78
chapter “Tropical Aquatic Ecosystems across Time, Space,
and Disciplines”). Untargeted metabolomics could be
applied to investigate these inferred substances as putative
components for medicinal use.
In fact, omic and multi-omic studies on functionality elucidate the significance of the molecular code on the phenotype level. They thereby contribute to the body of knowledge
by which we can extrapolate information from molecular
reads. Ultimately, they enable us to “write” in the book of life.
Systematics
Scientists often aim to explain complex natural phenomena
with comprehensive models, which are constantly adapted
and expanded. The species model, for example, is – if not
updated – at least constantly discussed in the scientific literature, especially regarding prokaryotes (e.g., Stackebrandt
et al. 2002; Wilmes et al. 2009; Amann and Rosselló-Móra
2016).
Mutations are an essential source of genetic variability,
which are estimated to occur at (region-specific) constant
rates per replication for closely related species (Gillooly
et al. 2005) and are used to resolve phylogenetic relationships. Polymorphisms (mostly single nucleotide polymorphism, SNP) in the genome create different alleles that may
be targeted by specific restriction enzymes. Some techniques
and methods, such as restriction site-associated DNA
sequencing (RADseq), enable the detection of many SNPs
on a population level in order to study the genetic background of populations and migratory dynamics (Andrews
et al. 2016; see also Box 1).
As some parts of the genome are more prone to mutations
which could lead to lethal dysfunctions of the encoded molecule, they depict highly preserved regions with significantly
lower mutation rates compared to other parts of the genome.
Non-lethal mutations that do happen within these regions
usually remain in the genome and may be queried by amplification and/or sequencing for phylogenetic assignment.
The DNA regions coding for the small subunit of ribosomal RNA in prokaryotes, the 16S rRNA gene, and the
mitochondrially encoded cytochrome c oxidase I in
eukaryotes, the COI gene, constitute such highly conserved
areas of genetic information, which are commonly used for
taxonomic characterization by barcoding approaches (Pimm
et al. 2014; see also McCarthy et al. within the abstracts
related to this chapter for an ancient DNA example).
Traditional microbial characterization approaches require
a cultivation prior to phenotypic classification. However,
most marine microbes are very challenging to or not at all
cultivable (Pedrós-Alió 2012; Epstein 2013; Amann and
Rosselló-Móra 2016). The advances of next-generation
sequencing methods provide the possibility to detect and
phylogenetically classify a vast amount of microbes simultaneously, including those that are not cultivable (see also
Weinheimer et al. within the abstracts related to this chapter). In a recently published microbial ecology study, Röthig
et al. (2016) aimed to characterize the microbiome of the
model metaorganism Aiptasia. Besides a metagenomics
approach by DNA isolation of Aiptasia tissue and subsequent high throughput 16S rRNA gene sequencing, the
authors applied a culture dependent approach. They detected
295 different taxa in the metagenomic data, while they were
only able to culture 14 of those (with a 100% match in the
16S rRNA) (Röthig et al. 2016; see also Slaby et al. within
the abstracts related to this chapter for a marine sponge
microbiome).
Similarly, the gastrointestinal (GI) microbial community
is an important component of a vertebrate metaorganism (see
Box 3). Dewar et al. (2013) assessed the residual GI microbiota in feces of the king, gentoo, macaroni, and little penguins by 16S rRNA gene sequencing. The authors detected a
diverse microbial community (>5 k operational taxonomic
units (OTUs) identified) with significant differences in relative abundances of microbial taxa across penguin species.
They further identified known human pathogens (including
Helicobacter, Veillonella, Mycoplasma, etc.), although their
virulence in penguins or other sea-birds remains questionable (Dewar et al. 2013, 2014).
Samples of environmental DNA (eDNA) may be subject
to similar analysis of highly conserved genome regions.
Such studies would aim to detect DNA traces in the environment to extrapolate on the presence and potentially even
abundance of the corresponding organisms (Taberlet et al.
2012; Valentini et al. 2016; see also Mauvisseau et al. within
the abstracts related to this chapter). A recent comparison of
the efficiency of traditional surveys and eDNA approaches
aimed to detect amphibians and fishes in natural aquatic
environments by designing group (i.e. amphibian and fish)
specific primers of mitochondrial DNA (mtDNA) (Valentini
et al. 2016). Two amphibian species (Triturus marmoratus
and Pelophylax sp.) were observed with conventional methods but not detected via barcoding, whilst a total of 64 species could exclusively be recorded in the eDNA samples.
The fish survey prompted a similar result. Thus, ecological
surveys using eDNA appear to be more thorough and accurate. In addition, they are less destructive, are more (cost)
efficient, and do not fully rely on the taxonomic knowledge
and species identification of experts, compared to commonly applied surveys. Furthermore, it will be less challenging to establish standardized protocols, thus, making
survey studies more comparable, especially if they are conducted across various scientific laboratories (Valentini et al.
2016).
