The use of gene array technology and proteomics in the search of new targets of diseases for therapeutics

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Toxicology Letters 186 (2009) 45–51

Contents lists available at ScienceDirect

Toxicology Letters

journa l homepage: www.e lsev ier .com/ locate / tox le t

The use of gene array technology and proteomics in thesearch of new targets of diseases for therapeutics

Marcel Ferrer-Alcóna,∗, David Arteta a, M.a José Guerrerob,Dietmar Fernandez-Ortha, Laureano Simónb, Antonio Martineza

a Progenika Biopharma, S.A., Zamudio Technology Park, 48160 Derio, Vizcaya, Spainb Proteomika S.L. Zamudio Technology Park, 48160 Derio, Vizcaya, Spain

a r t i c l e i n f o

Article history:Available online 28 October 2008

Keywords:

a b s t r a c t

The advent of functional genomics has been greatly broadening our view and accelerating our way innumerous medical research fields. The complete genomic data acquired from the human genome projectand the desperate clinical need of comprehensive analytical tools to study complex diseases, has allowedrapid evolution of genomic and proteomic technologies, speeding the rate and number of discoveries in

Functional genomicsGene expressionMicroarraysProtein expressionT

new biomarkers. By jointly using genomics, proteomics and bioinformatics there is a great potential tomake considerable contribution to biomarker identification and to revolutionize both the developmentof new therapies and drug development process.

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. Introduction

One of the principal points of interest in the medicine andiology of complex diseases is the development of new tech-ologies to use in the discovery of new biological targets forew drugs or for development of new therapies. During decades,rior to the development of functional genomics (mainly genomicsnd proteomics approaches), target discovery was relied on theobservation-based” approach (Rickardson et al., 2005; Rogawski,008; Schimmer et al., 2006; Wu et al., 2006; Yokoyama et al.,008). That is, the target strategy involved screening of large num-ers of small compounds against particular and desired phenotypesfor example anticancer agents blocking cell proliferation in cellnd animal models). From this approach, libraries of compoundsere constructed with biologically derived or chemically synthe-

ized agents which were used in a systematic manner. However, theesults of this approach produced a low number of drugs. In addi-ion, for a high number of drugs on the market their biological targets not known at all. Also, many of such drug candidates ultimatelyailed in clinical development, either due to poor pharmacokinetic

haracteristics (drug-likeness) or to intolerable side effects, whichay reflect insufficient specificity of the compound or unsuitability

f such a “target approach” (Schneider, 2004). In fact, a signifi-ant number of drug development projects have failed because

∗ Corresponding author. Tel.: +34 94 406 45 25 fax: +34 94 406 45 26.E-mail address: [email protected] (M. Ferrer-Alcón).

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378-4274/$ – see front matter © 2008 Elsevier Ireland Ltd. All rights reserved.oi:10.1016/j.toxlet.2008.10.014

© 2008 Elsevier Ireland Ltd. All rights reserved.

he underlying biological hypothesis about the target was incor-ect (Lindsay, 2003). Later, in the mid-1980s/early 1990s there was awitch from the phenotypic approach to a “target-based” approacho drug discovery in which modulation of a selected biochemical

echanism is hypothesized to be potentially useful in treatment ofparticular disease; but this switch has also been followed by a lowumber of new drugs entering the market. In the last years, phar-aceutical research and development spending has increased but

he number of new drugs has not increased in parallel. Recent stud-es showed that between 1996 and 2001, pharmaceutical researchnd development spending in the USA increased by 40% and newrug approvals declined by nearly 50%. Companies spend US $500illion and require at least 10 years to bring a new compound to

he market, and most drugs fail in development (Horrobin, 2001;rown, 2007; Blagosklonny, 2003). However, Brown has suggestedhat knowledge gained during the past 15 years with the target-ased approach is likely to lead, in the next decade, to an increase inhe productivity of small-molecule drug discovery. This assertion isased in the S-curve theory of new technology development whichtates that technology performance increases with investment butventually reaches a plateau where further improvement wouldither be impossible or prohibitively expensive. For this author, theew technology was not ready: it was still in the induction phase

ather than the payback phase (Brown, 2007).

