Metabolomics- Ijp

1 Metabolomics: Useful Tool for Functional Genomics Saraswati S Delhi institute of Pharmaceutical Sciences and Research, Sector III, Pushp Vihar, M B Road, New Delhi

Abstract Biology is in the midst of intellectual and experimental sea change. Essentially the discipline is moving from being a largely data poor science to a data rich science. Metabolomics has emerged as third major path of functional genomics besides transcriptomics and proteomics. Just as genomics is the omics for DNA sequence analysis, metabolomics is the omics approach to understand cell and systems biology level. Combined with information obtained on transcriptome and proteome, this would lead to nearly complete molecular picture of state of particular biological system at a given time.

Keywords: Metabonomics, metabolite profiling, Nuclear magnetic Resonance, Mass spectroscopy, metabolic database

2 Introduction

Metabolomics has been developed as one of the new ‘Omics’ joining genomics, transcriptomics and proteome as a science employed towards the understanding of global system biology. It is a large-scale study of all metabolites present in cell, tissue, or organs usually by high throughput screening

[1], [2], [3], [4].

Metabolomics

identify and quantify the complete set of metabolites present in a cell or tissue and to do so as quickly as possible and without bias

[2], [3].

It is a key aspect to phenotype;

hence, describing the distribution of metabolites is next logical step in elaboration of functional genomics morphology

[5]

and may be the best and most direct measure of cellular

[6]

Metabolomics is comprised of two words: Metabolome and Omics. Metabolome or Small Molecule inventory (SMI) is defined by entire complement of low molecular weight, non-peptide metabolite with in a cell or tissue or organism at a particular physiological rate

[1],

[4].

It defines metabolic phenotype thus is an important

biochemical manifestation, and useful tool for functional genomics

[7].

Another

definition states that metabolome consists only of those native small molecules (definable non polymeric compound) that are participant in general metabolite reactions and that are required for maintenance, growth and normal function of a cell [8].

“Omics” technologies are based on comprehensive biochemical and molecular

characterization of an organism, tissue or cell type. Omics is a high-through put screening based on biochemical and molecular characterization of an organ, tissue, or cell type. Metabolomics represents the logical progression from large-scale analysis of RNA and proteins at the systems level [8]

3 Metabolomics deals with the quantification of all or a substantial fraction of all metabolites within a biological sample and simultaneously identifying and quantifying their respective classes of biomolecules- mRNAs, proteins and metabolites. While the genome is representative of what might be proteome is and what it is expressed; it is the metabolome that represent the current status of the cell or tissue. To understand the basic metabolism and chemistry of metabolites, biochemical pathways should be first understood.[9] Measurement of metabolite provides basic information about biological response to physiological or environmental changes and thus improves the understanding of cellular biochemistry as networks of metabolite feedback regulate gene and protein expression and mediate signal between organisms.

Metabolomics allows a shift from hypothesis driven research to the

analysis of system-wide responses, especially when it is integrated with other profiling technologies.

At the analytical level both the functional genomics and Metabolomics rely on comprehensive profiling of large number of gene expression products, known as transcriptomics,

proteomics

and

metabolomics.

The

number

of

publications

stagnated from 407 in 2005 to 406 in 2006. (Fig 1). The use of these “omics” technologies in the biological research during the last 20 years is summarized in Fig. 2 based on number of publications per year for each area.

4

Fig 1. Pubmed literature search results document the continuously growing research areas of Metabolomics based on numbers of publication [10]

Fig 2. Pubmed literature search results document comparison of the continuously growing research areas of genomics, proteomics, metabolomics/metabonomics and transcriptomics

[10]

5 Metabolomics or Metabonomics

Metabolomics and metabonomics have been the subject of numerous reviews in recent years

[1], [11] [12], [13], [14], [15], [16], [17], [18], [19], [20],

was published in 2003

[21].

and a volume on metabolic profiling

Historically, metabolomics and metabonomics are

compared with GC/MS and NMR respectively

[22].

