Review
When metagenomics meets
stable-isotope probing:
progress and perspectives
Yin Chen and J. Colin Murrell
Department of Biological Sciences, the University of Warwick, Coventry, CV4 7AL, UK
The application of metagenomics, the culture-independent capture and subsequent analysis of genomic DNA
from the environment, has greatly expanded our knowledge of the diversity of microbes and microbial protein
families; however, the metabolic functions of many
microorganisms remain largely unknown. DNA stableisotope probing (DNA-SIP) is a recently developed
method in which the incorporation of stable isotope
from a labelled substrate is used to identify the function
of microorganisms in the environment. The technique
has now been used in conjunction with metagenomics
to establish links between microbial identity and particular metabolic functions. The combination of DNA-SIP
and metagenomics not only permits the detection of
rare low-abundance species from metagenomic libraries
but also facilitates the detection of novel enzymes and
bioactive compounds.
From 16S rRNA genes to whole community sequence
analyses of environmental microorganisms
One of the major breakthroughs in environmental microbiology in the past few decades is the application of cultureindependent techniques to the study of microbial ecology
and the distribution of microbes in the environment [1–4].
Because previously only cultivated microorganisms could
be studied, the development of culture-independent techniques has fundamentally changed our way of studying
microorganisms. Early work focused on the analyses of 16S
rRNA genes retrieved from the environment [5] and later
on the retrieval of larger DNA fragments and whole community sequences from environmental DNA, now referred
to as metagenomics [6]. Metagenomic approaches have
now been applied to a variety of environments, from the
human gut microbiome to soils [7–9], and from the deep-sea
to the indoor atmosphere [10,11]. The aims of these studies
were (i) to identify the diverse microorganisms inhabiting
various environments and (ii) to reconstruct metabolic
pathways, thus helping to predict environmental functions
of uncultivated microorganisms. Research in metagenomics is also driven by industry in the quest to retrieve
novel enzymes and bioactive compounds from the environment. Diverse enzymes and secondary metabolites have
been identified as arising from uncultivated microorganisms in the environment [12]. Conventional metagenomics
uses DNA directly extracted from environmental samples;
Corresponding author: Murrell, J.C. (J.C.Murrell@Warwick.ac.uk).
more recently DNA stable-isotope probing (DNA–SIP) has
been used as a filter to select DNA from relevant microorganisms [13,14]. DNA–SIP was originally developed as a
method to investigate the metabolic functions of microorganisms in the environment. The technique relies on the
incorporation of stable-isotope labelled compounds (e.g.
13
C, 15N) into microbial DNA [15] during microcosm or
in situ incubation of such compounds with environmental
samples (Box 1). Metagenomics combining with DNA–SIP
has developed from concept to reality, and is now attracting significant attention. We summarise recent progress in
SIP-metagenomic techniques and applications and discuss
future prospects for this combined approach in environmental microbiology and biotechnology.
Metagenomics: success and frustration
Key steps in metagenomic studies are summarised in
Figure 1. DNA is extracted directly from environmental
samples and then either is used to prepare a metagenomic
library by cloning DNA fragments into an appropriate
vector (plasmid, cosmid, fosmid, etc.), or the DNA is
directly used for high-throughput sequencing, for example
using 454 pyrosequencing technology [16]. The metagenome library can be screened for genes of interest by function-based approaches to look for key metabolic functions.
This depends on the expression of heterologous genes in a
host such as Escherichia coli (e.g. to screen for lipase
activity by the detection of clear zones around colonies
on tributyrin agar plates [17]). Metagenome libraries can
also be screened for target genes by sequence-based
approaches (such as PCR using degenerate primers or
by colony blotting and screening with radioactive probes)
(reviewed in Ref. [18]).