In this new context, the use of new technologies to discoveryf new biological targets has become high-priority in the modernedicine. The advances in “omics” disciplines such as genomics and

roteomics have had a crucial role in this search. The application

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f these methods in “target-based” drug discovery has proclaimednew era for target identification and these efforts have been

eviewed recently (Gupta and Lee, 2007). These genome-widepproaches have been adopted by fundamental scientists and byhe biotechnology and pharmaceutical industries to complementraditional approaches for target identification and validation, forypothesis generation and for experimental analyses in traditional-ased methods.

. Functional genomics

The discovery of new highly sensitive and specific biomark-rs for early disease detection coupled with the development ofersonalized “designer” therapies holds the key to future treat-ent of complex diseases such as cancer (Pujana et al., 2007;

lijn et al., 2008) or neuropsychiatric diseases (Schwarz andahn, 2008b; Papassotiropoulos et al., 2006). The current ongo-

ng revolution in molecular medicine has sought to understandhe molecular basis of human disease with an ultimate goal ofeveloping rationally designed therapies. The gene discovery phaseas been largely driven by key technological advances includingCR, high-throughput sequencing, automation of procedures andioinformatics. This phase recently culminated in the completionf the Human Genome Project and International HapMap Projectn 2003. This discovery has lead to the identification of some2,000 genes in human cells and allows us to obtain, with relativepeed, vast amount of deoxyribonucleic acid (DNA)-based infor-ation applicable to different research subjects. Consequently, the

uccessful sequencing of the human genome and the subsequentequencing of almost 150 other organisms genomes have set uphe scene for a new phase of life science. This new period, driveny the genomic data acquired from the human genome sequenc-ng project and the clinical need for comprehensive analytical toolso study complex disease, has made possible the rapid evolutionf the genomic and proteomic technologies and accelerated theate of discovery by providing a massive volume of data availableo clinical disease research. Functional genomics, which involvesstablishing the relationship between genes and their proteins, rep-esents the high point of a progression that started with structuralenomics, or high-throughput determination of protein structures,nd moved on to isolating and studying the structures of pro-eins, otherwise known as proteomics. The recent advances inenomic and proteomic technologies including gene array tech-ology, two-dimensional gel electrophoresis (2-DE) and new masspectrometric techniques (MS) coupled with advances in bioinfor-atics tools, show great promise of meeting the demand for the

iscovery of new biomarkers that are both sensitive and specificGupta and Lee, 2007). The use of these omics approaches implieshat studying relationships between a gene, its protein counter-arts, and a specific disease process requires scientists to blendogether huge amounts of data from different locations and in dis-arate formats. Bioinformatics tools often provide the only way toee the subtle relationships hidden deep within these huge setsf data (Kocher and Superti-Furga, 2007; Negrisolo et al., 2008;esai et al., 2008; Clarke et al., 2008). Nowadays, we can array DNArobes, proteins, antibodies and even biological samples enablingew types of research (Hocquette, 2005).

. Genomics

Genomics refers to a comprehensive analysis of gene expressionf a large number of genes by assessing relative or semi-uantitative amounts of RNA in biological specimens; that is, thenalysis of the genetic content of an organism. Genomics studies the

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Letters 186 (2009) 45–51

enome of organisms as a whole and it is based on high-throughputechniques allowing a wide picture of gene characteristics. Muta-ions, deletions and epigenetic alterations that directly or indirectlylter gene expression may also be uncovered by genomic analyses.

For years, scientists studied one gene at a time. Genes werendeed studied in isolation from the larger context of other genes.hat is, traditional methods in molecular biology generally workn a “one gene in one experiment” basis, which means that thehroughput is very limited and the “whole picture” of gene functions hard to obtain. In the past several years, a new technology, calledNA microarray or DNA chip, has been developed and has becomene of the most popular high-throughput techniques (Lockhart etl., 1996; Lockhart and Winzeler, 2000; Lipshutz et al., 1999; Cheet al., 1996). This technology is based on an orderly arrangement ofgreat number of specific probes in a reduced space allowing large-cale studies and monitorization of the whole genome on a singlehip. This way, researchers can have a better picture of the inter-ctions among several of genes simultaneously. The most popularype of microarrays that are being used for gene expression pro-ling studies is the short oligonucleotide chips GeneChip® systemroduced by Affymetrix (http://www.affymetrix.com/index.affx).lthough oncology is by far the field with the most data gener-ted by transcriptional studies (Eszlinger et al., 2007; Wang et al.,007; van de Vijver et al., 2002; Valk et al., 2004; Alizadeh et al.,000; Dyrskjot et al., 2004; Alon et al., 1999; Luo et al., 2001),any other fields have begun to follow their lead, including studies

