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JOURNAL OF RECENT ADVANCES IN APPLIED SCIENCES (JRAAS) 28:39-54, 2013 ISSN 0970 1990, ST CODE 08 ,www.jraas.org. E MAIL: mhcdr@rediff.com DISPLAYED ON LINE ON MARCH 2012 ROLE OF PROTEOMICS IN IDENTIFICATION OF NEW TARGETS FOR DRUG DISCOVERY AND NEW BIOMARKERS FOR DIAGNOSIS AND PROGNOSIS OF DISEASES DESH DEEPAK SINGH*, ASHOK KUMAR MOHANTY Proteomics and structural Genomic lab, Animal Biotechnology Centre, NDRI, Karnal, Haryana 132001, INDIA E mail: ddsbms@gmail.com * Present Address: Department of Microbiology, C. S. M. Medical University, Lucknow, U.P., 226003 INDIA- ABSTRACT

Proteomics is emerging technology that is approach to the study of complete complement of protein, trnascriptomics and genomics disparity in large scale as a result of beneficial in drug discovery and development process. A handful of disease biomarker is at present worn usually for population screening disease identification, prediction, monitoring of therapy, and expect of therapeutic response. Unluckily, most of the biomarker goes through from low sensitivity, specificity, and prognostic value mainly to unusual disease in inhabitant’s transmission program. Require new disease biomarkers so as to will additional improve our ability to detect, prognoses, and predict therapeutic response in many types of disease. Novel biomarker discovery and drug target justification are highly complex and supply-rigorous processes, requiring an integral use of various tools, approaches and information. The newly developed proteomic technology features elevated-throughput similar examination of thousands of proteins in individual patients and amount populations and thus opens up the opportunity of given that more details at a global level on the molecular mechanisms. With frequently efficient public databases, bioinformatics can contribute to these processes by providing functional information of target candidates and correlating this information to the biological pathways. In this review, we summarize up to role of drug discovery date advance of bioinformatics application in proteomic research on biomarker discovery and drug target rationale. Particularly, we highlight how Proteomics in identification of new targets for drug discovery and new biomarkers for diagnosis and prognosis of diseases can facilitate the proteomic studies of biomarker identification and drug target validation.

KEY WORDS Bioinformatics, biomarker discovery, drug design, drug development, proteomics, DIGE. INTRODUCTION Protein biomarkers are striking to drug discovery development because they can be used in the drug research channel, which allows in close proximity to the beginning elimination of ineffective or toxic compounds [1]. Although the promise of these biomarkers is clear, the path to successful implementation of protein biomarkers is may be the discovery-based approach is not the best way to tap the potential of protein biomarkers [2, 3]. Protein distribution / characterization in body tissues and fluids, in health as well as in disease, are the basis of the use of proteomic technologies for molecular diagnostics to providing a better understanding of pathomechanisms of human diseases, Analysis of different levels of gene expression in healthy and diseased tissues by the detection of mutations and polymorphisms at the genomic level and may be of more value in designing a rational therapy [4]. Proteomics are playing an important role in medicine of the future which are personalized and combine diagnostics with therapeutics [5]. This report describes and evaluates the proteomic technologies that will play an important role in drug discovery, molecular diagnostics and put into practice of medicine in the post-genomic era - the first decade of the 21st century [6]. Most commonly used technologies are 2D gel electrophoresis for protein

separation and analysis of proteins by mass spectrometry [7] Micro analytical protein characterization with multidimensional liquid chromatography/mass spectrometry improves the throughput and reliability of peptide mapping [8, 9]. Matrix-Assisted Laser Desorption Mass Spectrometry (MALDI-MS) has become a widely used method for determination of biomolecules including peptides, proteins [10]. Functional proteomics technologies include yeast two-hybrid system for studying protein- protein interactions [11]. Establishing a proteomics platform in the industrial setting initially requires implementation of a series of robotic systems to allow a high-throughput approach for analysis and identification of differences observed on 2D electrophoresis gels [12]. Protein chips are also proving to be useful. Proteomic technologies are now being integrated into the drug discovery process as complimentary to genomic approaches [13]. Major scientific Platforms for Proteomics 1. Proteomics make easy to rational analysis of proteins across any biological system or disease [fig: 1] forwarding new targets and information on mode of action, toxicology and substitute markers [15]. This technique is exceedingly corresponding to genomic approaches in the drug discovery process and offers scientists the ability to integrate information from the

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genome, expressed mRNAs, their respective proteins and sub cellular localization [16]. It is expected that this will go in front to significant new insights into disease mechanisms and better drug discovery strategies to produce novel therapeutics [17]. It is at the present sensible to recognize that corresponding equipment in the form ground-throughput analysis of the total protein range of selected biological samples [18]. In a corresponding approach to genomics, which aims to outline each gene expressed in a cell; proteomics seeks to profile every protein that is expressed [19]. Post-translational modifications of proteins, which can have thoughtful special effects on biological function, and their cellular localization. Significantly, provides knowledge that integrates the momentous advances in two-dimensional (2D) electrophoretic severance of proteins, mass spectrometry and bioinformatics [20, 21]. Sample Separation For the reason that of its difficulty the serum or plasma proteome cannot be determined entirely via a single proteomic equipment [22]. Manifold proteomics techniques for protein severance must be collective to examine and cover up a huge variety of the proteome [23]. Which methods are desired

depends on the fundamental and medical questions to be answered [24]. The last few years, enzymatic digestion of unseparated protein mixtures followed by separation of peptides by multidimensional liquid chromatography (LC) are uses as an alternative [27]. This LC-based approach lends itself to automated peptide separation together with the acquisition and analysis of MS data [28]. Despite concerns regarding the perils of fractionation to proteins, fractionating proteomes for analysis has an overriding advantage in that it simples them [29]. One's ability detects multiple protein species in a sample depends on one's ability to resolve many peptides from the sample and obtain MS data on as many of them as possible [30]. Selection of a sub cellular fraction for analysis reduces the task from analysis of perhaps 25000 proteins in a human cell sample to about 1000-2000 [31]. There is a better chance of identifying less abundant proteins when they are present in a mixture of 1000 proteins then when they are in a mixture of 14000 [32].

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Multidimensional Chromatography Multidimensional chromatography allows separation of complex mixtures by using multiple columns with different stationary phases [33].These columns are coupled orthogonally, which means that fractions from the first column can be selectively transferred to other columns for additional separation [34]. This enables separation of complex mixtures that cannot be separated using a single column [35]. Common applications for multidimensional LC are Proteins and peptides; Drug isolation from urine and plasma, Polysaccharides Homopolymers, oligomers, copolymers, Surfactants, Polycyclic aromatic hydrocarbons, DNA fragments [36]. One of the most important applications areas for this technique is proteomics, where complex protein digests are separated by multi-dimensional liquid chromatography instead of using the two dimension gel electrophoresis [37]. Proteomics applications in particular require very low flow rates in combination with small inner diameter columns for high detection sensitivity [38]. The micro valve, with low internal volume, can be positioned closely to the mass spectrometer for highest separation performance [39]. In the first dimension, fractions of the peptide mixture elute from an ion exchange column by a salt step gradient [40]. Each fraction is trapped on a small reversed-phase trapping column and then separated after the valve switches to a Reversed column (the second dimension). The trapping column is first used to prevent salt from entering the mass spectrometer (ion suppression). Second, the column allows an enrichment step, which together with the low flow rate in the 2nd dimension provides high detection sensitivity [41]. The vital theory is a soluble test to facilitate is estranged in a liquid-stage through a column, which is frequently a tube filled by way of small particles of exact surface chemistry [42]. The test is determined at the same time as it traverses the length of the column based on protein interactions with the solid-phase [43]. After the estranged test is detected at the last part of the column is the retention time and is quantitative if the peak contains a single protein [44]. One-dimensional liquid chromatography can be used to separate proteins according to their isoelectric point, molecular mass, and hydrophobicity, which are the three element individuality so as to describe several specified protein. The good number regularly used One-dimensional liquid chromatography is reversed Phase chromatography, during which proteins are estranged based on hydrophobicity [45]. Proteins are fractionated in two-dimensional liquid chromatography, by the first dimension