The power of the sequence-based approach for understanding the role of microorganisms in the environment
was first demonstrated in an elegant study carried out by
Tyson and colleagues using a less complex environment as
a model system [19]. Through the analyses of 16S rRNA
gene clones it was found that all biofims present in acidic
mine drainage (Iron Mountain, USA) were dominated by
two genera of bacteria (Leptospirillum and Sulfobacillus)
and one archeon (Ferroplasma). The authors then carried
out a shotgun-sequencing experiment, and from 100 Mb of
DNA sequence information they were able to reconstruct
near-complete genomes of the two dominant organisms
comprising these biofilms (Leptospirillum group II and
Ferroplasma type II), as well as partial genomes of three
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Trends in Microbiology Vol.18 No.4
Box 1. Top ten things you need to know before setting up a DNA–SIP experiment
1. When should I consider using DNA–SIP? DNA–SIP is one of the
many methods available to link microbial identity to function (for
more detail, see Ref. [27]). It is less sensitive compared to RNA–SIP
[50] and phospholipid fatty acid (PLFA)–SIP [51,52]; however, it
allows one to gain access to the DNA from the microorganisms of
interest.
2. What can DNA–SIP tell me? DNA–SIP will identify active microorganisms that metabolise and incorporate the labelled substrate
into their DNA during a given SIP incubation.
3. What substrate concentration should be used and how long
should the incubation be? Unfortunately, there is no standard
answer. However, typically, we have found that incorporation of
50 mmol 13C per g (for soil or sediment samples) and 5 mmol 13C
per ml (for water samples) is sufficient to detect active microorganisms above background. It is highly recommended that
target substrate utilisation activity for a given environmental
sample is measured before setting up SIP incubations. The overall
solution will be to consider the balance of the concentration of
labelled substrate (i.e. close to in situ concentrations), activity of
the samples and incubation time.
4. Do I need a 12C- or 14N-incubation control? This is highly
recommended. Fingerprints (see below) between labelled and
non-labelled DNA (both of which could be distributed over several
gradient fractions) can therefore be compared. This is extremely
useful in identifying ‘heavy’ DNA from SIP incubations.
5. Can I use partially labelled substrate? The use of partially-labelled
substrate for DNA–SIP is not recommended. This could cause
insufficient stable-isotope incorporation into DNA and further
complicate subsequent ‘heavy’ DNA identification and isolation;
however, recent studies have successfully used partially labelled
substrate for DNA–SIP (e.g. [53,54]).
6. Do I need to worry about cross-feeding? Although it could be
difficult to completely remove the problem of cross-feeding, SIP
incubations need to be optimised to label only primary consumers
while still yielding sufficient isotope-labelled DNA. However, in
some circumstances, cross-feeding of stable-isotope labeling is
desirable to track carbon flow through a food web (e.g. [55–57]).
7. What method should I use to extract DNA from SIP-incubated
samples? This depends on later applications using the ‘heavy’
DNA. For example, avoid methods that shear DNA if large DNA
fragments are needed. If high concentrations of humic substances
are present, extracted DNA could need to be purified before
isopycnic ultracentrifugation.
8. How do I know where the ‘heavy’ DNA is after isopycnic
centrifugation? There are six methods for identifying ‘heavy’
DNA. The more these are used, the more reliable the data are.
One should be aware that a second round of ultracentrifugation
with bis-benzimide can be required in some cases because
unlabelled DNA with a high G+C content can migrate at the same
position as labelled DNA with a relatively low G+C content [58].
other less-dominant microorganisms, thereby piecing
together the metabolic routes of this rather simple ecosystem. Inspired by this successful study, a number of wholecommunity shotgun-sequencing projects were carried out;
these have yielded a wealth of information on potential
metabolic pathways of uncultivated microorganisms in the
environment [7,20,21,22,23]. However, at the same time it
was realised that our ability to piece together metagenomic
DNA fragments decreases dramatically as the complexity
of the environment sampled increases. Aiming at assessing
the microbial diversity in Sargasso Sea, Venter and colleagues found it was only possible to assemble large scaffolds of genomic DNA from the most dominant species in
the samples, and the majority of sequences obtained
through shotgun sequencing could not be assigned to
any specific microorganism [24]. Inevitably this com158
Using isotope-ratio mass-spectrometry (IRMS). The most direct
way is to use an IRMS to determine the degree of labeling (i.e.
enrichment of 13C) in DNA fractions after isopycnic centrifugation.
This method relies on the availability of the IRMS instrument and
requires a relatively large DNA sample volume.
