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1. drug i n v e n t i o n today 5 ( 2 0 1 3 ) 3 2 1e3 2 6
Available online at www.sciencedirect.com
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journal homepage: www.elsevier.com/locate/di t
Review Article
Proteomics & metabolomics: Mapping biochemical
regulations
Anjum Gahlaut a,*, Vikas a, Monika Dahiya a, Ashish Gothwal a,
Mahesh Kulharia b, Anil K. Chhillar a, Vikas Hooda a, Rajesh Dabur c
a Centre for Biotechnology, Maharshi Dayanand University, Rohtak, Haryana 124001, India
b Centre for Bioinformatics, Maharishi Dayanand University, Rohtak, Haryana 124001, India
c Department of Biochemistry, Maharishi Dayanand University, Rohtak, Haryana 124001, India
a r t i c l e i n f o
Article history:
Received 28 June 2013
Accepted 27 August 2013
Available online 20 November 2013
Keywords:
Proteomics
Metabolomics
Chromatography
Spectrometry
Bioinformatics
a b s t r a c t
Large-scale studies in the field of omics have been successfully exploring the differences in
gene expression, protein and metabolite abundance and modification of post-translational
protein, and providing a different level of views for the cellular processes occur in cells.
Proteomics and metabolomics are new addition to the ‘omics’ field, but both of them are
still developing its own computational infrastructure by assessing the computational
needs of its own. Due to the strong knowledge on chemical information and the impor-tance
of linking this chemical information to biological consequences, proteomics and
metabolomics combines the elements of traditional bioinformatics and cheminformatics.
Copyright ª 2013, JPR Solutions; Published by Reed Elsevier India Pvt. Ltd. All rights
reserved.
1. Introduction
The study of biological entities at the system level is a clear
trend in the life sciences. Analytical tools are required to
identify the component parts of the system and determine
their responses to a changing environment. To achieve all
these requirements, a combination of transcriptomic, pro-teomic,
and metabolomic profiling technologies have been
developed, and among these technologies, proteomics is
continuing to evolve rapidly. Presently, there are large
numbers of proteomic studies have been published in the
literature, only a small portion has attempted to provide an
extensive quantitative description of the biological system
under investigation. Apart from the phenomenal contribu-tion
of the mass spectrometry and peptide separation
techniques in area of proteomics studies, there is so many
unsolved technical challenges for identification and quan-tification
of all of the proteins in the biological system is still
remain. While proteomic data for the genome of unicellular
organisms has been occasionally achieved beyond 50% but
the proteomic coverage for multicellular or higher organ-isms
strictly exceeds more than 10%. For protein quantifi-cation,
these figures have low data quality, in terms of
available information content, because the information
required for quantification are more than for protein
identification.
* Corresponding author.
E-mail address: anjumgahlaut@gmail.com (A. Gahlaut).
0975-7619/$ e see front matter Copyright ª 2013, JPR Solutions; Published by Reed Elsevier India Pvt. Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.dit.2013.08.007

2. 322 d r u g i n v e n t i o n t o d a y 5 ( 2 0 1 3 ) 3 2 1 e3 2 6
The use of fluorophores, dyes or radioactivity in classical
proteomic quantification methods provides very good line-arity,
sensitivity and dynamic range, but they have two
important drawbacks: (1) requirement of protein separation at
high resolution which is typically provided by 2D gels, so can
not be applicable to abundant and soluble proteins, and (2)
they do not provide the information related to the underlying
protein. Both of these problems can be solved by using the
modern LC-MS/MS techniques. However, mass spectrometry
is not used for quantitative purpose due to the wide range of
physicochemical properties such as size, charge, hydropho-bicity
etc. exhibited by proteolytic peptides; this causes the
large differences in mass spectrometric response. The accu-racy
in protein quantification can be achieved by comparing
each individual peptide between experiments.
In order to achieve a complete analysis of the biological
response of a complex system, it is important to monitor the
response of an organism to a conditional difficulty at the
transcriptome, proteome and metabolome levels.1 Integration
of experimental data with the results of functional genomics
is an important step to achieve this goal. Metabolomics can be
considered as the most recent contribution to this area. It
involves the qualitative and quantitative analysis of all the
metabolites in the cell (the metabolome). Moreover it is more
closely related to the organism’s actual phenotype and can be
linked to the genotype through the knowledge provided by the
biochemical pathways and gene regulatory networks.2
Comprehensive studies of metabolic processes have been
made possible and useful with the development of modern
analytical and computational tools.