chromatographic focusing (pI) and in the second dimension by reversed phase chromatography, which facilitates examination of a larger spectrum of the proteome [46]. One-dimensional liquid chromatography uses in proteomics mainly designs for peptide separation before MS analysis. It is important both to quantify and to identify proteins present in fractions generated by DLC or 2DLC [47]. One strategy is to normalize, overlay and compare elution profiles between different samples using specialized software packages (for which there is currently a need especially when analyzing a large number of samples) and analyze, using MS, only the fraction that varies between samples [48]. Existing information suggests that by means of manifold proteomic technologies considerably increases the number of proteins detected, particularly of individuals present in the test at very low abundance. Multidimensional chromatography is synergistic separation techniques provide a large active protein spectrum for biomarker validation [50]. Differential Gel Electrophoresis Differential Gel Electrophoresis (DIGE) is designed to provide a quantitative component to proteomics experiments utilizing two-dimensional (2D) gel electrophoresis .DIGE can provide detection of changes in protein abundance (sometimes subtle) with statistical confidence while controlling for gel-to-gel variation and other variations of non-biological origin [51,52]. Advantage of DIGE is the labeled samples are mix and then separated on the same 2-D PAGE gel [52, 53]. Consequently, for samples on the same DiGE gel, gel-to-gel variation is completely eliminated, and the number of gels needed for one experiment can be cut two- to three-fold [54, 55]. One of the three samples on a DiGE gel can be a mixture of equal amounts of all experimental samples, a “pooled internal standard”. This creates a standard for each protein in the analysis. Therefore, comparisons across different gels can be made with a high degree of assurance large format gels are cumbersome to handle [56,57]. Since in DIGE the proteins are pre-labeled, DIGE gels do not have to be manipulated after electrophoresis [58]. Additionally, the scanner that is used for imaging accepts gel sandwiches including the glass plates. This further reduces the variation between gels, and the risk of damaging or destroying gels [59]. Labeling Samples diseased and control are differentially labeled in the midst of spectrally resolvable fluorescent dyes; Cy2, Cy3 and Cy5 and co-resolved on a solitary 2D gel for absolute quantitation [Fig: 2].

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By means of internal standards and investigational replication, particular and multi-modification analyses

are able to be inter-compared with a comparatively little figure of synchronized DIGE gels [60]. 2-D DIGE

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technology is based on the precise properties of spectrally resolvable dyes, the CyDye DIGE Fluors [61]. Two sets of dyes are accessible—Cy2, Cy3, and Cy5 minimal dyes, and Cy3 and Cy5 diffusion dyes so as to have been designed to be both mass and charge coordinated [62]. Consequently, indistinguishable proteins labeled with each of the CyDye DIGE Fluors determination shift approximately to the same position on a 2-D gel .CyDyes propose immense compassion, detecting as modest as 125 pg of protein and charitable a linear response to protein awareness of up and regarding to four instructions of importance [63]. During assessment silver staining detects 1–60 ng of protein by means of less than a hundred-fold lively variety. Labeling does not hamper through succeeding recognition by mass spectrometry (MS) of proteins excised from 2-D DIGE gels, since a good number peptides will not contain a label. Nevertheless, together the mass and hydrophobicity of the CyDyes manipulate protein immigration for the duration of the second dimension of electrophoresis, SDS-PAGE [64]. Because a consequence, modestly labeled 2-D DIGE gels are frequently post-stained, the majority established 2-D DIGE procedure uses N-hydroxysuccinimide ester reagents for low-stoichiometry labeling of amino groups of lysine side chains labeling reactions are standardized so that only 2–4% of the lysine residues are labeled . During minimal labeling, the interior standard is characteristically labeled with Cy2, at the same time as samples are labeled with Cy3 and Cy5 [65]. This course of action uses maleimide reagents for labeling all cysteine sulfhydryls in a sample .It is measured meant for use in situation wherever sample profusion is limited [66]. Diffusion labeling is to a great extent more sensitive than minimal labeling, as additional fluorophore is included into each protein species. During diffusion labeling, where a Cy2 fluor is not available, the interior typical is labeled with one of the CyDyes and samples are labeled with the other CyDye. Suspicious optimization of diffusion labeling situation is appreciative to survive accepted out for every new sample set to make sure absolute decline and stiochiometric labeling of cysteine residues [67]. In addition, proteins so as to do not have cysteine will not be labeled and determination consequently not be imaged. Two-Dimensional Gel Electrophoresis Two-dimensional gel electrophoresis (2-D electrophoresis) is a powerful and extensively used method for the analysis of complex protein mixtures extracted from cells, tissues, or other biological samples [68]. This technique separate proteins in two steps, according to two independent properties: the first-dimension is isoelectric focusing (IEF), which separates proteins according to their isoelectric points (pI); the second-dimension is SDS-polyacrylamide gel electrophoresis (SDS-PAGE),

which separates proteins according to their molecular weights (MW) [69]. In this way, complex mixtures consisted of thousands of different proteins can be resolved and the relative amount of each protein can be determined. The procedure involves placing the sample in gel with a pH gradient, and applying a potential difference across [70]. It in the electrical field, the protein migrates along the pH gradient, until it carries no overall charge [71] this location of the protein in the gel constitutes the apparent pI of the protein. There are two alternatives methods to create the pH gradient - carrier ampholites and immobilized pH gradient (IPG) gels. The IEF is the most critical step of the 2-D electrophoresis process [72]. The proteins must be solubilizing without charged detergents, usually in high concentrated urea solution, reducing agents and chaotrophs. To obtain high quality data it is essential to achieve low ionic strength conditions before the IEF itself [73]. Since different types of samples differ in their ion content, it is necessary to adjust the IEF buffer and the electrical profile to each type of sample. The separation in the second dimension by molecular size is performed in slab SDS- PAGE. Twelve parallel gels can be separated in a fixed temperature to minimize the separation variations between individual gels [75]. Diversity and Typhoon Imaging of CY dye 1D and 2D gels Fluorescence imaging technology is extensively used for proteome examination due to its advantages of elevated sensitivity combined with broad linear dynamic range. Conversion metal chelate dyes, for example SYPRO Ruby protein gel stain, are usually used for staining protein gels. [76]. SYPRO Ruby has gained a great deal status as a responsive, reproducible stain that can be used for both one-dimensional (1-D) and 2-D gels. At what time scanned through an influential laser-based fluorescence imager, SYPRO Ruby offers low nanogram sensitivity plus a much wider linear dynamic range than the traditional silver staining method. SYPRO Ruby is also companionable with mass spectrometry and Edman sequencing [77]. Typhoon 8600 and 9200 series have two excitation sources for fluorescence imaging: a green (532 nm) and a red (633 nm) laser. Typhoon 9400 series has an additional blue laser with two excitation lines (457 nm and 488 nm). The green laser and the two laser lines from the blue laser are compatible with SYPRO Ruby excitation. Typhoon offers extremely high sensitivity and a wide linear range for SYPRO Ruby quantization [79]. Software-based image analysis is a crucial step in the proteomics understanding of two-dimensional gel electrophoresis experiments. Current important advances in image dispensation methods collective with powerful computing hardware have enabled the

