77
Llewellyn et al. 2015; Kim et al. 2016). In combination with
information on intrinsic or even environmental ontology, it
may provide insights into the plastic phenotypic range and
might suggest possible adaptation or acclimatization
responses (Dick 2017).
Functionality
A genome-wide survey on potential open reading frames and
prediction of gene function can help to characterize an organism or study its ecological background. An example from
marine plant genetics is the recently published genome of the
true seaweed Zostera marina (commonly referred to as eelgrass). It contains 20,450 genes, of which a majority (86.6%)
were validated using a transcriptomic approach (Olsen et al.
2016). Functional annotation revealed gene losses and gains
that could be attributed to the marine habitat. Those included
losses of stomatal differentiation, airborne communication,
and immune-system-related genes, to name only three examples (Olsen et al. 2016).
Using next-generation sequencing or quantitative PCR
(qPCR) approaches, transcript abundances may be assessed
(Liu et al. 2016; see also Box 1). As such, this provides a
good possibility to estimate biological activity rather than
the mere presence and abundance. In microbial ecology,
for example, the nifH gene is a common biomarker for
nitrogen- fixing bacteria, i.e. diazotrophs (Gaby and
Buckley 2012). Pogoreutz et al. (2017) queried gene and
transcript abundance of nifH in order to investigate nitrogen fixation in the coral holobiont (see Box 3 for details on
the metaorganism/holobiont concept). They detected autotrophic corals to exhibit a higher nifH gene abundance,
correlated with increased expression rates. Consequently,
the authors suggested that low nitrogen-uptake via heterotrophy may be compensated by the microbial component of
the holobiont.
Transcriptomes are interesting in another regard, as
some RNA species have regulatory functions, e.g. miRNAs which are short (about 22 base pairs) single-stranded
RNA molecules. They have the potential to align with
mRNA via sequence identity and thereby either inhibit the
translation or induce degradation (Gottlieb 2017). A single miRNA may bind to several different mRNAs and vice
versa (Selbach et al. 2008). In humans, the Chromosome
19 miRNA cluster (C19mc) is almost exclusively
expressed in the extra- embryonic tissue of the placenta
(Luo et al. 2009) and seems to be an important component
of the immune system during viral infections (DelormeAxford et al. 2013). C19mc has been suggested to be a key
component of embryonic- maternal communication, as
well as essential to suppress a maternal immune response
(Gottlieb 2017).
In a metagenomics and -proteomics study, Leary et al.
(2014) assessed the microbial community of biofilms on two
different navy ships. The metagenomics data revealed prokaryotic signature to be most abundant on both ships.
However, the meta-proteome on the first ship hull was dominated by eukaryotic cytoskeleton proteins, while diatom carbon fixation and photosystem related proteins were most
abundant on the second hull. The authors argue that observed
differences between metagenomics and -proteomic results
may be due to retention of prokaryotic DNA in the biofilm,
especially of inactivated or dead bacteria. Further, the
eukaryotic proteome is usually larger in size and may exhibit
a higher dynamic range of gene expression. In this case, a
single omics approach may have provided misleading results
(Leary et al. 2014; Beale et al. 2016).
The relatively novel field of untargeted metabolomics can
provide sufficiently broad information to infer previously
unknown functions. For example, stony corals are in constant association with a variety of microorganisms (Rohwer
et al. 2002). About a decade ago, a study by Ritchie (2006)
could show that bacteria isolated from the coral mucus
microbiome inhibit the growth of several gram-positive and
-negative bacteria, and suggested an antimicrobial activity.
In addition, Shnit-Orland et al. (2012) could show that
Pseudoalteromonas spp., a frequent coral mucus symbiont,
secrets antimicrobial agents against gram-positive strains
(see also Brüwer et al. within the abstracts related to the
Box 3: Metaorganisms
Evidence supports the notion that all multicellular
organisms live in synergistic interdependence with a
variety of microorganisms, including bacteria, archaea,
and viruses. Together, they form a complex entity,
termed holobiont or metaorganism (McFall-Ngai et al.
2013; Bosch and Miller 2016). The microbial community of a metaorganism constantly influences the performance of a metaorganism. The human
gastrointestinal tract (GI) microbiota, for example, is
of great importance for the digestion and ingestion of
nutrients and metabolites. In addition, the human
microbiota have been suggested to have great impacts
on the behavior and even neurological functions
(Turnbaugh et al. 2007; Biagi et al. 2012; Sampson and
Mazmanian 2015).
Various studies can show that a metaorganism’s
microbiome changes and that at least part of the metaorganism can compensate for environmental stressors
(Bosch and Miller 2016; Buck-Wiese et al. 2016;
Hernandez-Agreda et al. 2016; Ziegler et al. 2017). In
order to study the biology of a multicellular organism,
the whole metaorganism should therefore be respected.
