THE ‘-OMICS’ TECHNOLOGIES AND CROP IMPROVEMENT

1THE ‘-OMICS’ TECHNOLOGIES AND CROP IMPROVEMENT

R.C. Setia and Neelam SetiaDepartment of Botany

Punjab Agricultural University, Ludhiana-141004, Indiaemail: [email protected]

ABSTRACT

In view of ever-growing population and decreasing natural resources, there is need to enhancefood production that can possibly be achieved by improving upon qualitative and quantitativetraits of crop plants by adopting new analytical tools and technologies. During the last decadeor so rapid progress has been made in plant biology, especially with the introduction of highthroughput ‘omics’ technologies. The three main omics technologies – genomics, proteomicsand metabolomics involve quantification and characterization of genome, proteome andmetabolome, respectively, with extremely rapid, miniaturized and automated methods. Thesetechnologies are aimed at unraveling the overall expression of genes, proteins and metabolitesin a functionally relevant context, and provide insights into the molecular basis of variousfundamental processes involved in growth and development of plants and their environment.Advances in plant genomics research have opened up new perspectives and opportunitiesfor improving crop plants and their productivity. The development of sequencing techniquesand availability of genomes’ information on model organisms, Arabidopsis thaliana, rice,etc., have greatly influenced the disciplines of plant and crop sciences. Gene discovery andgene expression profiling technologies are creating an unprecedented opportunity for plantbreeders who can now apply molecular markers to assess and enhance diversity in theirgermplasm collections, to introgress valuable traits from new sources and identify genesthat control key traits. The genomics technologies have been found useful in decipheringthe multigenicity of biotic and abiotic plant stress responses through genome sequences,stress specific cell and tissue transcript collections, transcript, protein and metabolite profilesand their dynamic changes, protein interactions and mutant screens. As a consequence ofthe use of high throughput omics methods a vast amount of raw data is generated which isstored, processed and analyzed with the help of bioinformatics tools. This review providesan introduction to the three core omics technologies, relevant methodologies and applicationswith emphasis on crop improvement strategies.

Keywords: Omics, genomics, transcriptomics, proteomics, metabolomics, bioinformatics, cropimprovement

Crop Improvement: Strategies and Applications Editors: R.C. Setia, Harsh Nayyar and Neelam Setia

© 2008 I.K. International Publishing House Pvt. Ltd., New Delhi, pp 1-18

2 Crop Improvement: Strategies and Applications

INTRODUCTIONThe twentieth century has seen tremendous increase in food production with the introduction ofhigh yielding crop varieties, especially since the first “green revolution” that helped in keeping pacewith the population growth. The increase in productivity has been powered by changes in the geneticpotential of crops and relevant management practices. The conventional plant breeding strategiesbased on phenotypic selection and principles of statistical qualitative genetics have led to continuousincrease in seed yield (mainly due to increased harvest index) and improved yield stability(Wollenweber et al., 2005, Hammer and Jordan, 2007). Though long term selection experimentshave indicated sufficient potential for genetic improvement of qualitative traits over many generations(Dudley and Lambert, 1992), a decline in crop yield increase has become evident (Conway andToenniessen, 1999; Mann, 1999) that may result in a gap between demand and supply due to highpopulation growth rate (Wollenweber et al., 2005).

The global population projections are grim reminder of the imperative need to increase foodproduction and double it by the year 2040 from the present level. The future food security for ever-growing population will depend on acceleration of yield gains per unit land and per unit of input forthe major food crops at rates well above the historical trend of past 50 years. However, naturalresources are decreasing rapidly for agriculture as a result of economic development, which isdiverting these resources for non-agricultural uses. The leading resource and environmental constraintsfaced by the world’s farmers today include soil loss and degradation; water logging, drought andsalinity; the co-evolution of pests, pathogens and host; and impact of climate change (Tilman et al.,2001). In the arid and semiarid areas of world, water scarcity is becoming an increasingly seriousconstraint on growth of agriculture production (Raskin et al., 1998;Gleick, 2000). International WaterManagement Institute has projected that by the year 2025, most regions of the world will experienceeither absolute or severe water scarcity (Ruttan, 2005). Thus, drought has become the single mostlimiting constraint to crop production worldwide, and the need for a ‘blue revolution’ in whichwater use efficiency (WUE) of crop plants is improved, has been highlighted (Zhang and Yang,2004).

The growing worldwide demand for enhancing yields of major crops is placing pressure onbreeding programmes to provide elite cultivars that can adopt to range of environments withoutcompromising agronomic performance, grain quality or disease resistance. Plant scientists have beenmaking advances in understanding the biochemical and molecular processes that underlie importantmetabolic, physiological and developmental traits that affect the ability of plants to cope withunfavourable environmental conditions (abiotic and biotic stresses) for several decades. However, itwas often difficult to exploit information for plant breeding, because level of understanding was notdeep enough, and necessary techniques were not available. For closing the ‘yield gap’ and increasingyield for securing food security and food quality, identification of bottlenecks of plant developmentunder prevailing environmental conditions and their elimination is of pivotal interest. Moleculartransformation is commonly offered as a hope to overcome the apparent stagnation in crop yieldpotential.