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regular analysis of huge experiments [79]. We cover the procedure preliminary through the imaging of 2-D gels, quantitation of spots, and construction of appearance profiles to numerical face investigation followed by the presentation of results. The image has to be treated in a different way depending on whether you are with Image Master or Decyder [80]. Image Master Building of 2D-gel databases using Melanie 3.0 or Image MasterTM 2D Platinum software package .Complete corresponding information will be generated, i.e. each gel will be coordinated aligned with each other in order to obtain the most complete set of spot groups possible [82]. As a result, you will be completely free to choose the classes (control, treatment, disease, sex, age) you attribute the gels to during data assessment [83]. In view of the fact that each gel had been "reference gel" at one position throughout the matching custom, all protein spots which were found matching to a group for at least 3-times will be considered as a group surrounded by the coordinated proteome database [84]. Proteome Consult has widespread experience in assembling large 2D-gel databases. We have productively coordinated databases showing greater then high resolution 2D-gels containing greater than 106 spots devoid of encountering a scientific limit. No transitional standard gels need to be used [85]. Go for the square button on left section. Leave the cursor on the superior left place of the gel region you desire to box. Run square to right and subsequently down to bottom of gel. Exist sure to comprise orientation markers [86]. Make an effort to not comprise any dark areas typically on the edges this will get in the way with the spot detection. Edges of box are red. Choose desires Region of significance button, end button to right of second row of buttons. Boundaries of box are at this time blue and red. Regulate gray and contrast, choose Gray/Color regulate button, 2nd button from right on 2nd row of buttons [87]. Click lying on button in the center of the window to right. Has a curve and black arrow on it. This opens bend and bright side bars. Move these 2 up and down until you are happy with the image. You can see your changes in the small window at the top select Apply and your changes will be applied to your gel. File/Save Region of Interest as Name image and place in appropriate drive and folder. [88]. Decyder DeCyder™ 2-D Differential Analysis Software using for 2-D DIGE and is a main ingredient in the Ettan™ DIGE system. DeCyder 2-D discrepancy investigation Software significantly high throughput by accurately on behalf of measurement of protein differences with statistical buoyancy and reducing the time with minimal user-to- user variation [89].The

software regularly detects, matches, and analyzes protein spots in multiplexed fluorescent images, and is capable to identify minute differences between protein spots with high numerical buoyancy [90]. DeCyder 2-D Differential Analysis Software capable of a completely robotic image analysis software matching set that facilitate the recognition, quantitation, matching, and analysis of 2-D DIGE gels [91]. Using DeCyder 2-D software, expression differences identified by 2-D DIGE can be confidently assigned to induced biological change and are not due to system variability. Every difference is assigned a statistical confidence value [92]. DeCyder 2-D software is elite in entirely using the compensation obtainable by multiplexing, the co-migration of more than one sample per gel, by allowing the addition of an interior ordinary [93]. Multiplexing is performing with proprietary size- and indict-synchronized CyDye DIGE Fluor minimal or saturation dyes to label protein samples up and doing to three samples can be separated on the same 2-D gel, which is then scanned with the Typhoon 9400 series Variable Mode Imager [94]. Using DeCyder 2-D software, minute differences in protein expression can be intended with a high degree of self-assurance. Apply of an internal standard effectively eradicates gel-to-gel variation. Recognition of differences in expression of less than 10%, with over 95% confidence can be achieved within minutes [95]. Shut image so as to opens after scan [fig:3]. Every one images intended for a gel Cy3, Cy5 and Cy2 be obliged to be cropped the same and be obliged to be saved in the correct format for Decyder to work After scan, browse to My computer/C:/Data (with hand)/ your folde [96].Your folder should have a .dir folder and Dset box file are created. Only 1 image appears, select the book button and you can see all Select 1, 2 or 3 buttons to see each color [97]. select all 3 to see all 3 colors. Select Region of Interest button and draw a square. Place the cursor on the upper left corner of the gel area you want to box. Run square to right and then down to bottom of gel. Be sure to include reference markers. Try to not include any dark areas usually on the edges. File/Save Region of Interest as Name [100]. Imaging through the Diversity imaging system is quicker compared to the Typhoon laser scanner for analogous promise and this has been verified for 1D and 2D gels. In support of the BSA protein, the vibrant range, linearity and sensitivity are identical for both imaging systems [101]. Analysis of the image quality by viewing the background for 1D gel images and the signal for the uncorrected 2D gel images in 3D mode reveals the presence of background

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artifacts for the Typhoon image only. For the raw 2D gel images there is no apparent difference in the image quality indicating comparable performance [102]. As Diversity images the whole gel at once the scan times are not dependant on sample size but solely on the exposure time required to achieve the desired sensitivity [103]. 1x1 binning gives a resolution of 90 microns per pixel. However, using 8x8 binning it is possible to pre-scan the gels checking for saturation with exposure times of 1 or 2 seconds. This gives a live saturation preview mode [104]. Note that the exposure times used during the Dynamic Range Assessment, especially for the saturated images, longer exposures were used than those used on typical 2D gels, as we were testing the limits of the system’s sensitivity. On “normal” 2D Cy dye gels the imaging times typically seem to be under 60 seconds per channel [105]. Spot picking Subsequent the selection of the spot of interest protein spot have to be excised from the gel. This step performs manually or automatically with commercially available spot picker Protein bands or spots in PAGE gels need to be excised before the proteins can be identified [107]. When cutting them manually, please excise as close to the band or spot as possible. Large gel bands need to be cut further into 1 mm squares. Prevent keratin and dust contamination by wearing gloves, lab coats, masks, etc. Avoid sample cross contamination. For low abundant proteins, you may need to combine spots. Our facility is equipped with a high throughput and accurate spot picker (GelPix) that can pick spots from Silver or Coomassie stained gels as well as from Sypro stained gels [108]. The make use of self-adhesive orientation markers on backed gels ensures exceedingly perfect and competent picking of spots on both fluorescently and calorimetrically stained gels. The proprietary picker head propose be able to relocate gel plugs to microplate wells at a rate of 96 samples in < 30 min, and its hydrophobic coating ensures complete spot transfer [109]. Rinsing the picker head stuck between each pick minimizes contamination at the same time as picker head association gives a spotless and accurate cut in the region of the targeted spot [110]. During automated picking using a pick list imported from Image Master Software, instruction manual selection of spots is promising by selecting the spot click on that and pick it. Bioinformatics Bioinformatics is a branch of in sequence knowledge regarding the luggage compartment space, salvage, apparition, calculation and investigation of molecular data with biological consequence [111].

Bioinformatics can go faster proteomic studies in data pulling out, incorporated data supervision and association modeling [Fig:4]. Data taking out is a process which is at the present renowned as an involvement device in proteomics [112]. This is suitable to the expansion of a spacious variety of software programs enthusiastic to withdrawal data obtained by the side of dissimilar stages of proteomic study. MS results are capable to be compared through sets of hypothetical protein sequences accessible in databases [113]. Incorporated data used to put together data acquired beginning proteomics of a variety of areas of interest inside a software atmosphere in regulate to advance the consistency and to allocate improved thoughtful of results [114]. Arrangement modeling and systems biology make available in sequence intended for enhanced thoughtful of huge molecular networks in their cellular situation by in silico modeling of the intricacy of biological processes with reference interacting molecules [115]. In drug discovery channel, solitary of the preponderance significant stepladder is the resolve of three-dimensional arrangement of a target protein or nucleic acid. Bioinformatics software can use the three-dimensional structural in sequence of the unliganded objective to design entirely new lead compounds de novo [116]. Protein Identification Proteins are huge, multifarious molecules that bear away the tasks of life. They direct our bodies’ behavior, sort out our thoughts, and defend us against illness, maintenance us hale and hearty. Except in their mutant forms and as coats on disease-causing element, proteins can help make us ill and threaten our health [117]. Each protein is firstly formed as a sequence of amino acids whose characteristics and order are dictated by a gene according to the progression of its DNA bases. The orders of gene's carried by messenger RNA also call for this sequence to be folded into a three-dimensional molecule that has an complicated shape, ranging from a plate to a dumbbell to a twisting [118]. The ability to identify proteins is also important because it allows researchers to determine whether an organism has a genetic disease .A genetic disease is often caused by a mutant protein, which has a composition slightly different from that of the normal protein it replaces [119]. Solitary of the most powerful tools for detecting and identifying proteins is mass spectrometry, a method that has been improved and used for a variety of research projects for many years. A mass spectrometer sorts out charged particles according to their masses, allowing analysis of the elemental composition of complex molecules [120]. A mass spectrometer produces a spectrum consisting of peaks and valleys that indicate the identity and