While transcriptomics and proteomics studies provide
critical insight into sequential modulation of metabolic reac-tion
flux but metabolomics may provide information related
to regulation called, metabolic regulation.3 The metabolic
regulation can be described as the effect of metabolite con-centrations
on actual activity of enzyme through mass action,
kinetic and allosteric effects.4
2. Proteomics
Proteomics is the simultaneous and systematic analysis of the
diverse nature of proteins. The aim to develop proteomics is to
provide detailed information about the structure and function
of biological systems in different biological conditions. A
proteome may be defined as the total content of proteins
expressed by a genome in a cell or tissue at a certain time. The
term proteome was first introduced in the 1990s.5,6 Analysis of
proteome is most commonly performed by a combination of
two-dimensional gel electrophoresis (2-DE) and mass spec-trometry
(MS). With the help of 2-DE technique, a complex and
variable mixture of protein is separated and visualized and
then for identification of protein of interest, mass spectrom-etry
is applied. The proteome of one organism differs from
other organisms, depending on the genome and on external
and internal factors including health, stress, physiological
state, disease and drugs. The complexity of the proteome is far
higher as compared with the genome because of the protein
processing and modification. The main focus of proteomics
studies is to provide detailed descriptions of the diverse
properties of proteins in a variety of biological systems.7
Although proteomics is a relatively new field, but the new
methodologies in the proteomics studies have been under
development for decades.
Proteomic study of proteins is generally based on four
technological parameters, (i) a simple and fast method for
purification of proteins in small amounts from complex mix-tures,
(ii) a rapid and sensitive method to generate sufficient
detailed structural information for protein molecule being
studied, (iii) access to structural and sequence databases of
protein or DNA, and (iv) computer-based algorithms capable
of translating and linking the language of DNA sequence with
various types of structural information of protein like internal
peptide sequences or N-terminal protein, composition of
amino acids, p/peptide mass fingerprints, sequence tags of
selected peptides or mass spectrometry fragmentation
patterns.
2.1. Proteomics methodologies
The proteins separation on the whole protein level is usually
performed by gel-based electrophoretic or by liquid chro-matographic
methods. Peptide level separations or fraction-ations
can be achieved by chromatographic methods or by
peptide isoelectric focusing.8e10
2.1.1. Separation by gel-based method in proteomics
After obtaining the fraction of desired protein by purification,
this fraction is subjected to one-dimensional gel electropho-resis
(1-DE) for resolving the relatively simple protein mix-tures.
In 1-DE, the separation of proteins according to their
molecular weight is the basis of this method. 2-DE is used as a
standard gel-based separation method in proteomics, which
enabling the simultaneous separation and visualization of
thousands of proteins in one time. In 2-DE, isoelectric focusing
(IEF) is used to separate proteins in first dimension according
to their isoelectric point (pI) in a pH gradient, and after that
proteins are separated according to their molecular weight in
second dimension.
2.1.2. Separation by non-gel-based method in proteomics
The introduction of non-gel-based strategies in the field of
proteomics has provided high-throughput methodologies.
The emergence of non-gel-based proteomic methods in
recent years is due to the availability of proteins separation
by liquid chromatographic techniques, new protein chemis-try,
new enrichment methods and the development of mass
spectrometry and new software for data analysis. Quantita-tion
by mass spectrometry provides important addition to
quantitation by 2-DE. The use of mass spectrometric-based
technologies have several advantages over 2-DE-based
technology as they are automated and separated complex
peptide mixtures with high resolution and high sensitivity.11
To perform liquid chromatography/mass spectrometry (MS)
in proteome analysis, this require a complexity reduction in
order to detect and analyzemany possible components in the
sample.6 This can be achieved by combining the two
different orthogonal peptide separation methods such as
cation-exchange chromatography and capillary reversed
phase chromatography in combination with MS/MS. The

3. drug i n v e n t i o n today 5 ( 2 0 1 3 ) 3 2 1e3 2 6 323
combination of multidimensional chromatography and tan-dem
mass spectrometry method is known as MudPit, which
has been used for the identification of up to 10,000 of proteins
from complex protein mixtures.12e15 The use of multidi-mensional
separation techniques in proteomic analysis has
greatly increased the protein coverage and dynamics,
allowing the identification of many previously undetected
low abundance proteins.16
2.1.3. Mass spectrometry (MS) analysis
Due to its applicability in a wide range of applications, mass
spectrometry plays an important role in biological sciences. In
proteomic studies, mass spectrometry reached the new level
where it is routinely used worldwide and applied to solve a
wide range of biological problems due to its instrumentation
and the methods used for data acquisition and analysis.17e19
The collaboration of highly sensitive biological mass
spectrometry, and 2D PAGE (high resolution two-dimensional
polyacrylamide gel electrophoresis) and the fast growing da-tabases
on protein and DNA has opened the new way for high-throughput
proteomics.