In virology, characterization efforts demand the verification of newly identified viruses by visual evidence (e.g.,
J. D. Brüwer and H. Buck-Wiese
chapter “Tropical Aquatic Ecosystems across Time, Space,
and Disciplines”). Untargeted metabolomics could be
applied to investigate these inferred substances as putative
components for medicinal use.
In fact, omic and multi-omic studies on functionality elucidate the significance of the molecular code on the phenotype level. They thereby contribute to the body of knowledge
by which we can extrapolate information from molecular
reads. Ultimately, they enable us to “write” in the book of life.
Systematics
Scientists often aim to explain complex natural phenomena
with comprehensive models, which are constantly adapted
and expanded. The species model, for example, is – if not
updated – at least constantly discussed in the scientific literature, especially regarding prokaryotes (e.g., Stackebrandt
et al. 2002; Wilmes et al. 2009; Amann and Rosselló-Móra
2016).
Mutations are an essential source of genetic variability,
which are estimated to occur at (region-specific) constant
rates per replication for closely related species (Gillooly
et al. 2005) and are used to resolve phylogenetic relationships. Polymorphisms (mostly single nucleotide polymorphism, SNP) in the genome create different alleles that may
be targeted by specific restriction enzymes. Some techniques
and methods, such as restriction site-associated DNA
sequencing (RADseq), enable the detection of many SNPs
on a population level in order to study the genetic background of populations and migratory dynamics (Andrews
et al. 2016; see also Box 1).
As some parts of the genome are more prone to mutations
which could lead to lethal dysfunctions of the encoded molecule, they depict highly preserved regions with significantly
lower mutation rates compared to other parts of the genome.
Non-lethal mutations that do happen within these regions
usually remain in the genome and may be queried by amplification and/or sequencing for phylogenetic assignment.
The DNA regions coding for the small subunit of ribosomal RNA in prokaryotes, the 16S rRNA gene, and the
mitochondrially encoded cytochrome c oxidase I in
eukaryotes, the COI gene, constitute such highly conserved
areas of genetic information, which are commonly used for
taxonomic characterization by barcoding approaches (Pimm
et al. 2014; see also McCarthy et al. within the abstracts
related to this chapter for an ancient DNA example).
Traditional microbial characterization approaches require
a cultivation prior to phenotypic classification. However,
most marine microbes are very challenging to or not at all
cultivable (Pedrós-Alió 2012; Epstein 2013; Amann and
Rosselló-Móra 2016). The advances of next-generation
sequencing methods provide the possibility to detect and
phylogenetically classify a vast amount of microbes simultaneously, including those that are not cultivable (see also
Weinheimer et al. within the abstracts related to this chapter). In a recently published microbial ecology study, Röthig
et al. (2016) aimed to characterize the microbiome of the
model metaorganism Aiptasia. Besides a metagenomics
approach by DNA isolation of Aiptasia tissue and subsequent high throughput 16S rRNA gene sequencing, the
authors applied a culture dependent approach. They detected
295 different taxa in the metagenomic data, while they were
only able to culture 14 of those (with a 100% match in the
16S rRNA) (Röthig et al. 2016; see also Slaby et al. within
the abstracts related to this chapter for a marine sponge
microbiome).
Similarly, the gastrointestinal (GI) microbial community
is an important component of a vertebrate metaorganism (see
Box 3). Dewar et al. (2013) assessed the residual GI microbiota in feces of the king, gentoo, macaroni, and little penguins by 16S rRNA gene sequencing. The authors detected a
diverse microbial community (>5 k operational taxonomic
units (OTUs) identified) with significant differences in relative abundances of microbial taxa across penguin species.
They further identified known human pathogens (including
Helicobacter, Veillonella, Mycoplasma, etc.), although their
virulence in penguins or other sea-birds remains questionable (Dewar et al. 2013, 2014).
Samples of environmental DNA (eDNA) may be subject
to similar analysis of highly conserved genome regions.
Such studies would aim to detect DNA traces in the environment to extrapolate on the presence and potentially even
abundance of the corresponding organisms (Taberlet et al.
2012; Valentini et al. 2016; see also Mauvisseau et al. within
the abstracts related to this chapter). A recent comparison of
the efficiency of traditional surveys and eDNA approaches
aimed to detect amphibians and fishes in natural aquatic
environments by designing group (i.e. amphibian and fish)
specific primers of mitochondrial DNA (mtDNA) (Valentini
et al. 2016). Two amphibian species (Triturus marmoratus
and Pelophylax sp.) were observed with conventional methods but not detected via barcoding, whilst a total of 64 species could exclusively be recorded in the eDNA samples.
The fish survey prompted a similar result. Thus, ecological
surveys using eDNA appear to be more thorough and accurate. In addition, they are less destructive, are more (cost)
efficient, and do not fully rely on the taxonomic knowledge
and species identification of experts, compared to commonly applied surveys. Furthermore, it will be less challenging to establish standardized protocols, thus, making
survey studies more comparable, especially if they are conducted across various scientific laboratories (Valentini et al.
2016).
In virology, characterization efforts demand the verification of newly identified viruses by visual evidence (e.g.,
J. D. Brüwer and H. Buck-Wiese