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number of different atoms making up the molecule being analyzed [121]. The mass spectrometer is an idyllic instrument for identifying amino acids the building blocks of proteins and formative the order in which they are arranged [122]. The mass difference, or distance in atomic mass units between the peaks along the spectrum, allows each amino acid (e.g., alinine, arsenine, glycine, or lysine four of the 20 possible amino acids) to be identified [123]. Mass spectrometry In order to determine the characteristics of individual molecules, a mass spectrometer converts them to ions consequently with the intention of they can be moved about and manipulated by external electric and magnetic fields [124]. A small sample of compound is ionized, usually to cations by loss of an electron. The ions are sorted and separated according to their mass and charge; the separated ions are then detected and tallied [125].The proteolytic assimilates of a biological sample hold more than a few hundred thousand peptides [126]. Multidimensional chromatography (FPLC) and High performance liquid chromatography together with information needy The powerful capabilities of Matrix Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) Mass Spectrometry are realized with the fast and accurate determination of molar masses, the sequencing of repeat units, and recognition of polymer additives and impurities [127] The MALDI technique is based upon an ultraviolet absorbing matrix pioneered by Hillenkamp and Karas [128]. The matrix and polymer are mixed at a molecular level in an appropriate solvent with a ~104 molar excess of the matrix. The solvent prevents aggregation of the polymer. The sample/matrix mixture is placed onto a sample probe tip. Crystallized polymer molecules homogeneously dispersed within matrix molecules [5]. When the pulsed laser beam is tuned to the appropriate frequency, the energy is transferred to the matrix which is partially vaporized, carrying intact polymer into the vapor phase and charging the polymer chains [9] .Multiple laser shots are used to improve the signal-to-noise ratio the peak shapes, which increases the accuracy of the molar mass determination [29]. In the linear TOF analyzer (drift region), the distribution of molecules emanating from a sample are imparted identical translational kinetic energies after being subjected to the same electrical potential energy difference [48]. These ions will then traverse the same distance down an evacuated field-free drift tube; the smaller ions arrive at the detector in a shorter amount of time than the more massive ions. Separated ion fractions arriving at the end of the drift tube are detected by an appropriate recorder

that produces a signal upon impact of each ion group [57].The digitized data generated from successive laser shots are summed yielding a TOF mass spectrum. The TOF mass spectrum is a recording of the detector signal as a function of time [60]. The time of flight for a molecule of mass m and charge z to travel this distance is proportional to (m/z) 1/2. This relationship, t ~ (m/z)1/2, can be used to calculate the ions mass[73]. Through calculation of the ions mass, conversion of the TOF mass spectrum to a conventional mass spectrum of mass-to-charge axis can be achieved [76]. Although advances in software and instrumentation to facilitate contain enabled quick achievement of very huge data sets, the tremendous complication of proteomic samples characteristically leads to peptides individual introduced into the mass spectrometer at a time far more than that of MS/MS data acquisition [87]. This consequences in a below sampling of the proteome at least at the peptide level and a good number frequently furthermore at the protein level [97]. At present, two traditions comprises projected to increase proteome exposure in proteomics experiments [112]. The first is use of manifold magnitude of chromatography to further decrease the complication of the samples introduced into the mass spectrometer [127]. The subsequent projected move toward takes benefit of the corresponding character of ESI and MALDI [128]. The ESI and MALDI ionization processes are complementary in that they frequently suppress ion currents for dissimilar peptides. The amalgamation of both techniques on the identical illustration takes advantage of the settlement of both [129]. In meticulous, the combination of high-throughput real time ESI MS/MS spectra acquisition using an ion trap with high scanning speed followed by intelligent, non-redundant offline MALDITOF/TOF analysis will significantly increase the total number of detected peptides leading to the identification of more proteins with improved sequence coverage [130]. Validation for Biomarker Inadequate accomplishment in translation of protein disease biomarkers to the diagnostic ground has appeared as a bewildering progress of the last decade [ 131] a small number of new protein biomarkers have produced from the proteomic discovery team, development constant if the inspection of validation studies, and turns out to be integrated in diagnostic tools [132] . During a convincing analysis of the biomarker discovery problem [133] courageously posits that the biomedical community has a penchant to overestimate the biomarker discovery phase [134]. In fact, he asserts, researchers under-appreciate another true face up to facing adapted medicine in the 21st century the difficult mission of developing

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and undertaking meticulous, candid assessment of biomarker candidates within suspiciously designed validation schemes [135]. Devoid of a rigorous, consistent attempt to build up the instrumental infrastructure essential for high-throughput, productive validation studies, the serious gap between biomarker discovery and translation of said biomarkers to clinical and point-of-care diagnostic tools remains [136]. Validation studies are necessary for determining the statistically-demonstrable investigative potential of suspected disease biomarkers - both as ‘dissimilar’ and multianalyte diagnostic sections [137]. Researchers have in topical period speculated that biomarker validation may pose a greater challenge and have need of more considerable improvement than biomarker discovery. Several factors support this contention together with [1] like predictable validation schemes can be additional expensive and work hard than discovery activities [138]. Up to till now, the value of a hopeful biomarker rests on validation of the marker in the context of its intended use [60]. Inbuilt to whichever validation responsibility is abrasion of promising candidates - announcement of unconstructive consequences can be easier said than done and might pull towards you imperfect awareness commencing the society [154]. Therefore, validation happenings might be of inadequate attention to researchers [155]. Incredible modernization is truly requisite to get together validation proposal stipulation. Explanation provision includes tumbling the obligatory labor necessary to complete a validation study, growing, and providing reproducible protein quantitation [156]. Significant matter in validation is that a preponderance of the probable disease-precise biomarkers with the intention of has been discovered are not exact to the disease being deliberate. Several of these proteins reduce addicted to the categories of discriminating-phase response proteins whose concentrations transform in response to contagion or tissue grievance [157]. Biomarkers commencing this class of proteins would have very low specificity intended for a precise disease [158]. Supplementary proteins with the intention of have been reported to be possible biomarkers are induced by additional stresses such like go on a diet, medicine and might contain completely no association to the disease of curiosity [159]. Regrettably these proteins commonly category in the middle of the chief copious proteins in plasma, serum and cerebrospinal fluid and consequently infuriate the active variety difficulty in the innovation of subordinate profusion proteins so as to may be genuine biomarker [60]. Straightforward patient employment is one more confront so as to from time to time underappreciated

in biomarker validation. Commence is significantly greater in validation simply because a much larger number of samples need to be analyzed [90]. Not only necessity patients with the precise demographics for the disease of interest are located, their medical history, lifestyle, etc. must be cautiously examined and if a forthcoming lesson is worldly propose their sustained contribution be obliged to be assured [120]. Further than patient samples, proper joystick must be acquired from persons through analogous demographics seeing that the patients other than are disease-free [4]. Calculations require to be performed to conclude the number of cases and gearshift that endow with enough statistical power previously the consequences are analyzed [23]. All of this practical issue can be a key determinant seeing that to the concluding accomplishment of a biomarker innovation and validation endeavor []. Biomarkers can be correlated with biological events during drug development in order to validate drug targets or to predict drug response; [25] biomarkers can be used as companion diagnostics in drug development to characterize patient populations in order to better understand the extent to which new drugs reach intended therapeutic targets can alter proposed therapeutic pathways and achieve successful clinical[26].outcomes; biomarkers can be used to stratify patient populations for drug response in primary prevention or disease-modification studies, particularly in specific clinical areas such as neuron degeneration and cancer; clinically useful biomarkers are becoming increasingly useful to make proper therapeutic decisions regarding candidate drugs [57] and clinically useful biomarkers are becoming increasingly required and other outside authorities to make proper regulatory decisions regarding candidate drugs [29].This report describes new biomarker technology platforms developed for the analyses of drug targets that are connected to the effectiveness of therapeutic agents in a clinical setting [60,62] . The emphasis is on those companies that are actively developing and marketing new companion diagnostic tests for performing biomarker tests during drug development, as opposed to the more routine and clinically accepted companion markers that are manufactured and marketed by large diagnostic companies for routine clinical use [109,110] REFERENCES

