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developed to directly determine the whole collection of

genes within an environmental sample without construct-

ing a metagenomic library (Edwards et al., 2006; Turn-

baugh et al., 2006; Cox-Foster et al., 2007; Biddle et al.,

2008; Brulc et al., 2009; Palenik et al., 2009).

The four metagenomics approaches described above

can be characterized as unselective (shotgun analysis

and next-generation sequencing) and targeted (activity-

driven and sequence-driven studies) metagenomics

based on their random and directed sequencing strate-

gies respectively. Unselective metagenomics is becoming

an increasingly common strategy due to its simplicity and

cost-effectiveness in DNA sequencing. Many projects of

random sequencing of microbial communities, such as

bacteria, archaea and viruses, have been reported (Chen

and Pachter, 2005).

Here, we focus on targeted metagenomics studies,

which combine metagenomic library screening and sub-

sequent sequencing analysis. The approach is a more

effective means to understanding the content and com-position of genes for key ecological processes in microbial

communities.

Excessive advances in DNA

sequencing technology

The use of shotgun sequencing technology for under-

standing microbial species and their role in ecosystems

was first reported by Tyson and colleagues (2004) using a

low-complexity environment. The authors reported that

two bacterial genera (Leptospirillum, Sulfobacillus) and

one archaeon (Ferroplasma) dominated the biofilms

present in an acidic mine drainage habitat based on 16S

rDNA sequence analyses. A shotgun sequence experi-

ment was then performed. Nearly complete genomes of

the two dominant species and the partial genomes of

three less dominant species were reconstructed. As a

result, microbial metabolic pathways were discovered in

this particular ecosystem. Thus, the detection of metabolic

routes via metagenomics opened the door to post-

metagenomic studies that gain further insight into genetic

networks and metabolic pathways in environmental

microbes.

Subsequently, several whole-community shotgun

sequencing studies were performed (DeLong et al., 2006;Gill et al., 2006; Wegley et al., 2007; Gilbert et al., 2008).

These studies provided much useful information about

potential metabolic pathways of uncultivated environmen-

tal microorganisms. At the same time, the ability to recon-

struct metagenomic DNA fragments and determine

metabolic routes decreased dramatically as the genomic

complexity increased. In the microbial genomic diversity

assay of the Sargasso Sea, Venter and colleagues (2004)

determined the nucleotide sequences of over 1 billion

base pairs. Their results showed that large fragments

could only be assembled for the most dominant commu-

nity members and the majority of the fragments obtained

through shotgun sequencing could not be assigned to any

specific microorganism. In predicting the function of more

diverse ecosystems, the power of the environmental

shotgun sequencing approach is rather limited because of

inherent problems associated with limited accessibility to

the genomes of less abundant members of the commu-

nity. A rare biosphere, such as Methylophaga-like methy-

lotrophs, reported to play a significant role in methanol

metabolism in marine environments, could not be cap-

tured by such an approach (Neufeld et al., 2008).

The recent development of second-generation

sequence technologies has enabled us to obtain much

more DNA information from highly complex microbial

communities (Mardis, 2008). The analysis of longer con-

tiguous sequences allows us to identify not only open

reading frames, but also operons. Longer gene units,

such as mobile genetic elements, are evident only whenlarge genomic fractions can be assembled. However,

short sequence reads (varying between 20 and 700 bp

depending on the sequencing technology used) that are

dissociated from their original species can be joined to

lengths usually not exceeding 5000 bp due to the large

phylogenetic complexity. Consequently, the reconstruc-

tion of a whole genome is not possible (Wooley et al.,

2010). Even if reconstruction was successful, identifying

an entire transcriptional unit can be problematic

(Wommack et al., 2008; Hoff, 2009). Thus, these frag-

mented and lower-quality sequences lack sufficient infor-

mation to understand their function and ecological

relevance. Furthermore, another growing problem is the

handling and management of the massive amount of

metagenomic sequence data generated by these tech-

nologies, which requires computational infrastructure and

accessible tools for storage and further analysis.