Many crop traits are quantitative, complex and controlled by multiple interacting genes. Advancesin molecular biology are now providing the tools to study the genetical make-up of plants, whichallows us to unravel the inheritance of all traits whether they are controlled by single (major) genes,or many genes (of smaller effect) acting together, known as the quantitative trait loci (QTL). Themolecular marker technologies available since the 1980’s, enabled the variation in traits to be dissected

The ‘-OMICS’ Technologies and Crop Improvement 3

into the effects of QTL. With the progress of QTL mapping, new breeding approaches such asmarker-assisted selection and breeding by design (Miflin, 2000; Peleman and vander Voort, 2003)have emerged. But existing QTL-analysis methods do not have precision required to handle plant’sintrinsic complexities such as polygenic control, epistasis and genotype-environment interactions.Support from other disciplines like functional genomics, system biology and crop physiology canjointly improve the genetic analysis and breeding efficiency. Genetic improvements in crop plantsbeyond current capabilities are needed to meet the growing world demand not only for food, butalso for greater diversity of food, high quality of food and safer food produced in less land, whileat the same time conserving the soil, water and genetic resources.

During recent past, plant biologists witnessed the most explosive growth of information in thehistory of science. Not only is this avalanche of information providing new insights into how plantworks, but it is also creating entirely new scientific disciplines. The newly developed various ‘omics’technologies have brought revolution in plant science research. Consequently, the availability ofcomplete genome sequences for reference plant species Arabidopsis thaliana and more recently forrice, poplar and other organisms hold great potential for research aimed at crop improvement andcrop protection (Borovitz and Chory, 2004). Scientists, through a variety of functional genomicapproaches, are characterizing the genes that control key processes. Thus, in this era of new emergingtechnologies, plant scientists/breeders worldwide are recognizing the power that ‘-omics’ can bringto their efforts for crop improvement.

The emergence of the novel ‘omics’ technologies, such as genomics, proteomics andmetabolomics, is now permitting researchers to identify the genetic underpinnings of cropimprovement, namely the genes, that contribute to the improved productivity and quality of moderncrop varieties. These omics technologies enable a direct and unbiased monitoring of the factorsaffecting crop growth and yield formation, and provide the data that can be directly utilized toinvestigate the complex interplay between the plant, its metabolism, and also the stress representedby the environment or the biological threats by insects, fungi or other pathogens. These technologiesalso help in thorough investigation of the biology behind agronomic traits at the physiological,biochemical and molecular levels, and permit the elucidation of molecular circuitry of the cropplants, ultimately paving way for improved crop production (www.genedata.com). Indeed, the various-‘omics’ have become a staple of Plant Physiology (Raikhel, 2005).

The term ‘omics’ refers to the comprehensive analysis of the biological system. Informally, theneologism omics has come to refer to a comprehensive study involving the acquisition of vast datasets. An omics approach can be considered to be large-scale data rich biology consisting of a heavydata mining or bioinformatics component. The modern concept of omics was initiated by HumanGenome Project, which was launched in 1986 (Wheelock and Miyagawa, 2006). Omics has beendriven more by emerging experimental technologies than by the novel hypothesis (Yin and Struik,2007).

Genomics, proteomics and metabolomics are the three core omics technologies, which respectivelydeal with the analysis of genome, proteome and metabolome of cells and tissues of an organism.These technologies involve large data sets and high throughout methods (fast methods for gatheringdata). In the 1980’s, genomics arose as term describing the mapping and sequencing of genomes aswell as the analysis of the information content present in genetic sequences. Subsequently, when thecomplete genome sequences were considered as a basis for systematic functional analysis, thegenomics was divided into two disciplines—structural genomics and functional genomics. While the


structural genomics corresponds to the initial phase of genome analysis resulting ultimately in therevelation of the complete DNA sequence of an organism, the functional genomics makes the useof genome sequence to assess, on large scale, the functions of genes (Leister, 2005). Proteomics andmetabolomics are the main tools of functional genomics. Transcriptomics, which deals with thestudy of transcriptome (a set of gene transcripts or messenger RNAs in a cell), is also considered tobe a component of functional genomics. All these are generally referred to as post-genomictechnologies that provide primary omics methods of characterizing sets of molecules produced bygenomes. However, do these new fields fit under the larger umbrella of genomics, or are theydistinct? It is a matter of choice of the person who you ask, and in what context (Campbell andHeyer, 2006). Most of the people encompass all the three disciplines and technologies under‘genomics’ and use this term in a broader sense. For example, `genomics’ includes everything fromgene sequencing, annotation of function genes, and genome architecture to studying patterns ofgene expression at transcriptome level (transcriptomics), proteome level (proteomics) and metaboliteflux (metabolomics). Due to the magnitude and complexity of omic data, these disciplines areunderpinned by information technology support through bioinformatics. In the following text, weprovide a comprehensive introduction to the basic aspects of genomics, proteomics and metabolomics,and an overview of the associated techniques as well as (potential) applications of these technologiesin crop improvement strategies.

GENOMICSThe study of the way genes and genetic information are organized within the genome, the methodsof collecting and analyzing this information and how this organization determines their biologicalfunctionality is referred to as genomics (Campos-de Quiroz, 2002). The genetic material in plantsand animals is deoxyribosenucleic acid (DNA), which is present in the nucleus of every cell. DNAis the double stranded molecule made up of four different basic building blocks called nucleotidebases—adenine (A), cytosine (C), guanine (G) and thymine (T). The functional unit of DNA iscalled gene. In a gene, the sequence of ACGT on a strand of DNA specifies the sequence of aminoacids that make up a protein. In order for a protein to be synthesized, the DNA in a gene is firsttranscribed to messenger RNA (mRNA), also called the gene transcript, which is similar to DNAbut is single stranded. The mRNA is then translated into a sequence of amino acids through ribosomesfound in the cytoplasm of the cell. The proteins and products are fundamentally responsible forall cellular behaviour. The protein function is altered by changes in the sequence of amino acids(http://linux.ittoolbox.com). Elucidating the pattern of arrangement of nucleotide bases in the entiregenome is called genomic sequencing.

