Metabolomics- ijp

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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

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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 [5] and may be the best and most direct measure of cellular

morphology [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]

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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.

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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]

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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], and a volume on metabolic profiling

was published in 2003 [21]. Historically, metabolomics and metabonomics are

compared with GC/MS and NMR respectively [22]. L stands for plants and Nstands 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

eitherterm appropriate?The possible answer might be whom you target as both the

terms may be appropriate inmost cases and the distinctions are more a matter of

historicalusage 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 [23], [24] and was routinely used in

publications [25], 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] to be the "comprehensiveand quantitative analysis of all metabolites. ..."

Nicholson [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

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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 systems 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 RNA do not always correspondence to alteration in

biochemical phenotype [29] 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

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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 [33],

[34] 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 fordebate [38] but there is increasingevidence,

for example from investigations of transgenic plants [39] that metabolomic analysis isa

useful phenotyping tool. Moreover, the value of a metabolic phenotype, however

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defined, is greatly increased by the possibility of correlating the data with the

system-wide analysis of geneexpression 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

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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 [42], isoprenoids [43], saponins [44], carotenoids [45], steroids and acids

[46]. 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.

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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-, tri-

phosphates, sugar, nucleotide or intact membrane lipids cannot be detected by GC-

MS. 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

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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.

Using gas chromatography-mass spectrometry (GC-MS) [35], [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 [2]. 326 distinct

compounds were identified in A. thaliana leaf extracts [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) [52].

Improved Deconvulation algorithms and faster spectral acquisition by TOF

measurement [53] 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,

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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 ofArabidopsis thaliana[56]andOryza 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 [58]. The

problem, which arises with LC-MS, is ion suppression due to matrix effect [59]; that

can be circumvented by reducing the size of liquid droplets [60] Due to this reason

capillary electrophoresis (CE) has been taken in to consideration [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

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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 [70] and CE-MS and CE-DAD, 88 main

metabolites involved in glycolysis, TCA, PPP, Photorespiration, and amino

biosynthesis were measured in rice [71].

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 detectionschemes that

can be tailored to the nature of the sample andthe 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, andlocalizing the distribution of a metabolite in

a tissue areall 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 thesensitivity of the analysis is very different [75]. Third,

the natural abundance of some of the biologicallyrelevant magnetic isotopes is low

and this allows these isotopes,particularly 2H, 13C, and 15N, to be introduced into a

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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 [78]. Using LC-NMR nearly

2700 analytes were detected in plant extracts [78]. 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 [85].It has been used for characterization of lipo-

oligosaccharides [86] discovery of central nervous system agents [87] and high

throughput screening of combinatorial libraries [88] 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.

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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 20,00050,000 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

or ecotypes [1], [2], [90]. 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 ofArabidopsis [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

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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 [89], [95], aging and caloric restriction [96] , Duchenne muscular

dystrophy [97], multiple sclerosis, schizophrenia [98], Amyotrophic lateral sclerosis

(ALS) [99], [100] coronary heart disease [101] the analysis of cerebral spinal fluid [102].

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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 Peterskaps group at SRI. The aim of AraCyc

is to present Arabidopsis metabolism as completely possible with a user friendly web-

based 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

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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 OMEs ( 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.

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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.

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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.

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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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