Targeted metagenomics strategies

Two different metagenomics strategies are possible:

unselective and targeted metagenomics (Fig. 1). Random

sequencing of an environmental DNA pool using high-

throughput sequencing technology is becoming increas-

ingly common. However, as described above, methods forgenerating and interpreting metagenomics data remain in

the early stages of development. In a targeted metage-

nomics approach, a deliberately selected DNA pool is

subjected to sequencing to reduce genetic complexity.

The selection process is usually based on (i) sequence-

driven screening or (ii) function-driven screening. By

focusing efforts on sequence analysis, targeted metage-

nomics can provide broad coverage and extensive redun-

dancy of sequences for targeted genes and reveal

14 H. Suenaga

2011 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 14, 1322


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Table

1.

Comparisonofunselectiveandtargetedmetagenomics.

Environment

Markergene/phenotype

Screeningmeth

od

Sequence

size(Mb)

Reference

Unselectivemetagenomics

Acidminedrainage

76

Tysonetal.(2004)

Sargassosea

1625

Venteretal.(2004)

NorthPacificSubtropicalGyre

64

DeLongetal.(2006)

Humandistalgut

78

G

illetal.(2006)

Coral

32

W

egleyetal.(2007)

Controlledcoastaloceanmesocosm

323

G

ilbertetal.(2008)

Targetedmetagenomics

Sequence-drivenscreening

Antarcticcoastalwater

16SrDNA

PCR

0.260

G

rzymskietal.(2006)

Coastandshelfwater

16SrDNAofplanctomy

cete

PCR

0.233

W

oebkenetal.(2007)

Harboursediment

I-CeuIsite(23SrRNAg

ene)

0.438

Nesbetal.(2005)

Marinesediment

dsrAB,aprAB

PCR

0.111

M

ussmannetal.(2005)

Grasslandsoil

nirS,nirK,nirZ

Colonyhybridiz

ation

0.218

Demancheetal.(2009)

Function-drivenscreening

Faecalfromorganicallyrearedadultpig

Tetracyclineresistance

Growthassay

0.051

Kazimierczaketal.(2009)

Activatedsludge

Bleomycinresistance

Growthassay

0.099

M

orietal.(2008)

Cowrumen

Hydrolyticactivity

Enzymeactivity

assay

0.772

Ferreretal.(2005)

Activatedsludge

Extradioldioxygenase

Enzymeactivity

assay

1.37

Suenagaetal.(2009a)

Othermethods

Forestsoil

Incorporationofthe13C

H4

pmoA,nmoX,mxaF

SIPColonyhybridiz

ation

0.015

Dumontetal.(2006)

Lakesediment

IncorporationoflabelledC1compounds

SIP

255

Kalyuzhnayaetal.(2008)

Freshwaterandmarinesediment

Magnetotacticbacteria

Magneticcollec

tion

0.521

Jo

gleretal.(2009)

16 H. Suenaga



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taining an I-CeuI site (Marshall and Lemieux, 1992),

which exists at position 1923 in the 23S rRNA gene of

most bacteria, and specifically cloned DNA fragments

containing the 23S rRNA gene. Phylogenetic analysis of

the protein-coding sequences of 12 fosmids indicated that

a significant fraction of the genes was acquired by lateral

gene transfer. In several cases, co-transfer of functionally

related genes can be inferred.

The 16S rDNA sequences reflect only the phylogenetic

classification of host bacteria and not necessarily the

metabolic function of these organisms (Jaspers and Over-

mann, 2004). For example, Escherichia coli K-12 and

O157 : H7 (Perna et al., 2001) or strains of Bacillus

anthracis and Bacillus cereus (Ash et al., 1991), which

clearly exhibited different ecotypes based on their viru-

lence properties, showed identical 16S rRNA gene

sequences. Therefore, other genetic markers that are

associated with metabolic function are needed.

Dissimilatory sulfate reduction is a key process in the

mineralization of organic matter in marine sediments(Shen et al., 2001). Up to 50% of organic carbon in

coastal sediments is mineralized anaerobically by sulfate-

reducing prokaryotes (Jrgensen, 1982). From marine

sediments, Mussmann and colleagues (2005) screened

three fosmid DNA fragments harbouring a core set of

essential genes for dissimilatory sulfate reduction, includ-

ing genes associated with the reduction of sulfur interme-

diates (dsrAB gene) and the synthesis of the prosthetic

group of the dissimilatory sulfate reductase (aprA gene).