Plant genomes are best described in terms of genome size, gene content, extent of repetivesequences and polyploidy/duplication events (Campos-de Quiroz, 2002). Genomics investigates howvariation in genes affects protein structure and function throughout the life of a cell. When a cellsenses changes in its environment, it responds by accessing different components of genome.Normally, this access comprises the expression of genes encoding instructions for the production ofnew cellular proteins via production of mRNA molecules or the gene transcripts by a process calledtranscription. The set of all the transcripts produced in one or a population of cells is calledtranscriptome. Transcriptomics examines the systematic quantification of the levels of all or a largeportion of the transcripts expressed within a given cell population under particular environmentalcondition, often using high-throughput techniques (http.//www.en.wikipedia.org). Since sequences


of mRNAs (transcripts) mirror the DNA sequence of the genes from which they are transcribed, itis possible to determine when and where a gene is turned on or off in various types of cells andtissues by analyzing the transcriptome. It is often possible to count the number of transcripts todetermine the amount of gene activity, also called expression level, in a certain cell or tissue type.The higher the number of transcripts, generally the more important that transcript is to the functioningof cell or tissue (http://www.genome.gov/transcriptome). The identification and characterization ofdifferent candidate genes influencing economically important traits, and those involved in the responseto a particular stress enhance the possibility of promoting crop improvement through direct geneticmodifications (Dunwell et al., 2001).

Genomics TechniquesGenome is the total genetic content of the cells. Genome sequencing refers to the techniques usedto determine the order of the four nucleotides (adenine (A), guanine (G), cytosine (C) and thymine(T) that make up the genetic code in a DNA sample. While there are several methods for sequencingDNA, the most popular and commonly used DNA sequencing methods are the dideoxy or chaintermination method, which was developed by Sanger et al. in 1977, and the shotgun sequencingmethod.

Dideoxy or Chain Termination Method: Also known as Sanger method, it has undergoneseveral modifications (since its invention) including automation, and now-a-days sequencers areused in large scale DNA sequencing. This method employs DNA synthesis in the presence ofdideoxynucleotides in addition to the normal nucleotides found in DNA. Dideoxynucleotides (ddNTPs)are essentially the same as nucleotides (NTPs), except that they contain a hydrogen group on 3’carbon instead of hydroxyl group (OH).These modified nucleotides, when incorporated into thenewly synthesized DNA, prevent the addition of further nucleotides due to the absence of 3′-OHgroup. This occurs because a phosphodiester bond cannot form between the dideoxynucleotide andthe next incoming nucleotide. Thus, the DNA chain is terminated (http://statwww.berkeley.edu).The initial stage of sequencing is denaturation of double stranded DNA molecules into single strandsusing heat. The single stranded DNA molecule serves as a template for synthesizing a series ofcomplementary strands that terminate at specific nucleotides. Next, a short primer (a short strand ofDNA) is annealed to the template, and the primer bound DNA is distributed into four reactiontubes. Either the primer or one of the nucleotides should be radioactively labelled, so that the finalproduct can be detected on the gel. Then this primer is elongated by adding the DNA polymeraseand the four deoxyribonucleotide phosphates (dATP, dCATP, dGTP and dTTP). In addition, eachtube contains a small amount of one of the four base-specific analogs, the dideoxylnucleotidephosphate (ddNTP). As DNA synthesis proceeds, the DNA polymerase randomly incorporates theddNTP (instead of deoxynucleotide) into the growing DNA strand, thus, terminating DNA synthesisdue to the reasons mentioned above. As the reaction proceeds, the tubes accumulate a wholesuccession of DNA molecules that differ in length by one nucleotide at their 3′-ends. Once completed,the DNA fragments from each of the four reaction tubes are run in separate lanes on a polyacrylamidegel in order to separate the different sized bands from one another. After the contents have been runacross the gel, the gel is then exposed to either UV light or X-ray, depending on the method usedfor labelling the DNA. The nucleotide sequence of DNA can be read directly from the bottom totop, corresponding to the 5′→ 3′ sequence of DNA complementary to the template (Klug andCummings, 2003; Ban, 2006).


Automated sequencing has been developed for large scale DNA sequencing in a shorter periodof time. The new techniques are, however, based on the same principles of Sanger’s method. In theautomated procedure, the four dideoxynucleotide analogs (ddNTP – ddATP, ddCTP, ddGTP andddTTP) are labeled with different fluorescent dyes, so that at the end of the reaction, the chainsterminating in A are labelled with one colour, those ending with C with another colour, and soforth. The reaction is performed in a single tube and loaded into one lane on the gel, in contrast tofour lanes in the original Sanger’s method. The fluorescent detector or the laser in the sequencingmachine reads the gel to determine the identity of each band according to the wavelengths at whichis it fluoresces. The results are depicted in the form of chromatogram, which analyzed usingappropriate software for nucleotide sequencing (Russel, 2002). Now-a-days, the detection and readingof sequence is also completed by an automatic capillary array DNA sequencer. Each sample isloaded into capillary through which the fragments are separated according to their size. A laser isused to detect the fluorescence specific for each nucleotide, as the DNA migrates to the bottom ofthe capillary. Sequencing software is then applied to analyze the output from the DNA sequencerand provide a chromatogram of the DNA sequence—the end product (Ban, 2006).

Shotgun Sequencing Method: It is another widely used method that is particularly suited tohigh- throughput assembly-line style methodology for determining the entire genome of organismsto be sequenced relatively rapidly. The process of shotgun sequencing starts by shredding(‘shotgunning’) the DNA molecule into millions of random fragments. The fragments are then insertedinto cloning reactors in order to amplify the DNA to levels needed by the sequencing reaction. Thecommonly used cloning vectors are plasmids (circular pieces of DNA), which are then grown inEscherichia coli bacterium. The plasmid DNA sequence is engineered to enable sequencing reactionto proceed into the inserted fragments. The ends of each of the original fragments can thus be readby automated sequencing machines. The sequencing of these fragments is then ordered based onoverlaps in the genetic code followed by piecing together the original genome using specializedsoftware programs called assemblers(http://en.wikipedia.org/wiki/DNA_sequencing).