Ethidium bromide (EtBr) staining. Early DNA–SIP experiments used
ErBr to stain for both ‘heavy’ and ‘light’ DNA. The identification of
‘heavy’ DNA in this case is relatively straightforward because they
can be visualised under ultraviolet light. This method is rarely used
now due to the lack of sensitivity; however, recently, a more
sensitive staining-based method was developed using SYBR safeTM
to stain ‘heavy’ and ‘light’ DNA [59].
Fingerprinting analyses. After isopycnic centrifugation the gradient
is sub-fractionated and a fingerprinting method (e.g. denaturing
gradient gel electrophoresis, terminal restriction fragment polymorphism) is used to compare the profiles of all the fractions,
therefore helping to identify the ‘heavy’ DNA fraction. Once
the ‘heavy’ DNA fraction has been identified the DNA can
be precipitated using standard methods and used for further
analysis.
Measuring the density of each fraction. After isopycnic centrifugation and fractionation the density of each fraction can be measured
(e.g. using an analytical balance or a digital refractometer). This can
then be compared to the theoretical density of ‘heavy’ DNA fraction
from the literature or to control isopycnic centrifugation using a
mixture of 12C- and 13C-DNA from a pure culture.
Using a carrier DNA. 13C-carrier DNA (e.g. 13C-DNA from yeast or
archaea) can also be used (e.g. [60]) and in this case the
identification of ‘heavy’ DNA is straightforward because the 13C
carrier DNA can be identified easily.
Quantifying target genes by quantitative PCR. DNA precipitated
from each fraction can be quantified by real-time PCR for target
genes (e.g. 16S rRNA genes) [55]. A major peak will usually form at
a low density, indicating the position of the majority of the
background unlabelled DNA. A second peak can sometimes form
(often only a small shoulder adjoining the main peak), indicating
the position of labelled ‘heavy’ DNA.
9. What if the ‘heavy’ DNA retrieved is not sufficient for analysis?
The quantity of ‘heavy’ DNA retrieved ranges from ng to mg. One
way to obtain more ‘heavy’ DNA is, of course, to combine ‘heavy’
DNA from several CsCl gradients. However, if a substantial
amount of DNA is needed, such as for making a metagenome
library, multiple displacement amplification can be performed
from the ‘heavy’ DNA using Phi 29 DNA polymerase [25,35].
10. What are the minimum instruments required for carrying out a
SIP experiment? Instruments for measuring substrate utilisation
activity, an ultracentrifuge for isopycnic centrifugation, and an
analytical balance.
munity shotgun-sequencing approach is of rather limited
use in predicting the function of community members of
low abundance, and encounters the following two problems. First, in order to gain insights into the functions
of less-abundant community members (often referred to as
the ‘rare biosphere’), greater in-depth sequencing needs to
be carried out, and this is generally not cost-effective.
Second, the so-called ‘rare biosphere’ can be significant
players in a particular function (for example, methylotrophy in the marine water column) that cannot be captured
by such conventional approaches [25].
Meanwhile, the application of metagenomics has led to
the discovery and characterisation of a wide range of novel
biocatalysts and pharmaceutically important compounds
(reviewed in Ref. [12,26]). A number of small- to mediumscale companies (e.g. Diversa, now known as Verenium;
Review
Trends in Microbiology
Vol.18 No.4
‘Metagenomics, together with in vitro evolution and highthroughput screening technologies, provides industry with
an unprecedented chance to bring biomolecules into industrial application’ [26]. However, so far only relatively few
enzymes discovered through metagenomics are used in
established biotechnological processes [12]. One of the
reasons is probably ‘the low frequency of clones of a desired
nature’ [18] that can be retrieved from metagenome
libraries (Table 1).
Figure 1. Key steps in conventional and DNA–SIP-enabled metagenomics.