Sequence analysis of all fosmid inserts revealed the

genomic context of the key enzymes of dissimilatory

sulfate reduction as well as novel genes functionally

involved in sulfate respiration in their franking regions.

Based on the results of the comparative genome analy-

ses, a more comprehensive model of dissimilatory sulfate

reduction was presented. The results support the hypoth-

esis that the set of genes responsible for dissimilatory

sulfate reduction were concomitantly transferred in a

single event among prokaryotes.

A metagenomics approach combined with molecular

screening (colony hybridization) and pyrosequencing

was performed to gain insight into the genetic organiza-

tion and diversity of gene clusters or operons for denitri-

fication (Ginolhac et al., 2004; Demanche et al., 2009).

Denitrification is a microbial respiratory process withinthe nitrogen cycle responsible for the return of fixed nitro-

gen to the atmosphere. This targeted metagenomics

study indicated that the gene clusters involved in denitri-

fication were probably subject to shuffling by endog-

enous gene displacement or by horizontal gene transfer

between bacteria.

The advantage of using sequence-driven screening is

that it is not dependent on expression of cloned genes in

foreign hosts. Additionally, well-established techniques,

such as PCR or hybridization, can be employed for differ-

ent targets when using these metagenomic approaches.

On the other hand, the sequence-driven approach

involves designing DNA probes and primers derived from

conserved regions of known gene or protein families.

Various software (e.g. Edgar, 2004; Ludwig et al., 2004;

Zhang et al., 2007) and databases (e.g. FunGene, http://

fungene.cme.msu.edu/index.spr) can help to develop

effective screening strategies.

Targeted metagenomics based on

functional-driven screening

As an alternative to phylogenetic markers, the use of

known gene functions of interest is a more direct route to

the discovery of gene clusters with related metabolic roles

in microbial communities. Furthermore, function-driven

screening strategies can potentially provide a means to

reveal undiscovered genes or gene families that cannot

be detected by sequence-driven approaches, althoughspecific screening systems are often required.

Simple and certain activity-based strategies, such as

colony growth in the presence of antibiotics on agar

media, have often been used. Ten clones resistant to

tetracycline were identified from a bacterial artificial

chromosome (BAC) library (Kazimierczak et al., 2009)

and three clones resistant to bleomycin were identified

from a fosmid library (Mori et al., 2008). Both metage-

nomic libraries were constructed using DNA collected

from antibiotic-free environments (i.e. faecal samples of

organically reared adult pigs and activated sludge treat-

ing for coke-plant wastewater respectively). Most of the

genes resided on putative mobile genetic elements,

which presumably contribute to the maintenance and

dissemination of antibiotic resistance in even antibiotic-

free environments.

The diversity of the major rumen enzymes has

been characterized by an activity-based metagenomics

approach (Ferrer et al., 2005). The rumen habitat

consists mostly of obligate anaerobic microorganisms,

including fungi, protozoa, bacteria and archaea. Bacte-

rial and fungal components of this ecosystem are

responsible for the rapid degradation of plant polymers

(Tajima et al., 1999; Ramsak et al., 2000). A metage-

nomic phage library from the rumen content of a dairycow was screened for hydrolase activity directly on

agar medium. In total, 22 clones with distinct hydrolytic

activities were identified and characterized. Among

them, four hydrolases exhibited no sequence similarity

to enzymes from public databases and four clones did

not match any putative catalytic residues involved in the

catalytic function of esterase and cellulose-like enzymes.

These results suggest that function-based screening

of metagenomic libraries facilitates the functional

Targeted metagenomics 17

http://fungene.cme.msu.edu/index.sprhttp://fungene.cme.msu.edu/index.sprhttp://fungene.cme.msu.edu/index.sprhttp://fungene.cme.msu.edu/index.spr


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assignment of many properties that have been classified

as hypothetical proteins.