Polymerase Chain Reaction: The invention of polymerase chain reaction (PCR) by Mullis etal. (1986) made a significant breakthrough in the field of molecular biology. By using this technique,it is possible to generate many copies of a specific DNA sequence through a series of reactions ina test tube (in vitro), and rapidly amplify target DNA sequences initially present in infinitesimallysmall quantities in a population of other DNA molecules. This is essential in order to have enoughstarting DNA template for further experiments, such as sequencing. In PCR method, the targetDNA is denatured into single strands. Each strand is then annealed to a short, complementaryprimer. The primers are synthetic oligonucleotides that are complementary to sequences flankingthe region to be amplified. DNA polymerase and nucleotides extend the primers in 3′ directionusing the single stranded DNA as template, resulting in double stranded DNA molecules with theprimers incorporated into the newly synthesized strand. In a second PCR cycle, the products of thefirst cycle are denatured into single strands, primers are annealed, and DNA polymerase thensynthesizes new strands. Repeated cycles can amplify the original DNA sequence by more than amillion fold (cf. Klug and Cummings, 2003).

Genome Annotation: Once the genome sequence is available, it should be annotated, whichmeans finding the potential genes and assigning functions to them. Most of this is done in silico,i.e., with the aid of computer programs. Function assignment relies extensively on sequence similarity.This means that a function is assigned to a gene in a newly sequenced genome based on its similarity


to the already available gene sequences in sequence databases, such as GenBank. The most widelyused similarity detection tool is the BLAST programme (Alstchul et al., 1997; Setubal and da Silva,2004). The classification of genes is done according to their assigned function. Generally, eachgenome project develops its own classification scheme, but most are based on the one originallydeveloped for Escherichia coli bacterium (Riley, 1993).

There are various other methods available for genomic studies, and a number of them havebeen dealt with in some of the accompanying review articles in this volume.

Several genomes have been sequenced to a high quality in plants, including Arabidopsis thalianaand rice; draft genomes are available for poplar, lotus, and sequencing efforts are in progress forothers including tomato, maize, Medicago truncatula, sorghum, etc. (Rhee et al., 2006). DNAsequencing provides information about the number, nature, and organization of genes in a genomeand elucidates the mutational events that alter both genes and gene products, confirming that genesand proteins are colinear molecules.

TranscriptomicsAs mentioned above, transcriptomics deals with the analysis of gene expression patterns across awide array of cellular responses, phenotypes and conditions. The identification of candidate genesinfluencing any important trait can be approached through an analysis of gene transcripts or mRNAs.It involves detecting the expression level of one or more specific RNAs out of thousands of otherRNAs, and producing a snapshot image of genes being translated at any given moment. There areseveral systems available to analyze parallel expression of many genes, such as DNA microarraytechnology (Schena et al., 1995), or tag-based technologies, such as serial analysis of gene expression(SAGE) (Velculescu et al., 1995), which allow for exact measurement of any transcript, known orunknown (http://en.wikipedia.org/wiki/gene expression). Several other methods are also availablefor detection and quantification of mRNAs (see Meyers et al., 2004).

DNA microarray: The DNA microarray (also known as DNA chip or microarray) is a highthroughput, most widely used technique for gene expression studies and represents a key element intoday’s functional genomics research (Aharoni and Vorst, 2001). In brief, this technique involvesfirstly the extraction of specific cell(s) from two or more biological samples by using laser capturemicrodissection (LCM) technique, and then extracting the total RNA from the cells captured. Thisis followed by multiplication of the copies of each RNA expressed. RNAs thus produced (referredto as targets) can then be radioactively labelled or labelled with fluorescent dyes and hybridizedonto DNA chip under computer control. These chips (nylon membranes with glass or silicon surfacescontaining thousands of DNA probes) are designed to visualize which genes are being transcribedto RNA within the cell when the sample was taken. Scanners are used to read the signals andfluorescence measurements are made for each dye at each spot on chip. Specialized software anddata management tools are then used for data extraction and analysis (http://www.parisdevelopment.com). The microarray technology has been utilized to examine a range ofplant processes including circadian clock, plant defence, environmental stress responses, fruit ripening,phytochrome A signalling, seed development and nitrate assimilation (Aharoni and Vorst 2001).

Serial Analysis of Gene Expression (SAGE): The major difference between DNA microarrayhybridization and serial analysis of gene expression (SAGE) techniques is that the latter does notrequire prior knowledge of the sequences to be analyzed, as SAGE is a sequencing-based geneexpression profiling technique (Velculescu et al.,2000). For organisms with poorly characterized