L stands for plants and N stands for

animals. Before any further discussion a question, which arises, is what the difference between metabonomics and metabolomics is, and when is the use of either term appropriate? The possible answer might be whom you target as both the terms may be appropriate in most cases and the distinctions are more a matter of historical usage than meaningful scientific definition.

The concept of the metabolome has been in existence for years in the form of metabolic control theory and flux analysis publications

[25],

[23],

[24]

and was routinely used in

which indicated the total metabolite pool; “metabolome” analysis

offers a means of revealing novel aspects of cellular metabolism and global regulation. While not expressly defined, the term metabolomics was indicated by Fiehn

[22]

Nicholson

to be the "comprehensive and quantitative analysis of all metabolites. ..." [26]

coined Metabonomics in 1999 and defined as “the quantitative

measurement of the time-related multi-parametric metabolic response of living systems to pathophysiological stimuli or genetic modification”. Compounding the naming convention problem is the fact that metabonomics and metabolomics have been described as subsets of each other [22], [27].

Metabolomics is a direct approach to reveal the function of genes involved in metabolic processes and gene-to-metabolite networks. It offers a quick way to elucidate the function of novel genes and play important role in future plant, nutrition

6 and health, drug toxicity etc. Metabolism is the key aspect of phenotype, hence describing the distribution of metabolites in next logical step in elaboration of functional genomics. It is useful wherever an assessment of change in metabolite concentration is needed. In order to elucidate an unknown gene function, genetic alteration is introduced in system by analyzing phenotyping effect of such a mutation (i.e. by analyzing the metabolome functions may be assigned to respective gene

[28]

.

Metabolites are the result of interaction of system’s genome with its environment and are not merely end product of gene expression but also from part of regulatory system in an integrated manner and thus can define biochemical and phenotype of a cell or tissue

[3]

. Thus its quantitative and qualitative measurement can provide a

broad view of biochemical status of organism; that can be used to monitor and assess gene function

[1]

.

Exhaustive work has been done on genomics, proteomics and transcriptomics, which allowed establishing global and quantitating mRNA expression profile of cells and tissues in species for which the sequence of all genes is known

[29]

. Now question

which arises is why Metabolomics when transcriptome, genome and proteome are so popular? Probable reason for this may be: any change in transcriptome and proteome due to increase in biochemical phenotype

[29]

RNA do not always correspondence to alteration in and increase mRNA do not always correlated with

increased protein level. Translated protein may or may not be enzymatically active; thus it can be said that transcriptome and proteome do not correspondence to alteration in biochemical phenotype

[2]

. Identification of mRNA and protein is indirect

and yield only limited information. Another reason might be: if quantification of metabolite is known then long process like to know DNA protein sequence, micro array, 2 D Gel Electrophoresis need not to be done

[30]

. Thus, it is inferred that

metabolome provide the most functional information of Omics technology

[2]

. Unlike

7 transcripts and proteome, metabolite shares no direct link with genetic code and is instead products of concerted action of many networks of enzymatic reactions in cell and tissue. As such, metabolites do not readily tend themselves to universal methods for analysis and characterization

[31]

.

Metabolome data has twin advantage in systematic analysis of gene function; that metabolites are functional cellular entities that vary with physiological content and also the number of metabolites is far fewer than the number of genes or gene product. For this reason, Metabolomics requires the exploitation of knowledge of experimentally characterized gene in elucidation of function of unstudied gene. This may be achieved by comparing the change in cells metabolite profile that is produced by deleting a gene of unknown function with a library of such profiles generated by individually deleting genes of unknown function

[32]

Strategies for identifying the

function of unknown genes on the basis of metabolomic data have been proposed [34]

[33],

Silent phenotypes can be revealed by significant changes in concentration of

intercellular metabolites. FANCY approach is capable of revealing the function of gene that does not participate directly in metabolism or its control

[33]

. An advantage of

FANCY approach is that it assigns cellular rather than molecular function

[2]

Metabolite phenotypes are used as the basis of discriminating between plants of different genotypes or treated plants

[35], [36]

. Metabolic composition of a cell or tissue

influences the phenotype and it is the most appropriate choice for functional genomics and to use the fluxes between metabolites as the basis for defining a metabolic phenotype

[37]

is a matter for debate

[38]

but there is increasing evidence,

for example from investigations of transgenic plants [39] that metabolomic analysis is a useful phenotyping tool. Moreover, the value of a metabolic phenotype, however

8 defined, is greatly increased by the possibility of correlating the data with the system-wide analysis of gene expression and protein content

[40].