Conventional metagenomics uses DNA extracted directly from environmental
samples whereas DNA–SIP-enabled metagenomics uses ‘heavy’ DNA from SIP
experiments [that can be further subjected to multiple displacement amplification
(MDA) where necessary]. The DNA or ‘heavy’ DNA from SIP experiments is then
either used for making a metagenomic library or used for direct high-throughput
sequencing.
http://www.verenium.com) have been established and
many patents were filed regarding all aspects of metagenome-based bioactive-compound discovery, including
vector-construction methods, cloning strategies, and
high-throughput screening methods. It was believed that
Combining DNA–SIP with metagenomics: from concept
to reality
The problems that conventional metagenomics have
encountered imply that preselection of DNA from the
environment would be of considerable benefit for improving the performance of current metagenomic approaches
(i.e. to enhance gene detection frequency and to reduce the
complexity of the target microbial community). SIP is one
of a number of culture-independent methods developed to
link the identity of microorganisms in the environment to
particular functions (reviewed in Ref. [27]); however, SIP
also allows the simultaneous recovery of DNA from active
target microorganisms in the environment. Stable-isotope
labelled DNA (i.e. ‘heavy’ DNA) can be separated from
unlabelled DNA (background community) by density-gradient (isopycnic) ultracentrifugation (Figure 1). Traditionally, the ‘heavy’ DNA is subjected to PCR amplification of
ribosomal RNA and functional genes (e.g. pmoA, encoding
a key subunit of particulate methane monooxygenase – a
functional gene marker for aerobic methane-oxidising bacteria [methanotrophs]) to identify the taxonomic identity of
these microorganisms that feed on the labelled substrate
(reviewed in Ref. [28]).
The application of metagenomics based on shotgun
sequencing in determining microbial diversity is frustrated
by the inability to completely resolve more complex communities. It was proposed by Schloss and Handelsman that
combining DNA–SIP with metagenomics could help to
reduce sample complexity [18], whereby SIP is used as a
filter to enrich for the DNA of microorganisms that carry
out a particular function. A proof-of-concept study was first
carried out by Dumont et al. who were investigating
methane-utilising bacteria in a forest soil [13]. Using
‘heavy’ DNA from a 13CH4-labelled forest soil sample,
the authors constructed a bacterial artificial chromosome
(BAC) library. After screening 2,300 clones (average size
25 kb), one BAC clone was found to contain a pmoCAB
operon encoding the three subunits of particulate methane
monooxygenase, a key enzyme involved in the methaneutilisation pathway in aerobic methanotrophs. The
advantage of using DNA–SIP is highlighted by comparison
to a similar non-SIP study [29] where a metagenome
library was constructed using DNA directly extracted from
a forest soil sample and 250,000 fosmid clones (average size
40 kb) had to be screened to find one pmoCAB operon.
Although methanotrophs were detected in this study, they
are not usually a dominant group of microorganisms in
many soils and would have been missed in other conventional metagenomic studies. The study by Dumont and
colleagues demonstrated the feasibility of combining
DNA–SIP with metagenomics to determine the function
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Trends in Microbiology Vol.18 No.4
Table 1. Comparison of conventional metagenomics and DNA-SIP-enabled metagenomics
Key features
Allows culture-independent
analyses of microbial
communities
Allows the identification of novel
enzymes and bioactive compounds
from uncultivated microorganisms
in the environment
Limitations
Heavily biased towards the
most abundant species in the
environment
Reconstruction of individual
genomes from large-scale
sequencing is difficult
Cost-ineffective
Low success rate of
function-based screening for
bioactive compounds
from metagenome libraries
DNA–SIP-enabled
Can establish a direct link
between identity and function
metagenomics
Less abundant ‘rare species’ can
be targeted with the same
sequencing effort
Reconstruction of individual
genomes is feasible due to reduced
complexity
Gene-mining ‘hit-rate’ is improved
Isolating sufficient DNA for
metagenomic library construction
is an issue
Relatively low-throughput
for DNA–SIP experiments
Multiple displacement
amplification (MDA) can
introduce chimeras
Stable-isotope labelled
compounds are not always
available and can be expensive
Conventional
metagenomics
of less abundant members of the microbial community in
this environment [13].