genomic and expressed sequences, SAGE can be used to obtain complete transcriptional profiles ofexpressed genes, albeit unknown genes. A recent adaptation of SAGE, called Long SAGE, allowsthe derived transcriptome to be used in annotating expressed genes in the genome (Saha et al.,2002).In this sense, SAGE is a truly global and unbiased gene expression technique. The technique ofSAGE uses multiple enzymatic, PCR amplification, purification, and cloning steps. The SAGEprotocol starts with the purification of mRNA bound to solid phase oligo(dT) magnetic beads. ThecDNA is synthesized directly on the oligo(dT) bead and then digested with the anchoring enzymeNlaIII (AE) to reveal the 3'-most restriction site anchored to the oligo(dT) bead. Most SAGEexperiments have used the 4-bp recognition site anchoring enzyme NlaIII, predicted to occur every256 bp and thus present on most mRNA species. However, creating a second SAGE library with adifferent anchoring enzyme may be useful for detecting transcripts without a NlaIII site and also forreconfirming transcript identity in those with both anchoring restriction sites. This may significantlylessen the work associated with data analysis, but the marginal utility of such an approach remainsto be demonstrated. Next, the sample is equally divided into two separate tubes and ligated to twodifferent linkers, A or B. Both linkers contain the recognition site for BsmFI, a type IIS restrictionenzyme that cuts 10-bp 3' from the anchoring enzyme recognition site. BsmFI generates a uniqueoligonucleotide known as the SAGE tag, hence called the tagging enzyme (TE). The SAGE tagsreleased from the oligo(dT) beads are then separated, blunted, and ligated to each other to give riseto ditags. The ditags are PCR amplified, released from the linkers, gel purified, serially ligated,cloned, and sequenced using an automated sequencer (Patino et al., 2002).

ApplicationsThe plant genomics provides a platform for analyzing and understanding the genetic and molecularbasis of all biological processes in plants that are relevant to the species. The understanding isfundamental to allow efficient exploitation of plants as biological resources in the development ofnew cultivars with improved quality and reduced economic and environmental costs (Vassilevet al., 2005). The genomics allows the scientists to analyze thousands of genes in parallel and tounderstand the complex crop traits, such as yield and yield stability (Struik et al., 2007). It alsoreduces the gap between phenotype and genotype and helps to comprehend not only the isolatedeffect of a gene, but also the way its genetic content and genetic networks it interacts with canmodulate its activity (Campos-de Quiroz, 2002). Genomics has provided objectivity in plant breedingas never before. It helps in assaying genetic make up of the individual plants rapidly, so as to selectdesirable genotypes in breeding populations, and to design the superior genotypes for ‘breeding bydesign’ approach. With genomic approaches, the marker-assisted breeding or marker-assisted selectionwill gradually evolve into ‘genomics-assisted breeding’ for crop improvement (Varshney et al.,2005). Genomics research has also successfully unraveled various metabolic pathways and providedmolecular markers for agronomic traits. The identification of genes that control economically importanttraits provide the basis for new progress in genetic improvement of crop species, complementingtraditional methods based on assisted crosses (Dunwell et al., 2001). The knowledge of genomics isvalid for development of new plant diagnostic tools. It will help to find new solutions for improvedgermplasm in crop plants and for chemical protection of crops. Thus, a genome programme can beenvisioned as a highly important tool for crop improvement.


PROTEOMICSWhile the genome contains the coded information that allows an organism to live and reproduce,the essential functions of living cells are accomplished by gene products, mainly proteins. Althoughribonucleic acids are also essential, the proteins provide scaffold, regulatory and catalytic functionsthat drive metabolism (Whitelegge, 2002). Proteins are fairly large molecules made up of strings ofamino acids linked like a chain. While there are only 20 amino acids, they combine in differentways to form tens of thousands of proteins, each with a unique, genetically defined sequence thatdetermines the proteins specific shape and function. A full complement of proteins expressed bygenome of a cell, a tissue or an organism at a specific time point constitutes its proteome (Renautet al., 2006). Proteomics is a leading technology that covers the systematic analysis of proteinsexpressed by a genome, from identification of their primary amino acid sequence to the determinationof their relative amounts, their state of post-translocation modification and association with otherproteins or molecules of different types to characterization of protein activities and structures (Barbier-Brygoo and Joyard, 2004; Rhee et al., 2006). While the genome of an organism is a relatively fixedentity, the proteome is dynamic or changes constantly (similar to transcriptom). Therefore, there arepotentially many proteomes present in an organism, and the picture of one that is presented at anysingle point in time will depend on many factors including developmental stage of the plant, responseto abiotic and biotic stress, the organ or tissue being examined or even the cellular compartmentsbeing studied (Renaut et al., 2006).

In many ways proteomics runs parallel to genomics. Genomics starts with the gene and makesinferences about its products (proteins), whereas proteomics begins with the functionally modifiedproteins and works back to the gene responsible for its production. The path from gene to proteinis better described as gene to mRNA to polypeptide to protein, with the last step encompassing amyriad of alternative steps involving biochemistry and cell biology (Roberts, 2002). In fact, proteomicsis a tool for functional genomics in plants. Proteomics is also becoming a powerful tool to analyzebiochemical pathways and the complex responses of plants to environmental stimuli. This has enabledresearchers to associate changes in protein expression profiles in different physiological momentsand to define the functions of expressed proteins. Further, proteins serve as important componentsof major signaling and biochemical pathways. Therefore, studies at protein level are essential toreveal molecular mechanisms underlying plant growth, development, and interactions withenvironment (Chen and Harmon, 2006). The compatibility of proteomics is especially useful forcrops, as it may give clues not only about nutritional value, but also about yield and how thesefactors are affected by adverse conditions (Solekdeh and Komatsu, 2007).

