Targeted Metagenomics

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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

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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

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    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

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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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