n psychiatric disorders (Konradi, 2005; Pratt et al., 2008), in car-iovascular disease (Hiltunen et al., 2002; Tuomisto et al., 2003;iller et al., 2007), Alzheimer’s disease (Loring et al., 2001; Ho et

l., 2001; LaFerla, 2006), rheumatologic disorders (Baechler et al.,003; Bennett et al., 2003) and organ transplantation (Weintraubnd Sarwal, 2006). The microarray technology is having a sig-ificant impact on genomics study. Many fields, including drugiscovery and toxicological research, are certainly benefiting fromhe use of this technology. The publication of a number of semi-al papers describing the use of DNA microarray to detect globalene expression changes for clinical purposes has broadened theenomics technology and is now widely used in complex diseaseesearch.

As said before, the expression levels for a complete set of genesan now be assessed using microarray technology. This method-logical advance has fundamentally changed the way to discoverew drug targets and also the way investigators approach biomed-

cal questions, providing unparalleled opportunities for biomarkeriscovery. These current genomic technologies allow the evaluationf gene expression in thousands of genes in parallel and assess-ent of interactions between expressed genes to obtain a global

iew of a disease tissue in a single unbiased experiment ratherhan focusing on one or a handful of genes at a time. Thus, impres-ive technical advances, such as large-scale sequencing efforts andystematic functional genomics studies in model organisms, haveed to a massive increase in potential drug targets (Su et al., 2006;aletta and Hengartner, 2006; Renier et al., 2007). The main prob-

em of these results is that many target discovery groups are facinghe same dilemma, that is, they are confronted with a large num-er of targets which, in most cases, have been identified solely byirtue of their differentiated regulation under pathophysiologicalonditions. Microarray profiling of gene expression is a powerfuliscovery tool, but the ability to manage and compare the result-

ng data can be cumbersome. Even though many of these potential

argets may contribute in some way to disease phenotypes, fur-her functional characterization is required to identify key switchesn biochemical pathways as appropriate intervention points forrug treatment. For this reason is very important to demonstratehat a particular target plays an essential role in a disease-relevant

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Fig. 1. Workflow of the genomic analyse.

ellular process. The process of target validation plays an essentialole at this step (Egner et al., 2005; Sleno and Emili, 2008; Balganeshnd Furr, 2007). Development of microhybridization arrays hasowered the functional genomics phase in which gene expressionrofiling is being used to correlate gene expression patterns withisease classification and predict response to therapy. Hence, genexpression profiles have been demonstrated to be able to furtherubclassify and predict outcomes for complex entities such as lym-homa (Dave et al., 2004; Miyazaki et al., 2008), prostate cancerBibikova et al., 2007), and ovarian cancer (Marquez et al., 2005).early all these studies follow a similar workflow to that showed

n Fig. 1 where the primary points are data analysis and targetalidation.

But not only is the gene expression profile interesting in thisenomic approach. Now that the whole genome is sequenced, therere ongoing efforts to identify genetic polymorphisms (e.g. singleucleotide polymorphisms [SNPs]) that may point to disease pre-isposition, or unique response to therapy such as untoward drugide effects (Namjou et al., 2007; Beaudet and Belmont, 2008; Deellis et al., 2007; Tebbutt et al., 2007; Sripichai and Fucharoen,007). In addition, the expression chips do not give informationbout the genetic polymorphisms. For that reason, is important toomplete the genomic studies with studies of genome wide anal-sis (GWA) directed to detection of SNPs that are implied in theodification of the genome.Consequently, the post-genomic era is characterized by huge

mounts of data and the identification of increasing numbers ofargets. Many of the new molecules exist in low abundance in theell or have unknown functions. But as the research shifts fromtructural genomics to proteomics and to functional genomics, lifecientists can gain better understanding of how these molecules

ork together to provide a homeostatic environment for organ-

sms. Emerging tools and technologies such as DNA and proteinrrays, mass spectrometry (MS), yeast two-hybrid, RNA interfer-nce, and increased use of robotics, has facilitated the progress of

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Letters 186 (2009) 45–51 47

he “omics” disciplines (genomics, proteomics, metabolomics, etc.)hat are moving forward very quickly.