1. He QY, Chiu JF. Proteomics in biomarker discovery and drug development. (2003) J Cell Biochem; 89: 868-86.

2. Wang Y, Chiu JF, He QY. Proteomics in computer-aided drug design. (2005) Current Computer Aided Drug Design; 1: 43-52.

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3. Simpson RJ, Dorow DS. Cancer proteomics: from signaling networks to tumor markers. (2001) Trends Biotechnol; 19:S40–8

4. Diamandis EP. (2004) Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations. Mol Cell Proteomics; 3:367 – 78.

5. Kageyama S, Isono T, Iwaki H, Wakabayashi Y, Okada Y, Kontani K, et al\. (2004) Identification by proteomic analysis of calreticulin as a marker for bladder cancer and evaluation of the diagnostic accuracy of its detection in urine. Clin Chem; 50:857–66.

6. Anderson NL, Anderson NG. (2002) The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics; 1: 845 – 67.

7. Kane RC, Bross PF, Farrell AT, Pazdur R (2003) Velcade: U.S. FDA approval for the treatment of multiple myeloma progressing on prior therapy. Oncologist; 8: 508-13.

8. Dihazi H, Muller G A. (2007) Urinary proteomics: a tool to discover biomarkers of kidney diseases. Expert Rev Proteomics; 4: 39-50.

9. Zhang X, Leung SM, Morris CR, Shigenaga MK. (2004) Evaluation of a novel, integrated approach using functionalized magnetic beads, bench-top MALDI-TOF-MS with prestructured sample supports, and pattern recognition software for profiling potential biomarkers in human plasma. J Biomol Tech; 15: 167-75.

10. Boden M, Hawkins J. (2005) Prediction of subcellular localization using sequence-biased recurrent networks. Bioinformatics; 21: 2279-86

11. Domon B, Aebersold R. (2006) Mass spectrometry and protein analysis. Science; 312:212–17.

12. McDonald T, Sheng S, Stanley B, Chen D, Ko Y, Cole RN, Pedersen P and Van Eyk JE. (2006) Expanding the subproteome of the inner mitochondria using protein separation technologies: one- and two-dimensional liquid chromatography and two-dimensional gel electrophoresis. Mol Cell Proteomics; 5: 2392-411.

13. Levy RH, Hachad H, Yao C, Ragueneau-Majlessi I. (2003) Relationship between extent of inhibition and inhibitor dose: literature evaluation based on the metabolism and transport drug interaction database. Curr Drug Metab; 4: 371-80.

14. Jeong H, Mason SP, Barabasi AL, Oltvai ZN. Lethality and centrality in protein networks. Nature 2001; 411: 41-42.

15. Stanislaus R, Chen C, Franklin J, Arthur J, Almeida JS. (2005) AGML Central: web

based gel proteomic infrastructure. Bioinformatics; 21: 1754-57.

16. Keseru GM. (2001) A virtual high throughput screen for high affinity cytochrome P450cam substrates. Implications for in silico prediction of drug metabolism. J Comput Aided Mol Des; 15: 649-57

17. Bensmail H, Golek J, Moody MM, Semmes JO, Haoudi A. (2005) A novel approach for clustering proteomics data using Bayesian fast Fourier transform. Bioinformatics; 21: 2210-24

18. Houseman BT, Huh JH, Kron SJ, Mrksich M. (2002) Peptide chips for the quantitative evaluation of protein kinase activity. Nat. Biotechnol.; 20:270–74.

19. Blalock EM, Geddes JW, Chen KC, Porter NM, Markesbery WR, Landfi eld PW.(2004) Incipient Alzheimer's disease: Microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc Natl Acad Sci U S A; 101:2173-8.

20. tastna M and van Eyk J. (2007) Liquid Chromatography. In: Clinical Proteomics: From Diagnosis to Therapy. WiLEY-VCH Verlag GmbH&Co. KGaA, Weinheim,

21. Guo Y, Fu Z and Van Eyk JE. (2007) A proteomic primer for the clinician. Proc Am Thorac Soc.; 4: 9-17.

22. Jin G, Zhou X, Wang H, Zhao H. (2008) The Knowledge-integrated network biomarkers discovery for Major Adverse Cardiac Events. J Proteome Res; 7: 4013-4021

23. Anderson L, Hunter CL. (2006) Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell. Proteomics; 5:573 – 88.

24. Arthur JM. Proteomics. (2003) Curr Opin Nephrol Hypertens; 12: 423-30.

25. Jin T, Hu LS, Chang M, Wu J, Winblad B, Zhu J.(2007) Proteomic identification of potential protein markers in cerebrospinal fluid of GBS patients. Eur J Neurol;14:563-8.

26. Vitzthum, F., Behrens, F., Anderson, N. L., Shaw, J.H. (2005) Proteomics: From basic research to diagnostic application, A review of requirements and needs. J Proteome Res; 4:1086-97.

27. Alaiya A. (2005) Al-Mohanna M, Linder S. Clinical cancer proteomics: Promises and pitfalls. J Proteome Res; 4:1213-22.

28. Hye, A., Lynham, S., Thambisetty, M., Causevic, M., (2006) Campbell J, Byers HL, et al. Proteome-based plasma biomarkers for Alzheimer's disease. Brain; 129:3042-50.

29. Gramolini, A.O., Kislinger, T., Liu, P.P.,(2006) MacLennan DH, Emili A. Analyzing the cardiac muscle proteome by

50 | JRAAS 28:39-54,2013

liquid chromatographymass spectrometry (LC-MS) based expression proteomics. Methods in Molecular Biology; 357:15–32.

30. Hanash, S. M., Madoz-Gurpide, J. , Misek, D. E. (2002) Identification of novel targets for cancer therapy using expression proteomics. Leukemia; 16: 478–485.

31. Karp NA, Kreil DP, Lilley KS (2004) Determining a signifi cant change in protein expression with DeCyder during a pair-wise comparison using two-dimensional difference gel electrophoresis. Proteomics; 4:1421-32

32. Anderson TJ, Tchernyshyov I, Diez R, Cole RN, Geman D, Dang CV, Winslow RL (2007) Discovering robust protein biomarkers for disease from relative expression reversals in 2-D DIGE data. Proteomics. 7:1197-207

33. Fu Q and Van Eyk J,. (2006) Proteomics and heart disease: identifying biomarkers of clinical utility. Expert Rev Proteomics. 3: 237-49.

34. Brown SM (2003)Bioinformatics becomes respectable. Biotechniques 34: 1124-27.

35. Hu Y, Zhang S, Yu J, Liu J, Zheng S. (2005) SELDI-TOF-MS: the proteomics and bioinformatics approaches in the diagnosis of breast cancer. Breast 14: 250-55.