Proteomics TechniquesThere are numerous emerging techniques available for proteome analysis, each attempting to provideimproved separation, resolution and automation depending on different experimental purposes andthe chemical and physical properties of the target proteins. In general, two analytical techniques areemployed in current proteomic research: two-dimensional polyacrylamide gel electrophoresis (2D-PAGE or 2-DE) for the separation and visualization of crude extracts (Klose, 1975), and massspectrometry (MS) for the identification and characterization of the separated proteins (Fenn etal.,1989; Karas et al., 1989). 2-DE is based on isoelectric focussing (IEF), by which the proteinsare separated according to their pI in pH gradient polyacrylamide gels (first dimension) and SDS(sodium dodecyl sulphate)-PAGE, by which the proteins are separated according to their molecular


weights (second dimension) (Klose, 1975,1999; Klose and Kobalz, 1995). Visualization of separatedprotein spots is achieved by use of visible staining techniques. Proteins within the spots of interestare then identified by firstly digesting to peptides, typically with trypsin, and subsequently analysedby MS. The utility of this method is hampered for being low throughput, costly and a labour andtime consuming, and can also be tedious due to gel-to-gel variations that can complicate the imageanalysis process. Difference gel electrophoresis (2D-DIGE) circumvents many of these issues andallows for more accurate determination of both qualitative and quantitative variations of samples(Ünlü et al., 1997). DIGE is a prelabelling technique using separate spectrally resolvable fluorescentdyes and allows multiple samples to be co-separated and visualized on one 2-DGel (Tongs et al.,2001). Currently, the most commonly used methods for the study of complex mixtures are based onmass spectrometry (Aebersold and Mann, 2003) according to the following outline: Proteins inmore or less complex samples derived from 2-D gels are proteolytically cleaved into smaller peptides.The resulting peptides are then analyzed using MS based automated matrix assisted laser desorption/ionization time of flight mass spectrometric (MALDI TDF-MS) peptide mapping followed byextensive data based searches (Henzel et al.,1993). An alternative method to analyze proteins directlyby MS, without gel separation, has been developed and is referred to as multidimensional proteinidentification technology (MudPIT) or liquid chromatography (LC)-MS/MS that couples capillary,high-performance liquid chromatography (HPLC) to MS/MS and allows automated analyses of peptidemixtures that are generated from complex protein samples (Appella et al., 1995; Washburn et al.,2001). Further, quantitative proteomics becomes feasible using an innovative reagent, termed isotope-coded affinity tag (ICAT), in the LC-MS/MS system (Han et al., 2001). The development of theyeast two-hybrid (Y2H) system allows identification of protein-protein interactions (Fields and Song,1989). This genetic procedure allows the rapid identification of in vivo protein-protein interactionsdand the simple isolation of corresponding nucleic acid sequences encoding the interacting partners(Kersten et al., 2002). Recently, protein microarray technology has emerged following successfulapplications of DNA chip methods for large-scale studies on protein profiling and functioncharacterization (MacBeath, 2002; Mitchell, 2002). Quantitative non-2 D gel based methods, especiallythose centred on stable isotope labelling and multidimensional chromatography, such as clearableisotope code affinity tagging (cICAT) and isobaric tagging for relative and absolute quantification(iTRAQ) will hopefully increase the scope and power of future proteomics in vivo metabolic labellingand in vitro chemical derivation experiments in plants (Wu et al., 2004; Gruhler et al., 2005;Rampitsch and Srinivasan, 2006; Lilley and Dupree, 2006). The current and developing proteomicsapproaches have allowed large scale determination of protein patterns, structure and function inplant organs, tissues and cells.

Recent years have also seen an explosion in the development and employment of organelleproteomics (Millar, 2004; Warnock et al., 2004). These methods depend primarily on subcellularfractionation by MS for protein identification (Dreger, 2003); however, new microscopy-basedapproaches for determination of protein localization include immunoelectron microscopy, in situlabelled with fluorescently-tagged antibodies, and pluorescent protein fusions (e.g., green fluorescentprotein – GFP) coupled with confacal microscopy (Pan et al., 2005). Assuming that the members oflarge protein families may share significant identities, their function can widely vary (Dunwell etal., 2004). Knowing the ultimate localization of these proteins, as well as the pathways used forgetting these, may give clues to their definite functions (Nair and Rost, 2005). Several studies ofplant proteomics have been reported on plant tissues and purified cellular compartments including


nucleus, plastids, mitochondria, ER, Golgi apparatus, cell wall, plasma membrane, peroxisomes andvacuoles (see Pan et al., 2005). The identification of those proteins recruited to fulfil the specificfunction of subcellular compartments has given an additional dimension to the proteome analysis(Canovas et al., 2004).

ApplicationsProteome analysis is becoming a powerful tool to monitor developmental changes or influence ofenvironmental stimuli on protein patterns, so as to gain insight into the functioning of plants atmolecular level. Detailed information on the application of proteomics has been published in excellentreviews by Canovas et al. (2004) and Rampitsch & Srinivasan (2006). A brief account of some ofthe major applications of proteome studies is given here. In Arabidopsis, while studying the role ofGAs during initial stages of seed germination, and the impact of scarification on seed germination,application of proteome analysis resulted in better understanding of the complex cellular events.Similarly, in barley, the proteome analysis revealed new insights into cellular mechanisms underlyingseed development during grain filling and seed maturation phases. In rice, proteome studies havehelped in detecting novel traits useful for breeding (Yu et al., 2002). Mutants are generally subjectedto proteome analysis to compare their responses to different hormonal treatments, nutritional factors,and photosynthetic traits. In maize, a number of previously unknown novel genes coding for enzymesin metabolic pathways were identified during grain development following proteome analysis.Proteomics has been widely used to assess genetic variability at the level of expressed proteins(Canovas et al., 2004). Accordingly, closely related lines were successfully differentiated in wheatcultivars, barley and rice lines, maize genotypes, and a number of other crop species.