Nevertheless, although the “blueprints” of human disease maye genetically encoded, the execution of the disease processccurs through altered protein function. Thus, identifying theenetic or epigenetic events leading to disease requires subsequentnderstanding of the proteomic consequences of these events. Con-equently, the completion of the human genome project has fuelledtudies aimed at unraveling the human proteome, which are theomplete collection of proteins expressed in a given cell type or tis-ue (Fels et al., 2003; Ornstein and Tyson, 2006; Huang et al., 2008;eidan and Townsend, 2008). While gene microarray studies eluci-ate gene expression patterns associated with disease, they give no

ndication of the complexity of protein–protein interactions, theirocalization, or whether the encoded proteins are stably expressed,hosphorylated, cleaved, acetylated, glycosylated or functionallyactive”. As proteins are the ultimate effector molecule, proteomics the ideal complement of genomic approach.

. Proteomics

After applied genomics technologies to target discovery, theext phase of the new molecular medicine involves the use ofhese technologies combined with newly evolving omics tech-ologies to diagnose, subclassify, and drive the development of

ndividualized, molecularly targeted therapies, ushering in a newra of clinical medicine. The advent of these multi-omic approacheshat merge proteomics, transcriptomics, metabolomics, lipidomics,lycomics, etc. . . is facilitating a more detailed and more compre-ensive molecular description of dynamic biological systems thanas been ever been possible. Proteomics has emerged in the last fewears as a multi-disciplinary and technology-driven science thatocuses on proteomes: the complex of proteins expressed in a bio-ogical system, their structures, interactions, and post-translational

odifications. Hence, Proteomics refers to a set of analytical strate-ies aimed at identifying, describing and quantifying the completeet of proteins expressed by a particular cell, tissue or organism at apecific time and is thus the perfect companion to genomics analy-is. In particular, proteomics examines changes in protein levels andther protein alterations that result from or foster specific diseases,r are induced by various external factors, such as toxic agents.n analogy to gene expression analysis based on DNA microar-ays, global proteome profiling will play a key role in identifying,haracterizing, and screening all proteins encoded by the genome.owever, contrary to the genome, the proteome is composed of anctive array of molecules constantly being modified and with spe-ial localization. Whereas it is estimated that the human genomeonsists of about 32,000 different genes, due to alternative splicing,equence deletions and post-translational modifications occurringuring protein production, it is estimated that the total proteomeonsists of well over a million different protein species defined ashe proteome. Further, the dynamic range of expression of thesearies over 108–109 orders of magnitude. Likewise, the other so-alled ‘omic’ approaches attempt to do the same for other molecularomponents of the cell (transcriptomics refers to mRNA transcripts,etabolomics to small molecule metabolites, etc.) (Schwarz and

ahn, 2008b; Kim et al., 2008; Natt, 2007).The challenge in proteomic analyses is to some extent much

reater than in genomic analyses. Some proteins are expressed

ousekeeping gene products, are extremely abundant. Also, someroteins, or protein forms, may be expressed during very short timeeriods during the life of an individual, for example during embry-nic development, while others may be continually expressed but

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ith very short half lives, rendering these very difficult to isolatend characterize. The growing interest in explaining biological phe-omena in terms of molecular structure has lead to an enormousffort to identify all the molecular elements of biological systemsnd their mechanism of function (Schwarz and Bahn, 2008a; Issaqnd Veenstra, 2008; List et al., 2008).