36. Bensmail H, Golek J, Moody MM, Semmes JO, Haoudi A. (2005) A novel approach for clustering proteomics data using Bayesian fast Fourier transform. Bioinformatics; 21: 2210-24.

37. Aytur T, Foley J, Anwar M, Boser B, Harris E, Beatty PR. (2006). A novel magnetic bead bioassay platform using a microchip-based sensor for infectious disease diagnosis. J. Immunol. Method;.314:21–29.

38. Pang S, Smith J, Onley D, Reeve J, Walkerb M, Foy C (2005) A comparability study of the emerging protein array platforms with established ELISA procedures. J Immunol Methods; 302: 1-12.

39. Pal S K, Pegram, M (2007) HER2 targeted therapy in breast cancer beyond Herceptin. Rev Endocr Metab Disord; 8: 269-277

40. Buckingham S.(2004) Bioinformatics: data's future shock. Nature, 428: 774-77.

41. Kastenbauer S, Angele B, Sporer B, Pfister H W, Koedel U (2005) Patterns of protein expression in infectious meningitis: a cerebrospinal fluid protein array analysis. J. Neuroimmunol.; 164:134–39.

42. Coombes K R, (2005) Analysis of mass spectrometry profiles of the serum proteome. Clin Chem, 51:1 – 2.

43. Rai A J, Chan D W. (2004) Cancer proteomics: Serum diagnostics for tumor marker discovery. Ann N Y Acad Sci; 1022: 286-94.

44. Bonetta L. (2006) Protein purification: fast forward. Nature; 439: 1017-21.

45. Miller B B, Mandell J W (2005). Multiplex method for measuring biomarkers of Alzheimer disease in cerebrospinal fluid. Clin Chem, 51: 289-90

46. Fu Q, Bovenkamp D E, Van Eyk, J E, (2007) A rapid, economical, and reproducible method for human serum delipidation and albumin and IgG removal for proteomic analysis. Methods Mol Biol; 357: 365-71

47. Gillette M A, Mani D R, Carr S A . (2005) Place of pattern in proteomic biomarker discovery. J Proteome Res; 4:1143-54

48. Neverova I and Van Eyk, J E. (2005) Role of chromatographic techniques in proteomic analysis. J Chromatogr B Analyt Technol Biomed Life Sci; 815: 51-63.

49. Nedelkov D, Kiernan U A, (2005) Niederkofler EE, Tubbs KA, Nelson RW. Investigating diversity in human plasma proteins. Proc Natl Acad Sci. U S A; 102:10852 – 7

50. Fliser D, Wittke S, Mischak H (2005) Capillary electrophoresis coupled to mass spectrometry for clinical diagnostic purposes. Electrophoresis. 26: 2708-16

51. Kopf E, Shnitzer D, Zharhary D. (2005) Panorama Ab Microarray Cell Signaling kit: a unique tool for protein expression analysis. Proteomics; 5: 2412-16

52. Jia Tang, Mingxia Gao, Chunhui Deng, Xiangming Z (2008) Recent development of multi-dimensional chromatography strategies in proteome research. J Chromatography. 866: 123-132.

53. Herrero M, Ibáñez E, Cifuentes A, Bernal J. (2009) A Multidimensional chromatography in food analysis. J Chromatogr, 1216:7110-29.

54. Maikhunthod B et. al. (2010) Development of a switchable multidimensional / comprehensive two-dimensional gas chromatographic analytical system. J. Chromatogr. 1217: 1522-1529.

55. Rühle C. (2009) Multiple component isolation in preparative multidimensional gas chromatography with mass spectrometry and nuclear magnetic resonance spectroscopy. J. Chromatogr; 1216: 5470-5747.

56. Bieri S, Marriott P J (2008) Dual injection system with multiple injections for determining bi- dimensional retention indices in comprehensive two-dimensional gas chromatography. Anal Chem; 80: 760-768.

57. Keshishian H, Addona T, Burgess, M., Kuhn, E., Carr, S. A. (2007) Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass

51 | JRAAS 28:39-54,2013

spectrometry and stable isotope dilution. Mol. Cell. Proteomics, 6: 2212–2229.

58. Anderson L, Hunter C L (2006) Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell. Proteomics. 5:573 – 88.

59. Sheng S, Chen D, Van J E (2006) Multidimensional liquid chromatography separation of intact proteins by chromatographic focusing and reversed phase of the human serum proteome: optimization and protein database; Mol Cell Proteomics. 5:26-34.

60. Listgarten J, Emili A. (2005) Statistical and computational methods for comparative proteomic profiling using liquid chromatography- tandem mass spectrometry. Mol Cell Proteomics. 4: 419-34.

61. Qian W J, Jacobs JM, Liu T. (2006) Camp DG 2nd, Smith RD. Advances and challenges in liquid chromatography-mass spectrometry based proteomics profiling for clinical applications. Mol Cell Proteomics; 5:1727-44.

62. Li J, Yin C, Okamoto H, Mushlin H, Balgley B M, Lee C S (2008) Identification of a novel proliferation-related protein, WHSC1 4a, in human gliomas. Neuro Oncol. 10:45-51

63. Sitnikov D, Chan D,Thibaudeau E, Pinard M, Hunter J M. (2006) Protein depletion from blood plasma using a volatile buffer. J Chromatogr B Analyt Technol Biomed Life Sci; 832:41-6.

64. Zurbig P, Renfrow MB, Schiffer E et al. (2006) Biomarker discovery by CE-MS enables sequence analysis via MS/MS with platform-independent separation. Electrophoresis; 27: 2111-25.

65. McDonald T, Sheng S, Stanley B, Chen D, Ko Y, Cole RN, Pedersen P, Van Eyk JE, (2006) Expanding the subproteome of the inner mitochondria using protein separation technologies: one- and two-dimensional liquid chromatography and two dimensional gel electrophoresis. Mol Cell Proteomics; 5: 2392-411.

66. Shafer M W, Mangold L, Partin AW, Haab B B (2007) Antibody array profiling reveals serum TSP-1 as a marker to distinguish benign from malignant prostatic disease. Prostate; 67:255–67

67. Mio Iwasaki S M, Tohru I, Masaru T, Nobuo T, Yasushi I (2010). One-Dimensional Capillary Liquid Chromatographic Separation Coupled with Tandem Mass Spectrometry Unveils the Escherichia coli Proteome on a Microarray Scale. Anal. Chem; 82, 2616–2620.

68. Stephen E (2009) Reichenbach. Quantification in Comprehensive Two-Dimensional Liquid Chromatography. Anal Chem. 81: 5099–5101.

69. Tannu N, Hemby S E. (2006) Quantitation in two-dimensional fluorescence difference gel electrophoresis: effect of protein fixation. Electrophoresis. 27: 2011–2015.

70. Tan H T, Zubaidah R M, Tan S, Hooi S C, Chung M C (2006) 2-D DIGE analysis of butyrate-treated HCT-116 cells after enrichment with heparin affinity chromatography. J Proteome Res;5: 1098–1106.

71. Sontheimer E J, Carthew R W (2005). Silence from within: endogenous siRNAs and miRNAs. Cell 2005; 122, 9–12.

72. Slabas A R, Suzuki I, Murata N, Simon W J , Hall J J (2006) Proteomic analysis of the heat shock response in Synechocystis PCC6803 and a thermally tolerant knockout strain lacking the histidine kinase 34 gene. Proteomics; 6: 845–864.

