75
Box 2: Mass-Per-Charge of Peptides and Metabolites
Protein and targeted metabolite analyses, including
antibody, ionization, and spectroscopy approaches,
date back more than a century. Technical advances in
the field of mass spectrometry (MS) are, however, revolutionizing the possibilities in these fields, now supporting proteome-wide peptide sequence identification
and untargeted metabolome characterization and
comparison.
Protein studies have traditionally been relying on
the usage of antibodies on a small scale but as a precisely localizing method. Nevertheless, limited availability of antibodies for different protein structures,
comparatively low throughput, high costs for antibody
production, and low quantitative comparability due to
lacking standards have hampered proteome-scale
assessments. Deep high-throughput MS has emerged
as an opportunity to read-out relative and absolute concentrations of proteins genome-wide. Label-free quantification via tandem mass spectrometry (MS/MS)
allows the recognition of individual peptide spectra.
These are compared to entries in databases, optimally
containing all peptide sequences expected to be present, but few irrelevant ones. Current developmental
and research efforts, though, target the de novo determination only from the peptide’s spectrum (Liu et al.
2016; Ruggles et al. 2017).
Current-standard for untargeted metabolome analysis is a liquid chromatography coupled with mass spectrometry. Since theoretically every type of small
molecule possesses a unique retention time and a
unique mass-per-charge ratio, this procedure separates
and characterizes each metabolite. Adjustments in liquid phases regarding hydrophilic and hydrophobic
components and their directions can improve the resometagenome data analyses has to be treated with caution due to exponential amplification steps. However,
normalization steps to account for differential amplification within samples, as well as differential sequencing depth across samples, may be used to gain better
estimates of quantities as well as maintaining data
comparable. This may be achieved by calculation of
“Fragments Per Kilobase of exon model per million
Mapped reads” (FPKM). Further biostatistic normalization to eliminate sequencing biases, e.g., using
nCounter (Geiss et al. 2008), may be helpful in evaluation of the data (Liu et al. 2016).
lution achieved by retention. The experimental
approach requires a comparison of the metabolic profile yielded by the mass spectrometer either to a standard or between two or more samples. A bioinformatic
overlay of the produced profiles provides information
on significant differences in abundance and thereby
delineates molecules of interest. Their mass-per-charge
ratios now serve to find reference molecules in databases. However, due to the novelty of metabolomewide studies, there is a considerable number of
molecules, which remains to be identified and entered
into the repositories. If there is a mass-to-charge hit
and standards are available for the molecules of interest, the identity can be confirmed via retention times
and MS/MS profiles (Patti et al. 2012).
and micro RNA (miRNA). The study of the protein content
of an organism and its respective functions is comprised by
proteomics. Metabolomics deals with any small molecules
that are produced or ingested by an organism (Handelsman
2004; Patti et al. 2012; Pascault et al. 2015; Beale et al. 2016;
Liu et al. 2016).
In this review, we will delineate the physiological background of omics research and will exemplify the wide spectrum of applicability under aspects of functionality,
systematics, and response to environmental cues. Finally, we
aim to highlight the significance of multi-omics for an indepth understanding of complex systems.
Physiological Background
The genome depicts the inherited foundation within a cell
and is – apart from epigenetic changes – consistent in almost
every healthy somatic cell of a multicellular organism. It
encodes for the high variety of proteins, as well as nonprotein coding sequences, such as ribosomal RNA (rRNA),
transfer RNA (tRNA), and micro RNA (miRNA) (Alberts
et al. 2008).
Gene expression begins with the transcription of a DNA
sequence into a pre-mRNA. The newly synthesized nucleotide sequence constitutes a reverse complement of the coding
strand with ribose phosphates instead of deoxyribose phosphates forming the backbone, and Uracil pairing with
Adenine instead of Thymine (Alberts et al. 2008).