The major challenge faced by metabolomics is unable to comprehensively profile of all metabolites. Plants have enormous biochemical diversity, which is estimated to exceed 200,000 different metabolites

[1]

and therefore large-scale comprehensive

metabolite profiling meets its greater challenge. Metabolites are not linear polymers composed of a defined set of monomeric units but rather constitute a structurally diverse collection of molecule with widely varied chemical and physical properties. The chemical nature of metabolites ranges from ionic, inorganic species to hydrophilic carbohydrate, hydrophilic lipids and complex natural products. The chemical diversity and complexity of metabolome makes it extremely challenge to profile all of metabolome simultaneously

[3].

To find changes in metabolic network

that are functionally correlated with the physiological and developmental phenotype of the cell, tissue or organism is the bottleneck of metabolomics

[31].

If one general

extraction and analytical system is used it is likely that many metabolites will remain in plant matrix and will not be profiled

[32].

Analytical variance (the coefficient of

variance or relative standard deviation that is directly related to experimental approach), Biological variance (arises from quantitative variation in metabolite levels between plants of same species grown under identical or as near as possible identical conditions), Dynamic range (concentration boundaries of an analytical determination over which instrumental response as a function of analyte concentration is linear)

[2]

represent the major limitations of resolution of Metabolomics approach.

Metabolome analysis can be roughly grouped in to four categories

[14],

which require

different methodologies for validation of results. For the study of primary effects of any alteration, analysis can be restricted to a particular metabolite or enzyme that

9 would be directly affected by abiotic or biotic perturbation. This technique is called metabolite target analysis and is mainly used for screening purpose. Sophisticated methods for the extractions, sample preparation, sample clean ups, and internal references may be used, making it much more precise than other methods

[22], [41].

Metabolic fingerprinting classifies samples according to their biological relevance and origin and used for functional genomics, plant breeding and various diagnostic purposes. In order to study the number of compounds belonging to a selected biochemical pathway, metabolite profiling is employed. The term metabolite profiling was coined by Horning and Horning in 1970, defined as ‘quantitative and qualitative analysis of complex mixtures of physiological origin’. It has been employed for the analysis of lipids [46]

[42]

, isoprenoids

[43]

, saponins

[44]

, carotenoids

[45]

, steroids and acids

. Only crude sample fractionation and clean-up steps are carried out [22], [41].

Next step in metabolome analysis is to determine metabolic snapshots in a broad and comprehensive way, widely known as metabolomics. In this, both sample preparation and data acquisition aimed at including all class of compounds, with high recovery and experimental robustness and reproducibility.

Metabolomics has been developing as an important functional genomic tool. For continued maturation of it, following objectives need to be achieved

[14]

:

1. Improved comprehensive coverage of plant metabolome. 2. Facilitation of comparison of results between laboratory and experiments 3. Enhancement of integration of metabolomics data with other functional genomic strategies.

10

Metabolomics technologies:

Metabolites are chemical entities

[47]

and be can be analyzed by standard tools of

chemical analysis much molecular spectroscopy and MS. For better resolution, sensitivity and selectivity, these technologies can be hyphenated. Type of sample decides the use of different technologies and strategies

[47]

. It is not yet technically

possible, and will probably require a platform of complementary technologies, because no single technique is comprehensive, selective, and sensitive enough to measure them all [48].