Although the term proteomics is only a few years old, its rootso back to the 1970–1980s. Some techniques that are standard inlinical laboratories such as enzyme-linked immunosorbent assayELISA) and immunohistochemistry (IHC), and in basic science labo-atories such as Western blot and immunoprecipitation (IP), are alsoxamples of proteomic techniques. However, these conventionalechniques have been limited to evaluation of one or a handful ofroteins at a time, and comprehensive analyses of complex pro-ein mixtures from clinical samples, as in tumor lysates or serum,ave only become feasible in the last few years. The developmentf proteomics can be attributed primarily to the refinements in MS,mprovements in computer and software sciences, and the floodf data now available from genomic sequencing of many organ-sms. Recent technological advances have increased the resolution,ccuracy and speed in methods to: (1) fractionate (or separate) pep-ide or protein mixtures to smaller number of proteins per fractionsing chromatographic techniques such as high-performance liq-id chromatograph (HPLC), polymeric reverse-phase columns or-DE gel electrophoresis; (2) label and detect proteins and anti-odies using multi-color fluorophores, imaging equipment andomputer software; and (3) analyze clinical samples without anyxtensive preparation by mass spectrometry-based techniques forlinical purposes with greatly increased throughput capacity. Theuture envisions the release of much more information. As a con-equence, several recent studies have used this new technology tonvestigate common health concerns, including, but not limited to,ging (Yang et al., 2008; Chakravarti et al., 2008; Willis, 2007),ancer (Chumbalkar et al., 2008; Micallef et al., 2008; Bertuccind Goncalves, 2008), atherosclerosis (Martin-Ventura et al., 2007),lzheimer disease dementia (Papassotiropoulos et al., 2006; Ward,007) and more recently psychiatric diseases (Mu et al., 2008;ockstone et al., 2007; Huang et al., 2006; Beasley et al., 2006) haveeen investigated.

Protein expression profiling provides an opportunity for a syner-istic systems biology approach to the understanding of complexesisease that, when combined with gene transcript profiling, canmplify our knowledge repertoire. Proteomics technologies allows to understand proteins and their modifications, which may elu-idate properties of cellular behaviour that may not be reflectedn an analysis of gene expression. However, proteomic studiesre more technically challenging because of the multitude ofotential post-translational modifications, compartmentalizationf proteins and the formation and regulation of multi-proteinomplexes. Therefore, proteomic approaches are able to character-ze post-translational modifications, a method by which the cellynamically and quickly modifies protein function and regulatesoth creation and degradation in response to cellular perturba-ions. Similar to gene mRNA expression profiling, several proteinrofiling techniques emerged in the last decade that did not requiren a priori knowledge of candidate genes or proteins. From varia-ions of gel electrophoresis to the advent of peptide specific masspectrometry, each modality confers another method of differentialrotein expression analysis. Profiling the proteome of diseased andealthy tissues allows for the discovery of peptide or protein molec-

lar change, which potentially reveals information on pathogenesisr diagnosis, or both. In the last decade, proteomics has beenncreasingly applied to different fields of research to advance thenderstanding of disease pathogenesis, develop targets biomark-rs for diagnosis, and early detection of disease using proteomic

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Fig. 2. Workflow of the proteomic analyse.

ortrait of samples. This intense interest in applying proteomicsas been developed to identify the protein profiling by means of 2-E and with the wide-spread introduction of mass spectrometry, ofioinformatics and the proteinchip. This interest can lead to the dis-overy, identification, and characterization of protein biomarkersifferentially expressed in the diseased state versus the control.

A proteomic analysis comprises two principal steps: (1) separa-ion of the protein mixture, mainly by 2-DE, to allow the efficientetection of the particular proteins included in the mixture and (2)

dentification of the separated proteins by various analytic meth-ds, mainly by MS. The most efficient and most widely used proteindentification method in proteomics is the peptide mass finger-rint approach, which is mainly performed by matrix-assisted laseresorption/ionization time-of-flight mass spectrometry (MALDI-OF-MS) (Pusch and Kostrzewa, 2005), surface-enhanced laseresorption and ionization (SELDI)—a variant of MALDI (Kiehntopft al., 2007), and multi-dimensional liquid chromatography cou-led to electrospray ionization tandem mass spectrometry (LC-ESIS/MS) (Galasinski et al., 2003). Finally, the bioinformatics tools

llow to analyse the results and to identify possible biological tar-ets (Fig. 2).

All MS instruments analyze biopolymers such as peptides, pro-eins and polynucleotides as ions, which can be distinguished basedn their mass-to-charge ratio (m/z). All MS instruments have threeasic components: (1) an ion source which volatizes and ionizeshe analyte, (2) a mass analyzer which separates ions based on their/z values, and (3) a detector which detects ions after separation.

he most popular MS technique is MALDI-MS. The popularity of thisechnique stems from the advantages of simple sample preparation,

inimal sample volume, its ability to analyze a complex mixtureuch as serum and whole tissues and coordinated programming of

ata acquisition (Nakazawa et al., 2008; Albrethsen, 2007).

