1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 15 Understanding Stress-Responsive Mechanisms in Plants: An Overview of Transcriptomics and Proteomics Approaches Naser A. Anjum, Sarvajeet Singh Gill, Iqbal Ahmad, Narendra Tuteja, Praveen Soni, Ashwani Pareek, Shahid Umar, Muhammad Iqbal, Mario Pacheco, Armando C. Duarte, and Eduarda Pereira Plants are static in nature and, therefore, they encounter a number of biotic and abiotic stress factors during their life cycle. Plants responses to these stress factors are differential and complex. Since the past decade, omics technologies are providing the major clues for understanding plant stress response mechanisms important for crop improvement. This chapter will critically evaluate the current literature on the plant transcriptomics and proteomics for understanding plant stress responses in detail in addition to the basic concept, principles, and procedure outlines of these approaches, and will also suggest important future perspectives. 15.1 Introduction Abiotic stresses negatively impact plant growth and development and hence are the primary cause of crop loss worldwide. Plants adaptation to these stresses is very differential and complex and depends on the activation of cascades of molecular networks involved in stress perception, signal transduction, and expression of specific stress-related genes and metabolites [1]. Although the development of omics technologies including transcriptomics and proteomics is in its infancy, it indeed has helped, to a great extent, unravel the possible mechanism of plant responses to a number of stress factors. The following sections will present introduction, principle and advantages, and limitations of, first, the transcriptomic and, subsequently, of the proteomic approaches in detail. Improving Crop Resistance to Abiotic Stress, First Edition. Edited by Narendra Tuteja, Sarvajeet Singh Gill, Antonio F. Tiburcio, and Renu Tuteja Ó 2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA. j 337 Druckfreigabe/approval for printing Without corrections/ ` ohne Korrekturen After corrections/ nach Ausfçhrung ` der Korrekturen Date/Datum: ................................... Signature/Zeichen: ............................
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15Understanding Stress-Responsive Mechanisms in Plants:An Overview of Transcriptomics and Proteomics ApproachesNaser A. Anjum, Sarvajeet Singh Gill, Iqbal Ahmad, Narendra Tuteja, Praveen Soni,Ashwani Pareek, Shahid Umar, Muhammad Iqbal, M�ario Pacheco, Armando C. Duarte,and Eduarda Pereira
Plants are static in nature and, therefore, they encounter a number of biotic andabiotic stress factors during their life cycle. Plants� responses to these stress factorsare differential and complex. Since the past decade, �omics� technologies areproviding the major clues for understanding plant stress response mechanismsimportant for crop improvement. This chapter will critically evaluate the currentliterature on the plant transcriptomics and proteomics for understanding plant stressresponses in detail in addition to the basic concept, principles, and procedureoutlines of these approaches, and will also suggest important future perspectives.
15.1Introduction
Abiotic stresses negatively impact plant growth and development and hence are theprimary cause of crop loss worldwide. Plants� adaptation to these stresses is verydifferential and complex and depends on the activation of cascades of molecularnetworks involved in stress perception, signal transduction, and expression ofspecific stress-related genes and metabolites [1]. Although the development of�omics� technologies including transcriptomics and proteomics is in its infancy, itindeed has helped, to a great extent, unravel the possible mechanism of plantresponses to a number of stress factors. The following sections will presentintroduction, principle and advantages, and limitations of, first, the transcriptomicand, subsequently, of the proteomic approaches in detail.
Improving Crop Resistance to Abiotic Stress, First Edition.Edited by Narendra Tuteja, Sarvajeet Singh Gill, Antonio F. Tiburcio, and Renu Tuteja� 2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA.
15.2Transcriptomic Approaches and Plant Stress Responses
In the layman�s language, the transcriptome refers to the pools of RNA transcripts ina cell and the transcriptomics is the global analysis of gene expression at the RNAlevel and provides tools for the study of gene function. In fact, transcriptomicsprovides information on the presence and relative abundance of RNA transcripts andthus offers a better view of the active components in the cell than a genomicapproach [2].
Transcriptomic approaches can be divided into two broad categories, hybridiza-tion-based approaches and sequencing-based approaches.
15.2.1Hybridization-Based Approaches
15.2.1.1 Suppression Subtractive HybridizationA detailed study involving identification and cloning of the relevant subsets ofdifferentially expressed genes of interest is required to understand the molecularregulation of the major biological processes such as cellular growth and organo-genesis. The subtractive cDNA hybridization has been a powerful approach in thisregard to identify and isolate cDNAs of differentially expressed genes. This techniquecan be used to compare two mRNA populations and obtain cDNAs representinggenes that are either overexpressed or exclusively expressed in one populationcompared to another. It can also be used for comparison of genomic DNA popula-tions. In general, cDNA subtraction methods involve hybridization of cDNA fromone population (tester) to excess ofmRNA (cDNA) fromother population (driver) andthen separation of the unhybridized fraction (target) from hybridized commonsequences. However, these subtraction techniques are labor-intensive, involvemultiple or repeated subtraction steps, and often require more than 20mg of poly(A)þ RNA. In fact, suppression subtractive hybridization (SSH) is a PCR-based cDNA subtraction method that is used to selectively amplify target cDNAfragments (differentially expressed) and simultaneously suppress nontarget DNAamplification.
