79
electron microscopy), as well as molecular methods (e.g.,
sequencing). However, a panel of leading virologists recently
proposed that viruses only known from metagenomic samples need to be incorporated into the official classification
scheme by the International Committee on Taxonomy of
Viruses (ICTV) (Fauquet and Mayo 2001; Simmonds et al.
2017). Paez-Espino et al. (2016), for example, analyzed
around 5 terabyte metagenomics data of diverse samples,
which led to the discovery of 125,000 new predicted viral
genomes as well as a massive increase in the number of putatively identified viral genes (Paez-Espino et al. 2016;
Simmonds et al. 2017).
To conclude, metagenomic and metatranscriptomic data
may be used to detect, characterize, and taxonomically rank
all ‘lifeforms’, including previously unknown and uncultured organisms and viruses.
Metabolomic analyses, for example of lipids, are commonly performed in marine plankton research to identify the
dietary preferences of plankton species. These analyses are
based on the fact that some fatty acids (biomarkers) are characteristic of specific groups of phyto- or zooplankton (e.g.,
16:1(n-7) for diatoms, 18:4(n-3) for dinoflagellates and
18:1(n-9) for metazoans) and are incorporated into the consumers body tissue largely unmodified, thus retaining a
dietary signature (Graeve et al. 1994; Dalsgaard et al. 2003).
For example, the fatty acid pattern of the Arctic copepod
Calanus finmarchicus typically reflects a dinoflagellate
nutrition. However, in in vitro feeding experiments with the
diatom Thalassiosira antarctica over several weeks, the fatty
acid composition depicted a change towards a diatom-like
signature/profile (Graeve et al. 1994) showing the unchanged
incorporation of dietary fatty acids. Thus, the fatty acid biomarker concept might allow differentiating the source of
phytoplankton (diatom vs. dinoflagellate) and if a species
mostly feeds on phytoplankton (herbivory) or other zooplankton (carnivory) giving hints on its trophic position and
role in the food web. In this regard, studying the proteome or
metabolome can provide clues on the functional role of an
organism and might even provide a glimpse on the phylogeny (Jones et al. 2014; Llewellyn et al. 2015).
Functional groups of proteins are not necessarily linked to
phylogeny. The Gene Ontology (GO) provides a hierarchical
structure based on functional grouping of the gene products.
Thus, it helps to identify the physiological role based on
molecular function, cellular component, and biological process (The Gene Ontology Consortium 2004, 2017). The
Kyoto Encyclopedia of Genes and Genomes (KEGG) has
been established to interrelate genes based on their highlevel function. Identifying a queried gene in the KEGG
PATHWAY database integrates it in a corresponding pathway and may show its connectivity to other genes, thus
allows to extrapolate on its physiological impact (Kanehisa
et al. 2016, 2017). In summary, GO-terms and the KEGG
database may help to identify involved physiological processes, as well as potentially easing the comparison amongst
different organisms or species.
Response to Environmental Cues
The recent geological era is called the Anthropocene, because
human activities have severely impacted geology and ecosystems, including the alteration of carbon fluxes. Long-term
temperature rise and increased short-term fluctuations (IPCC
2007, 2014), as well as pollution accidents, such as oil spills
(e.g., McNutt et al. 2012), challenge the adaptive capacities
of species. However, technological advances allow us to utilize those capacities to increase the yield of biological products (e.g., Park et al. 2015). To subsequently suggest
strategies for conservation and bioengineering, the different
omics fields can be a useful tool to identify genetic, transcriptomic, proteomic, or metabolic responses to environmental cues.
A consortium of marine geneticists proposed to investigate the adaptability and resilience to environmental stress in
a three-step approach (Voolstra et al. 2015). Firstly, species
along a natural gradient should be queried for genetic, epigenetic, or transcriptional differences, to secondly experimentally test the resilience of specimen from each extremum
to the corresponding opposite poles. Thirdly, possible (epi-)
genetic or transcriptional differences should be investigated
and their impacts regarding the environmental parameter
evaluated (Voolstra et al. 2015). Following this approach,
Ziegler et al. (2017) assessed the plasticity of the microbial
community within a metaorganism in response to temperature regimes (Box 3). They were able to show that the microbiome significantly contributed to thermal-stress resilience
(Ziegler et al. 2017). This underlines the role of a metaorganism’s microbial community when reacting to and coping
with environmental change (Bosch and Miller 2016; BuckWiese et al. 2016).
Liew et al. (2017) exposed a facultative endosymbiotic
dinoflagellate to acute light and temperature stress and
sequenced the transcriptome, to investigate stress-induced
RNA editing (see section “Physiological background”).
They observed base exchanges from RNA editing most
prominently responsive to a heat stress treatment (Liew et al.
2017; see also Olschowsky et al. within the abstracts related
to this chapter). RNA editing may induce non-synonymous
substitutions which could alter protein functioning as well as
stability and thereby contribute to acclimatization (see section “Physiological background”).
The exact same dataset was used by Brüwer et al. (2017),
who in silico detected a diverse viral community associated
with the dinoflagellate and observed differential viral gene
expression upon heat treatment. As in this example, nextgeneration sequencing often contains “by-catch” of hostassociated microorganisms and viruses, which may be
Reading the Book of Life – Omics as a Universal Tool Across Disciplines
electron microscopy), as well as molecular methods (e.g.,
sequencing). However, a panel of leading virologists recently
proposed that viruses only known from metagenomic samples need to be incorporated into the official classification
scheme by the International Committee on Taxonomy of
Viruses (ICTV) (Fauquet and Mayo 2001; Simmonds et al.
