota from the superficial forearms in six healthy subjects
indicated a small core set of phylotypes (2.2%) and a
high degree of inter-individual variability in the microbiota (Gao et al, 2007). The distribution of the 182 identified
phylotypes at the phylum level was 29% Proteobacteria,
35% Actinobacteria, 24% Firmicutes, 8% Bacteroidetes,
1.6% Deinococcus-Thermus, 0.5% Termomicrobia, and
0.5% Cyanobacteria (Gao et al, 2007). However, only the
three most abundant phyla were observed in all subjects.
Although many phylotypes overlap between the inner elbow
and forearm skin microbiota, the predominant phylotype
belonging to the Proteobacteria phyla of the inner elbow
microbiota is missing in the forearm microbiota. In addition,
the forearm skin microbiota possesses more members of
the Actinobacteria and Firmicutes phyla (Grice et al, 2008).
It is difficult, however, to compare studies that employed
different methods and skin sample locations. |
The characterization of skin microbial communities and
their interactions is still in its infancy, since the impacts of
sex, age, clothing, and others factors have not been clearly
determined. However, a recent study on the reduction of
disease transmission by hand washing used a barcoded
pyrosequencing approach to characterize the hand surface
microbiota of 51 healthy men and women to determine how
specific factors could affect the community structure (Fierer
et al, 2008). Although the authors detected a core set of
bacterial taxa on the hand surface, the results mainly revealed
a high intra- and inter-individual variation in community
structure when sex, hand washing, or handedness factors
were considered. In addition, though the diversity observed
in hand surfaces is high (sequences from >25 phyla
were identified), 94% of the sequences belong only to three
of these phyla (Actinobacteria, Firmicutes and
Proteobacteria) (Fierer et al, 2008). Finally, independently
of the skin site sampled (Fierer et al, 2008, Gao et al, 2007,
Grice et al, 2008), all of the studies shared the same predominant
phyla: Proteobacteria, Actinobacteria, and
Firmicutes. |
Limits of 16S Analyses |
The 16S is an efficient phylogenic marker for bacteria
identification and microbial community analyses. However,
the multiple pitfalls of PCR-based analyses, including sample
collection, cell lysis, PCR amplification, and cloning, can
affect the estimation of the community composition in mixed
microbial samples (Farrelly et al, 1995, von et al, 1997). |
Although one or two 16S gene copies are commonly exhibited
by a single genome, multiple and heterogeneous 16S
genes in a single microbial genome are not rare and can lead to an overestimation of the abundance and bacterial
diversity using culture-independent approaches (Acinas et
al, 2004). Multiple copies of rRNA operons (rrn) per genome
are generally found in rapidly growing microorganisms,
especially in soil bacteria (Klappenbach et al, 2000).
As a result, the number of copies of the 16S gene in a microbial
genome can reach 10 or 12 copies in Bacillus subtilis
(Stewart et al, 1982) or Bacillus cereus (Johansen et al,
1996), respectively, and up to 15 copies in Clostridium
paradoxum (Rainey et al, 1996). In addition to the heterogeneity
of the 16S gene copy number per genome, a bacterial
species can display important nucleotide sequence variability
among its 16S genes (Acinas et al, 2004, Rainey et
al, 1996, Turova et al, 2001). Furthermore, it is well known
that 16S gene sequencing lacks taxonomic resolution at the
species level for some closely related species (Janda and
Abbott, 2007), subspecies, or recently diverged species (Fox
et al, 1992). In this way, Escherichia coli and E. fergusonii
species, as well as the subspecies Bartonella vinsonii
subsp. arupensis and B. vinsonii subsp. vinsonii, are indistinguishable,
when using 16S sequence similarity comparisons
(Adekambi et al, 2003). |
Likewise, PCR amplification bias in a mixed microbial
sample causes the taxon amplicon abundance to differ from
the real proportions present in the community. Notably, PCR
amplification bias can be induced by the choice of primer
set, the number of replication cycles, or the enzyme system
used (Qiu et al, 2001, Suzuki and Giovannoni, 1996). An
obvious example is that the sensitivity of universal primers
is limited to the currently known 16S sequences and does
not reach 100% coverage. What is the primer sensitivity
for unknown microbial sequences, and how can the same
hybridization efficiency be guaranteed for all targets in the
sample? However, metagenomic studies, which can theoretically
access up to 100% of the microbial diversity in a
sample, have yielded powerful alternatives to bypass primer
and cloning bias using 454 pyrosequencing. |
Another aspect limiting the capture of the true microbial
diversity in a mixed sample by 16S surveys is inherent to
the cloning step. The efficiency of ligation to the plasmid,
transformation, and amplification in the host can all have an
effect. For instance, it has been suggested that many
unclonable genes in the E. coli host are present in a single
copy per genome and hence are under-represented in clone
libraries due to inactive promoters or toxic effects induced
by gene transcription/translation into the host (Kunin et al,2008, Sorek et al, 2007). Finally, horizontal gene transfer
and recombination events have also been reported in ribosomal
genes (Miller et al, 2005), which distort phylogenetic
signals and thereby affect the phylogenetic classification |
Conclusions and Perspectives |
Culture-independent methods based on the 16S rRNA
gene yield a useful framework for exploring microbial diversity,
by establishing the taxonomic composition and/or
structure present in environmental samples using both a
and b-diversity measures, phylogenetic tree construction,
and sequence similarity comparison. |
Unlikely to culture methods, these recent high-throughput
methods allow accessing to the true microbial diversity. In a
point of view of clinical research, new or uncultured etiologic
agents from poly-microbial samples (pulmonary infections,
brain abscess) of disease states can be identified and
will lead to elaborate more appropriate antibiotherapies
rather than the use of broad range antibiotics. In addition,
compared to lean controls, the reduction of the Bacteroidetes
members and the increase of the methanogen
Methanobrevibacter smithii in obese patients were revealed
by high-throughput sequencing methods. These results suggested
that modulate the relative abundance of some microbial
groups of the gut microbiota could be beneficial for
obese treatment. The huge amount of sequences provided
by these new sequencing methods hugely increase the number
of 16S sequences in databases, and thus improve the
ability of 16S sequence identification using sequence similarity
search tools. In a near future, the accuracy of classification
methods of short 16S sequences will be improved
by the increase of read length (450pb) produced by the new
454 FLX Titanium apparatus. In addition, the increase in
sequence production capabilities of the 454 FLX Titanium
associated with the barcoding strategy will allow examining
much more different samples in a single pyrosequencing
run. |
However, 16S high-throughput methods can not characterize
the functional component (defined as the microbiome)
of an environmental sample. Such limitations arise by targeting
a sole gene marker. This limitation can be overcome
by a metagenomic approach, which focuses on the full gene
content (gene-centric analysis) of a sample. Therefore, in
addition to providing species richness and evenness information,
the relatively unbiased metagenomic approach can
also identify the metabolic capabilities of a microbial community and disclose specific adaptive gene sets that are
potentially beneficial for survival in a given habitat. |
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