15.2.1.1.1 Principle The SSH method is based on a suppression PCR effect,introduced by Lukyanov et al. [3]. In this method, the normalization and subtractionsteps are simultaneously performed, where the normalization step equalizes theabundance of DNA fragments within the target population and the subtractionstep excludes sequences that are common to the two populations being compared [4].It is pertinent to mention here that SSH eliminates any intermediate stepsdemanding the physical separation of single-stranded (ss) and double-stranded (ds)DNAs, requires only one round of subtractive hybridization, and can achieve amore than 1000-fold enrichment for differentially presented DNA fragments [5](Figure 15.1).
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15.2.1.2 Serial Analysis of Gene ExpressionSerial Analysis of Gene Expression (SAGE) is a method developed by Velculescuet al. [6] for a comprehensive analysis of gene expression patterns where it allows arapid and detailed analysis of thousands of transcripts. SAGE technology is thesecond most popular high-throughput gene expression technology after microarrayand does not require preexisting knowledge of the genome that is being examinedand therefore SAGE can be applied to many different model systems.
Although SAGE has the capability of producing large amounts of gene expressiondata with the potential of providing novel insights into fundamental processesunderlying (a) plant–pathogen, (b) plant–diseases, and (c) plant–stresses interac-tions, it has been found very effective for small-scale sequencing. Most importantly,SAGE provides an affordable and fast comparison of many experiments, stages, andso on and altogether it requires a very small amount of starting material (single-cellstudies are possible).
15.2.1.2.1 Principle SAGE is based on three major principles: (a) a short oligonu-cleotide sequence, defined by a specific restriction endonuclease (anchoring enzyme,AE) at a fixed distance from the poly(A) tail, contains sufficient information touniquely identify a mRNA transcript. As there are four nitrogen bases (A, T, G, andC), a 10 bp tag theoretically can give 410 different possible sequence combinations. (b)End-to-end concatenation of short oligonucleotides form the long serial moleculesthat can be cloned and sequenced. (c) Quantization of the number of times aparticular tag observed provides the expression level of the corresponding tran-script [7] (Figure 15.2).
15.2.1.3 MicroarraysThis is one of the hybridization-based approaches. Amicroarray is a glassmicroscopyslide onto which gene fragments are spotted, in the form of cDNA fragments (cDNAmicroarray) or in-situ-synthesized oligonucleotides (oligonucleotide microarray).Therefore, the microarray technology employing cDNAs or oligonucleotides isa powerful tool for analyzing gene expression profiles of plants exposed tovarious environmental stress factors. In fact, depending on the target nucleic acid
conventional/SMARTof TM cDNASynthesis TM cDNA
I digestionRsa
ligationAdapter
Subtractive hybridization
PCR amplification
[→MOS hydridization → PCR amplification]
Differentially expressed cDNAs
Figure 15.1 Schematic representation of the SSH procedure.
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components, cDNA microarray-based technologies have been subdivided intofollowing two formats:
i) Oligonucleotide arrayii) cDNA microarray
15.2.1.3.1 Oligonucleotide Array The oligonucleotide type of array consists ofoligonucleotide targets, generally less than 25 mer in length, which are generatedin situ on a solid surface by light-directed synthesis [8, 9]. Synthetic linkers modifiedwith photochemically removable protecting groups are attached to the glass sub-strate. Light is thendirected through aphotolithographicmask to specific areas on thesurface to produce localized photodeprotection. Hydroxyl-protected deoxynucleo-tides are incubated with the surface so that chemical coupling occurs at the sites thathave been illuminated in the preceding step. By repeating these procedures with newmasks, hundreds of thousands of oligonucleotides can be synthesized in a very smallarea [9, 10]. Alternatively, oligonucleotide arrays can be constructed by spottingpresynthesized oligonucleotides on the solid surface [11–13].
Because oligonucleotide arrays are designed and synthesized on the basis ofsequence information, physical intermediates such as cloning and polymerase chainreaction (PCR) are not required. Specific sequences, which are nonoverlapping ifpossible or minimally overlapping if necessary, can be designed to increase thehybridization sensitivity, even through their shorter sequences [10]. The oligonu-cleotide array is applied when more precise analysis, including the detection ofsingle-nucleotide polymorphisms, is required [14].
Extraction of RNA
Immobilization of mRNA (present in a sample of total RNA) onto poly T beads
Synthesis of double-stranded cDNA by reverse transcription using universal oligo dT primers
Release of the ditags from the linkers by digestion with the anchoring enzyme
Separation of the released ditags on a gel
Cutting and serial ligation of the bands formed
Cloning and subsequent sequencing of the resultant serial tags into an appropriate vector
Figure 15.2 Schematic overview of major events in SAGE.
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15.2.1.3.2 cDNA Microarray The principle of microarray studies is based on theability of anmRNAmolecule to hybridize to its originalDNA sequence spotted on thearray. Messenger RNA is extracted from samples such as control organisms and testorganisms. ThemRNA is reverse transcribed to cDNA and labeled with a fluorescentlabel. One sample is labeled with a green fluorescent dye (Cy3), whereas the cDNAfrom the other sample is labeled with a red fluorescent dye (Cy5). Cy3- and Cy5-labeled samples are mixed together in equal quantities and hybridized to themicroarray. The array is then scanned using laser emission. A software is used tovisualize the expression levels ofmRNAs of the genes and the amount of each labeledtarget bound to each spot on the array is quantified. Now, it has become possible toidentify induced, repressed, or unchanged mRNA expression by determining theratio of signal intensities between control and test cDNA. However, readers mayconsult review article byHegde et al. [15] for insights into themajor technical aspectsof microarray fabrication, hybridization, and analysis; in addition, an article byLettieri [16] may be useful on the applications of the microarray technique in atoxicological context.