2017). Paez-Espino et al. (2016), for example, analyzed
around 5 terabyte metagenomics data of diverse samples,
which led to the discovery of 125,000 new predicted viral
genomes as well as a massive increase in the number of putatively identified viral genes (Paez-Espino et al. 2016;
Simmonds et al. 2017).
To conclude, metagenomic and metatranscriptomic data
may be used to detect, characterize, and taxonomically rank
all ‘lifeforms’, including previously unknown and uncultured organisms and viruses.
Metabolomic analyses, for example of lipids, are commonly performed in marine plankton research to identify the
dietary preferences of plankton species. These analyses are
based on the fact that some fatty acids (biomarkers) are characteristic of specific groups of phyto- or zooplankton (e.g.,
16:1(n-7) for diatoms, 18:4(n-3) for dinoflagellates and
18:1(n-9) for metazoans) and are incorporated into the consumers body tissue largely unmodified, thus retaining a
dietary signature (Graeve et al. 1994; Dalsgaard et al. 2003).
For example, the fatty acid pattern of the Arctic copepod
Calanus finmarchicus typically reflects a dinoflagellate
nutrition. However, in in vitro feeding experiments with the
diatom Thalassiosira antarctica over several weeks, the fatty
acid composition depicted a change towards a diatom-like
signature/profile (Graeve et al. 1994) showing the unchanged
incorporation of dietary fatty acids. Thus, the fatty acid biomarker concept might allow differentiating the source of
phytoplankton (diatom vs. dinoflagellate) and if a species
mostly feeds on phytoplankton (herbivory) or other zooplankton (carnivory) giving hints on its trophic position and
role in the food web. In this regard, studying the proteome or
metabolome can provide clues on the functional role of an
organism and might even provide a glimpse on the phylogeny (Jones et al. 2014; Llewellyn et al. 2015).
Functional groups of proteins are not necessarily linked to
phylogeny. The Gene Ontology (GO) provides a hierarchical
structure based on functional grouping of the gene products.
Thus, it helps to identify the physiological role based on
molecular function, cellular component, and biological process (The Gene Ontology Consortium 2004, 2017). The
Kyoto Encyclopedia of Genes and Genomes (KEGG) has
been established to interrelate genes based on their highlevel function. Identifying a queried gene in the KEGG
PATHWAY database integrates it in a corresponding pathway and may show its connectivity to other genes, thus
allows to extrapolate on its physiological impact (Kanehisa
et al. 2016, 2017). In summary, GO-terms and the KEGG
database may help to identify involved physiological processes, as well as potentially easing the comparison amongst
different organisms or species.
Response to Environmental Cues
The recent geological era is called the Anthropocene, because
human activities have severely impacted geology and ecosystems, including the alteration of carbon fluxes. Long-term
temperature rise and increased short-term fluctuations (IPCC
2007, 2014), as well as pollution accidents, such as oil spills
(e.g., McNutt et al. 2012), challenge the adaptive capacities
of species. However, technological advances allow us to utilize those capacities to increase the yield of biological products (e.g., Park et al. 2015). To subsequently suggest
strategies for conservation and bioengineering, the different
omics fields can be a useful tool to identify genetic, transcriptomic, proteomic, or metabolic responses to environmental cues.
A consortium of marine geneticists proposed to investigate the adaptability and resilience to environmental stress in
a three-step approach (Voolstra et al. 2015). Firstly, species
along a natural gradient should be queried for genetic, epigenetic, or transcriptional differences, to secondly experimentally test the resilience of specimen from each extremum
to the corresponding opposite poles. Thirdly, possible (epi-)
genetic or transcriptional differences should be investigated
and their impacts regarding the environmental parameter
evaluated (Voolstra et al. 2015). Following this approach,
Ziegler et al. (2017) assessed the plasticity of the microbial
community within a metaorganism in response to temperature regimes (Box 3). They were able to show that the microbiome significantly contributed to thermal-stress resilience
(Ziegler et al. 2017). This underlines the role of a metaorganism’s microbial community when reacting to and coping
with environmental change (Bosch and Miller 2016; BuckWiese et al. 2016).
Liew et al. (2017) exposed a facultative endosymbiotic
dinoflagellate to acute light and temperature stress and
sequenced the transcriptome, to investigate stress-induced
RNA editing (see section “Physiological background”).
They observed base exchanges from RNA editing most
prominently responsive to a heat stress treatment (Liew et al.
2017; see also Olschowsky et al. within the abstracts related
to this chapter). RNA editing may induce non-synonymous
substitutions which could alter protein functioning as well as
stability and thereby contribute to acclimatization (see section “Physiological background”).
The exact same dataset was used by Brüwer et al. (2017),
who in silico detected a diverse viral community associated
with the dinoflagellate and observed differential viral gene
expression upon heat treatment. As in this example, nextgeneration sequencing often contains “by-catch” of hostassociated microorganisms and viruses, which may be
Reading the Book of Life – Omics as a Universal Tool Across Disciplines