60
A. Meyerdierks and F.O. Glöckner
sound hypotheses about how the organisms share their resources and energy (Tyson
et al. 2004, Meyerdierks et al. 2005, Martin et al. 2006, Woyke et al. 2006). The
challenge is now to expand the bioinformatic toolbox so that it can deal with complex environments and provide an integrated understanding of marine ecosystem
functioning.
2.5 Outlook
In the current context of global change, it is crucial to generate a broad understanding of the key players and processes that regulate life on earth. Marine ecosystems,
which cover more than 70% of the earth surface, represent the majority of the planetary biomass and contribute significantly to the global cycles of matter and energy.
Microorganisms are known to be the “gatekeepers” of these processes and insights
into their life-style and fitness will enhance our ability to monitor, model and predict
future changes. The capability of sequencing DNA samples from natural environments without prior cultivation of the organisms present in these samples provide
an unprecedented opportunity to investigate the microbial diversity and functions on
the molecular level. In the long run this will allow us to address questions which are
central for marine ecology: (1) Which microbes are in the environment? (2) How
abundant are they? (3) What is their functional potential? and (4) How are their
activities and adaptations linked to environmental conditions?
The power of metagenomics to provide descent answers to some of the questions has been recently shown. Dinsdale et al. (2008) demonstrated that functional
differences of nine qualitatively categorized, discrete biomes can be used for discrimination. Gianoulis et al. (2009) took this a step further by calculating metabolic
footprints based on an ensemble of weighted pathways that maximally covaries with
a combination of environmental variables. They even suggest that such footprints
can be used as environmental indicators when no measurable environmental factors
are available.
Although metagenomics has been shown to be a formidable tool, there are still
several limitations that need to be addressed and solved. A major obstacle is that
it is often impossible to assign specific abilities to individual organisms, especially
if highly diverse environments are sampled. The general descriptors indicating the
prevailing phylogenetic and functional diversity cannot provide answers to questions such as: “Who does what?” and “How do they work together and exchange
energy and nutrients?” Single cell genomics based on physical separation of cells
and subsequent whole-genome multiple displacement amplification (MDA) (Lasken
2007) is an emerging technology. This approach has been shown to provide valuable insights into the genomic potential of large sulfide oxidisers such as Beggiatoa
sp. (Mussmann et al. 2007) and a set of marine organisms isolated from the Gulf of
Maine bacterioplankton (Stepanauskas and Sieracki 2007).
Another common criticism of sequence dominated metagenomics is the discrepancy between the rate of data accumulation and the rate of knowledge generation.
A. Meyerdierks and F.O. Glöckner
sound hypotheses about how the organisms share their resources and energy (Tyson
et al. 2004, Meyerdierks et al. 2005, Martin et al. 2006, Woyke et al. 2006). The
challenge is now to expand the bioinformatic toolbox so that it can deal with complex environments and provide an integrated understanding of marine ecosystem
functioning.
2.5 Outlook
In the current context of global change, it is crucial to generate a broad understanding of the key players and processes that regulate life on earth. Marine ecosystems,
which cover more than 70% of the earth surface, represent the majority of the planetary biomass and contribute significantly to the global cycles of matter and energy.
Microorganisms are known to be the “gatekeepers” of these processes and insights
into their life-style and fitness will enhance our ability to monitor, model and predict
future changes. The capability of sequencing DNA samples from natural environments without prior cultivation of the organisms present in these samples provide
an unprecedented opportunity to investigate the microbial diversity and functions on
the molecular level. In the long run this will allow us to address questions which are
central for marine ecology: (1) Which microbes are in the environment? (2) How
abundant are they? (3) What is their functional potential? and (4) How are their
activities and adaptations linked to environmental conditions?
The power of metagenomics to provide descent answers to some of the questions has been recently shown. Dinsdale et al. (2008) demonstrated that functional
differences of nine qualitatively categorized, discrete biomes can be used for discrimination. Gianoulis et al. (2009) took this a step further by calculating metabolic
footprints based on an ensemble of weighted pathways that maximally covaries with
a combination of environmental variables. They even suggest that such footprints
can be used as environmental indicators when no measurable environmental factors
are available.
Although metagenomics has been shown to be a formidable tool, there are still
several limitations that need to be addressed and solved. A major obstacle is that
it is often impossible to assign specific abilities to individual organisms, especially
if highly diverse environments are sampled. The general descriptors indicating the
prevailing phylogenetic and functional diversity cannot provide answers to questions such as: “Who does what?” and “How do they work together and exchange
energy and nutrients?” Single cell genomics based on physical separation of cells
and subsequent whole-genome multiple displacement amplification (MDA) (Lasken
2007) is an emerging technology. This approach has been shown to provide valuable insights into the genomic potential of large sulfide oxidisers such as Beggiatoa
sp. (Mussmann et al. 2007) and a set of marine organisms isolated from the Gulf of
Maine bacterioplankton (Stepanauskas and Sieracki 2007).
Another common criticism of sequence dominated metagenomics is the discrepancy between the rate of data accumulation and the rate of knowledge generation.