The cDNA microarray can also be differentiated on some other ground such asthe fabrication of cDNA microarray by printing cloned and amplified cDNAs ontothe solid surface. Furthermore, the advantages of the cDNA microarray comparedto the oligonucleotide array include less susceptibility and higher specificity due tothe longer sequences of the targets [17, 18]. However, cDNA may contain repetitivesequences that are often observed in various genes, or similar sequences that arefound in family member genes. These nonspecific sequences may affect thesensitivity of the cDNA microarray. The cDNA microarray can be easily used forscreening steady-state mRNA expression levels [14] (Figure 15.3).
Cloning and subsequent amplification of target cDNAs
Printing of purified PCR products onto glass microscope
microarrayerroboticawithslides
Synthesis of cDNA probes (test or reference) labeled with different
fluorescent dyes (Cy3-dUTP and Cy5-dUTP) from total RNA or
mRNA derived from test and reference samples
Hybridization of the pooled probes to the microarray
Detection of the hybridized fluorescent signals
with a dual-wavelength laser scanner
Separate scanning of images combined and pseudocolored
Figure 15.3 Outline of the principle of the cDNA microarray analysis system.
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Advantages of Microarray Technology
. Microarray technology has largely helped in the global gene expression analysis.
. Analyses of plant defense responses.
. Microarray has been used in genomic-wide research, mutational analyses,pharmacology, toxicology, aging research, and molecular analyses of fataldiseases.
Disadvantages of Microarray Technology In addition to the significant advantageslisted above, several weaknesses of microarray technology can be summarized asfollows:
. High cost and time consumption, and necessity of special devices.
. Difficulty of data interchanges between individual microarrays.
. Microarray is difficult for the expression levels between individual targets to becompared in the same RNA sample (because of different hybridization rates dueto variations in melting temperature depending on sequence and length of targetgene fragments).
15.2.2Sequencing-Based Approaches
Sequencing-based approaches have largely replaced the hybridization-basedapproaches and significantly helped gene expression analysis over the past 5 years.For the study point of view, sequencing-based approaches can be divided into two:(a) DNA sequencing of expressed sequence tag (EST) libraries and (b) next-generation sequencing (NGS).
15.2.2.1 DNA Sequencing of Expressed Sequence Tag LibrariesExpressed sequence tags represent short, unedited, and randomly selected single-pass sequence reads derived from cDNA libraries, providing a low-cost alternative(also called �poorman�s genome) towhole genome sequencing, with a glimpse of thetranscriptome of an organism at various stages of development. EST sequences aregenerated by single-pass DNA sequencing of clones randomly selected from cDNAlibraries and represent partial descriptions of the transcribed portions of gen-omes [19]. EST sequences are widely used for a rapid and cost-effective discoveryof new genes, verification of the exon–intron structure of predicted genes, and asresources for genemapping and cDNAarray construction [20]. ESTs are used as a fastand efficient method of profiling genes expressed in various tissues, cell types, ordevelopment stages [21]. One of the many interesting applications of EST database(dbEST) is gene discovery where many new genes can be found by querying thedbEST with a protein or DNA sequence.
ESTs have become an invaluable resource for gene discovery, genome annotation,alternative splicing, SNP discovery, molecular markers for population analysis,and expression analysis in animal, plant, and microbial species [22]. Although
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several alternatives have been described since the emergence of EST sequencingprojects, none has yet totally supplanted the use of bacterial vectors and Sangersequencing [23].
15.2.2.2 Next-Generation SequencingNext-generation sequencing technologies are a set of new, state-of-the art, high-throughput sequencing technologies that came into existence after the dramaticprogress in sequencing instrumentations, from Sanger-based methods, using slabgels, to capillary electrophoresis (CE). In fact, the NGS technologies differ fromconventional capillary-based sequencing in that NGS has departed from Sangersequencing chemistry and sequencing is often performed on templates formed asbeads or spots ofDNA [24].NGSplatformsare beingutilized for targetedsequencingofcandidate genes or genomic intervals to perform sequence-based association studies.
Several NGS technologies have recently emerged that can be discussed underfollowing subheadings:
. Pyrosequencing
. Fluorescent-labeled sequencing by synthesis
. Sequencing by hybridization and ligation, and microchip-based CE
15.2.2.2.1 Pyrosequencing The pyrosequencing technique is based on the processof sequencing by synthesis. It was developed by 454 Life Sciences and Roche AppliedScience.
Principle Pyrosequencing is based on the principle that when a nucleotide is incor-porated into the growing DNA strand, the pyrophosphate is released, which issubsequently converted to ATP by enzyme; a light is produced when ATP comes incontactwithenzymeluciferase(Luc). IndividualandsequentialadditionofdNTPsto thegrowingDNAmoleculestakesplace.Theincorporationofanucleotideemits theflashoflight signals that can be easily correlated with the incorporation of specific nucleotide.