73. Friedman D B, Wang S E, Whitwell CW, Caprioli R M, Arteaga CL (2007) Multivariable Difference Gel Electrophoresis and Mass Spectrometry. Molecular & Cellular Proteomics 6:150-169

74. Bandow JE, Baker JD, Berth M, Painter C, Sepulveda O J, Clark KA, Kilty I (2007) VanBogelen RA New image analysis workflow for 2D gels enables large-scale 2D gel-based proteomics studies— example: COPD biomarker discovery study (in press) Benjamini Y, Hochberg Y Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B, 57:289–300.

75. Dowsey AW, English J, Pennington K, Cotter D, Stuehler K, Marcus K, Meyer H E, Dunn M J, Yang G Z (2006). Examination of 2-DE in the human proteome organization brain proteome project pilot studies with the new RAIN gel matching technique. Proteomics; 6:5030–5047

76. Eravci, M., Fuxius, S., Broedel, O., Weist, S., Eravci, S., Mansmann, U., Schluter, H., Tiemann, J., Baumgartner, A. (2007) Improved comparative proteome analysis based on two-dimensional gel electrophoresis. Proteomics 7:513–523.

77. Höper D, Bernhardt J, Hecker M (2006) Salt stress adaptation of Bacillus subtilis: a hysiological proteomics approach. Proteomics; 6:1550–1562

78. Tamle T, Antelmann H, Eymann C, Albrecht D, Bernhardt J, Hecker M (2006) Proteome signatures for stress and starvation in Bacillus subtilis as revealed by a 2-D gel

52 | JRAAS 28:39-54,2013

image color coding approach. Proteomics. 6:4565–4585.

79. Tan HY, Ng T W, Liew OW (2007) Effects of light spectrum in flatbed scanner densitometry of stained polyacrylamide gels. BioTechniques; 42:474–478

80. Tokarski C, Cren-Olivé C, Fillet M, Rolando C (2006) Highsensitivity staining of proteins for one- and two-dimensional gel electrophoresis using post migration covalent staining with a Ruthenium fluorophore. Electrophoresis. 27:1407–1416.

81. Wolff S, Otto A, Albrecht D, Zeng J S, Buttner K, Gluckmann M, Hecker M, Becher D (2006) Gel-free and gel-based proteomics in Bacillus subtilis: a comparative study. Mol Cell Proteomics; 5:1183–1192.

82. Wolff S, Antelmann H, Albrecht D, Becher D, Bernhardt J, Bron S, Buttner K, van Dijl JM, Eymann C, Otto A, Tam LT, Hecker M (2007) Towards the entire proteome of themodel bacterium Bacillus subtilis by gel-based and gel-free approaches. J Chromatogr B Analyt Technol Biomed Life Sci; 849:129–140.

83. Fu Q, Bovenkamp D E,Van Eyk, J.E. (2007) A rapid, economical, and reproducible method for human serum delipidation and albumin and IgG removal for proteomic analysis. Methods Mol Biol. 357: 365-71

84. Bandow J E, Baker JD, Berth M, Painter C, Sepulveda OJ, Clark KA, Kilty I, VanBogelen, R A (2007) . New image analysis workflow for 2D gels enables large-scale 2D gel-based proteomics studies— example: COPD biomarker discovery study (in press) Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B; 57:289–300.

85. Dowsey AW, English J, Pennington K, Cotter D, Stuehler K, Marcus K, Meyer HE, Dunn MJ, Yang G Z (2006) Examination of 2-DE in the human proteome organisation brain proteome project pilot studies with the new RAIN gel matching technique. Proteomics; 6:5030–5047.

86. Eravci M, Fuxius S, Broedel O, Weist S, Eravci S, Mansmann U, Schluter H, Tiemann J, Baumgartner A. (2007) Improved comparative proteome analysis based on two-dimensional gel Electrophoresis. Proteomics; 7:513–523.

87. Eymann C, Becher D, Bernhardt J, Gronau K, Klutzny A, Hecker M (2007) Dynamics of protein phosphorylation on Ser/Thr/Tyr in Bacillus subtilis. Proteomics (in press) Fodor IK, Nelson DO, Alegria-Hartman M, Robbins K, Langlois RG, Turteltaub KW, Corzett TH, McCutchen- aloney SL . Statistical

challenges in the analysis of two-dimensional difference gel electrophoresis experiments using DeCyder. Bioinformatics. 21:3733–3740.

88. GE Healthcare. DeCyder™ 2-D differential analysis software, user manual. Available from www.gelifesciences.com

89. Gustafsson J S, Blomberg A, Rudemo M (2007). Warping twodimensional electrophoresis gel images to correct for geometric distortions of the spot pattern. Electrophoresis; 23:1731–1744.

90. Höper D, Bernhardt J, Hecker M (2006). Salt stress adaptation of Bacillus subtilis: a physiological proteomics approach. Proteomics; 6:1550–1562.

91. Karp NA, Kreil DP, Lilley KS. (2004) Determining a significant change in protein expression with DeCyder during a pair-wise comparison using two-dimensional difference gel electrophoresis. Proteomics; 4:1421–32.

92. Shafer M W, Mangold L, Partin AW, Haab B B. (2007) Antibody array profiling reveals serum TSP-1 as a marker to distinguish benign from malignant prostatic disease. Prostate. 67:255–67.

93. Oh J H, Nandi A, Gurnani P, Knowles L. (2006) Proteomic biomarker identification for diagnosis of early relapses in ovarian cancer. J Bioinform Comput Biol; 4:1159-1179.

94. Kondo T, Hirohashi S. (2006) Application of highly sensitive fluorescent dyes (CyDye DIGE Fluor saturation dyes) to laser microdissection and two-dimensional difference gel electrophoresis (2D-DIGE) for cancer proteomics. Nat Protoc; 1: 2940-2956.

95. Sitek B, Luttges J, Marcus K, Kloppel G, Schmiegel W. (2005) Application of fluorescence difference gel electrophoresis saturation labelling for the analysis of microdissected precursor lesions of pancreatic ductal adenocarcinoma. Proteomics; 5: 2665-2679.

96. Greengauz RO, Stoppler H, Nomura S, Yamaguchi H, Goldenring J R.(2005) Saturation labeling with cysteine- reactive cyanine fluorescent dyes provides increased sensitivity for protein expression profiling of laser-microdissected clinical specimens. Proteomics; 5: 1746-1757.

97. Fliser D, Wittke S, Mischak H. (2005) Capillary electrophoresis coupled to mass spectrometry for clinical diagnostic purposes. Electrophoresis; 26: 2708-16.

98. Qiao J, Kang J, Cree J, Evers BM, Chung D H (2005). Gastrin-releasing peptide-induced down-regulation of tumor suppressor protein PTEN (phosphatase and tensin homolog

53 | JRAAS 28:39-54,2013

deleted on chromosome ten) in neuroblastomas. Ann Surg. 241: 684-91.

1. Haab B B. (2005) Antibody arrays in cancer research. Mol Cell Proteomics 4:377–83

2. Saviranta P, Okon R, Brinker A, Warashina M, Eppinger J (2004) Geierstanger BH. Evaluating sandwich immunoassays in microarray format in terms of the ambient analyte regime. Clin Chem 50:1907–20.

3. Zheng T, Peelen D, Smith L M. (2005) Lectin arrays for profiling cell surface carbohydrate expression. J Am Chem Soc; 127:9982–83

4. Zheng T, Peelen D, Smith L M(2005)Lectin arrays for profiling cell surface carbohydrate expression. J Am Chem Soc 127:9982–83.

5. Patwa TH, Zhao J, Anderson MA, Simeone DM, Lubman DM (2006) Screening of glycosylation patterns in serum using natural glycoprotein microarrays and multi-lectin fluorescence detection. Anal. Chem; 78:6411–21.