Promoter sequences upstream of open reading frames, the
DNA region to be transcribed, contribute significantly to
expression by recruiting the RNA polymerase. However,
expression profiles remain a complex puzzle due to influences of cis- and trans-regulatory motifs and binding of transcription factors. Further, epigenetic modifications as
Box. 1: (continued)
Reading the Book of Life – Omics as a Universal Tool Across Disciplines
Box 2: Mass-Per-Charge of Peptides and Metabolites
Protein and targeted metabolite analyses, including
antibody, ionization, and spectroscopy approaches,
date back more than a century. Technical advances in
the field of mass spectrometry (MS) are, however, revolutionizing the possibilities in these fields, now supporting proteome-wide peptide sequence identification
and untargeted metabolome characterization and
comparison.
Protein studies have traditionally been relying on
the usage of antibodies on a small scale but as a precisely localizing method. Nevertheless, limited availability of antibodies for different protein structures,
comparatively low throughput, high costs for antibody
production, and low quantitative comparability due to
lacking standards have hampered proteome-scale
assessments. Deep high-throughput MS has emerged
as an opportunity to read-out relative and absolute concentrations of proteins genome-wide. Label-free quantification via tandem mass spectrometry (MS/MS)
allows the recognition of individual peptide spectra.
These are compared to entries in databases, optimally
containing all peptide sequences expected to be present, but few irrelevant ones. Current developmental
and research efforts, though, target the de novo determination only from the peptide’s spectrum (Liu et al.
2016; Ruggles et al. 2017).
Current-standard for untargeted metabolome analysis is a liquid chromatography coupled with mass spectrometry. Since theoretically every type of small
molecule possesses a unique retention time and a
unique mass-per-charge ratio, this procedure separates
and characterizes each metabolite. Adjustments in liquid phases regarding hydrophilic and hydrophobic
components and their directions can improve the resometagenome data analyses has to be treated with caution due to exponential amplification steps. However,
normalization steps to account for differential amplification within samples, as well as differential sequencing depth across samples, may be used to gain better
estimates of quantities as well as maintaining data
comparable. This may be achieved by calculation of
“Fragments Per Kilobase of exon model per million
Mapped reads” (FPKM). Further biostatistic normalization to eliminate sequencing biases, e.g., using
nCounter (Geiss et al. 2008), may be helpful in evaluation of the data (Liu et al. 2016).
lution achieved by retention. The experimental
approach requires a comparison of the metabolic profile yielded by the mass spectrometer either to a standard or between two or more samples. A bioinformatic
overlay of the produced profiles provides information
on significant differences in abundance and thereby
delineates molecules of interest. Their mass-per-charge
ratios now serve to find reference molecules in databases. However, due to the novelty of metabolomewide studies, there is a considerable number of
molecules, which remains to be identified and entered
into the repositories. If there is a mass-to-charge hit
and standards are available for the molecules of interest, the identity can be confirmed via retention times
and MS/MS profiles (Patti et al. 2012).
and micro RNA (miRNA). The study of the protein content
of an organism and its respective functions is comprised by
proteomics. Metabolomics deals with any small molecules
that are produced or ingested by an organism (Handelsman
2004; Patti et al. 2012; Pascault et al. 2015; Beale et al. 2016;
Liu et al. 2016).
In this review, we will delineate the physiological background of omics research and will exemplify the wide spectrum of applicability under aspects of functionality,
systematics, and response to environmental cues. Finally, we
aim to highlight the significance of multi-omics for an indepth understanding of complex systems.
Physiological Background
The genome depicts the inherited foundation within a cell
and is – apart from epigenetic changes – consistent in almost
every healthy somatic cell of a multicellular organism. It
encodes for the high variety of proteins, as well as nonprotein coding sequences, such as ribosomal RNA (rRNA),
transfer RNA (tRNA), and micro RNA (miRNA) (Alberts
et al. 2008).
Gene expression begins with the transcription of a DNA
sequence into a pre-mRNA. The newly synthesized nucleotide sequence constitutes a reverse complement of the coding
strand with ribose phosphates instead of deoxyribose phosphates forming the backbone, and Uracil pairing with
Adenine instead of Thymine (Alberts et al. 2008).
Promoter sequences upstream of open reading frames, the
DNA region to be transcribed, contribute significantly to
expression by recruiting the RNA polymerase. However,
expression profiles remain a complex puzzle due to influences of cis- and trans-regulatory motifs and binding of transcription factors. Further, epigenetic modifications as
Box. 1: (continued)
Reading the Book of Life – Omics as a Universal Tool Across Disciplines