2.2. Proteomics applications
Oneof the key developments arises fromthe analysis ofhuman
genes and proteins are the identification of putative novel drug
molecules for the treatment of various diseases. This depends
on the information obtained from the genome and proteome
which identify the proteins associated with a disease, and then
computer software can be used as targets for novel drug mol-ecules.
For instance, if a specific protein is found altered in a
diseased condition, its 3D structure can be used to generate the
information and then this information helps in designing the
drugs which can interfere with the protein action. A molecule
that binds to the active site of an enzyme and can not be
released by the enzyme will inhibit the action of the enzyme.
This is the basic tools for discovering the new drug, which
provides information to findnewdrugs that inactivate proteins
responsible for disease. After finding the genetic differences
among the individuals, researchers used these techniques for
developing the more personalized drugs which shows more
efficacies on the individual suffering from disease.20
3. Metabolomics
Metabolomics is the scientific study of the metabolites of
chemical processes. Metabolomics is defined as the system-atic
analysis of the unique chemical fingerprints that are
produced from specific cellular processes and the study of the
metabolic profile of small molecules.21 The metabolome is the
collection of all metabolites, which are the end products of
cellular processes in a biological cell, tissue, organ or organ-ism.
22 When gene expression data and proteomic studies do
not tell about the whole information of a cell, metabolic
profiling offers an instantaneous physiology of that cell.
The first metabolomics web database called METLIN,23 was
created in the Scripps Institute in 2005 for characterizing the
metabolites of human and the database contained about
10,000 metabolites and tandem mass spectral data. This figure
reaches to over 60,000 metabolites in September 2012 and
METLIN database contains the largest storehouse of tandem
mass spectrometry data in metabolomics field.
In 2007, Dr. DavidWishart, completed the first framework of
the human metabolome which consists of a database with
approx. 2500 metabolites, 1200 drugs and 3500 food compo-nents.
24,25 In 2012, Gahlaut et al developed a simple and fast
protocol to discriminate between various components from a
plant extract. The protocol was a sequential amalgamation of
UPLC-QTOFMS and multivariate analysis.26 Metabolome is the
small molecule such as hormones, other signaling molecules,
metabolic intermediates and secondary metabolites found
within a biological sample.27,28 The term metabolome was
coined in similarity with transcriptome and proteome. Similar
to the transcriptome and the proteome, the metabolome is
dynamic in nature and changes every second.Metabolome can
be defined quickly enough, but it is not possible to analyze the
entire range ofmetabolites in sample by a single analytical tool.
Metabolomics is an analytic tool used in combination with
pattern recognition approaches and bioinformatics to study
metabolites and pursue their remarkable changes in tissue or
biofluids.2,29,30 There are precise numbers of human metabo-lites
are still unknown, may be ranging from 1000 to 10,000.
Metabolomics, an extension of the traditional analysis of
targeted metabolites, employs four levels of methodologies:
(1) metabolite fingerprinting, (2) metabolite profiling, (3)
metabolome analysis, and (4) systems biology. While the basis
of these strategies is not new, but the availability of new
technologies with high resolution and simultaneous detection
have facilitated their expansion to include large-scale
metabolome data. Metabolite fingerprinting identifies and
compares the overall nature of samples. It is not restricted in
merely identifying the metabolites. The output of sensors
(analytical detectors) is termed as fingerprints. These are
classified and statistically analyzed to mark out their differ-ences.
Metabolite fingerprinting can be applied for simulta-neous
determination technology, comparative analysis
fingerprints of wild/mutant or healthy/sick. This can yield
valuable insights for biotechnology and diagnostics.27,31
Metabolite profiling involves identification of metabolites as
the analysis is based on their spectral peaks and calibration
curves. For a number of years, metabolite profiling has been
used to identify quantitative differences for amino acids and
other diagnostic biomarkers.32,33 The focus on time-dependent
resolution based analysis of metabolite com-pound
leads to metabolome analysis. Metabolome analysis
comprehensively monitors entire gamut of metabolites in a
sample by the synergistic application of various analytical
techniques. To elucidate the functional aspect of metabolites,
proteomic and transcriptomic information is required. Finally,
systems biology carries out partial to full integration of tran-scriptome,
proteome and metabolome data.34 Known meta-bolic
networks and other data need to be integrated to gain
insight into biological processes, and ultimately to model
these processes mathematically. Such modeling should
explain the properties of biological systems and predict their
behavior in response to experimental intervention.