454 Life Sciences has developed several machines for pyrosequencing. GenomeSequencer (GS)-20 was the first next-generation DNA sequencer on the marketreleased in 2005. Margulies et al. [25] reported that GS-20 was able to read up to 25million bases of bacterial genome in a single 4 h run. The Genome Sequencer FLX(GS-FLX) was released in 2007. This instrument is able to read lengths of 250 basesand is able to performmate-paired reads. In addition, an average of 100millionDNAbases can be sequenced in a 7.5 h run [26]. In 2008, 454 Life Sciences launched theGS-FLX Titanium series reagents for use on the present instrument, with the abilityto sequence 400–600million base pairs with 400–500 base pair read lengths.With itshigh accuracy, low cost, and long reads, many researchers have switched fromtraditional Sanger capillary sequencing instruments over to the 454 sequencingplatform for a variety of genome projects.
15.2.2.2.2 Fluorescent-Labeled Sequencing by Synthesis Genome Analyzer (GA)system developed by Illumina uses a polymerase-based sequencing-by-synthesis
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(SBS) chemistry. This platform utilizes fluorescent-labeled and reversible terminatorchemistry, unlike the instrument by 454 Life Sciences, but produces read lengths ofapproximately 50 bp and >2000 Mb of sequence data per run over the course ofapproximately 4 days [24]. In addition, Illumina�s GA system can be used in geneexpression, SNP discovery, base resequencing, and ChIP experiments (ChIP-seq).
15.2.2.2.3 Sequencing by Hybridization and Ligation, and Microchip-Based CapillaryElectrophoresis The instrument based on the method of sequencing by hybridiza-tion followed by ligation was developed by Applied Biosystems (ABI) generally calledsequencing by oligonucleotide ligation and detection (SOLiD) [27]. In fact, TheSOLiD technology platform uses emulsion PCR and sequencing by oligonucleotideligation and detection. SOLiD technology can be potentially used in gene expressionanalysis and other approaches. The overall accuracy rate for the SOLiD system isgreater than 99.94% and this applies to paired end runs that produce 50 bp reads.
The microchip-based capillary electrophoresis-based sequencing systems involvethe separation of fluorescent-labeled sequencing samples on hair-thin, 30–50 cmlong capillary gels. CE array chips have been fabricated on the basis of the well-understood behavior of a single-channel chip system. Different materials, forexample, silicon [28], glass [29], and plastics [30, 31] have been used. A variety ofdifferent fabrication processes have also been developed to accommodate thecomplicated requirements and materials used for making such a device. Micro-chip-based CE systems have demonstrated use in diverse applications such as theseparation of amino acids [32], analysis of blood serum cortisol [33], examination ofpolymerase chain reaction amplicons [34], and analysis of metal–ion complexes [35].However, readers are advised to consult recent, excellent reviews by Hert et al. [26]and Simon et al. [24] for a detailed working principle and application of ABI-SOLiDand CE array-based sequencing.
Almost all the instruments for next-generation sequencing are able to generatethree to four orders of magnitude more sequences and are considerably lessexpensive than the Sanger method on the ABI 3730xL platform (hereafter referredto as ABI Sanger) [36–39]. These next-generation sequencing methods promise acost-effective means of either deeply sampling or fully sequencing an organism�stranscriptome, with even small experiments tagging a very large number ofexpressed genes.
15.3Proteomic Approaches in Plant Stress Responses
The study of complex biological questions through comparative proteomics isbecoming increasingly attractive to plant biologists. Since the past few decades, themajor aim of proteomic studies is to decipher the constituents of a proteome, thus toreveal the basic mechanism of plant responses to various environmental stresses byanalyzing changes and the dynamics of changes on the protein level. It is pertinent tomention here that attention has been focused on the determination of the function
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and functional network of proteins by proteome analysis after the completion ofgenome sequences of several organisms. Although sequence analysis of genomicDNA started in the 1990s on a full scale, developed rapidly during the past decade,and made available the entire sequence of genomic DNA for many organismsincluding higher plants, animals, and human, and the expression of genes can beanalyzed at the transcriptional level, the expression of proteins cannot always beanalyzed from gene expression because there exists a relatively low correlation(correlation coefficient about 0.5) in quantity between mRNA and protein [40,41]. Moreover, information on protein posttranslational modification, structure, andprotein–protein interaction cannot be provided by the DNA sequence and/or theexpression ofmRNA. In addition, almost all proteins are posttranslationallymodifiedand then form specific structure and function through protein–protein (ligand)interaction. Therefore, the analysis of proteins assumes great importance. Althoughproteome research started after the genome sequence analysis was accomplished, thedevelopments over the last few years have been remarkable [41]. The systematicanalysis of proteins in plants has greatly helped researchers to understand genefunctions through complementation of gene and gene expression analysis in detail.Recently, the development of advanced techniques for revealing coding genes of theorganism under study, gene annotation, and functional characterization have addedgreat momentum to plant proteome analysis.
Proteomic analysis is a multistep process that typically involves protein extraction,fractionation, separation, and mass spectrometry (MS). However, a classical prote-omics work involves the following two major steps: (i) separation of proteins step,usually 2-dimensional gel electrophoresis (2DE), and (ii) identification of separatedproteins step, usually mass spectrometry.