The primary drive in Metabolomics is to improve analytical techniques to provide an ever-increasing coverage of the complete metabolome of an organism. The most common and mature technique used is GC-MS analysis. It is a hyphenated system where GC first separates volatile and thermally stable compounds and then eluting compounds are detected traditionally by EI-MS. In metabolomics GC has been described as GOLD STANDARD

[48]

: in spite of its biasedness against non-volatile,

high MW metabolites. Thermo-labile and large metabolites such as organic bis-, triphosphates, sugar, nucleotide or intact membrane lipids cannot be detected by GCMS. Non-volatile polar metabolites often need to be derivatised by converting carbonyl group to oximes with O-alkyl hydroxylamine solution, followed by formation of

TMS

ester

with

slightly

reagents

(typically

N-methyl-N-(trimethylsilyl

trifluoroacetamide) to replace exchangeable protons with TMS groups. Oxime formation is required to eliminate undesirable slow and reversible slow and reversible silylating reaction with carbonyl groups, whose products can be thermally labile. The presence of water can result in breakdown of TMS esters, although extensive sample drying and presence of exceeds silylating reagents can limit the process. Small

11 aliquots of derivatised samples are analyzed by split and split-less technique on GC columns of differing polarity, which provides both high chromatographic resolution of compounds

and

high

sensitivity.

Deconvulation

is

then

needed

to

quantify

metabolites that are unresolved by GC. It can detect co-eluting peaks with peak, apexes separated by less than 1s and also detect low-absorbance peaks co-eluting in presence of metabolites at much higher concentration.

[35],

Using gas chromatography-mass spectrometry (GC-MS)

[50]

comprehensive

metabolite profiling of potato (Solanum tuberosum) tuber detected 150 compounds, out of which 77 could be chemically identified as amino acids, organic acids or sugars, and 27 saponins in Medicago truncatula were identified compounds were identified in A. thaliana leaf extracts

[2]

. 326 distinct

[1]

, further elucidating the

chemical structure of half of these compounds. Different compound classes have been investigated using fractionation techniques and about 100 compounds were identified in rice grains via fractionation techniques by employing GC-MS

[51]

. In GC-

MS recent advances with respect to fast acquisition as well as accurate mass determinations have been achieved by applying time-of-flight technology (TOF) Improved

Deconvulation

measurement

[53]

algorithms

and

faster

spectral

acquisition

by

[52]

.

TOF

have however resulted in detection of over 1000 components from

plant leaf extracts at a throughput of over 1000 sample per month . Recent advance is MSFACTs (Metabolomics Spectral Formatting and Conversion Tools)

[54]

which

comprises of two tools, one for alignment of integrated chromatographic peak list and another for extracting information from raw chromatic ASC II formatted data files. Another recent advance is MET- IDEA (Metabolomics Ion-based Data Extraction Algorithm) which is capable of rapidly extracting semi-quantitative data from raw data files, which allows for more rapid biological insight

[55]

.Over 300 metabolites

were covered in a proof-of-concept study on functional genomics in Arabidopsis,

12 using GC-MS technology. Although, it has been shown that the number of detected peaks in typical GC-MS plant chromatogram can be multiplied by deconvolution algorithm, the de novo identification of GC-MS peaks remains cumbersome. Therefore, needs for development of the complementary technique allowing plant sample analysis without chemical modification and providing enhanced qualitative characterization of the components are clear.

LC-MS techniques were developed employing soft ionization methods like electro spray (ESI) or photo ionization (APPI) and, simultaneously, mass spectrometers became both more sophisticated and more robust for daily use. More recently, achievements in separation sciences propose much better solutions for the separation of the complex mixtures than it was attainable before. The objective of this study was to develop LC-MS methods of analysis suitable for the plant metabolomics studies, and to apply this for Arabidopsis and rice plants. LC-MS for metabolite analysis of Arabidopsis thaliana [56] and Oryza sativum

[57]

was used based

on RP and HILIC chromatography that is a complementary technique to GC-MS. Capillary LC/MS using monolithic columns have been applied to metabolome profiling of Arabidopsis

[57]

. More than 1400 compounds from Arabidopsis leaf extract using a

quadrapole time-of-flight (QTOF) mass spectrometer were identified problem, which arises with LC-MS, is ion suppression due to matrix effect can be circumvented by reducing the size of liquid droplets

[60]

capillary electrophoresis (CE) has been taken in to consideration

[58]

. The

[59]

; that

Due to this reason [61]