Both abiotic and biotic stresses can bring about dramatic changes to the plant proteome, andthese are manifested as the up- or down- regulation of proteins, or their posttranslation modification(Rampitsch and Srinivasan, 2006). Salinity stress results in change in the proteome, as the plantattempts to restore homeostasis in osmolarity to resume growth and development. Detailed analysisindicated that during salt stress, plant diverts carbon to glycolysis to provide the energy required toreturn the plant to homeostasis (Yan et al., 2005; Zhu, 2001; Kleczowski et al., 2004). In case ofpathogen attack (biotic stress), the proteome analyses have indicated involvement of defense andstress related proteins, metabolic enzymes, translocation and protein turnover proteins, and proteinsof unknown functions in the defense response (Campo et al., 2004; Kim et al., 2004 a,b; Rampitschand Srinivasan, 2006). The application of proteomics has also been used to decipher the highlycomplex genetic interactions involved in plant-microbe interactions and for studying symbioses(nitrogen symbiosis, ecto- and endo-mycorrhizal symbiosis) in plants. Proteome approaches havealso provided new insights into molecular controls regulating wood formation in plants, especiallypine species (for details refer the two reviews mentioned above).

METABOLOMICSThe quality of crop plants and their derived products is the direct function of their metabolites (i.e.,the biochemical component of cells and tissues). The metabolites determine the flavour, aroma,colour and texture of crops, their storage properties and performance in field (Memelink, 2005).The metabolite content of a plant constitutes its metabolome. Metabolomics is a technology thatdeals with comprehensive analysis in which all the metabolites of an organism are identified andquantified at a given time (Fiehn, 2002). It has emerged as a genome functional methodology that


contributes to our understanding of the complex molecular interactions in the biological systems(Hall et al 2002). As such, metabolomics represents the logical progression from large scale analysisof RNA and proteins at the system level (Weckwerth, 2003).

The metabolites are the end products of cellular regulatory processes, and their levels can beviewed as response of biological systems to environment or genetic manipulations (Maloney, 2004).The metabolome is very diverse. It includes lipid soluble chemical that are normally found inmembranes, polar chemicals for aqueous parts of the cell, acidic and basic ions, stable structuresand structures that oxidize at the slightest mistreatment (Maloney, 2004). The total number ofmetabolites, which are produced within the plant kingdom, including secondary metabolites, isestimated to range between 100000 to 200000 (Oksman-Caldentey and Inze, 2004). The quantitativeand qualitative measurements of such a large number of cellular metabolites provides a broad viewof the biochemical status of an organism, which can then be used to monitor and assess genefunction (Fiehn et al., 2000). Further, the importance of metabolites in control, communication, andas building blocks and energy transporters within the biological systems provides metabolomics aunique opportunity for phenotypic and genotypic profiling (Tolstikov et al., 2003). Plants react toany change in their surroundings. The metabolites are reported to function in many resistance andstress responses in plants (Bino et al., 2004). The biochemical response of an organism to a conditionalperturbation can be characterized by its effect on the differential accumulation of individual metabolites(Raamsdonk et al., 2001). As metabolites represent the catabolic and anabolic activities beingperformed by proteins at any given time (Maloney, 2004), it is increasingly understood that they(metabolites) themselves modulate macro-molecular processes through, for example, feedbackinhibition and signaling molecules (Dixon et al., 2006).The cascading effect begins with a modifiedgenome leading to modified proteins, and consequently, change in the pattern and/or concentrationof metabolites. Therefore, change in genotypes will be manifested through an observed change inmetabolome, which helps in better understanding of the correlation between genes and functionalphenotype of an organism (Sumner et al., 2003; Bino et al., 2004).

Metabolomics research is proving an invaluable tool for generating information of use in manyfields. For functional genomics strategies, potentially fast track methods exploiting metabolomicsanalyses of tagged lines or known mutants are likely to prove invaluable. Further, metabolomicsinformation is not only assisting in the establishment of deeper understanding of the complexinteractive nature of plant metabolic network and their responses to environmental and genetic change,but also is providing unique insights into the fundamental nature of plant phenotypes in relation todevelopment, physiology, tissue identity, resistance, biodiversity etc.

Metabolomics TechniquesMetabolites have a much greater variability in the order of atoms and subgroups compared togenes and proteins. Therefore, there is no single analytical technique that can be used in metabolomicsto define, identify and quantify all metabolites in a biological sample. Nevertheless, metabolomicsis driven primarily by advances in mass spectrometry (MS), nuclear magnetic resonance (NMR) andfourier transform infrared (FT-IR) spectroscopy coupled with chromatographic separationprocedures. The success of metabolome analysis is dependent on a few key aspects—production ofthe biological materiall/sample and sample extraction/metabolite detection (Hall 2006). Severaltechnical and review reports are available on appropriate strategies for the design of metabolicexperiments (Fiehn, 2002; Gulberg et al., 2004; Hall, 2006). Bino et al. (2004) have suggested


minimum information about a metabolomics experiment (MIAMET), which incorporates experimentaldesign; sampling; preparation, metabolite extraction and derivation; metabolite profiling design, andmetabolite measurement and specifications. Due to convoluted nature of plant metabolism, themetabolic data is quite complicated. Different analytical approaches have been, therefore, designedfor specific analytes. These approaches include target analysis, metabolite (or metabolic) profiling,metabolomics and metabolic fingerprinting. Metabolite target analysis involves a combination oftechniques to prepare and analyze samples for one or a small number of compounds from complexmixtures. It is the most wide-spread technique, and is applied in all areas of research such as themonitoring of phytohormones and also to directly study the primary effect of a genetic alteration(Fiehn, 2002). Metabolite profiling is the measurement of hundred or thousands of metabolites. Itrequires streamlined extraction, separation and analysis in highly throughput manner, so as to measurelarge number of metabolites in the presence of extraordinarily complex mixture of chemicals (matrix),which are found in cellular extracts (Kopka et al., 2004). Metabolic fingerprinting looks at a fewmetabolites to help differentiate samples according to their genotype, phenotype or biological relevance(Shanks, 2005). Metabolomics in the strict sense is the measurement of all metabolites in a givensystem. It is not yet technically possible, and will probably require platform of complementarytechnologies, because no single technique is comprehensive, selective, and sensitive enough to measurethem all (Weckwerth, 2003). For target compound analysis and metabolic profiling, main techniquesare gas chromatography (GC), high performance liquid chromatography (HPLC) and NMR. Theseapproaches rely on chromatographic separation, often coupled with well developed calibrations forspecific analytes. In metabolic fingerprinting, samples are analyzed as crude extracts without anyseparation step using NMR, direct injection mass spectrometry (MS), or FT-IR spectroscopy. Thesefingerprinting approaches are often combined with multivariate analyses to get most out of the data.For the widest coverage of metabolomics, the hyphenated complementary analytical techniques ofliquid chromatography (LC)/MS, LC/MS, LC/MS/MS and LC/NMR are likely to make increasedimpact in the future (cf.www.metabolomics-nrp.org.uk, also see for technological details). A referencemay be made to a review by Hall (2006) for details regarding various metabolomics techniques,their applications and limitations.