The following sections will review various gel- and nongel-based approaches thatare used in a wide range of biological systems for studying differentially expressedproteins including multidimensional protein identification and labeled or nonla-beled approaches.
15.3.1Gel-Based Approaches
Gel-based proteomics generates qualitative and quantitative protein behavioral dataand as such it provides a core technology to integrate information produced usingvarious �omic� technologies.
15.3.1.1 One- or Two-Dimensional Polyacrylamide Gel ElectrophoresisDepending on the plane of separation, the gel-based approaches for proteomeanalysis may be one or two dimensional. The 1-dimensional electrophoresis (1DE)is used for most routine protein and nucleic acid separations. The support mediumfor electrophoresis can be formed into a gel within a tube or it can be layered into flatsheets. In general, the tubes are used for easy 1DE separations.
O�Farrell in 1975 [42] first described the two-dimensional polyacrylamide gelelectrophoresis (2D-PAGE). This is the simplest, most popular, and versatile method
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of protein separation among a rapidly growing array of comparative proteomictechnologies. In fact, 2DE allows for the separation of complex protein and isbased on the orthogonal separation of proteins according to their isoelectric pointsthrough isoelectric focusing and molecular mass separation using SDS-polyacryl-amide gel electrophoresis (PAGE). An SDS-PAGE is run first in one direction andthen again at right angles. In the first dimension, an isoelectric focusing (IEF) gel isrun and in the second dimension the proteins are separated in SDS-PAGE. A greaternumber of individually different proteins can be resolved in a highly repeatablefingerprint-like pattern. Two-dimensional gel-based strategies separate intact pro-teins on the basis of both charge (isoelectric point, pI) and mass, and therefore havethe ability to resolve multiple charged isoforms (that may result from phosphory-lation or other charged posttranslational modifications) and biologically significantproteolytic products.
Advantages of 2DE
i) Represents entire proteome.ii) Can resolve up to 5000 different proteins simultaneously (�2000 proteins
routinely).iii) It can detect and quantify <1 ng of protein per spot.iv) Provides more than a raw list of proteins, and also intensities.v) Can track posttranslational modifications.vi) Preset conditions can bemanipulated to enhance resolution (pH ranges, size of
gel, staining methods, solubility, etc.).
vii) Delivers a map of intact proteins that can be stored and analyzed at will.
Limitations of 2DE
i) Reproducibility and sensitivity are less.ii) Poor resolution of hydrophobic or membrane-bound and nuclear proteins.iii) Sample loading/sample size capacity can limit experiments.iv) Hard to resolve very acidic and/or very basic proteins (pH range from 2.5 to 12),
very small, or very large proteins.v) Difficult to automate process or create accurate databank standards.vi) Only highly abundant proteins from total cell lysates are visualized and low-
abundance proteins of physiological relevance, such as regulators or signalingproteins, are difficult to detect.
The introduction of immobilized pH gradients (IPGs) has largely overcome themajor shortcomings listed above for the first dimension of 2DE [43]. A pH gradientformed bymixtures of acrylamide buffers is covalently fixed to the acrylamidematrixduring gel polymerization. The gradient does not drift and cannot be distorted.Here,a series of chemically well-defined acrylamide derivatives with the general structureCH2¼CH�CO�NH�R (where, R contains either a carboxyl or an amino group) areused that form a series of buffers with different pK values ranging between 1 and 13.A true steady-state IEF with increased reproducibility is allowed because of the
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generation of extremely stable pHgradients due to occurrence of copolymerization ofthe reactive end with the acrylamide matrix. With this improved first dimension, notonly a substantially wider spectrum of proteins can be resolved throughout the entirepHgradient in one gel but also lower abundance proteins caused by increased sampleloading capacity can be detected.
Significant improvements to the 2DE are beingmadewith the advancement of newtechnologies such as (a) the use of IPG DryStrip technology, (b) the use ofsemiautomated devices such as the IPGphor in the first dimension, and (c) the useofmultiple SDS-PAGE apparatus for running up to 20 different samples in parallel inthe second dimension. With the application of fluorescent dyes or isobaric tags, nowwe can have (a) improved solubilization and (b) separation of hydrophobic proteins,(c) display of low abundance proteins, and (d) reliable protein quantization. Inaddition, the use of multiplexed fluorescent Cy-Dye staining of different proteomestates in difference gel electrophoresis (DIGE) technology has largely eliminated thetechnical irreproducibility of 2DE.
Unlu et al. [44] first introduced the two-dimensional differential gel electro-phoresis (2D-DIGE) technology. 2D-DIGE uses three spectrally resolvable fluo-rescent dyes (Cy2, Cy3, and Cy5) to label up to three samples to be run togetheron the same 2D gel, adds an essential quantitative component to 2D-GE, andallows for the detection of subtle changes in protein abundance with statisticalconfidence. As discussed above, a classical 2DE approach lacks the intrinsic gel-to-gel variation that requires several replicate gels of each sample that are notdirectly overlapped. With the use of multiplexing methods such as fluorescent2D-DIGE, substantial variability can be reduced by displaying two or morecomplex protein mixtures labeled with different fluorescent dyes in a single 2Dgel. In addition, the use of spectrally resolvable fluorescent dyes also renders 2D-DIGE much more quantitative than colorimetric methods. The detection ofproteins in samples in 2D-DIGE has a large dynamic range of 104–105, and herethe dye sensitivity is capable of detecting 0.25–1 ng of sample, thus enabling thedetection of relatively low copy-number proteins. Therefore, with this excellentsensitivity, DIGE can be used to analyze relatively small amounts of even verycomplex cell extracts.