The profiling strategy allows for the direct discovery of pro-ein markers potentially indicative of disease, disease progression,nd outcome. For profiling experiments, there are two main

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pproaches: (a) mass spectrometry-based and (b) antibody-based.rotein profiling can be performed on complex peptide or proteinixtures from whole or partially fractionated tissue extracts or

iofluids such as serum (Gagnon et al., 2008; Cazares et al., 2008;opez et al., 2007), urine and cerebrospinal fluid, or directly on thinissue sections (Chaurand et al., 2004; Yanagisawa et al., 2003).

For the antibody-based profiling, the antibodies against pre-etermined protein lists that are important in a given biologicalrocess, pathway or clinical outcome are used to determine the pro-ein signatures (Tong et al., 2008; Mathivanan and Pandey, 2008;

ang et al., 2005). The negative aspects of antibody-based Pro-eomics are that the assay is limited by the availability of antibodiesnd the need for having the prior knowledge of proteins of interest.ntibody-based microarrays are among the novel classes of rapidlymerging proteomic technologies, enabling multiplexed proteinxpression profiling of clinical samples in a high-throughput minia-urized format. Protein arrays can be used to detect specific proteinsn a non-MS based approach. In general, the technique providesmmune detection of multiple proteins by printing specific anti-odies or antigens on a slide or a membrane. A single sample

s hybridized to the array followed by the detection of the cap-ured antigens or antibodies. The first report of using protein arraysor protein–protein interaction, ligand binding and biochemicalnvestigations was from MacBeath and Schreiber (2000). Since thisrst work, protein microarrays have generally seen major develop-ents in two major aspects, in terms of immobilization methods

or anchoring huge repertoires of proteins and expanding areas ofpplications using novel strategies. However, not only the need forhighly specific probe for every target molecule (in contrast to

ucleic acid arrays that can use the anti-sense sequence) remainshurdle limiting the applicability of the approach. The usual lowensity design, allowing detection of only a few proteins with-ut addressing post-translational modifications, is also a challenge.here is much potential in screening whole proteome microarraysor a variety of different purposes. It provides a unique windownto the ensemble of proteins present in an organism for parallelnalysis; offering huge opportunities in screening potential drugnteractions as well as in detecting post-translational modificationshat regulate protein behaviour (Uttamchandani et al., 2008, 2006).he use of proteomics for clinical diagnosis or prognosis is one ofhe most attractive applications, and in this context the expressionattern of biomarkers could be directly correlated with diagnosisnd prognosis of complex disease such as cancer (Chung et al., 2007)r psychiatric disease (Huang et al., 2007).

. Conclusion

Advances in genomic and proteomic technologies are trans-orming the drug development process and have also evolved from

great promise to technologies that are beginning to contributealuable new information in both basic and clinical research and, inome cases, clinical care. Therefore, the analysis of the large datasetsenerated by such technological applications can be greatly bene-cial in understanding living systems. Additionally, these gene androtein expression data catalogued the differences between nor-al and disease tissues. These information can be used to identify

ovel therapeutic targets, and may ultimately guide physicians inheir decisions on patient-specific personalized therapies in theuture. These beneficial effects on the industry are more clear at the

iscovery level because of the relatively larger investment madet this level. However, the impact on process development andanufacturing should not be underestimated. There are increasing

fforts to apply these technologies to understanding cell culturerocess development, with the goal of strain improvement or pro-

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ess improvement. Unfortunately, a substantial number of thesefforts results only in lists of proteins with significant changes inoncentration, and fall short of demonstrating the effect of theontrolled expression of those genes or proteins on the desired phe-otypes. The difficult question is how to translate the vast amountsf data into a meaningful clinical context, and how to translate thisnformation into clinical trial design and subsequently into rou-ine clinical use. Thus, although there is considerable work beingone in using these methods, and there is great promise in theirpplication, there are, at this time, relatively few examples thatemonstrate the substantial benefit of genomics and proteomics onrocess development and to develop an effective assay or therapy.inally, both genomics and proteomics approaches are complemen-ary to be based on the study of the two fundamental pieces ofhe biology of the cell: genes and proteins. They control directlyhe cellular and pathological behaviour of the cell and for that it ismportant to carry out parallel approaches.

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