. It is relatively a

new technology, which has been widely used for both targeted and non-targeted analysis of metabolites

[62]

. It has been used to analyze a variety of compounds

including organic, inorganic ions, amino acids, nucleotides, nucleotides, iriods, flavonoids, vitamins, thiols, carboxylic acid metabolites, carbohydrate and peptide due to its high resolving power and small sample requirement with short analysis

13 time

[63], [64], [65], [66], [67], [68], [69]

. CE is advantageous for measuring water-soluble

metabolites for several reasons: high sensitivity (up to nanolitres), sample preparation is rapid and common to all type of compounds; every type of compound can be analyzed without derivatizations. CE separates molecules with respect to their apparent charge radius, and it is therefore best applicable to analysis of easily ionisable or ionic compounds

[22], [41]

Capillary electrophoresis-mass spectroscopy was

used to measure the intracellular levels of ionic and polar metabolites in bacterial cells were developed

[64], [ 73], [74]. Using CE-MS, 1692 metabolites were

identified in Bacillus subtilis extracts metabolites

involved

in

glycolysis,

biosynthesis were measured in rice

[71

[70]

and CE-MS and CE-DAD, 88 main

TCA,

PPP,

Photorespiration,

and

amino

].

In addition to MS based approaches, nuclear magnetic resonance (NMR) is also being used in metabolomic analysis

[72], [73]

. NMR has low sensitivity than MS and suffers

from overlapping signals, leading to smaller numbers of absolute identifications, but still it is used in metabolomics study as it is non-destructive, and spectra can be recorded from cell suspensions, tissues, and even whole plants, as well as from extracts and purified metabolites

[74], [75]

. It offers an array of detection schemes that

can be tailored to the nature of the sample and the metabolic problem that is being addressed

[75]

. Thus analyzing the metabolite composition of a tissue extract,

determining the structure of a novel metabolite, demonstrating the existence of a particular metabolic pathway in vivo, and localizing the distribution of a metabolite in a tissue are all possible by NMR. However, the nature of the NMR measurements that are required for these tasks, particularly in relation to the hardware requirements, the detection scheme, and the sensitivity of the analysis is very different

[75]

. Third,

the natural abundance of some of the biologically relevant magnetic isotopes is low and this allows these isotopes, particularly 2H,

13

C, and

15

N, to be introduced into a

14 metabolic system as labels prior to the NMR analysis

[37], [76], [77]

. Hyphenating NMR

with liquid chromatography can increase its efficiency by reducing the co-resonant peaks and improving dynamic range. It has been reported that a combination of HPLC-NMR spectroscopy with rudimentary data analysis has been employed for the evaluation of metabolic changes in transgenic food crops 2700 analytes were detected in plant extracts

[78]

[78]

. Using LC-NMR nearly

. Directly coupled HPLC-NMR and

HPLC-NMR-MS has been used that allows rapid identification of metabolites with little sample preparation

[79], [80]

.

Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS or simply FTMS) has so far been used only in a handful of published studies into metabolomics [81], [82], [83], [84]

. However, the technique has great potential as a technology to unravel

metabolomes. FT- MS is a system

for metabolome analysis in which crude plant

extract is introduced by means of direct injection without prior separation of metabolites by chromatography oligosaccharides

[86]

[85]

.It has been used for characterization of lipo-

discovery of central nervous system agents

throughput screening of combinatorial libraries

[88]

[87]

and high

The extreme mass accuracy of the

technique, coupled to ultra high resolution of mass species means that thousands of metabolites can be identified simultaneously without the need for chromatographic separations.

15

Application of Metabolomics

Plants are of pivotal importance to sustain life on Earth because they supply oxygen, food, energy, medicines, industrial materials and many valuable metabolites. Plant metabolomics is a huge analytical challenge as despite typical plant genomes containing 20,000–50,000 genes there are currently estimated 50,000 identified metabolites with this number set to rise to 200,000

[ 89]

. These plants metabolites are

synthesized and accumulated by the networks of proteins encoded in the genome of each plant.