One of the major criticisms of DNA–SIP is that usually
relatively high concentrations (compared to in situ concentrations) of 13C compounds need to be added to SIP incubations in order for the desired amount of stable isotope to
be incorporated into DNA over a reasonable timescale, a
prerequisite for making a metagenome library from stableisotope-labelled DNA (i.e. ‘heavy’ DNA). The problem is
that isotope label can flow from primary utilisers to secondary consumers by ‘cross-feeding’ [13,30]. In addition, it
is documented that some microorganisms can be inhibited
by higher substrate concentrations (e.g. Candidatus Nitrososphaera gargensis, an ammonia-oxidising archaeon, can
be partially inhibited by ammonium at a concentration of
3.08 mM [31]) whereas others can be inactive if substrate
concentrations are too low (e.g. a Pseudomonas strain
isolated from a polycyclic aromatic hydrocarbon-contaminated groundwater only utilises naphthalene at concentrations higher than 30 mM [32]). Successful labeling with
near in situ concentrations of substrate was achieved in a
study with the goal of finding methanol-utilising microorganisms in the ocean [25]. Using 1 mM 13C-methanol it was
found that Methylophaga-related microorganisms were
metabolising methanol in sea water. However, only nanograms of ‘heavy’ DNA were obtained from the DNA–SIP
experiment and multiple displacement amplification
(MDA) was necessary to generate sufficient amounts of
DNA to make a fosmid library. After screening fosmids by
PCR, a novel methanol dehydrogenase cluster was found in
the metagenomic library; this was clearly from a bacterium
closely related to Methylophaga spp., thus indicating that
these bacteria are actively involved in the metabolism of
methanol in the marine environment. The MDA method
has only recently begun to be used by environmental
microbiologists (reviewed in Ref. [33]) and it was noted
that chimeras could be introduced by mechanisms that are
160
Research needs
Improved bioinformatics for the
reconstruction of metabolic pathways
from massive gene-sequence
datasets and to assist binning of
these sequences for improving
sequence assembly
Technology development for
large-scale sequencing; reducing cost
Automated screening methods
and technologies for function-based
screening for novel enzymes and
pharmaceutically relevant compounds
Better understanding of genes of
unknown function in current databases
Prevent chimera formation during
MDA of 13C-DNA
Development of high-throughput
methods for DNA–SIP analysis of
multiple samples
not fully understood [34]. Another study [35] showed that it
was possible to minimise chimera formation by treating
MDA-generated DNA with a series of enzymes before
making a fosmid library. This technique did not significantly affect the size of MDA-generated DNA. These two
studies demonstrated that it is possible to combine DNA–
SIP, using near in situ substrate conditions, with metagenomics to fully characterise the function of microorganisms
which may well be present in low numbers. Taking the
advantage of the high sequencing capacity at the Joint
Genome Institute (USA), an elegant study was carried out
by Kalyuzhnaya et al. [36], where the authors were interested in the one-carbon (C1) cycling in sediment from Lake
Washington. DNA–SIP experiments were carried out
using a number of 13C1 compounds and metagenome
libraries were made from purified ‘heavy’ DNA. Rather
than screening for particular functions, the libraries were
subjected to a shotgun-sequencing approach and a nearcomplete genome of the methylotroph Methylotenera mobilis was reconstructed from the sequences retrieved
from the 13C-DNA metagenome library. This bacterium
is clearly a key player in C1 cycling in the Lake Washington
sediment [37]. Furthermore, this study proved the
notion that whole-genome shotgun metagenomics can be
used to reconstruct genomes and metabolic pathways of
‘rare’ microorganisms if combined with the DNA–SIP
approach.
Having seen the inherent problems associated with
conventional function-based metagenomics, a number of
methods have been evaluated to improve gene detection
frequency. One of these is to establish enrichment cultures
for microorganisms harbouring the desired functions
before DNA extraction is carried out (e.g. Refs. [38,39]).
However, this inevitably results in the loss of microbial
diversity because a few fast-growing microorganisms can
outcompete the rest of the microbial community. Another
method that can potentially be applied to enrich for DNA
Review
Trends in Microbiology
from organisms of interest involves the use of 5-Bromo-2deoxyuridine (BrdU) [40,41]. BrdU can be incorporated
into newly synthesised DNA by metabolically active community members in response to stimuli (such as substrates) and BrdU-labelled DNA can then be separated
from community DNA using anti-BrdU antibodies [40,41].