Reading the Book of Life – Omics as a Universal Tool Across Disciplines
Llewellyn et al. 2015; Kim et al. 2016). In combination with
information on intrinsic or even environmental ontology, it
may provide insights into the plastic phenotypic range and
might suggest possible adaptation or acclimatization
responses (Dick 2017).
Functionality
A genome-wide survey on potential open reading frames and
prediction of gene function can help to characterize an organism or study its ecological background. An example from
marine plant genetics is the recently published genome of the
true seaweed Zostera marina (commonly referred to as eelgrass). It contains 20,450 genes, of which a majority (86.6%)
were validated using a transcriptomic approach (Olsen et al.
2016). Functional annotation revealed gene losses and gains
that could be attributed to the marine habitat. Those included
losses of stomatal differentiation, airborne communication,
and immune-system-related genes, to name only three examples (Olsen et al. 2016).
Using next-generation sequencing or quantitative PCR
(qPCR) approaches, transcript abundances may be assessed
(Liu et al. 2016; see also Box 1). As such, this provides a
good possibility to estimate biological activity rather than
the mere presence and abundance. In microbial ecology,
for example, the nifH gene is a common biomarker for
nitrogen- fixing bacteria, i.e. diazotrophs (Gaby and
Buckley 2012). Pogoreutz et al. (2017) queried gene and
transcript abundance of nifH in order to investigate nitrogen fixation in the coral holobiont (see Box 3 for details on
the metaorganism/holobiont concept). They detected autotrophic corals to exhibit a higher nifH gene abundance,
correlated with increased expression rates. Consequently,
the authors suggested that low nitrogen-uptake via heterotrophy may be compensated by the microbial component of
the holobiont.
Transcriptomes are interesting in another regard, as
some RNA species have regulatory functions, e.g. miRNAs which are short (about 22 base pairs) single-stranded
RNA molecules. They have the potential to align with
mRNA via sequence identity and thereby either inhibit the
translation or induce degradation (Gottlieb 2017). A single miRNA may bind to several different mRNAs and vice
versa (Selbach et al. 2008). In humans, the Chromosome
19 miRNA cluster (C19mc) is almost exclusively
expressed in the extra- embryonic tissue of the placenta
(Luo et al. 2009) and seems to be an important component
of the immune system during viral infections (DelormeAxford et al. 2013). C19mc has been suggested to be a key
component of embryonic- maternal communication, as
well as essential to suppress a maternal immune response
(Gottlieb 2017).
In a metagenomics and -proteomics study, Leary et al.
(2014) assessed the microbial community of biofilms on two
different navy ships. The metagenomics data revealed prokaryotic signature to be most abundant on both ships.
However, the meta-proteome on the first ship hull was dominated by eukaryotic cytoskeleton proteins, while diatom carbon fixation and photosystem related proteins were most
abundant on the second hull. The authors argue that observed
differences between metagenomics and -proteomic results
may be due to retention of prokaryotic DNA in the biofilm,
especially of inactivated or dead bacteria. Further, the
eukaryotic proteome is usually larger in size and may exhibit
a higher dynamic range of gene expression. In this case, a
single omics approach may have provided misleading results
(Leary et al. 2014; Beale et al. 2016).
The relatively novel field of untargeted metabolomics can
provide sufficiently broad information to infer previously
unknown functions. For example, stony corals are in constant association with a variety of microorganisms (Rohwer
et al. 2002). About a decade ago, a study by Ritchie (2006)
could show that bacteria isolated from the coral mucus
microbiome inhibit the growth of several gram-positive and
-negative bacteria, and suggested an antimicrobial activity.
In addition, Shnit-Orland et al. (2012) could show that
Pseudoalteromonas spp., a frequent coral mucus symbiont,
secrets antimicrobial agents against gram-positive strains
(see also Brüwer et al. within the abstracts related to the
Box 3: Metaorganisms
Evidence supports the notion that all multicellular
organisms live in synergistic interdependence with a
variety of microorganisms, including bacteria, archaea,
and viruses. Together, they form a complex entity,
termed holobiont or metaorganism (McFall-Ngai et al.
2013; Bosch and Miller 2016). The microbial community of a metaorganism constantly influences the performance of a metaorganism. The human
gastrointestinal tract (GI) microbiota, for example, is
of great importance for the digestion and ingestion of
nutrients and metabolites. In addition, the human
microbiota have been suggested to have great impacts
on the behavior and even neurological functions
(Turnbaugh et al. 2007; Biagi et al. 2012; Sampson and
Mazmanian 2015).
Various studies can show that a metaorganism’s
microbiome changes and that at least part of the metaorganism can compensate for environmental stressors
(Bosch and Miller 2016; Buck-Wiese et al. 2016;
Hernandez-Agreda et al. 2016; Ziegler et al. 2017). In
order to study the biology of a multicellular organism,
the whole metaorganism should therefore be respected.
Reading the Book of Life – Omics as a Universal Tool Across Disciplines