Metabolomics permits an overall calculation of a cellular
condition in terms of its environment, in consideration to the
gene regulation, modulated enzyme kinetics, and variations

4. 324 d r u g i n v e n t i o n t o d a y 5 ( 2 0 1 3 ) 3 2 1 e3 2 6
in metabolic reactions.29,35,36 In variance to the genomics or
proteomics, metabolomics reflects the phenotypic changes in
the function. However, it is important to mention here that
the omic sciences are complementary as “upstream” changes
in genes and proteins are measured “downstream” as changes
in physiological function.29,37
The contrary of metabolomics is that it is a terminal view of
the biological system, not allowing for representation of the
increased or decreased genes and proteins. The similar fea-tures
of metabolomics with proteomics and transcriptomics
include the ability to assay the biofluids or tumor samples and
are relatively rapid, inexpensive and automated techniques.
3.1. Analytical technologies for metabolomics
3.1.1. Separation methods in metabolomics
3.1.1.1. Gas chromatography (GC). When gas chromatography
combined with mass spectrometry (GCeMS), become one of
the most widely used and powerful methods. Gas chroma-tography
offers a high chromatographic resolution, but re-quires
derivatization of chemical for many biomolecules, but
volatile chemicals can be analyzed without the requirement of
derivatization. Gas chromatography can not analyzed few
large and polar metabolites.38
3.1.1.2. High performance liquid chromatography (HPLC).
HPLC has lower chromatographic resolution than gas chro-matography.
The use of high performance liquid chromatog-raphy
offers advantage over gas chromatography as it
measured a much wider range of analytes.39
3.1.1.3. Capillary electrophoresis (CE). The theoretical sepa-ration
efficiency of capillary electrophoresis is much higher
than HPLC and is suitable for working with a wider range of
metabolite classes. Among the all electrophoretic techniques,
capillary electrophoresis is most appropriately used for
analyzing the charged analytes.40
3.2. Detection methods in metabolomics
3.2.1. Mass spectrometry (MS)
Mass spectrometric analysis is used to identify and quantify
metabolites after separation through gas chromatography,
high performance liquid chromatography (LC-MS) or capillary
electrophoresis techniques. GCeMS was the first method to be
developed as the most ’natural’ combination of the three
techniques. In mass spectrometry studies, identification of a
metabolite according to its fragmentation pattern through the
mass spectral fingerprint libraries. Mass spectrometry tech-nique
is sensitive and very specific. There are several other
studies which use mass spectrometry technique as a stand-alone
tool: the sample is directly placed into the mass spec-trometer
without any need of separation steps, and then MS
serves to separate and detect metabolites in the sample.
3.2.2. Surface-based mass analysis
The surface-based mass analysis has undergone a sea change
in the last few years, with the advent of new Mass spectro-metric
technologies. These techniques have increased sensi-tivity
and minimized background noise. However, there is still
a paucity of techniques hat can analyze the biofluids directly
from tissues in the current MS technology. This is because of
the limits by virtue of the complexity in sample preparation
involving thousands to tens of thousands of metabolites.
Nanostructure-Initiator MS is a novel technology that is being
developed to overcome this problem.41,42 It is a revolutionary
approach which does not require desorption and ionization
approach; hence, it facilitates rapid identification of metabo-lite.
Even though MALDI is often used, the use of a MALDI
matrix can increase the noise significantly around 1000 Da.
This makes it extremely difficult to analyze the low-weight
metabolites. Moreover, the size of the matrix material puts
limits on the resolution possible in study of tissue. Secondary
ion mass spectrometry (SIMS) was developed as a non-matrix
approach for the analysis of metabolites among biological
samples. SIMS involves usage of a high-energy ion beam to
cause desorbtion. This generates secondary molecular ions
from a surface. The major advantage of SIMS lies in its high
resolution (as small as 50 nm). This helps in imaging of tissues
with MS. Inspite of this the technique has yet to be readily
applied to analyze biofluids because of low sensitivity at
500 Da. Another drawback of the technique is the analyte
fragmentation generated by the ion beam. Desorption elec-trospray
ionization (DESI) is yet another non-matrix tech-nique
for the analysis of biological samples using a charged
solvent spray for desorbtion. Advantages of DESI include its
freedom from the requirement of any special characteristics
of a surface. DESI however suffers from a disadvantage that its
resolution is not optimal. This is due to the fact that focusing
of the charged solvent spray is problematic. In recent years a
new technique called as “Laser ablation ESI” (LAESI) has been
developed to overcome this limitation.