However, the application of this method in capturing
functionally-relevant organisms responding to stimuli is
probably heavily biased because two of the four bacterial
strains tested did not incorporate BrdU into their DNA
[40]. The use of ‘heavy’ DNA from SIP experiments in order
to improve the detection frequency of genes of interest for
biotechnological applications has also been investigated
[14]. The authors were screening environmental samples
for novel glycerol dehydratases, key enzymes in the production of 1,3-propanediol, an important building block for
biopolymer synthesis. It was realised that the chance of
finding a positive clone from metagenome libraries using
function-based screening was rather low (one positive clone
per 1.14 Gbp metagenomic DNA screened) [38]. Therefore,
the authors compared two approaches, and used either
DNA from an enrichment culture (40 mM of 12C-glycerol)
or ‘heavy’ DNA from a stable-isotope labeling experiment
using 13C3-glycerol (40 mM) to make a metagenomic
library. The gene detection frequency using the ‘heavy’
DNA from SIP was 2–3 times higher than with DNA from
an enrichment culture. A similar study was published
recently [42]. The authors retrieved biphenyl-dioxygenase
sequences from the ‘heavy’ DNA extracted from a 13Cbiphenyl-amended microcosm using a sediment sample
from a contaminated river. The organisation of biphenyldioxygenase genes in the retrieved clone differed from that
of the upper bph operons from known biphenyl-degrading
microorganisms, thus indicating the novelty of this
enzyme. These two studies clearly demonstrate the feasibility and great potential of SIP-enabled metagenomics in
the discovery of novel industry-relevant biocatalysts from
the environment.
Vol.18 No.4
Future perspectives for DNA–SIP-based metagenomic
studies
Until now, only a few studies have been published that
combine DNA–SIP techniques with metagenomics
(Table 2). It is clear from these studies that this approach
can have advantages over conventional metagenomics.
Although combining DNA–SIP with metagenomics can
improve gene detection frequency for enzyme discovery
and significantly facilitate the reduction of community
complexity for whole-genome shotgun sequencing, these
two factors are not the only bottlenecks in current metagenomic studies (Table 1). For example, data analysis of
massive metagenome sequences is not trivial [43]. One of
the most serious challenges is population heterogeneity. In
fact, the assembled genome of Methylotenera mobilis from
SIP ‘heavy’ DNA is thought to be from a few closely related
strains rather than from a clonal strain [36]. Genome
assembly can benefit from better sequence coverage, and
therefore more sequencing effort can be helpful for later
genome assembly. However, although next-generation
sequence technologies have significantly reduced the cost
of DNA sequencing, cost is still an issue for many researchers involved in metagenomic research projects, and access
to sophisticated bioinformatics expertise is also required.
This is reflected in the literature – large-scale shotgun
metagenomic studies have so far only been carried out by
relatively few research groups worldwide.
Another major issue for current metagenomic studies is
that it is at best a guess to infer the metabolism of microorganisms from their DNA sequences alone, and subsequent experimental validation is required. Combining
DNA–SIP with metagenomics can help to resolve the
functions of target microorganisms at the population level
(i.e. depending on the uptake of particular substrates). In
many cases it is also desirable to pinpoint the function of
microorganisms from the environment at the single-cell
level. There is now tremendous technological development
in this regard. This includes combining cell-sorting and
Table 2. Studies combining DNA–SIP with metagenomics
Environmental samples
Acidic soil from a forest
Marine sediment
Marine surface water
Acidic peat soil
Sediment and
water from a lake
Contaminated
river sediment
Procedures and results
Forest soil (5 g) was exposed to 50 ml of 13CH4; ‘heavy’ DNA was digested with BamHI and ligated
to a BAC vector pCC1BACTM (average insert size 25 kb); 2300 colonies were screened by colony
blot and two clones containing a pmoCAB operon were found.
Enrichment culture was exposed to 13C3-glycerol; ‘heavy’ DNA was ligated to plasmid pBluescript
(insert size 2 kb); a library (87.8 Mbp) was screened by colony blot, 27 positive clones containing
B12-dependent glycerol- and diol-dehydratases were isolated.
Surface seawater was exposed to 1 mM 13C-methanol; ‘heavy’ DNA was subjected to MDA
amplification; MDA-generated DNA was size-selected (25–60 kb) and ligated to fosmid vector pCC1FOS;
1500 colonies were screened by colony PCR with degenerate primers for mxaF; one colony containing
a Methylophaga-related methanol-dehydrogenase gene cluster was obtained.