In many cases, prokaryotic phenotypes are the result of

the concerted expression of many genes that are often

arranged into adjacent operons or super-operonic clus-

ters. Thus, the entire gene set of a pathway of interest can

be captured by targeting the central or essential gene in

that metabolic pathway.

The ability to use various aromatic compounds as

sources of carbon and energy is widespread in bacteria.

In the natural environment, these bacteria contribute

greatly to the breakdown of aromatic compounds and to

the global carbon cycle (Esteve-Nez et al., 2001;

Furukawa et al., 2004). Among the enzymes that degrade

aromatic compounds, extradiol dioxygenases (EDOs)

catalyse ring cleavage of catecholic compounds to

produce yellow meta-cleavage compounds (Eltis and

Bolin, 1996). Metagenomic DNA extracted from activated

sludge for industrial wastewater was cloned into fosmids

(Suenaga et al., 2007). The resulting E. coli library wasscreened for EDO activity using catechol as a substrate

and 38 clones were subjected to sequence analysis

(Suenaga et al., 2009a,b). As a result, various types of

gene subsets were identified that were not similar to pre-

viously reported pathways that complete degradation. The

distribution of these genes among the different genome

segments was reported in some isolated sphingomonads

(Miller et al., 2010) or rhodococcal (McLeod et al., 2006)

strains. The metagenomic data revealed that the com-

plete degradation pathways identified in many isolated

bacteria were, in fact, rare. Additionally, through in silico

assembly of the inserts from several fosmid clones, a

meta-plasmid designated as pSKYE1 was reconstructed.

Plasmid pSKYE1 seems to act as a detoxification appa-

ratus that may confer survival capabilities to its bacterial

host in the microbial community. Thus, targeted metage-

nomic approaches based on functional-driven screening

can be used to obtain novel findings of targeted biological

functions.

Other targeted metagenomics studies

The pre-treatment of a collected environmental sample

before DNA extraction for the enrichment of target genes

is also effective in targeted metagenomics. Stable-isotopeprobing (SIP) allows the simultaneous recovery of

labelled DNA from active target microorganisms that play

a particular function in the environment (Radajewski et al.,

2000; Wellington et al., 2003). These collected DNAs

have often been directly subjected to PCR amplification of

rRNA genes to taxonomically identify microorganisms that

feed on the labelled substrate. Recently, the combination

of SIP with shotgun sequencing was used to determine a

complete pmoCABoperon encoding the three subunits of

a particular methane monooxygenase (Stolyar et al.,

1999). PmoCAB is a key enzyme involved in the methane

utilization pathway of aerobic methanotrophs, a subgroup

of the methylotrophs (Dumont et al., 2006). A metage-

nomic BAC clone library was constructed with 13C-DNA

from a 13CH4-labelled forest soil sample. After colony

hybridization and subsequent sequence determination,

one clone was found to contain the pmoCAB operon.

Although methanotrophs were detected in this study, they

usually are not the dominant group of microorganisms in

soils and would have been missed by a random metage-

nome shotgun method. More recently, the nearly com-

plete genome of a novel methylotroph Methylotenera

mobilis, which comprises less than 0.4% of the total bac-

terial population in the sampled environment, was deter-

mined by using SIP followed by shotgun sequencing

(Kalyuzhnaya et al., 2008). Methylotrophy, the metabo-

lism of organic compounds containing no carboncarbon

bonds (i.e. C1 compounds) such as methane, methanol

and methylated amines, is an important component ofthe global carbon cycle (Hanson and Hanson, 1996).

Genome analysis of methylotrophs will expand our

current knowledge of the microbial metabolisms of the C1

compound in environment.

An enrichment procedure was also performed for the

mass collection of environmental magnetotactic bacteria

(MTB) virtually free of non-magnetic contaminants

(Jogler et al., 2009). MTB synthesize magnetosomes,

which are membrane-enclosed organelles consisting of

magnetite (Fe3O4) crystals or, less commonly, greigite

(Fe3S4) (Bazylinski and Frankel, 2004). MTB do not form

a coherent phylogenetic group, but the trait of magneto-

taxis is found in species within different phylogenetic

clades, which are mostly unavailable in pure culture

(Faivre and Schler, 2008). The selective cloning of

larger DNA fragments from magnetotactic metagenomes

was achieved by a two-step magnetic enrichment.