3.2.3. Nuclear magnetic resonance (NMR) spectroscopy
Nuclear magnetic resonance does not require the separation
of the analytes, this helps in the recovery of the sample for
further analysis. Nuclear magnetic resonance can be consid-ered
as a universal detector because it can be used to measure
all types of metabolites/small molecules simultaneously.
Nuclear magnetic resonance has main advantages as it has
high analytical reproducibility and simplicity in the sample
preparation. In practical, it is relatively insensitive than
techniques based on mass spectrometry.43,44
Although nuclear magnetic resonance and mass spec-trometry
are the most widely used techniques but there are
several other detection methods that have been used in
combination with nuclear magnetic resonance and mass
spectrometry techniques include radiolabel (when combined
with thin-layer chromatography), ion-mobility spectrometry
and electrochemical detection (coupled to high performance
liquid chromatography).
3.3. Statistical methods in metabolomics
The output generated in metabolomics generally comprise of
data measurements performed on subjects under varying
conditions.45 These can be digitized spectra of metabolites
mass spectrometric analysis, or a list of metabolite levels. This
usually is corresponds to a matrix with rows representing to
subjects and columns representing to the respective levels of
metabolite.46 Numerous statistical tools have been designed

5. drug i n v e n t i o n today 5 ( 2 0 1 3 ) 3 2 1e3 2 6 325
for the analysis of mass spectrometry data. In the Siuzdak
laboratory at Scripps Research Institute software named as
XCMS was developed to carryout the analysis of global mass
spectrometry-based metabolomics datasets.47 It has become
one of the most popular and cited metabolomics-MS software
programs in literature. A cloud based version of XCMS has also
been put forward by the same group.48,49 In addition to XCMS
other widely used metabolomics programs for mass spectral
analysis are MetAlign,50 MZmine,51 and MathDAMP.52 These
softwares also compensate for retention time variation during
analysis of sample. LCMStats is a package of R which can
carryout a meticulous analysis of LCMS output data.53 Its
primary use has been in identification of ions that co-elute
especially isotopologues. It involves a combination of XCMS
package functions and is carries numerous statistical func-tions
for the correction of sensor saturation using heat maps.
3.4. Applications of metabolomics
3.4.1. Toxicology
Physiological changes in biological samples especially urine or
blood plasma samples caused due to the toxic nature of a
chemical can be detected by metabolic profiling.
3.4.2. Functional genomics
Phenotype produced due to the genetic manipulation (such as
insertion or deletion of gene) can be detect by the help of
metabolomics. Metabolomics is a great tool for identification of
the phenotypic changes in a genetically-modified plant which
is used for animal or human consumption. More exciting
feature of metabolomics is the possibility of function predic-tion
of hypothetical genes by comparing the metabolic diffi-culties
arises due to the deletion/insertion of known genes.
3.4.3. Nutrigenomics
Nutrigenomics is a commonly used term for describing the
links between metabolomics, transcriptomics, proteomics,
genomics and human nutrition. In general, the factors
affecting a metabolome in a given body fluid include sex, age,
body composition, genetics and underlying pathologies. The
micro floras can also be classified as an endogenous or exog-enous
factor. The drugs and diet are the main exogenous
factors. Diet can be broken into nutrients and non-nutrients
diet. Metabolomics reflects the balance of all these factors
on an individual’s metabolism determining the biological
endpoint or metabolic fingerprint.54
4. Conclusion
Understanding the cellular processes at molecular level and
biological interpretation of high-throughput omics experi-ments
can be achieved by analyzing the function of genes,
metabolites, enzymes, pathways and their relationships in
the biological system. The ability to combine biological data
from different sources and analyze them within the same
framework also enhances this understanding. Proteomics and
Metabolomics, both are relatively young disciplines in the field
of genomics and system biology, they have already considered
as an important integrative part of the field of biological
research. Further developments in the field of analytical sci-ence
and bioinformatics are required for the improvements
in Proteomics and Metabolomics. New researches in the
analytical techniques can helps in improving the resolution,
comprehensiveness, speed and throughput of analytical ex-periments
and miniaturization of equipment.
Conflicts of interest
All authors have none to declare.
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