Peat soil (5 g) was exposed to 10,000 ppm 13CH4; ‘heavy’ DNA was subjected to MDA and subsequent
enzyme treatments to minimise potential chimera formation, and then size-selected (30–50 kb) and
ligated to fosmid vector pCC1FOS; 2300 colonies were screened by PCR and colony blot; three clones
containing 16S rRNA genes from methanotrophs and one clone containing genes involved in methanol
utilisation were found.
10 g of sediment and 90 ml of water were exposed to 1–10 mM 13C substrates (methane, methanol,
methylamine, formaldehyde and formate); ‘heavy’ DNA was used to construct a plasmid library in
pUC18 (insert size 1–3 kb); 225 Mbp of DNA sequence was obtained using a shotgun-sequencing
approach; a nearly complete genome of Methylotenera mobilis was obtained.
5 g of sediment were exposed to 10 mg of 13C-biphenyl; ‘heavy’ DNA was size-selected (25–40 kb)
and ligated to a cosmid vector pWEBTM; the cosmid library (1568 colonies, average size 30–40 kb)
was screened by PCR. This yielded one positive clone containing an aromatic-ring-hydroxylating
dioxygenase within a 31 kb cosmid insert.
Refs
[13]
[14]
[25]
[35]
[36]
[42]
161
Review
microfluidics with metagenomics, combining Raman
microscopy with single-cell genomics [44], combining
secondary ion mass spectrometry with fluorescence
in situ hybridisation [45], and combining microautoradiography with fluorescence in situ hybridisation [46]. Readers are referred to other recent reviews and references
therein for further coverage of these exciting new technologies [27,47].
The other difficulty in inferring function based on metagenome sequence lies in the fact that the majority of the
genes in public databases lack a definitive functional
assignment. A review of prokaryotic protein diversity in
different shotgun metagenome studies indicated that 30–
60% of the proteins cannot be assigned to known functions
using current public databases [16]. In fact, even for the
best studied model organism, E. coli, the functions of
approximately 50% of the genes in its genome have not
yet been experimentally confirmed [48]. Current functional
assignment for genes from metagenomes as well as from
individual genomes is based on homology searches using
BLAST tools that heavily depend on the quality as well as
the completeness of current databases. On the one hand,
the fact that two genes share high sequence-similarity does
not guarantee that they perform a similar biological function; on the other hand, functional redundancy implies that
one particular function could be performed by several
proteins with no significant homology (e.g. Ref. [49]). It
is therefore possible that large errors (or at least imprecisions) are present in current metagenome function assignments, and therefore also in metabolic pathway
reconstruction and prediction. Improvement in the standard of gene annotation in public databases is urgently
needed, not only for environmental microbiologists but for
all users of these databases.
The biotechnological potential of combining DNA–SIP
with metagenomics could offer considerable advances in
the near future, particularly in view of the urgent need for
novel enzymes in industry. Significant improvements in
gene-detection frequency can reduce the cost of finding a
novel enzyme. However, a number of issues need to be
resolved before this technique can be widely used. The
major difficulty is probably the development of a highthroughput production line for analysing multiple DNA–
SIP incubations and 13C-DNA isolation. DNA–SIP was
originally designed to analyse just a few samples simultaneously and is time-consuming. Bioindustry uses highthroughput methods for screening multiple samples, and
conventional metagenomics has already been implemented in this process. Similar high-throughput methodology will need to be developed in order that SIPmetagenomics can be used for large-scale enzyme discovery. This is certainly achievable in the future, but will need
close collaboration between environmental microbiologists
and bioindustries.
SIP-metagenomics, as are many other approaches
developed in the past few years, is aimed at improving
the usefulness of metagenomics in understanding the
unseen microbial majority. The combination of metagenomics with other contemporary and complementary technologies will guarantee a productive future for
metagenomics.
162
Trends in Microbiology Vol.18 No.4
Acknowledgements
Y.C. and J.C.M. acknowledge the Natural Environment Research Council
(U.K.) for funding. We thank Dr. K. Purdy for his comments on the
manuscript.
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