6. Li Y, Lee HJ, Corn R M. (2006) Fabrication and characterization of RNA aptamer microarraysfor the study of protein-aptamer interactions with SPR imaging. Nucleic Acids Res; 34:6416–24

7. Li Y, Lee H J, Corn RM (2007) Detection of protein biomarkers using RNA aptamer microarrays and enzymatically amplified surface plasmon resonance imaging. Anal. Chem;79:1082–88.

8. Smith JE, Medley CD, Tang Z, Shangguan D, Lofton C, Tan W. (2007) Aptamerconjugated nanoparticles for the collection and detection of multiple cancer cells. Anal. Chem. 79:3075–82.

9. Schwenk J M, Lindberg J, Sundbergn M, Uhlen M, Nilsson P. (2007) Determination of binding specificities in highly multiplexed bead-based assays for antibody proteomics. Mol. Cell. Proteomics. 6:125–32.

10. Seshi B. (2006) An integrated approach to mapping the proteome of the human bone marrow stromal cell. Proteomics. 6:5169–82.

11. Abdi F, Quinn JF, Jankovic J, McIntosh M, Leverenz J B . (2006) Detection of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal fluid of patients with neurodegenerative disorders. J Alzheimers Dis; 9:293–348.

12. Hood BL, Lucas D A, Kim, G., Chan, K.C., Blonder, J. (2005) Quantitative analysis of the low molecular weight serum proteome using 18O stable isotope labeling in a lung tumor xenograft mouse model. J Am Soc Mass Spectrom; 16:1221–30.

13. Thiery G, Shchepinov MS, Southern, E.M., Audebourg, A., Audard, V. (2007) Multiplex target protein imaging in tissue sections by

mass spectrometry: TAMSIM. Rapid Commun. Mass Spectrom; 21:823–29

14. Amann, J.M., Chaurand, P., Gonzalez, A., Mobley, J.A., Massion, P.P. (2006) Selective profiling of proteins in lung cancer cells from fine-needle aspirates by matrixassisted laser desorption ionization time-of-flight mass spectrometry. Clin. Cancer. Res, 12:5142–50.

15. Srinivasan R, Daniels J, Fusaro V, Lundqvist A, Killian J K. (2006) accurate diagnosis. of acute graft-versus-host disease using serum proteomic pattern analysis. Exp. Hematol.34:796–801.

16. Meilhac, O., Delbosc, S., Michel, J. B. (2007) Cardiovascular biomarker discovery by SELDITOF mass spectrometry. Methods Mol. Biol.357:331–41.

17. Engwegen, J.Y., Mehra, N., Haanen, J.B., Bonfrer, J.M., Schellens, J.H. (2007) Validation of SELDI-TOF MS serum protein profiles for renal cell carcinoma in new populations. Lab. Invest, 87:161–72.

18. Hueber W, Kidd B A, Tomooka BH, Lee BJ, Bruce B. (2005). Antigen microarray profiling of autoantibodies in rheumatoid arthritis. Arthritis Rheum; 52:2645–55.

19. Sheng S, Chen D, Van Eyk J E. (2006) Multidimensional liquid chromatography separation of intact proteins by chromatographic focusing and reversed phase of the human serum proteome: optimization and protein database. Mol Cell Proteomics.5: 26-34.

20. Malik G, Rojahn E, Ward M D, Gretzer M B . (2007) SELDI protein profiling of dunning R-3327 derived cell lines: identification of molecular markers of prostate cancer progression. Prostate; 67: 1565-1575.

21. Forrest MS, Lan Q, Hubbard A E, Zhang L. (2005) Discovery of novel biomarkers by microarray analysis of peripheral blood mononuclear cell gene expression in benzene-exposed workers. Environ Health Perspect; 113:801-807.

22. Izuhara K, Saito H. (2006) Microarray-based identification of novel biomarkers in asthma. Allergol Int; 55: 361-367Mayr M, Zhang J, Greene A S, Gutterman D. (2006) Proteomics-based development of biomarkers in cardiovascular disease: mechanistic, clinical, and therapeutic insights. Mol Cell Proteomics. 5:1853-1864.

23. Issaq H J, Veenstra TD, Conrads T P, Felschow D. (2002) The SELDI-TOF MS approach to Proteomics: protein profiling and biomarker identification. Biochem Biophys Res Commun; 292: 587-592

54 | JRAAS 28:39-54,2013

24. Izuhara K, Saito H. Microarray-based identification of novel biomarkers in asthma. (2006) Allergol Int; 55: 361-367.

25. Mayr M, Zhang J, Greene A S, Gutterman D. (2006) Proteomics-based development of biomarkers in cardiovascular disease: mechanistic, clinical, and therapeutic insights. Mol Cell Proteomics; 5:1853-1864.

26. Coombes KR. Analysis of mass spectrometry profiles of the serum proteome. Clin Chem 2005;51:1 – 2.

27. Forrest M S, Lan Q, Hubbard A E, Zhang L. (2005) Discovery of novel biomarkers by microarray analysis of peripheral blood mononuclear cell gene expression in benzene-exposed workers. Environ Health Perspect; 113:801-807.

28. Meilhac O, Delbosc S, Michel JB.(2007) Cardiovascular biomarker discovery by SELDITOF mass spectrometry. Methods Mol. Biol. 357:331–41.

29. Issaq H J, Veenstra TD, Conrads T P, Felschow D. (2002) The SELDI-TOF MS approach to Proteomics: protein profiling and biomarker identification. Biochem Biophys Res Commun; 292: 587-592

30. Gramolini AO, Kislinger T, Liu PP, MacLennan DH, Emili A. (2006) Analyzing the cardiac muscle proteome by liquid chromatographymass spectrometry (LC-MS) based expression proteomics. Methods in Molecular Biology; 357:15–32

31. Kuhn E, Wu J, Karl J, Liao H, Zolg W, Guild B. Quantification of Creactive protein in the serum of patients with rheumatoid arthritis using multiple reaction monitoring mass spectrometry and 13C-labeled peptide standards. Proteomics 2004; 4:1175–1186.

32. Cargile BJ, Bundy JL, Stephenson JL Jr. Potential for false positive identifications from large databases through tandem mass spectrometry. J Proteome Res 2004; 3:1082–1085.

33. Ettan MALDI-ToF User Manual, Amersham Biosciences 2001; 18-1144-01 Edition AA.

34. Fliser D, Wittke S, Mischak H. Capillary electrophoresis coupled to mass spectrometry for clinical diagnostic purposes. Electrophoresis 2005; 26: 2708-16.

35. Zhang X, Leung SM, Morris CR, Shigenaga MK. Evaluation of a novel, integrated approach using functionalized magnetic beads, bench-top MALDI-TOF-MS with prestructured sample supports,and pattern recognition software for profiling potential biomarkers in human plasma. J Biomol Tech 2004; 15: 167-75

36. Barnidge DR, Goodmanson MK, Klee GG, Muddiman DC. (2004) Absolute quantification of the model biomarker

prostate-specific antigen in serum by LC-MS/MS using protein cleavage and isotope dilution mass spectrometry. J. Proteome Res.; 3:644–52.

37. Thiery G, Shchepinov MS, Southern EM, Audebourg A, Audard V. (2007) Multiplex target protein imaging in tissue sections by mass spectrometry: TAMSIM. Rapid Commun. Mass Spectrom. 21:823–29.

38. Hood BL, Lucas DA, Kim G, Chan KC, Blonder J. (2005) Quantitative analysis of the low molecular weight serum proteome using 18O stable isotope labeling in a lung tumor xenograft mouse model. J. Am. Soc. Mass Spectrom; 16:1221–30.

39. Amann JM, Chaurand P, Gonzalez A, Mobley JA, Massion PP. (2006) Selective profiling of proteins in lung cancer cells from fine-needle aspirates by matrixassisted laser desorption ionization time-of-flight mass spectrometry. Clin Cancer Res; 12:5142–50.