Sequence analysis of fosmid clones revealed that uncul-

tivated MTB exhibited similar, yet different, organizations

of the magnetosome island, which may account for the

diversity in biomineralization and magnetotaxis observed

in MTB from various environments.

Targeted metagenomics analyses based on pre-

treatment for enrichment of specific microorganisms or

genes can reveal relevant genome areas directly linked toan ecological function even in low abundance within a

metagenome.

Future aspects

The accumulation of data sets of microbial genes and

genomes in metagenomics is growing rapidly, but our

understanding of their functional and ecological relevance

is proceeding more slowly. Sequencing technology is

18 H. Suenaga



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accelerating and third-generation sequencers that will

enable reading of the DNA sequence of a single chromo-

some in a single pass with few or no fragments will

likely be established in the near future (Clarke et al., 2009;

Eid et al., 2009). One of the most serious problems

with metagenomics methods using second-generation

sequencing technology related to fragment assembly

may be solved by using these long-read sequencing

technologies.

However, the other serious problem related to gene

prediction and annotation remains unsolved. Current

functional assignment for genes from metagenomes is

based on homology searches using BLAST tools (Altschul

et al., 1990) that depend heavily on the quality and

completeness of current databases. Homology-based

methods are effective only when the information of the

reference sequences is accurate. Actually, a number of

genomes in the current databases contain misannota-

tions (Schnoes et al., 2009). Furthermore, this method

is not sensitive, particularly for genomes that lacksequence relatives, and can miss novel genes that may

potentially be the most interesting. A review of prokary-

otic protein diversity in different shotgun metagenome

studies indicated that 3060% of the proteins cannot be

assigned to known functions using current public data-

bases (Vieites et al., 2009). Many hypothetical proteins

that have been identified may be ecologically important.

Targeted metagenomics, based on function-driven

screening, can reveal specific genome sequences

directly linking an ecological function although the ORF

lacks sequence homology to known genes and is iden-

tified as a hypothetical protein in current databases.

Furthermore, the functions of genes located in the neigh-

bourhood of the target genes would also be revealed by

complete sequence analysis of the inserts. This was sup-

ported by the observation that intergenic distances tend

to be shorter between genes of the same operon than

between operons (Salgado et al., 2000) and neighbour-

ing ORFs are more likely to be functionally associated

(Overbeek et al., 1999; Korbel et al. 2004). Time series

studies at one site are also being used to investigate

how microorganisms and their activities co-vary with

environmental changes. The transition of metadata,

which contains various environmental measurements as

well as metagenomic sequence data, will help to predictthe key genes for crucial environmental processes and to

understand the variability of microbial genomes in the

process of adaptive evolution.

Sequence data of metagenomics tell us what is there

and what it is capable of doing. What it is actually doing

is revealed by evaluating transcription (mRNA) and trans-

lation (protein) data. The correlation between specific

gene expression patterns and changes in community

composition or the functional property of the microbial

ecosystem can be addressed more directly by applying

these omics approaches (Benndorf et al., 2007; Maron

et al., 2007; Shrestha et al., 2009). The combined analy-

sis of targeted metagenomics with targeted metatran-

scriptomics or metaproteomics has the potential to

provide a novel perspective on microbial community

dynamics.

In this review, the targeted metagenomics approaches

based on the screening step are mainly presented. Selec-

tive DNA collection from the environment also seems to

be efficient for targeted metagenomics approaches. For

example, plasmids represent a comparatively uncharac-

terized metagenomic space and they frequently carry

useful genes for their host such as those involved in

biodegradation and metal or antibiotic resistance. Total

plasmid DNA can be targeted as a metamobilome study.

In the near future, it will be possible to develop targeted

metagenomics methodologies with improved experimen-

tal techniques such as environmental sampling, DNA

extraction, cloning as well as library screening.

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