Due to its possibility off making economical worthwhile discoveries,

plants have been the subject of many metabolomics research programs. It has been applied in plant biology by analysis of differences between plant species, genotypes [1], [2], [90]

or ecotypes

. It helps us to gain insight in the cellular regulation of plant

biosynthetic network and to link changes in metabolite levels to differences in gene expression and protein production

[2]

. One of the first applications of the approach

was to genotype Arabidopsis thaliana leaf extracts. However, even after the completion of the genome sequencing of Arabidopsis

[91]

and rice

[92]

function of these

genes and networks of gene-to-metabolite are largely unknown. To reveal the function of genes involved in metabolic processes and gene-to-metabolite analysis is shown to be an innovative way for targeted metabolite analysis is shown to be an innovative way for identification of gene function for specific product accumulation in plants

[93], [94]

. Metabolomics can provide research a new tool to identify the functions

of unknown genes in Arabidopsis and other plants. Understanding plant metabolism could lead to the engineering of the higher quality food or material producing plants. Metabolic profiling has been used in number of areas to provide biological information beyond the simple identification of plants constituents. The powerful approaches in metabolic profiling and metabolomics now enable us to study the

16 plants in broader sense and possibly to unravel yet unknown changes in the plant metabolome.

Metabolomics has the potential to bring the assembled knowledge of biochemical to bear in quest to achieve fully personalizes and preventive health care. Bases of most diseases are found in faulty enzyme activity (genetics, toxicology) improper substrate balance or faulty metabolic regulation (genetics, nutrition, lifestyle etc).All of these effects are observable through quantitative metabolic assessment i.e. Metabonomics. By measuring metabolites comprehensively, treatment can be tailored to molecular basis for disease consequences. A key advantage of NMR spectroscopy-based metabolomics is that the approach is high-throughput, allowing the rapid acquisition of large data sets. This makes it ideal as a screening tool, particularly for human populations where there can be significant environmental and dietary influences on tissue and biofluid ‘metabolomes’

[89]

. Metabolomics, in

conjunction with other "-omics" approaches, offers a new window onto the study of cancer and tumor dystrophy (ALS)

[89], [95]

, aging and caloric restriction

[97]

[99], [100]

, multiple sclerosis, schizophrenia

coronary heart disease

[101]

[96]

, Duchenne muscular

[98]

, Amyotrophic lateral sclerosis

the analysis of cerebral spinal fluid

[102]

.

17 Database for Metabolomics

Biggest challenge of metabolomics is the current lack of appropriate database and data exchange format. Large amount of data can be transmitted stored safely with adequate curation and made available in convenient and supportive ways for statistical analyses and datamining. To do this, well designed data standards are required .The DNA microarray community has developed MIAME as a definition of what should be recorded for a transcriptome experiment. Possibly the most advanced database for plant is The Arabidopsis Information Resources (TAIR). It is supported by pathways tool software developed by Peterskap’s group at SRI. The aim of AraCyc is to present Arabidopsis metabolism as completely possible with a user friendly webbased interface. It is a tool to visualize biochemical pathways of Arabidopsis. The software allows querying and graphical representation of biochemical pathway and expression data.

ArMET: ArMet

[103]

is proposed framework for description of plant metabolomics

experiments and their result. It encompasses the entire experiment time line and organizes it in into nines subunits termed components. In this data are specified by a way of a core set of data items for each component. These core data provide a basis for cross laboratory data exchange and datamining. This components based approach for Armet provide a basis for definition of extension to core data to support the

requirement

of

range

of

methodologies

employs

by

different

projects,

experiments and laboratories. ArMet compliant databases and data handling systems are in use on two major projects involving a complete set of subcomponents to support experiment with Arabidopsis thaliana and Solanum tuberosum. ArMet and MIAMET can be viewed as complementary proposals, where MIAMET provides a checklist of information that should be described in metabolomics publications. ArMet

18 provides a formal data definition to support automatic data set comparison and mining and development of system for data storage and exchange. It works in synergy with laboratory information management system (LINS) and other existing standards. In appropriate

circumstances,

ArMet

could provide a

design for

customization of LINS to support metabolomics process, which therefore becomes an implementation vehicle for ArMet.