ApplicationsPlant metabolomics is still a field in its infancy, but has a potentially broad field of applicationsaimed at facilitating an improved understanding of dynamic biochemical composition within theliving systems. This knowledge will prove to be fundamental in monitoring crop quality characteristics(Hall et al., 2005), identifying potential biochemical markers to detect product contamination andadulteration (Reid et al., 2004), and optimizing trait development in agricultural products and inbiorefining (Dixon et al .,2006). Metabolomics offers the unbiased ability to characterize anddifferentiate genotypes and phenotypes based on metabolic levels. In a detailed review, Hall (2006)has discussed applications of plant genomics in six major areas—genotyping and phenotyping;population screening, understanding physiological processes, biomarkers and bioactivity, quality andbreeding, and substantial equivalence. He has included several examples where metabolite datahave been found useful in differentiating various genotypes and understanding plant responses tobiotic and abiotic stresses, characterization of the novel plant products, breeding of crops based onspecific biochemical composition and assessing the substantial equivalence i.e., comparison betweentransgenic and wild-type plants.


The knowledge of metabolomic also has great potential for application in efficient engineeringof crops that combine an attractive appearance and taste with improved levels of phytonutrientssuch as flavonoids and carotensids (Bino et al., 2004). Plant properties can be improved in variousways, such as by increasing metabolic fluxes into valuable biochemical pathways using metabolicengineering (e.g., enhancing the nutritional value of foods, decreasing the need for pesticide orfertilizer application etc.), or into pathways needed for the production of pharmaceuticals in plants(Giddings et al., 2000). Similarly, metabolic shortcuts can be created by introducing foreign set ofenzymes(s) that lead to the production of desired end products from other or more upstreamprecursores, and the foreign enzymes can also lead to the production of new metabolites (Memelink,2005).

BIOINFORMATICSAdvances in the field of genomics technologies are generating biological information in the form ofdigital data, which is too much for human brain to comprehend and analyze. Bioinformatics is aknowledge based theoretical discipline that allows the scientists to store, analyze, and compare theotherwise overwhelming amounts of genomic data. Bioinformatics involves biology, computer scienceand information technologies to form a single discipline (Rhee et al., 2006; Vassilev et al., 2005).It uses computers and sophisticated software to search and analyze databases accumulated fromgenome sequence projects and other sources. Bioinformatics has facilitated the analysis of genomicand post-genomic data, and the integration of data from the related fields of transcriptomics,proteomics and metabolomics (Varshney et al., 2005). Several bioinformatics tools and databaseshave been developed for DNA sequencing analysis, transcriptomics, proteomics and metabolomics,and a comprehensive list of the same has been provided separately by various workers (Bino et al.,2004; Varshney et al., 2005; Rhee et al., 2006). The integration of knowledge from bioinformatictools, databases and other different fields enables the identification of genes and gene products, andhelps in elucidating the functional relationships between genotypes and phenotypes (Edwards andBatley, 2004). Using bioinformatics tools, scientists can search genomic data and identify a regionimportant for a desired trait. Then, through biotechnology methods, they can transfer that trait toanother organism to create a useful product or outcome, e.g., converting a drought-sensitive crop toa drought-tolerant crop. The computational approaches facilitate the understanding of variousbiological processes by providing a more global perspective in experimental design, and ability tocapitalize on the emerging technology of database mining by which testable hypothesis are generatedregarding the function or structure of a gene or protein of interest by identifying similar sequencesin characterized organisms. Bioinformatics will continue to play an important role to glue basicresearch with applied research, and biotechnology will play an essential role in solving urgentproblems of our society, such as developing renewable energy, reducing world hunger and poverty,and preserving the environment (Rhee et al., 2006).

CONCLUSIONConsiderable progress has been made building infrastructure for applying knowledge and tools ofgenomics (and other ‘omics’) to allow the characteristics of crop plant to be altered for improvedactual and potential yields, increased resource use efficiency and enhanced crop system health.However, there is a continuing need to coordinate disciplines such as structural genomics,transcriptomics, proteomics and metabolomics with plant physiology, biochemistry and plant breeding


for formulating strategies to work on problem-oriented and process-oriented goals leading to cropimprovement. Recently, a new scientific discipline—crop system biology—has been proposed byyin and Struik (2007) as a complementary approach to connect functional genomics and crop modelingfor assisting plant breeding programmes to omprove the yield related characteristics of major crops.It is envisaged that this science will develop into a highly computer-intensive discipline, whichshould enable in silico assessment of crop response to genetic fine-tuning under defined environmentalscenarios, thereby becoming powerful tool in supporting breeding for complex crop tyraits. Thegenomics revolution is creating its own revolution in plant breeding that cannot be ignored. Thepromotion of all the innovative approaches is fundamentally important in addressing the need forenhancing agricultural productivity and sustainability, and to put them to use for the public good.

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