It is pertinent to mention here the other gel-based approach in which metaboliclabeling of proteins is done using radioactive isotope-labeled amino acids, and 2DEand recording are done on color negative film by radiographic exposure [45].Spandidos and Rabbitts [46] described another gel-based subproteome differentialdisplay method in which the radiolabelled proteins are used from one source andsilver-stained proteins from a second source, which are mixed in a gel in a 1 : 100ratio, to allow the precise discrimination between members of each subproteome(chromatographic fractions) using commonly available software. In the same year,Gerner et al. [47] developed a quantitative proteome profiling method where precisequantitation both of the protein amount and of the 35S incorporated is allowed using acombination of radiolabeling and SYPRO ruby staining of the same gels. In addition,this method also determines the absolute values of cell protein amounts, as well assynthesis and turnover rates.
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15.3.2Nongel-Based Approaches
It is evident from above discussion on gel-based approaches for proteome analysisthatmost of the classical proteomics approaches suffer from anumber of limitations.The identification of proteins by mass spectrometry was made possible by thedevelopment of �soft� ionization techniques, namely, matrix-assisted laser desorp-tion ionization (MALDI) and electrospray ionization (ESI) developed in the late 1980sin Europe by Michael Karas and Franz Hillenkamp and in the United States by JohnFenn, respectively. MS is now firmly entrenched as the method of choice for bothprotein identification and characterization of posttranslational modifications. Inaddition, MS has become an increasingly attractive analytical instrument for biol-ogists due in part to new ionization methods and major improvements in massaccuracy, resolution, sensitivity, and ease of use. Therefore, with the introduction ofvarious nongel-based approaches for proteomics studies, we can achieve a dynamicrange of analysis (usually 103–105) and identify low-concentration proteins. In fact,themost of nongel-based approaches digest complexmixtures of proteins in solutionwhere the resulting peptidemixture is fractionated by one or several steps of capillarychromatography and analyzed in a data-dependent manner by MS/MS. We maysummarize themajor steps inMS as follows: MS consists of (i) an ion source, (ii) themass analyzer, and (iii) an ion detection system. Analysis of proteins byMS occurs inthree major steps: (a) protein ionization and generation of gas-phase ions, (b)separation of ions according to their mass to charge ratio, and (c) detection of ions.In nongel-based approaches such as isotope-coded affinity tag (ICAT) and multidi-mensional protein identification technology (MudPIT), samples are directly analyzedby MS, whereas in gel-based proteomics (2DE and 2D-DIGE), the protein spots arefirst excised from the gel and then digested with trypsin. The resulting peptides arethen separated by liquid chromatography (LC) or directly analyzed by MS. Theexperimentally derived peptidemasses are correlatedwith the peptide fingerprints ofknown proteins in the databases using search engines (e.g., Mascot and Sequest).
15.3.2.1 One-Dimensional LC-MS/MS TechnologyIdentification of proteins is required for understanding the complex and highlydynamic proteome of a cell/tissue/organ/organism. For this purpose,mass spectros-copy has beenwidely used. Butmass spectrometers alone cannot resolvemore than acertainnumberof ionsignals; therefore,before identificationofproteins, reductionofsample complexity by using an advanced separation technique is necessary. Massspectroscopycoupledwithliquidchromatographynowhasbecomeamethodofchoicefor identification of proteins present in a complexproteomic sample. LC-MS/MSis anadvanced technique that combines the separation capabilities of liquid chromatog-raphy with the mass analysis capabilities of mass spectrometry.
15.3.2.1.1 Principle In LC-MS/MS, complex proteinmixtures arefirst digested intosmall peptides that are separated by liquid chromatography. It is important to notethat this method is based on peptide separation, instead of protein separation,
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because peptides in complex mixtures exhibit a more uniform behavior thanindividual subclasses of proteins [48]. It is also important that these small peptidescan be easily ionized in the mass spectrometer in comparison to the large proteins.
For single-dimensional separation of protein digests, that is, peptides by liquidchromatography, a nanocolumn packed with reverse-phase C18 resin is used.Peptides bind to C18 chain by hydrophobic interactions. After loading onto thereverse-phase C18 resin nanocolumn, they are directly eluted into the ionizationchamber of mass spectrometer.
For ionization, generally electrospray method is used. These ionized peptides arefirst detected as charged ions that are separated by mass/charge ratio. Peptide with aspecific mass/charge is then selected and further fragmented using �collision-induced dissociation� [49, 50]. Collision-induced dissociation takes place in acollision cell filled with N2 gas with a certain pressure. The selected peptide ionis excited by applying a certain voltage and then it undergoes a collision-induceddissociation by energetic collision with N2 molecules. After that, they are sentthrough a second mass spectrometer that scans and detects fragmentation pattern.This CID fragmentation pattern is used to determine the sequence of the peptide andthis sequence information is then used to search against databases using computersoftware for protein identification. This approach is known as MS/MS technique ortandem mass spectroscopy (Figure 15.4).