AraCyc

[104]

is a database contains biochemical pathways of Arabidopsis, developed at

The Arabidopsis Information Resources. It presently features more than 170 pathways hat include information on compounds, intermediates, cofactors, reactions, genes, proteins, and protein subcelluar locations.

DOME

[105]

is composed

of

various

subsection: one

counting

details

about

experimental design (metadata), another with raw data, another one with processed data ( i.e. analysis result) and finally an ontology describing the known molecular biology of species of interest (thus is called as B-Net).Results are processed using multiple statistical tool and visualize using a Brower for OME’s ( BROME).

MetaCyc

[106]

is a database of non-reluctant, experimentally elucidated metabolic

pathways. MetaCyc comprised of near about 700 pathways from more than 600 different organisms. It stores pathways involved in both primary and secondary metabolism as well as associated compounds enzymes and genes. It stores predominantly qualitative information rather than quantitative data although we have recently began capturing qualitative data such as enzyme kinetics data. Goal of MetaCyc is to catalog universe of metabolism by storing a representative sample of each experimentally elucidated pathways.

19 MetNet

[107]

is a database contains information on networks of regulatory and

metabolic interaction in Arabidopsis. This information is based on input from biologists in this area of expertise. Types in interaction include transcription, translation, protein modification assembly, allosteric regulation, translocation from one subcellular compartment to another. Network information from MetNet database can be converted to an XML file. From this XML file, it can be transferred to Gene Gobi which uses the network in conjunction with statistical analysis of expression data, to FC Modeler, which find cycles and pathways in the networks, visualizes and models in combination with expression data and to MetNet, where networks can be visualized in 3D. It features graph visualization and modeling with interactive displays. FC modeler is a unique multivariate display and analysis tool with functionality to do statistical analyses (Gene Gobi) and versatile text mining (Path binder A). This set of applications seeks how they interact in context of metabolic networks. The MetNet software enables analyses of disparate data types (microarray, metabolomics, and proteomics) in context of known information about metabolic network.

Map Man

[108]

is a user driven tool that displays large datasets on to diagrams of

metabolic pathways or other processes. It is composed of multiple modules for hierarchical grouping of transcript and metabolite data can be visualized using a separate user-guided module. Editing existing module and creation of new categories or module is possible and provide flexibility.

BioCyc

[109]

is a collection of pathways/ genome database provide electron references

sources on pathways and genomes of different organism. Databases within BioCyc collection are recognized into tiers according to the amount of manual review.

20 BRENDA

[110]

represents the most comprehensive information system on the enzymes

and metabolic information. The database contains data from atleast 83,000 different enzymes from 9800 different organisms, classified in approximately 4200 EC number. It includes biochemical and molecular information on classification and nomenclature, reaction and specificity, functional parameter, occurrence, enzyme structure, application, engineering, stability, disease, isolation, preparation, links and literature references.

21

Future directions

Metabolomics is an emerging technology that has lot of scope and needs lots of efforts to improve the sensitivity of metabolomic experiments. Targeted approaches are need that can focus on the specific classes of small molecules so that remarkable sensitivity can be achieved. Efforts should be made to develop of fractionation and enrichment methods for specific classes of aqueous metabolites should prove particularly valuable. As compared to genomics and proteomics, major problem faced by metabolomics is the determination of metabolite structures as they constitute a family of biomolecules of near limitless structural diversity unlike genes and proteins. Increased sensitivity and high resolution tools combined with the exhaustive searchable databases that contain all biochemical information of all known metabolites should facilitate the future characterization of metabolites. Just increase in the number of instruments like NMR, MS, IR or any other technique will not solve this problem, instead new technologies are needed and real jump in innovation or even more important- better software technologies and curated and unified open access database are needed.

Metabolomics is emerging as a powerful high throughput platform complementing other genomics platform like transcriptomics and proteomics. Combination of these high throughput data generation techniques with mathematical modeling of biochemical and signaling network is essential; for the systems biology and will help us to deeper understand how biological systems work as a whole.

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