2 Metagenome Analysis
35
keeping pace with the flood of molecular studies of microbial communities which
are identifying an increasing number of species and phyla. Examples of these molecular tools include powerful PCR-based methods that have been established for the
direct amplification, cloning, and analysis of ribosomal RNA (rRNA) genes from
the environment (Pace et al. 1985, Olsen et al. 1986, Giovannoni et al. 1990, Ward
et al. 1990). Recently, with the development of a new generation of DNA amplification and sequencing techniques, a new dimension in 16S rRNA diversity analysis
was opened, allowing massively parallel sequencing of a variable region of the 16S
rRNA gene from environmental samples (Sogin et al. 2006).
It is often difficult to predict the ecophysiology of uncultivated microorganisms
solely based on rRNA phylogeny. For that, also the “adaptive” pool of metabolic,
resistance, and defence genes has to be investigated, because they ensure survival in
the environment. The addition of metagenomic techniques to the toolbox available
to molecular ecologists has opened a new window to study the metabolic equipment
of uncultured microorganisms in detail, and has allowed bridging between diversity
and function.
2.2 History and Application of Metagenomics
The term “metagenome analysis” was introduced by Jo Handelsman (1998). The
term is derived from the statistical concept of meta-analyses, i.e. the process of statistically combining separate analyses, and genomics, the comprehensive analysis of
an organism’s genetic material. The method involves sequence-based or functional
analysis of the collective genomes contained in an environmental sample based
on genomic DNA fragments retrieved from a habitat, or an enrichment of target
cells. Popular synonyms of “metagenome analysis” are “environmental genomics”,
“ecogenomics”, and “community genomics”.
Metagenome analyses were actually carried out before the term metagenomics
was coined. Thomas M. Schmidt, Edward F. DeLong and Norman R. Pace were
the first to screen a metagenomic library, with the objective of obtaining a PCRindependent, unbiased access to the microbial diversity in a marine ecosystem. They
extracted DNA from about 8,000 l of an oligotrophic picoplankton sample from the
north central Pacific Ocean and cloned it into a bacteriophage λ derived vector. Part
(3.2 × 10 4 clones) of the resulting library (10 7 clones), with insert sizes of 10–
20 kbp, was screened for 16S rRNA genes by hybridization with a mixed kingdom
probe. This resulted in the identification of 16 unique clones. This group had already
noted the power of metagenomics for the retrieval of sequence information associated with the 16S rRNA gene (Schmidt et al. 1991). The next step was made five
years later by DeLong. With colleagues, he sequenced a metagenomic fosmid originating from an uncultured planktonic archaeon (Stein et al. 1996). With improved
sequencing techniques and bioinformatic tools, metagenomics became increasingly
popular in the late 1990s. Large metagenomic fragments were preferably cloned
into high capacity vectors to create cosmids (Collins and Hohn 1978), fosmids (Kim
35
keeping pace with the flood of molecular studies of microbial communities which
are identifying an increasing number of species and phyla. Examples of these molecular tools include powerful PCR-based methods that have been established for the
direct amplification, cloning, and analysis of ribosomal RNA (rRNA) genes from
the environment (Pace et al. 1985, Olsen et al. 1986, Giovannoni et al. 1990, Ward
et al. 1990). Recently, with the development of a new generation of DNA amplification and sequencing techniques, a new dimension in 16S rRNA diversity analysis
was opened, allowing massively parallel sequencing of a variable region of the 16S
rRNA gene from environmental samples (Sogin et al. 2006).
It is often difficult to predict the ecophysiology of uncultivated microorganisms
solely based on rRNA phylogeny. For that, also the “adaptive” pool of metabolic,
resistance, and defence genes has to be investigated, because they ensure survival in
the environment. The addition of metagenomic techniques to the toolbox available
to molecular ecologists has opened a new window to study the metabolic equipment
of uncultured microorganisms in detail, and has allowed bridging between diversity
and function.
2.2 History and Application of Metagenomics
The term “metagenome analysis” was introduced by Jo Handelsman (1998). The
term is derived from the statistical concept of meta-analyses, i.e. the process of statistically combining separate analyses, and genomics, the comprehensive analysis of
an organism’s genetic material. The method involves sequence-based or functional
analysis of the collective genomes contained in an environmental sample based
on genomic DNA fragments retrieved from a habitat, or an enrichment of target
cells. Popular synonyms of “metagenome analysis” are “environmental genomics”,
“ecogenomics”, and “community genomics”.
Metagenome analyses were actually carried out before the term metagenomics
was coined. Thomas M. Schmidt, Edward F. DeLong and Norman R. Pace were
the first to screen a metagenomic library, with the objective of obtaining a PCRindependent, unbiased access to the microbial diversity in a marine ecosystem. They
extracted DNA from about 8,000 l of an oligotrophic picoplankton sample from the
north central Pacific Ocean and cloned it into a bacteriophage λ derived vector. Part
(3.2 × 10 4 clones) of the resulting library (10 7 clones), with insert sizes of 10–
20 kbp, was screened for 16S rRNA genes by hybridization with a mixed kingdom
probe. This resulted in the identification of 16 unique clones. This group had already
noted the power of metagenomics for the retrieval of sequence information associated with the 16S rRNA gene (Schmidt et al. 1991). The next step was made five
years later by DeLong. With colleagues, he sequenced a metagenomic fosmid originating from an uncultured planktonic archaeon (Stein et al. 1996). With improved
sequencing techniques and bioinformatic tools, metagenomics became increasingly
popular in the late 1990s. Large metagenomic fragments were preferably cloned
into high capacity vectors to create cosmids (Collins and Hohn 1978), fosmids (Kim