Advantages of One-Dimensional LC-MS/MS Technology By coupling an LC systemwith a tandemmass spectrometer, it is possible to distinguish individual proteins incomplex mixtures containing more than 50 components without prior purifica-tion [51–53].
Disadvantages of One-Dimensional LC-MS/MS Technology While LC-MS/MS isroutinely used to sequence peptides and identify proteins directly from complexmixtures, some samples present complexity beyond the separation capacity of a 1D-LC technique.
Sample preparation and enzymatic digestion
resinC18phasereversewithpackagingColumn
Sample loading on prepared column
Separation and then elution of peptides into ionization
chamber of mass spectrometer
Ionization of peptides
ofa peptide ion and transfer into collision chamberbeSelection
Analysis and detection of fragmentation pattern by MS
Collection and processing of data
Figure 15.4 Schematic representation of the one-dimensional LC-MS/MS technology.
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15.3.2.2 Multidimensional Protein IdentificationMudPITwas developed by Washburn et al. in 2001 [54]. It is an advanced technologyfor large-scale analysis of proteome. In general, MudPIT is a combination of two ormultidimensional liquid chromatography systems with tandem mass spectrometer.
15.3.2.2.1 Principle In this technology, a mixture of proteins is first subjected toreduction, alkylation, and digestion. Reduction breaks cysteine disulfide bonds,alkylation prevents reformation of these bonds, and digestion converts the proteinmixture to a mixture of peptides. The digested sample is directly loaded onto a 50–100 mm i.d. nanocolumn that has a tip with an inner diameter of 2–5mm [55–58]. Thenanocolumn is packed with C18 resin followed by strong cation exchange (SCX)resin. This is known as biphasic column. Desalting of samples containing high saltconcentration is required before loading onto this biphasic column. Alternatively, atriphasic column containing C18 resin, SCX resin, and C18 resin in a sequentialmanner is also generally used. After loading, the column is attached to the tandemmass spectrometer. A high-performance liquid chromatography (HPLC) pump isused to supply different buffers through the column for separation and elution ofpeptides. In a triphasic column, peptides are desalted in thefirst step byC18 resin andthen they are eluted onto the SCX phase [59]. In SCX, separation is based uponcharge. Peptides of similar isoelectric point are sequentially advanced to next C18resin where separation takes place on the basis of size and hydrophobicity. Thus,peptides are stepwise separated using SCX and C18 resins. After separation andelution from the nanocolumn, peptides are ionized by ESI method and thensubjected to the mass spectrometer, where they are separated on the basis of theirmass-to-charge ratio (m/z). Selected peptide ions are fragmented via collision-induced dissociation in the tandem mass spectrometer. Tandem mass spectra aregenerated and are searched against a protein database to determine the peptidesequence and their proteins [53, 60] (Figure 15.5).
Reduction, alkylation and enzymatic digestion of protein sample
Column packing with C18 resin and SCX resin and
bufferwithequilibration
Sample loading on column
Separation and than elution of peptides into ionization
chamber of mass spectrometer
Ionization of peptides
chambercollisionintotransferpeptideionaofSelection and
Analysis and detection of fragmentation pattern by MS
Collection and processing of data
Figure 15.5 Schematic representation of the multidimensional protein identification technology.
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Advantages of Multidimensional Protein Identification Technology The use of Mud-PIT has rapidly increased in proteomics research as it has revolutionized large-scaleanalysis of complex proteome. It is unbiased, as proteins of extreme values ofmolecular weights, pI, hydrophobicity, and abundance can be identified with equalsensitivity. MudPIT has been used in a wide range of proteomics experiments[61–66]. These include the following:
i) Identification of protein complexes.ii) Profiling of organelle/membrane/cell/tissue-specific proteins.iii) Identification of posttranslational modifications.iv) Quantitative comparison of protein expression level by coupling this technology
to labeling methods such as stable isotope labeling, SILAC or iTRAQ [67, 68].
Disadvantages (Limitations) of Multidimensional Protein Identification TechnologyThe success of MudPIT greatly depends upon the chromatographic separation ofmixtures of peptides. The limitations of MudPIT are as follows:
i) Requirement of high-quality nanocolumn and solvents of highest purity.ii) MudPIT column is a nanocolumn; therefore, it has a limited sample loading
capacity.iii) Successful packing of column for good reproducibility is required.iv) MudPIT analysis of complex proteome generates huge amount of data. For
analysis of such data, advanced computational tools are necessary, and thisanalysis step can take time from few hours to few days depending upon thesample complexity, the size of database being searched, and the computationaltools being used.
15.3.3Labeled or Nonlabeled Approaches
Mass spectrometry-based methods have become popular not only for qualitative butalso for quantitative analysis of a proteome. Information onwhat types of proteins areexpressed in a proteome and what is the level of expression of these proteins is alsoimportant.
In classical quantitative proteomics, proteins are separated by one-/two-dimen-sional polyacrylamide gel electrophoresis and then identified by mass spectrome-try [69]. Here, quantification is based upon intensity of staining of a protein. Anothertechnique 2D-DIGE providesmore precise quantification as samples to be comparedare run together on the same gel; therefore, errors due to separate gel runs getremoved [70]. These classical approaches have gel-based limitations such as low-resolution protein separation and difficulty in identification of proteins of extrememolecular weights and pI values and that of low solubility [71–73]. These limitationshave been overcomebymodernmass spectrometry-basedmethods. Two types ofMS-based quantification methods have been developed for extensive comparison ofmultiple proteomes (Figure 15.6):
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In labeled quantitative methods, samples to be compared are first separatelylabeled with different isotopes after which they are pooled together and then they aresubjected to sample preparation, separation, and analysis byMS/MS.While in case oflabel-free quantitative methods, there is no labeling and no pooling of samples; eachsample is separately prepared, separated, and analyzed.
15.3.3.1 Principle of QuantificationIn label-free quantitativemethods, quantification is based on �spectral counting� [74].Spectral counting is based upon the number of times a particular peptide is identifiedby MS/MS, which is directly proportional to abundance of corresponding proteinpresent in the sample.
In labeled quantitative methods, during separation by liquid chromatographydifferentially labeled peptides elute simultaneously, but due to mass difference, twoforms of a peptide can be detected by mass spectroscopy. Quantification is done bycomparing their signal intensities [75–77]. Peptide and protein ratio is calculated andthen fold change is found outQ1 . Thus, relative quantification is performed by labelingapproaches.
15.3.3.2 Types of MethodsLabeling approaches include15N/14Nmetabolic labeling [78], stable isotope labelingby amino acids in cell culture (SILAC) [79], 18O/16O enzymatic labeling, ICAT [75],isotope-coded protein labeling (ICPL) [80], isobaric tags for relative and absolutequantification (iTRAQ) [81], tandem mass tags (TMTs) [82], and other chemicallabeling. In case of label-free approaches, normal LC-MS and LC-MS/MS are widelyused methods [83, 84].
Although labelingmethods have provided high delectability and reproducibility ofprotein quantification, they have some limitations including complexity in samplepreparation, requirement of high amount of sample, expensive reagents, increasedtime span and specific computational tools, and importantly incomplete labeling of
Sample preparation
(Protein extraction, reduction of disulfide bonds,
alkylation of free cysteine residues and digestion)
Sample separation
(By liquid chromatography LC or LC/LC)
Mass spectroscopy analysis
(by MS/MS)
Data analysis for identification and quantification
of peptides/proteins
Figure 15.6 Outline of the procedure of the MS-based quantitative methods.
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samples. Therefore, label-free methods being cleaner and relatively cheaper aregaining more interest [78, 85].
15.3.4Data Mining Tools
Software is used for analysis of a large amount of data generated by modern massspectroscopy-based techniques. Different search methods are used for peptideidentification. These methods can be divided into three types: peptide mass finger-print, sequence query, andMS/MS ion search. Digestion of a protein using a specificenzyme results inmixture of peptideswhosemass spectrumprovides afingerprint ofsuch specificity that protein identification is possible. Therefore, it is known aspeptide mass fingerprinting. But it has one drawback that only proteins with alreadyknown sequence can be identified. In sequence query method, molecular massinformation of peptides is combined with sequence, composition, and fragment iondata. The source of information about sequence is the analysis of a series of peaksof an MS/MS spectrum. This method was developed by Mann et al. in 1994 [86]Q2 . InMS/MS, ions search data are accepted in different types of peak list formats. A singleMS/MS spectrumor amultidimensional LC-MS/MS run containing data frommanythousands of peptides may be searched.
Peptide identification algorithms fall into two broad classes: database searchand de novo search. The former search takes place against a database containingall amino acid sequences assumed to be present in the analyzed sample, whereasthe latter infers peptide sequences without knowledge of genomic data.SEQUEST, Mascot, X!Tandem, Phenyx, OMSSA, MyriMatch, Graylag, ByOnic,InsPecT, SIMS, and MassWiz are some database search algorithms used foridentification of peptides. For de novo sequencing, DeNoS, PEAKS, and Lutefiskalgorithms are used.
15.4Conclusions and Prospects
Advanced new technologies for proteomic analysis have accelerated biologicalresearch. Improvement became possible because of the development of advancedseparation processes, mass spectrometers, and computer software tools. This hashelped us in making new discoveries in the field of proteomics. However, furtherimprovement in proteomics techniques is required in some fields such as quanti-tative analysis of posttranslationalmodifications. It will not be wrong to say that thereis a huge scope for further improvement of proteomic technology so that thecomprehensive analysis of complex biological processes can be done. Because newtechniques generate large volume of data in exponential manner, there is need todevelop new statistical tools required for finding out logical interpretations of thedata. Finally, as these techniques will be developed and popularized, more andmorebiological information will be dug out.
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Acknowledgments
The financial support to NAA from Portuguese Foundation for Science and Tech-nology (FCT) provided through contract (SFRH/BPD/64690/2009) and to IA, IM,MP, ACD, and EP from the FCT and the Aveiro University Research Institute/CESAM is gratefully acknowledged. Praveenwould like to acknowledge the receipt ofJRFand SRF fromCSIR and SSG, NT, AP, SU, andMIwould like to acknowledge thereceipt of funds from DST, DBT, and UGC, Govt. of India, New Delhi.
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